Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
As the return to normality in the wake of the COVID-19 pandemic enters its early stages, the necessity for accurate, quick, and community-wide surveillance of SARS-CoV-2 has been emphasized. Wastewater-based epidemiology (WBE) has been used across the world as a tool for monitoring the pandemic, but studies of its efficacy in comparison to the best-known method for surveillance, randomly selected COVID-19 testing, has limited research. This study evaluated the trends and correlations present between SARS-CoV-2 in the effluent wastewater of a large university campus and random COVID-19 testing results published by the university. A moderately strong positive correlation was found between the random testing and WBE surveillance methods (r = 0.63), and this correlation was strengthened when accommodating for lost samples during the experiment (r = 0.74).
An analysis of university flight emissions, carbon neutrality goals, and the global impact of university sanctioned flight.
Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.
Phthalates are ubiquitous in the built environment and are used across various fields, despite known endocrine disruptive properties, and other associated health hazards, including abnormalities in reproductive health and development. I investigated the presence of phthalates in the built environment using the Health Product Declaration (HPD) repository to survey for products containing these chemicals, investigated the literature for possible health effects and alternatives to phthalates, and conducted a laboratoy-based feasibility study of urinary biomarkers associated with phthalates using wastewater-based epidemiology (WBE) on a US university campus at the building-scale. Of the 5,278 products in the HPD repository, 73 contained phthalates and were most commonly found in windows, doors, flooring, sealants, insulations, and furnishings. Alternative plasticizers (cardanol, epoxidized soybean oil, hydrogenated castor oil) usage were identified in 10 products from HPD repository. The two wastewater samples analyzed by liquid chromatography-tandem mass spectrometry (LC-MS-MS) showed that dimethyl phthalate (DMP) was detectable, as well as its human metabolite, monomethyl phthalate (MMP), observed at a concentration of 163-202 ng/L. These results indicate low human exposure from the building materials in the limited convenience sample investigated. Future studies of building scale wastewater-based epidemiology are recommended to investigate these and other phthalates commonly found in the built environment, including diisononyl phthalate (DINP) and diisononyl hexahydrophthalate (DINCH).
The field of quantum computing is an exciting area of research that allows quantum mechanics such as superposition, interference, and entanglement to be utilized in solving complex computing problems. One real world application of quantum computing involves applying it to machine learning problems. In this thesis, I explore the effects of choosing different circuit ansatz and optimizers on the performance of a variational quantum classifier tasked with binary classification.