Physical Sciences and Mathematics Commons^™

Bioanalytical Studies Of Disease Protein Profiles: Maldi-Tof Ms, Prajkta Satish Chivte

Graduate Research Theses & Dissertations

Coronavirus disease-2019 (COVID-19), which is caused by a novel coronavirus named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has ravaged the world for the past 3 years. Even today, there still exists a need for rapid, accurate, economical and non-invasive diagnostic testing platforms that yield high specificity and sensitivity towards the constantly mutating SARS-CoV-2. Research has consistently indicated saliva to be a more amenable specimen type for early detection of SARS-CoV-2, compared to the oral and nasopharyngeal swabs. Considering the limitations and high demand of the existing COVID-19 testing platforms, this dissertation work studies used MALDI-ToF MS (matrix-assisted laser desorption/ionization-time of …

Alignment And Range Verification In Proton Therapy Using Proton Radiography And Proton Ct, Joseph Piet

Graduate Research Theses & Dissertations

Protons are used in radiation therapy to lower doses to healthy tissues by utilizing their Bragg peak. Protons can be used both in imaging and treatment. One of the uses of protons in imaging we tested is its use to align patients using a single beam's eye proton radiograph (pRad). By using a beam's eye pRad, and comparing the water equivalent thickness (WET) to proton digitally reconstructed radiographs (pDRRs), we show that we can measure the best alignment on six axes, three translational and three rotational. This is done by defining a cost function, chi squared, which quantifies the misalignment …

Structural Analysis Of Polar Aggregates And Inter-Ionic Distances In Room Temperature Ionic Liquids, Emily Elizabeth Dalbey

Graduate Research Theses & Dissertations

Ionic liquids have a wide variety of applications in chemistry due to their unique properties such as low flammability, low volatility, and high thermal and chemical stability. These properties make them great solvents, electrolytes for lithium-ion batteries, and more. Ionic liquids are salts whose components are mis-matched in size and shape leading to low melting points. To design an ionic liquid with a desired property it is key to first understand their structure. Lineshape analysis of the charge alternation peak from the experimentally measured or computationally calculated X-ray scattering data can give information about the size of the polar aggregates …

Visualizing Dynamic Multivariate Networks, Bharat Kumar Kale

Graduate Research Theses & Dissertations

Many real-world networks are multivariate in nature and also evolve over time. Such a network whose nodes and/or edges along with their associated attributes, evolve over time is called A dynamic multivariate network (DMVN). For instance, in the scholarly community, researchers often collaborate with others. These collaborations have various attributes, such as scientific domain and number of publications, as well as they change over time. At any given point in time, the collaborations among researchers can be modeled as a network resulting in a collaboration network. When the changes in collaborations and associated attributes are also included in the network, …

The Impact Of Faculty Composition On Cost Per Student: A Mixed Model Approach, Arun Sleeba

Graduate Research Theses & Dissertations

This thesis aims to explore whether research universities in the United States, specifically those classified as Carnegie I or II institutions, utilize part time contingent faculty(rPTF) as a cost saving strategy. Additionally, it sought to determine if there was a differential impact on total costs when comparing public and private universities. Employing a linear mixed effects model with random intercept and slopes, this study analyzed the relationship between rPTF (ratio of part-time to total faculty) and total cost. This study did not provide substantial evidence to support the notion despite observing a negative correlation between rPTF and total cost. Regarding …

A State Space Modeling Approach To Eeg Artifact Removal, Patrick B. Rafael

Graduate Research Theses & Dissertations

In this work, a state space modeling approach is applied to an Electroencephalography(EEG) recording for the purpose of artifact removal, and is compared against Independent Components Analysis (ICA), the current gold standard. Issues of model identifiability are touched on, and Hamiltonian Monte Carlo (HMC) is used to estimate a linear non-Gaussian state space model. Results show that estimating such a model is a nontrivial matter, and the full utility of the state space approach remains to be demonstrated.

Enhanced Maximum Likelihood Models For Underreported Variables: Extending To Multiple Claims Dimension, Shalaka Sudhanshu Sarpotdar

Graduate Research Theses & Dissertations

This thesis builds upon the foundations laid out in Xia et al. [2023], which explored the utilizationof Maximum Likelihood approach to model misrepresentation data in Generalized Linear Models (GLM) ratemaking models. We introduce the concept of “underreported variables”, a form of insurance misrepresentation where insured individuals provide inaccurate information about risk factors that influence insurance eligibility, premiums, and insured amounts. Unlike fraudulent misrepresentation, underreported variables arise from a lack of awareness regarding the insured’s mental and physical health conditions, rather than fraudulent intent. The study rigorously tests the proposed model using health insurance data and extends its applicability to other …

Systematic Study Of Projection Biases In Weak Lensing Analysis, Prudhvi Raj Varma Chintalapati

Graduate Research Theses & Dissertations

The nature of Dark Energy is possibly one of the most fundamental questions in Physicstoday. Comprising 70% of the energy density of the Universe, and being responsible for the accelerated expansion of the Universe, this mysterious form of energy with negative pressure is challenging our current understanding of the fundamental laws of nature. Cosmological parameters like total relative matter Density Ωm, the normalization of power spectrum σ8 and their combination S8 = σ8 (Ωm/0.3)**0.5 are used to quantify our understanding of the evolution of the large-scale structure in the Universe and shed light on the accelerated expansion, caused by the …

Mechanical Behavior Of Metallic Core-Shell Nanoparticles Under Compressive Loading, Phillip Tomich

Graduate Research Theses & Dissertations

Core/shell metallic nanoparticles have been shown to be a promising material type for additive manufacturing in the aerospace and automotive fields. Within additive manufacturing they will be used to accurately make an array of nanoparticles within the grains of metal matrix composites. This in turn will help to strengthen the material while remaining ductility and light weight. In this study, copper/aluminum core/shell nanoparticles are compressed under [100], [110], [111], and [112] directions to showcase their anisotropic material properties. Models of their individual counterparts were also investigated. There are no previous works showing the deformation mechanisms of copper/aluminum core/shell nanoparticles. Molecular …

Methods For Investigating Geo-Stem Learning Ecosystems: A Case-Study Of Illinois Earth Science Teachers, Cheryl L.B. Manning

Graduate Research Theses & Dissertations

The complex interactions between people and resources in communities that foster innovation can be thought of as ecosystems. These ecosystems have the potential to broaden participation in the geoscience community if we invest in collaborations that map out long-term solutions and career pathways for young community members. Geo-STEM learning ecosystems (GLE) engage local communities in sustainable programs that promote geoscience literacy and inspire people to learn the geosciences. A goal of GLE is to leverage existing social systems and accelerate geo-STEM solutions for society. Thus far, few studies in the geosciences have interrogated what is meant by a “learning ecosystem” …

Insight Into The Nanostructure Of “Water-In-Salt” Solutions: A Saxs/Waxs Study On Imide-Based Lithium Salts Aqueous Solutions, Xinyi Liu

Graduate Research Theses & Dissertations

The fundamental understanding of the liquid electrolytes (LEs) solvation structure and electrode-electrolyte interface behavior is important to the entire electrochemical energy storage field. Understanding the solvation structures from a few angstroms to hundreds of nanometers will undoubtedly lay a good foundation for studying the macroscopic transport properties, such as viscosity and ionic conductivity, of the LEs."Water-in-salt" electrolytes that contain 21 m lithium bis(trifluoromethane sulfonyl)imide (LiTFSI) with excellent electrochemical and physical properties have been extensively investigated. However, the structural understanding of the LiTFSI in water is still lacking. Here, we perform synchrotron X-ray scattering to systemically study the structural variation of …

Using Machine Learning To Predict Student Outcomes, Saba Fatima

Graduate Research Theses & Dissertations

Predicting students’ performance to identify which students are at risk of receiving aD/Fail/Withdraw (DFW) grade and ensuring their timely graduation is not just desirable but also necessary in most educational entities. In the US, not only is the Science, Technology, Engineering, and Mathematics (STEM) major becoming less popular among students, the graduation rate of STEM students is steadily declining. The lack of STEM graduates in the US is a serious problem that will place this country at a disadvantage as a competitor in international technological advancement. In order to secure its status as a technological leader internationally, the US institutions …

Dynamic Overnight Effect On Next Day Stock Market Forecasting, Thomas J. Lee

Graduate Research Theses & Dissertations

Using a cross section of stocks that have high frequency trading data from 2007 to 2018, we document whether various intraday momentum patterns found in the financial literature over the years continue to hold over time. The first half hour return on the market is often seen as having predictive power over the last half hour of trading, or overnight returns are thought to reverse in the next day's first half hour of trading. We find that while there is some evidence for these patterns, especially in the earlier years, these patterns tend to weaken over time as investors take …

Macroeconomic Factors Influencing Foreign Direct Investment In Some Selected Countries In Africa, Richard Essel Mensah

Graduate Research Theses & Dissertations

This paper investigates the possible factors that influence foreign direct investment inflow rate to Africa after controlling for other macroeconomic factors. Using the heterogenous Toeplitz mixed method on a sample of 23 countries from 1998 – 2020, we find evidence of the statistical significance of a relationship between the amount of trade done in Africa and the FDI inflow rate in Africa. We also find a statistical relationship between the labor force participation rate and the FDI inflow rate to Africa. Although the Fixed effect and GLM method did not find the relationship between LFP rate and FDI inflow to …

Coulomb Blockade-Mediated Field Emission Sources Using Ultra-Nanocrystalline Diamond, Jevin Jensen

Graduate Research Theses & Dissertations

Coulomb Blockade effects in field emission provide interesting means of achieving brighter electron sources for numerous applications, ranging from vacuum electronics to the next generation of electron beam technology. Microelectronics cleanroom methods are presented in this thesis for production of field emission sources moderated by the Coulomb Blockade. The use of common processes is an essential step toward widespread experimentation with Coulomb Blockade-mediated field emission apparatuses. The main feature to be explored is the use of nano-diamond films for their potential applicability for this desired outcome. Ultra-Nanocrystalline Diamond is used in two different ways to achieve this, both as a …

The Role Of Machine Learning And Network Analyses In Understanding Microbial Composition In An Experimental Prairie, Ali Eastman Oku

Graduate Research Theses & Dissertations

Machine learning and network analyses are powerful modern tools can process and map out connections between large amount of ecological data from complex environmental communities. Random forests, an ensemble machine learning algorithm, are particularly powerful as they can capture complex patterns in data while remaining easily interpretable. These tools are specifically useful in experimental settings where different types of data are collected. The aim of this study was to demonstrate the utility of machine learning models and network analyses at analyzing diverse ecological data from dynamic plant-soil microbial communities in a prairie ecosystem. Our experimental system is an experimental prairie …

Design And Cold Test Of A Metamaterial Accelerating Structure For Two-Beam Acceleration, Dillon Christopher Merenich

Graduate Research Theses & Dissertations

Structure-based wakefield acceleration (SWFA) is an advanced accelerator concept that can achieve higher accelerating gradients than conventional accelerators. Advanced structures are required for SWFA, with metamaterial (MTM) structures as a promising candidate. MTMs are periodic sub-wavelength structures engineered to exhibit exotic electromagnetic properties, such as simultaneously negative permittivity and permeability. Because of their unique electromagnetic properties, MTMs are particularly interesting to SWFA. Previous studies at the Argonne Wakefield Accelerator have demonstrated efficient wakefield power extraction using MTM structures. This thesis presents the design, fabrication, and cold test of an X-band MTM accelerating structure for two-beam acceleration. An MTM structure was …

Thermal Analysis Of Thermal Shield For Pip-Ii Ssr1 Cryomodule, Victor Aguado Rodriguez

Graduate Research Theses & Dissertations

Fermilab's PIP-II project plans to enhance the onsite accelerator into the world's most intense neutrino beam, bringing Fermilab into the next generation of particle accelerators. The five different types of cryomodules are vital parts of the PIP-II project and each requires numerous engineering analyses to prove operation. For example, the thermal shield of the Single Spoke Resonator-1 (SSR1) cryomodule required a thermal analysis to ensure the design was operating as intended before fabrication commenced. The thermal shield is cooled by the supercritical helium inside of the extrusion and must remain between 45 and 80 Kelvin. The main purpose of the …

Carbonate Chemistry And Carbon Sequestation Driven By Inorganic Carbon Outwelling From Mangroves And Saltmarshes, Gloria M. S. Reithmaier, Alex Cabral, Anirban Akhand, Matthew J. Bogard, Alberto V. Borges, Steven Bouillon, David J. Burdige, Mitchell Call, Nengwang Chen, Xiaogang Chen, Luiz C. Cotovicz Jr., Meagan J. Eagle, Erik Kristensen, Kevin D. Kroeger, Zeyang Lu, Damien T. Maher, J. Lucas Pérez-Lloréns, Raghab Ray, Pierre Taillardat, Joseph J. Tamborski, Rob C. Upstill-Goddard, Faming Wang, Zhaohui Aleck Wang, Kai Xiao, Yvonne Y.Y. Yau, Isaac R. Santos

OES Faculty Publications

Mangroves and saltmarshes are biogeochemical hotspots storing carbon in sediments and in the ocean following lateral carbon export (outwelling). Coastal seawater pH is modified by both uptake of anthropogenic carbon dioxide and natural biogeochemical processes, e.g., wetland inputs. Here, we investigate how mangroves and saltmarshes influence coastal carbonate chemistry and quantify the contribution of alkalinity and dissolved inorganic carbon (DIC) outwelling to blue carbon budgets. Observations from 45 mangroves and 16 saltmarshes worldwide revealed that >70% of intertidal wetlands export more DIC than alkalinity, potentially decreasing the pH of coastal waters. Porewater-derived DIC outwelling (81 ± 47 mmol m^{−2 …}

Providing A Framework For Seagrass Mapping In United States Coastal Ecosystems Using High Spatial Resolution Satellite Imagery, Megan M. Coffer, David D. Graybill, Peter J. Whitman, Blake A. Schaeffer, Wilson B. Salls, Richard C. Zimmerman, Victoria Hill, Marie Cindy Lebrasse, Jiang Li, Darryl J. Keith, James Kaldy, Phil Colarusso, Gary Raulerson, David Ward, W. Judson Kenworthy

OES Faculty Publications

Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no …

Quantum Computing And Its Applications In Healthcare, Vu Giang

OUR Journal: ODU Undergraduate Research Journal

This paper serves as a review of the state of quantum computing and its application in healthcare. The various avenues for how quantum computing can be applied to healthcare is discussed here along with the conversation about the limitations of the technology. With more and more efforts put into the development of these computers, its future is promising with the endeavors of furthering healthcare and various other industries.

Machine Learning Methods For Prediction Of Human Infectious Virus And Imputation Of Hla Alleles, Xiaoqing Gao

Dissertations, Master's Theses and Master's Reports

This dissertation contains three Chapters. The following is a concise description of each Chapters.

In Chapter 1, we introduced the Random Forest, a machine learning method, to foresee whether a virus is capable of infecting humans. The Covid pandemic informs us the importance of predicting the ability of a zoonotic virus that can infect humans from its genomic sequence. We used the -mer with and as features of a virus to predict if it can affect humans. We further employed the Boruta algorithm to select the important features, then fed those important features into the Random Forest method to train …

Biophysical Insights Into Peptide And Alcohol Perturbations On Biomimetic Membranes, Michael Hai Nguen

Electronic Theses and Dissertations

Biological membranes exist in every domain of life. Life exists due to the presence of these special structures for which we take for granted. They are composed of fatty lipids and workhorse proteins and act as the premier interface of biological processes. Due to the sheer quantity and complexity within their thin boundary, studying their actions and properties pose challenges to researchers. As a result, simplified biomembrane mimics are employed regularly. We will use several types of biomembrane mimics to understand fundamental properties of membranes. In the present thesis, we also attempt to move beyond the canonical structure-based theories upon …

Stem Education And Retention For Black Women Using High-Impact Practices: Historically Black Colleges And Universities Vs. Predominantly White Liberal Arts Colleges, Annette Njei

CMC Senior Theses

Black women are significantly underrepresented within the fields of science, technology, engineering, and mathematics (STEM). To address this, the Association of American Colleges & Universities crafted ten high-impact practices to increase student engagement and promote retention. This research paper examines how three specific high-impact practices (learning communities, mentoring, and undergraduate research experience) are utilized in STEM education.This research paper explores and compares the best high impact approaches that successfully teach and retain Black women within the various fields of STEM within the differing academic environments of historically Black colleges & universities ( HBCUs) and predominantly white liberal art colleges (PWLACs). …

High Performance Computing Algorithms For Accelerating Peptide Identification From Mass-Spectrometry Data Using Heterogeneous Supercomputers, Muhammad Haseeb, Fahad Saeed, Ed.

School of Computing and Information Sciences

Fast and accurate identification of peptides and proteins from the mass spectrometry (MS) data is a critical problem in modern systems biology. Database peptide search is the most commonly used computational method to identify peptide sequences from the MS data. In this method, giga-bytes of experimentally generated MS data are compared against tera-byte sized databases of theoretically simulated MS data resulting in a compute- and data-intensive problem requiring days or weeks of computational times on desktop machines. Existing serial and high performance computing (HPC) algorithms strive to accelerate and improve the computational efficiency of the search, but exhibit sub-optimal performances …

Automating Intersection Marking Data Collection And Condition Assessment At Scale With An Artificial Intelligence-Powered System, Kun Xie, Huiming Sun, Xiaomeng Dong, Hong Yang, Hongkai Yu

Civil & Environmental Engineering Faculty Publications

Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with …

More On Complex Hesitant Fuzzy Graphs, Abdulazeez Alkouri, Eman A. Abuhijleh, Ghada Alafifi, Eman Almuhur, Fadi M.A. Al-Zubi

All Works

Correctly determining a company’s market worth during an entire year or a certain period presents a difficulty to decision-makers. In the case of the merger of companies, the need performs heavier when both the companies’ owners are attracted to establishing a fair price at the optimal time to merge. The effectiveness of representing, connecting and manipulating both uncertainty and periodicity information becomes highly required. Hence, study and nhance some properties and conditions of the algebraic structure of complex hesitant fuzzy graphs. Therefore, the degree of composition between two complex hesitant fuzzy graphs is proposed. Also, the formal definitions of union, …

Implementation Of Course Structure In Stem Courses For Student Motivation And Learning, And Lab Innovation, Muzammil Arshad, Mamoona Muzammil

Chemistry Faculty Publications and Presentations

The present study is an extension of implementation of the course structure which was initially designed, developed and implemented at Texas A&M University for engineering courses. This study extends its implementation to other STEM courses to assess its applicability and effectiveness in science related courses. The course structure is employed at the Chemistry department at University of Texas Rio Grande Valley (UTRGV). The present study is an autoethnography of the implementation of the course structure and its effectiveness assessment. This study highlights the implementation of the course structure considering student motivation and learning since student motivation is an important research …

Maximizing Productivity And Quality In Senior Thesis Writing With Artificial Intelligence And Natural Language Processing Driven Tools, Lauren Leadbetter

CMC Senior Theses

This project is a Python program designed to generate a senior thesis on a user-
inputted topic using natural language processing techniques. The program takes in a
topic from the user and then uses OpenAI API to deploy text models for text genera-
tion and evaluation, such as GPT-3 and Davinci-003. The resulting output is in .tex
format and includes a first-draft outline and paper, followed by self-generated assessment, with scoring, revisions, and feedback comments instructing manual revisions.

This submission is a sample using one available model of the project, meant to
demonstrate it’s functionality and limitations. Further model versions …

Statistical And Machine Learning Analysis Of The Human Brain Functional Network In A Multi-Site Resting-State Functional Mri Database Framework, Oswaldo Artiles, Fahad Saeed, Ed.

School of Computing and Information Sciences

The human brain has a complex network structure that is non-random and multiscale. It consists of subsystems coupled by a nonlinear dynamic, enabling it to produce complex responses to various external inputs and self-organize. To understand the physical structure and specific brain functions, it is essential to comprehend the connectivity of the hundreds of billions of neurons in the human brain. Functional connectivity (FC) in modern neuroscience is the statistical temporal dependencies between neuronal activation events occurring in spatially separated brain regions. Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the …