Electronic Thesis or Dissertation

My fascination with the human figure remains a prominent part of my work as a familiar and complex path to formalist traditions. But as I have sought to expand my engagement with representational figuration, I look towards themes of universal emotions and conditions - grief, remembrance, anger, affection and resolve. Within this, I often allude to socio-political content, art historical references or personal experiences. In integrating representational figuration with a contemporary approach, I incorporate techniques of drawing, painting and printmaking into narrative formats while preserving the figure as the protagonist.
I lean into gestural lines, expressive marks and abstract color fields to breathe life into the work and enhance the emotive context. I embrace the intuitive process as I believe it reflects and retains a certain spiritual spontaneity and response.
Recently, I have experienced deep loss. It is the grief associated with this that has informed these pieces. Grief is a process that can be present, historical, anticipatory or prolonged. I have been stricken by a wide range of losses –personal, societal, political and cultural - death of loved ones, risk of severe limitations to civil liberties and bodily autonomy, global wars and violence, xenophobia, brutal attacks on democratic ideals and even the inevitable passage of time have precipitated my personal and visceral grief.

In the current world of fast-paced data production, statistics and machine learning tools are essential for interpreting and utilizing the full potential of this data. This dissertation comprises three studies employing statistical analysis and Convolutional Neural Network models. First, the research investigates the genetic evolution of the SARS-CoV-2 RNA molecule, emphasizing the role of epistasis in the RNA virus’s ability to adapt and survive. Through statistical tests, this study validates the significant impacts of genetic interactions and mutations on the virus’s structural changes over time, offering insights into its evolutionary dynamics. Secondly, the dissertation explores medical diagnosis by implementing Convolutional Neural Networks to differentiate between lung CT-scans of COVID-19 and non-COVID patients. This portion of the research demonstrates the capability of deep learning to enhance diagnostic processes, thereby reducing time and increasing accuracy in clinical settings. Lastly, we delve into gravitational wave detection, an area of astrophysics requiring precise data analysis to identify signals from cosmic events such as black hole mergers. Our goal is to utilize Convolutional Neural Network models in hopes of improving the sensitivity and accuracy of detecting these difficult to catch signals, pushing the boundaries of what we can observe in the universe. The findings of this dissertation underscore the utility of combining statistical methods and machine learning models to solve problems that are not only varied but also highly impactful in their respective fields.

Over the past decade, hydrogen gas generation has been a critical component toward clean energy due to its high specific energy content. Generating hydrogen gas from water is crucial for future applications, including space transportation. Recent studies show promising results using silicon nanoparticles (SiNPs) for spontaneous hydrogen generation, but most methods require external energy like high temperature or pressure. In this work, we investigated hydrogen production from SiNPs without external energy by leveraging high pH water using sodium hydroxide and optimizing the process with a microfluidic approach. When comparing the physical dispersion methods using the 0.1 mg/mL case, ultrasonic bath produced more hydrogen than magnetic stirrer. In this thesis, 0.01% dextran with pure SiNPs at concentrations of 0.1 mg/mL, 0.2 mg/mL, and 0.3 mg/mL was analyzed. The highest concentration with dextran generated at least 40% less hydrogen than silicon alone, thus dextran did not increase hydrogen gas.

In recent decades, developments in glycobiology have enabled the use of glycopeptides as tools for studying complex diseases such as cancer. Mucin 1 (MUC1) is a heavily glycosylated transmembrane protein, altered in both expression and glycosylation pattern in human carcinomas of the epithelium. The presence of incomplete or truncated glycan structures, often capped by sialic acid, commonly known as tumor-associated carbohydrate antigens (TACAs), on the cell surface is a well-known cancer biomarker and therapeutic target for different types of cancer. Accumulating evidence suggests that TACAs are recognized by the endogenous carbohydrate binding proteins (lectins). These interactions frequently result in the development of a protumor microenvironment, favoring tumor initiation, progression, metastasis, and immune evasion. Macrophage galactose binding lectin (MGL) is a C-type lectin receptor found on antigen-presenting cells (APCs) which facilitates the uptake of carbohydrate antigens for antigen presentation, modulating the immune response in homeostasis, autoimmunity, and cancer. Considering the crucial role of tumor-associated forms of MUC1 and MGL in tumor immunology, a thorough understanding of this interaction is essential for it to be exploited for cancer vaccine strategies. The specific goal of this research is to synthesize structurally well-defined chemical probes, mono and multiple glycosylated MUC1 glycopeptide models bearing the Tn or sTn antigens, that provide control over the complexity of the chemical space of multivalent ligands. For this purpose, a concise scheme was developed for the large-scale synthesis of the Tn and sTn antigen building blocks in a relatively high yield with moderate stereoselectivity. Thiophenyl glycoside donors, in the presence TfOH/NIS or TMSOTf/NIS as promoter systems, were used for the galactosylation and sialylation steps of the amino acid building block synthesis, respectively. We explored the effect of the activator, temperature, solvent, and excess equivalent of sialic acid thioglycoside donor on the sialylation reaction.

We explore a novel method of approximating contractible invariant circles in maps. The process begins by leveraging improvements on Birkhoff's Ergodic Theorem via Weighted Birkhoff Averages to compute high precision estimates on several Fourier modes. We then set up a Newton-like iteration scheme to further improve the estimation and extend the approximation out to a sufficient number of modes to yield a significant decay in the magnitude of the coefficients of high order. With this approximation in hand, we explore the phase space near the approximate invariant circle with a form numerical continuation where the rotation number is perturbed and the process is repeated. Then, we turn our attention to a completely different problem which can be approached in a similar way to the numerical continuation, finding a Siegel disk boundary in a holomorphic map. Given a holomorphic map which leads to a formally solvable cohomological equation near the origin, we use a numerical continuation style process to approximate an invariant circle in the Siegel disk near the origin. Using an iterative scheme, we then enlarge the invariant circle so that it approximates the boundary of the Siegel disk.

This study aimed to understand the relationship between School Counseling Site Supervisors’ (SCSS) characteristics, Social Determinants of Health (SDOH) training, cultural humility, advocacy competency, and SDOH competency (n = 69). SDOH refers to the factors that inform an individual’s physical and mental health. Cultural humility refers to an innate openness and curiosity about individual experiences, perspectives, and culture. Advocacy competency refers to the ability to implement advocacy efforts within an individual’s community. Having competency with addressing SDOH in schools, practicing cultural humility, and advocacy competency can help SCSS improve supervision practice within school communities.
This study followed a non-experimental, correlational survey research design. Multiple linear regression analyses were conducted to measure the strength of the relationships between the variables. The data supported statistically significant relationships between SDOH-based supervision training (F(12,51) = 2.59, p < .05, R2 = .38), cultural humility (F(1,67) = 6.17, p < .015, R2 = .08), and advocacy competency (F(1,67) = 9.7, p < .003, R2 = .13) as predictors of SDOH competency.

This systematic literature review investigates K-12 social studies educators' perspectives on digital game-based learning (DGBL) from 2015 to 2024, focusing on its impact on student engagement, motivation, and learning outcomes. Using Braun and Clarke’s (2006) reflexive thematic analysis, data from 10 studies were synthesized. Four main themes emerged regarding engagement and motivation, and three concerning learning outcomes. Findings indicate that while teachers recognize DGBL's potential benefits, they prioritize meeting learning objectives over new instructional strategies due to teaching demands and limited suitable digital games. Barriers such as time constraints and resource limitations hinder broader DGBL implementation. Overcoming these challenges requires collaboration among educators, administrators, and policymakers to leverage DGBL in K-12 social studies education fully.

Antibiotic resistance is a growing concern due to the improper use of antibiotics. Not only is antibiotic resistance increasingly occurring in human populations, but it appears to be spreading in wildlife populations too due to drug overuse and misuse in medicine, farming, and industrial settings, and the subsequent release into watersheds. This project examined the prevalence of antibiotic resistant bacteria in the hindgut microbiome of green (Chelonia mydas) (n=60) and loggerhead (Caretta caretta) (n=57) sea turtles. Hindgut swabs were cultured for gram negative bacteria and exposed to 6 antibiotics. 83.3% of samples were resistant or intermediately resistant to at least one antibiotic, and 27.7% of samples were resistant or intermediately resistant to three antibiotics. This study provides more information regarding the relationship between turtle characteristics and the presence of antibiotic resistance in the hindgut of Florida sea turtles, as well as examine the types of bacteria found in the hindgut.

The study of non-invasive techniques to analyze the propagation of corrosion in steel reinforced concrete structures proves to be a great alternative to better understanding the corrosive process of rebar and increasing its useful life. The study presented in this document examines the evolution of steel reinforced concrete corrosion over time by applying a small anodic current over four samples, one with a single rebar (16X) and three with three rebars. The rebars were interconnected to apply the anodic current and accelerate their corrosion. Galvanostatic Pulse (GP) was used. This method applies a constant current pulse to the rebar for 150 seconds while monitoring the potential of the rebars. Each rebar's corrosion current was assessed using GP measurements when no anodic current was applied, and the rebars were disconnected. Sample 16X additionally underwent ultrasonic acoustic analysis by collecting the surface and rebar echo response with a transducer and modeling the sound propagation for poroelastic media with an adapted version of the novel Biot-Stoll method.

Due to technological advancement, energy consumption and demand have been increasing significantly, primarily satisfied by fossil fuel consumption. This reliance on fossil fuels results in substantial greenhouse gas emissions, with CO₂ being the most prominent contributor to global warming. To mitigate this issue and prevent CO₂ emissions, Carbon Capture, Utilization, and Storage (CCUS) technologies are employed. Among these, the amine scrubbing method is widely used due to its high CO2 capture efficiency and regenerative ability. However, this method has drawbacks, including high toxicity, corrosion, and substantial freshwater consumption.
To develop an environmentally sustainable carbon capture solution, researchers are exploring alternatives such as the use of seawater and enhanced CO2 capture with catalysts. In this study, we analyze the catalytic performance of nickel nanoparticles (NiNPs) in seawater with carboxymethyl cellulose (CMC) polymers. Using flow-focusing geometry-based microfluidic channels, we investigated CO₂ dissolution at various concentrations of nanoparticles and CMC polymers. The objective is to optimize the concentration of nanoparticles and CMC polymers for effective CO₂ dissolution. We utilized NiNPs with diameters of 100 nm and 300 nm in CMC concentrations of 100 ml/L, 200 ml/L, and 300 ml/L. Additionally, NiNP concentrations ranging from 6 mg/L to 150 mg/L were tested for CO₂ dissolution in seawater. The results indicated that a concentration of 10 mg/L NiNPs in 100 mg/L CMC provided a CO₂ dissolution of 57%, the highest for this specific CMC concentration. At CMC concentrations of 200 ml/L and 300 ml/L, NiNP concentrations of 70 mg/L and 90 mg/L achieved CO₂ dissolution rates of 58.8% and 67.2%, respectively.