Physical Sciences and Mathematics Commons^™

The Fifth Element In Three Dimensions: Boron Systematics Along And Across The Aleutian Volcanic Arc, Owen Jensen

Theses and Dissertations

This study uses abundances and isotope ratios of the fluid-mobile element boron (B) to better understand the role of fluids in volcanism at the Aleutian arc in Alaska. In volcanic rocks from the Aleutian volcanic front, patterns in B abundance and isotope ratios (δ11B) point to three endmembers: a sediment melt, a slab interior melt, and a slab-top melt. Most lavas from the eastern Aleutian Islands have δ11B of -2 to +5‰, similar to Aleutian sediment. Hence, sediment is likely to be the dominant B source in the Aleutian Island samples. In lavas from the western Aleutian seafloor, high δ11B …

Topics In Beyond The Standard Model Physics, Sapan Karki

Theses and Dissertations

Standard Model and General relativity are two pillars of modern physics, and their reconciliation would bring a long-sought harmony in physics along with insights into the secrets of the universe. It is then natural to try and modify these theories by searching for the fundamental symmetry describing our world. In this work, I work on the implication of breaking of Lorentz symmetry and the existence of topological objects called solitons. In chapter 3, I show how a cosmologically generated Lorentz- Violating model has instability issues. I then show how the quantum corrections on Lorentz-Violating models can produce ambiguity in β-function …

Antitrust After The Coming Wave, Daniel A. Crane

Articles

A coming wave of general-purpose technologies, including artificial intelligence ("AI"), robotics, quantum computing, synthetic biology, energy expansion, and nanotechnology, is likely to fundamentally reshape the economy and erode the assumptions on which the antitrust order is predicated. First, AI-driven systems will vastly improve firms' ability to detect (and even program) consumer preferences without the benefit of price signals, which will undermine the traditional information-producing benefit of competitive markets. Similarly, these systems will be able to determine comparative producer efficiency without relying on competitive signals. Second, AI systems will invert the salient characteristics of human managers, whose intentions are opaque but …

On Localization Of Eigenfunctions Of The Magnetic Laplacian, Jeffrey S. Ovall, Hadrian Quan, Robyn Reid, Stefan Steinerberger

Mathematics and Statistics Faculty Publications and Presentations

Let Ω ⊂ ℝd and consider the magnetic Laplace operator given by H(A) = (–i∇ – A(x))2, where A : Ω → ℝd, subject to Dirichlet boundary conditions. For certain vector fields A, this operator can have eigenfunctions, H(A)ψ = λψ, that are highly localized in a small region of Ω. The main goal of this paper is to show that if |ψ| assumes its maximum at x0 ∈ Ω, then A behaves 'almost' like a conservative vector field in a -neighborhood of x0 in a precise sense. In particular, we expect localization in regions where |curl A| is small. …

Real-Time Synthesis Of A Nonuniformly Correlated, Partially Coherent Beam Using An Optical Coordinate Tansformation, Milo W. Hyde Iv

Faculty Publications

We design, build, and validate an optical system for generating light beams with complex spatial coherence properties in real time. Beams of this type self-focus and are resistant to turbulence degradation, making them potentially useful in applications such as optical communications. We begin with a general theoretical analysis of our proposed design. Our approach starts by generating a Schell-model (uniformly correlated or shift-invariant) source by spatially filtering incoherent light. We then pass this light through an optical coordinate transformer, which converts the Schell-model source into a nonuniformly correlated field. After the general analysis, we discuss system engineering, including trade-offs among …

Festival Of Research Abstracts, Fall 2024, College Of Science And Mathematics, Wright State University

Festival of Research

The collection of abstracts accepted for the Fall 2024 Festival of Research hosted by the Wright State University College of Science and Mathematics.

Proceedings Of Wmu Maritime Week 2024 Beyond Horizons : Maritime Sustainability, World Maritime University, Korea Maritime Institute, Korea Research Institute Of Ships And Ocean Engineering

WMU Maritime Week 2024 Beyond Horizons: Maritime Sustainability

This proceedings is the output of the WMU Maritime Week 2024 Conference, which was held from August 27 to 30, 2024, at WMU in Malmö. As the first conference of WMU Maritime Week, it focused on themes such as the IMO World Maritime Day theme 2024: 'Navigating the Future: Safety First!', Maritime Sustainability, Digitalization, Decarbonization, Maritime Business and Logistics, and Capacity Development. A total of 42 experts in these fields presented across 8 sessions. This proceeding provides a summary of the presentations from all the speakers.

Leveraging Imitation Learning In Agricultural Robotics: A Comprehensive Survey And Comparative Analysis, Siavash Mahmoudi, Amirreza Davar, Pouya Sohrabipour, Ramesh Bahadur Bist, Yang Tao, Dongyi Wang

Biological and Agricultural Engineering Faculty Publications and Presentations

Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise across diverse domains. In recent years, its integration into robotics has sparked significant interest, offering substantial advancements in autonomous control processes. This paper presents an exhaustive insight focusing on the implementation of imitation learning techniques in agricultural robotics. The survey rigorously examines varied research endeavors utilizing imitation learning to address pivotal agricultural challenges. Methodologically, this survey comprehensively investigates multifaceted aspects of imitation learning applications in agricultural robotics. The survey encompasses the identification of agricultural tasks that can potentially be addressed through imitation learning, detailed analysis of specific models …

Making Plans Findable, Accessible, Interoperable, And Reusable With Data Infrastructure: A Search Engine For Constructing, Analyzing, And Visualizing Planning Documents, Lindsay Poirier, Dexter Antonio, Makenna Dettmann, Tiffany Eng, Jennifer Ganata, Sujoy Ghosh, Mirthala Lopez, Ranesh Karma, Asiya Natekal, Catherine Brinkley

Statistical and Data Sciences: Faculty Publications

Local land-use plans help guide future development, but it is often difficult to compare content across jurisdictions, making regional coordination and plan evaluation challenging. This research reviews federal, state, and local data infrastructure guidance for land-use plans and compares such guidance to compliance with a California use-case. Findings indicate a number of obstacles to fostering data sharing and comparative analysis of plans: there is currently no central repository of land-use plans; plans are not uniform in format and are often out of date; many plans are not machine-readable thereby inhibiting text extraction, and planning language varies so greatly that there …

A Survey Of Unikernel Security: Insights And Trends From A Quantitative Analysis, Alex Wollman, John Hastings

Research & Publications

Unikernels, an evolution of LibOSs, are emerging as a virtualization technology to rival those currently used by cloud providers. Unikernels combine the user and kernel space into one ``uni''fied memory space and omit functionality that is not necessary for its application to run, thus drastically reducing the required resources. The removed functionality is significant however, and includes components that have become common security technologies such as Address Space Layout Randomization (ASLR), Data Execution Prevention (DEP), and Non-executable bits (NX bits). This raises questions about the security of unikernels. This research presents a quantitative methodology using TF-IDF to analyze the focus …

Age And Provenance Relationships Between The Basal Great Valley Group And Its Underlying Basement: Implications For Initiation Of The Great Valley Forearc Basin, California, U.S.A., Mariah C. Romero, Devon A. Orme, Kathleen D. Surpless, Chance B. Ronemus, Zachary Morrow

Earth and Environmental Geosciences Faculty Research

The Great Valley forearc (GVf) basin, California, records deposition along the western margin of North America during active oceanic subduction from Jurassic through Paleogene time. Along the western GVf, its underlying basement, the Coast Range Ophiolite (CRO), is exposed as a narrow outcrop belt. CRO segments are overlain by the Great Valley Group (GVG), and locally, an ophiolitic breccia separates the CRO from basal GVG strata. New stratigraphic, petrographic, and geochronologic data (3865 detrital and 68 igneous zircon U-Pb ages) from the upper CRO, ophiolitic breccia, and basal GVG strata clarify temporal relationships among the three units, constrain maximum depositional …

Solubility Characterization Of Organic Molecules For Aqueous Organic Redox Flow Batteries, Anthony W. Ferrell, Harkeerith K. Vij, Seamus D. Jones

College of Engineering Summer Undergraduate Research Program

The major obstacle to renewable energy sources is a lack of long-term energy storage capabilities. Energy produced during the day dissipates, leaving insufficient electricity for at night. The goal of the project is to design an Aqueous Organic Redox Flow Battery (AORFB) to act as long-term energy storage. Work has been done using machine learning to identify suitable compounds for the batteries. In this work there was no indication as to the aqueous solubility of the molecules; this controls the device’s energy storage capabilities. We used a machine learning model to determine the aqueous solubility of slightly more than 3000 …

Targeting Federated Learning: A Study Of Membership Inference Attacks On Healthcare Data, Brett W. Hillyard, Aditi S. Lappathi

College of Engineering Summer Undergraduate Research Program

This study investigates the vulnerabilities of federated learning models in the healthcare domain, specifically focusing on membership inference attacks (MIA). Federated learning allows local models to train on sensitive healthcare data without sharing the data itself, making it an attractive method for protecting privacy. However, even in this decentralized framework, models remain vulnerable to MIAs, where attackers can infer whether certain data points were used to train a model by analyzing model updates. Using the Texas100 dataset, this study demonstrates that as the number of local models increases, the attack accuracy of MIAs also increases due to higher bias …

Fast And High-Resolution View Synthesis From A Single Input Panorama, Nam Nguyen, Angela V. Chen, Theresa Zhu, Seth Johnson, Pranav Dumpa, Benjamin Geil

College of Engineering Summer Undergraduate Research Program

We introduce a novel method to convert a single input panorama into a 3D colored mesh representation of the scene. Unlike recent methods based on neural rendering, which are limited to low-resolution inputs and offline rendering, our approach supports 4k resolution inputs and real time rendering in a virtual reality headset. We first estimate a depth map and produce an initial layered depth image (LDI) representation. We fill unseen regions behind objects by iteratively cutting and inpainting the LDI. We then convert the LDI into an optimized, texture mapped mesh to achieve a compact representation

"Deep Learning For Microscope Image Denoising", Nasreen Buhn, Sriya Adunur, Guy Hagen, Jonathan Ventura

College of Engineering Summer Undergraduate Research Program

In order to avoid damaging live cells, optical microscope imaging must be conducted under low-excitation light intensity and/or short exposure times, resulting in low signal-to-noise ratios (SNR). Deep learning methods offer an effective solution for removing microscope noise, utilizing algorithms that are able to reconstruct finer features in low SNR images. This research explores the denoising capability of several deep learning methods based on PSNR and SSIM. Tested methods include traditional approaches (BMED), supervised learning (CARE and Restormer), and unsupervised methods (Noise2Fast, N2V, SSD-Unsupervised, and SASSID). The Restormer model, which employs an encoder-decoder transformer architecture and progressive learning, stood out …

Digital Twin For Shelf Intelligence: Ai-Driven Inventory Management For Minimizing Food Waste, Charlotte Maples, Marvin Velazquez

College of Engineering Summer Undergraduate Research Program

This project aims to develop a solution for improving grocery store inventory management by leveraging AI-driven image recognition. Traditional inventory methods, which rely on manual counting or barcode scanning, are inefficient, labor-intensive, and prone to human error. Over an 8-week period, we designed and developed a basic iPad app capable of identifying specific types of fruit and automatically updating inventory records in real time. By utilizing the iPad’s camera and machine learning algorithms, the app demonstrates the potential to streamline inventory tracking, reduce manual labor, and improve accuracy in managing perishable goods. Future work will focus on expanding the app’s …

Optimizing Medical School Enrollment, Justin Koida, Nicholas Quattrocchi

College of Engineering Summer Undergraduate Research Program

The increasing physician shortage, coupled with an aging population, presents significant challenges for healthcare systems. With higher education facing a projected enrollment cliff, and a decline in youth math and reading scores, identifying the most qualified medical school applicants is imperative. With thousands of applications received annually for only 300 spots at Western University of Health Sciences (WesternU), it is crucial to streamline the selection process while minimizing applicant attrition and melt.

In our research project, we develop a predictive model to identify candidates for interviews based on success data from students at WesternU. We utilized a dataset provided by …

Seismic Resilience Of Timber Homes, Melia R. Costa

College of Engineering Summer Undergraduate Research Program

The vast majority of residential buildings in California are wood-frame structures. A home is typically a very large investment for families, and damage to the home can be detrimental to a family’s economic success. Thus, reducing the economic impacts of an earthquake on residential timber housing is crucial. A full scale, two-story section of a residential timber house is in construction in the Parson’s Geotechnical and Earthquake Engineering Laboratory on the Cal Poly campus in San Luis Obispo, California. Once built it will be put through earthquake simulation tests on a shake table with various magnitudes of seismic activity. Next, …

Leveraging Tradespace-Exploration For A Senior Project Team Formation Application, Miguel Saenz

College of Engineering Summer Undergraduate Research Program

This project revolves around the development of an app in MATLAB that leverages the VASSAR rule-based system and a genetic algorithm to form groups of teams for the Mechanical Engineering Senior Design project class. We leveraged the iterative design process to eventually attain a functional app with a reasonable runtime that works provided correctly formatted rulesheets describing student project preference and member preference.

Tabular Data-Centric Ai: Challenges, Techniques And Future Perspectives, Yanjie Fu, Dongjie Wang, Hui Xiong, Kunpeng Liu

Computer Science Faculty Publications and Presentations

Tabular data are the most widely used data formats in almost every application domain, such as, biology, ecology, and material science. The purpose of tabular data-centric AI is to use AI to augment the predictive power of tabular data to get better AI. Tabular data-centric AI is essential because it can reconstruct distance measures, reshape discriminative patterns, and improve data AI readiness (structural, predictive, interaction, and expression levels), which is significant in industries and real-world deployments. Therefore, our tutorial is designed to capture the interest of professionals with expertise in artificial intelligence, machine learning, and data mining, as well as …

Investigating In Vivo Tumor Biomolecular Changes Following Radiation Therapy Using Raman Spectroscopy, Varsha Karunakaran, Sina Dadgar, Santosh K. Paidi, April Mordi, Whitney A. Lowe, Umme Marium Mim, Jesse D. Ivers, Joel Rodriguez Troncoso, Jared A. Mcpeake, Alric Fernandes, Sanidhya D. Tripathi, Ishan Barman, Narasimhan Rajaram

Biomedical Engineering Faculty Publications and Presentations

Treatment resistance is a major bottleneck in the success of cancer therapy. Early identification of the treatment response or lack thereof in patients can enable an earlier switch to alternative treatment strategies that can enhance response rates. Here, Raman spectroscopy was applied to monitor early tumor biomolecular changes in sensitive (UM-SCC-22B) and resistant (UM-SCC-47) head and neck tumor xenografts for the first time in in vivo murine tumor models in response to radiation therapy. We used a validated multivariate curve resolution-alternating least-squares (MCR-ALS) model to resolve complex multicomponent Raman spectra into individual pure spectra and their respective contributions. We observed …

Measuring The Ion Temperature Of A Plasma Jet Undergoing Instability-Driven Heating, Mario Avila

Doctoral Dissertations and Master's Theses

Outlined are arrangements to measure the ion temperature of a plasma jet undergoing instabilities and an examination of experiment findings. The jet, formed by puffing a controlled amount of Nitrogen or Argon gas into Embry-Riddle's cylindrical vacuum chamber and then ionizing it via high-voltage electronically switched capacitor banks, is regulated to undergo three distinct instability regimes, stable, current-driven, and pinched (or detached), dependent on the voltage of an externally applied bias field and initial conditions. Ion temperature measurements of plasma are inferred through analysis of the Doppler-broadening observed spectroscopically at relevant wavelengths. To improve measurements, this arrangement implements a unique …

Assessing The Impact Of Femur Morphological Variations On Pediatric Hip Joint Biomechanics Using Statistical Shape Modeling, Tamara Chambers

Doctoral Dissertations and Master's Theses

This dissertation aimed to (1) quantify morphological variations in the pediatric hip joint and (2) evaluate the sensitivity of an infant musculoskeletal model (MSM) to these variations, considering hip joint center estimation errors. A shape statistical model (SSM) of decedent infant femurs from the Ortolani collection was created using ShapeWorks, capturing key morphological features, such as variations in the femoral neck-shaft and anteversion angles. Seven synthetic femurs were generated from the SSM to create SSM-informed MSMs, which were systematically evaluated through kinematics and kinetics analyses in OpenSim. Incorporating the SSM led to slight changes in the pediatric MSMs’ kinematics but …

Crystal Archer: Profile Of The Newest Chbc Faculty Member, Danna Jaimes-Fajardo, Jeremy J. Smith, Lutishoor Salisbury

University Libraries Faculty Publications and Presentations

Presents the research profile of Crystal Archer, Assistant Professor, Chemistry and Biochemistry department. It highlights her research interests, education, and impact of her research.

The P-Adic Schrödinger Equation And The Two-Slit Experiment In Quantum Mechanics, Wilson A. Zuniga-Galindo

School of Mathematical & Statistical Sciences Faculty Publications

p-Adic quantum mechanics is constructed from the Dirac-von Neumann axioms identifying quantum states with square-integrable functions on the N-dimensional p-adic space, Q_{p}^{N}. This choice is equivalent to the hypothesis of the discreteness of the space. The time is assumed to be a real variable. p-Adic quantum mechanics is the response to the question: what happens with the standard quantum mechanics if the space has a discrete nature? The time evolution of a quantum state is controlled by a nonlocal Schrödinger equation obtained from a p-adic heat equation by a temporal Wick rotation. This p-adic heat equation describes a particle performing …

Optical Investigation Of A Low-Pressure Plasma In A Superconducting Radio-Frequency Cavity, Zachary A. Caudell

Electrical & Computer Engineering Theses & Dissertations

The state-of-the-art cryomodules for particle accelerators use superconducting radio-frequency (SRF) cavities. It is known that accumulation of hydrocarbon impurities reduces the cavity surface work function, decreasing cryomodule performance metrics such as field emission, thermal stability, and accelerating gradients. An in-situ method of plasma processing has recently been developed to clean the cavities and restore cryomodule performance [1]. This study is to understand the properties of the low-pressure plasmas generated in a C100 SRF cavity and ultimately to improve the performance of plasma surface cleaning using optical emission spectroscopy. Using a 500 mm focal length Czerny-Turner monochromator coupled with a photomultiplier …

A Quantitative Study Assessing The Impact Of Financial Sector Safeguards Rules On Us Publicly Traded Companies' Cybersecurity Breach Frequency And Severity, Alan Dinerman

School of Public Service Theses & Dissertations

Cybersecurity failure in the private sector is a growing federal policy priority. Nevertheless, US policy makers struggle with policy instrumentation in this domain. Behavioral public policy theory asserts that a linkage exists between policy target behavioral & cultural attributes and effective policy tool instrumentation. The federal government is now embracing a regulation heavy approach, but little empirical study exists at the nexus of behavioral public policy theory and use of regulatory tools in the cybersecurity policy domain. Since the early 2000s, the financial sector has employed a regulation heavy approach using the Safeguards regulations to facilitate cybersecurity in financial private …

Symbolic Regression For Data-Driven Equation Discovery: A Physics-Informed Approach, Anusha Reddy Singireddy

Computer Science Theses & Dissertations

Symbolic Regression (SR) is a cutting-edge machine learning technique that discovers mathematical expressions representing the underlying patterns in data. Unlike traditional regression, SR explores a wide range of mathematical models, allowing for flexible and interpretable solutions. We utilize PySR, a highly customizable symbolic regression package, which combines genetic programming and modern optimization methods to efficiently search for interpretable equations. PySR balances model complexity with performance by penalizing overly complex expressions while optimizing accuracy. In this thesis, we apply Physics-informed Symbolic Regression through PySR to model the x and t dependence of the flavor isovector combination H_u − d( …

Simulating The Compound Impacts Of Climate Change And Sea-Level Rise On Coastal Shallow Groundwater Using An Integrated Surface-Subsurface Hydrologic Model, Zhaoyi Cai

Civil & Environmental Engineering Theses & Dissertations

As climate change intensifies and sea levels rise, coastal aquifers become increasingly vulnerable to interacting oceanic and terrestrial processes. This dissertation examines the combined impacts of climate change and sea-level rise (SLR) on coastal shallow groundwater using an integrated surface-subsurface hydrologic model, focusing on the low-lying Little Creek watershed (LCW) in Norfolk, Virginia. The SWAT-MODFLOW model, a coupled system, unifies land surface, streamflow, and groundwater processes, providing a comprehensive framework for watershed-aquifer analysis. Model parameters were derived from U.S. Geological Survey datasets, relevant literature, station records, and technical reports, followed by meticulous calibration. The validated model first simulated baseline groundwater …

Refinement Of Alphafold-Predicted Models Using Cryo-Em Density Maps And Enhancement Of Protein Secondary Structure Topologies With Residue Contacts, Maytha Naif Alshammari

Computer Science Theses & Dissertations

Proteins play an important role in almost every biological process. Understanding the mechanism of protein function requires knowledge of three-dimensional (3D) structures. Traditionally, the determination of 3D structures has presented significant challenges. However, Cryo-Electron Microscopy (Cryo-EM) has revolutionized the field of structural biology, providing a powerful technique for atomic structure determination. This dissertation delves into the potential of cryoEM in two ways. First, this dissertation presents a new flexible fitting approach, utilizing Normal Mode Analysis (NMA) and Elastic Network Models (ENMs) to refine AlphaFold-predicted models by optimizing the structures to match cryo-EM density maps. This approach identifies the optimal mode …