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Description
In 2022, a previous team of computer science and accounting students worked together to design and build a fully-functioning website to automate accounting transactions. They created dynamic accounting applications using software frameworks such as React and Express. They then used the services provided by Amazon Web Services to make the website available online. The stakeholders of the project wanted to expand upon the services provided by the website so they entrusted our team with implementing new features and applications to the software system. Using the same software frameworks and services of the previous team, we redesigned the website and increased its functionality to better meet the needs of accounting automation.
ContributorsJain, Sejal (Author) / Macabou, Elise (Co-author) / Lim, Jonathan (Co-author) / Villani, Jacob (Co-author) / Chen, Yinong (Thesis director) / Hunt, Neil (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Computer Science and Engineering Program (Contributor) / School of Public Affairs (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2024-05
Description
This thesis examines the discourse on firearm suicide in the United States, focusing on the contrasting narratives of gun rights and gun control advocacy groups. It analyzes how these groups use rhetorical, visual, and linguistic strategies to shape public perception and policy discussions about the link between gun ownership and suicide rates. The study highlights the role of digital media in amplifying these divergent narratives, revealing a polarized discourse that simplifies the complexities of firearm suicide. The findings advocate for a more nuanced approach to the public and policy dialogue, aiming to foster more effective suicide prevention strategies.
ContributorsSinger, Sean (Author) / Young, Alex (Thesis director) / Cohen, Adam (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2024-05
Description
This thesis project focused on determining the primary causes of flight delays within the United States then building a machine learning model using the collected flight data to determine a more efficient flight route from Phoenix Sky Harbor International Airport in Phoenix, Arizona to Harry Reid International Airport in Las Vegas, Nevada. In collaboration with Honeywell Aerospace as part of the Ira A. Fulton Schools of Engineering Capstone Course, CSE 485 and 486, this project consisted of using open source data from FlightAware and the United States Bureau of Transportation Statistics to identify 5 primary causes of flight delays and determine if any of them could be solved using machine learning.
The machine learning model was a 3-layer Feedforward Neural Network that focused on reducing the impact of Late Arriving Aircraft for the Phoenix to Las Vegas route. Evaluation metrics used to determine the efficiency and success of the model include Mean Squared Error (MSE), Mean Average Error (MAE), and R-Squared Score.
The benefits of this project are wide-ranging, for both consumers and corporations. Consumers will be able to arrive at their destination earlier than expected, which would provide them a better experience with the airline. On the other side, the airline can take credit for the customer's satisfaction, in addition to reducing fuel usage, thus making their flights more environmentally friendly.
This project represents a significant contribution to the field of aviation as it proves that flights can be made more efficient through the usage of open source data.
ContributorsRosenbloom, Yonatan (Author) / Chavez Echeagaray, Maria Elena (Thesis director) / Govindillam, Sreenivasan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor)
Created2024-05
Description
The project investigates the differences in the left and right hemispheres during a sensory gating paradigm in people with dyslexia compared to neurotypicals. The sensory gating paradigm included repeated pure tones, and each response's negative amplitudes during the first and second tones were recorded. It was determined that the response to the second tone can predict the response to the second tone. Still, there was no significant difference between participants with dyslexia and controls in the gating magnitude. There were no significant results when determining if gamma and beta power could predict the level of gating magnitude.
ContributorsBienz, Owen (Author) / Peter, Beate (Thesis director) / Daliri, Ayoub (Committee member) / Kim, Yookyung (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2024-05
Description
The present study was conducted in order to better understand how stuttering appears in bilingual Spanish-English (SE) speakers in Arizona. The primary purpose was to determine whether the frequencies and types of speech disfluencies that are produced by bilingual speakers vary depending on the language they are speaking in. In addition, the study attempted to determine whether there exists a variation of the frequencies or types of speech disfluencies that are produced by a bilingual speaker based on their approximate dominance of the language they are speaking in. For the purpose of the study, two elementary school children (2 boys, 7 and 10 years old), who were identified as diagnosed stutterers by a speech-language pathologist (SLP), were recruited and interviewed for 45 minutes. The two participants were identified as typically fluent in both English and Spanish from conversations with their parents and the pre-interview parental questionnaire in which their level of exposure to and approximate competence in each language was established. The interviews consisted of a speech and reading portion in both English and Spanish, in which spontaneous and non-spontaneous speech data was recorded. The results of the study indicate that there does seem to be a difference in the frequencies and types of speech disfluencies that appear depending on the language that a bilingual individual is speaking in. Additionally, there seems to be a relationship between approximate language dominance and the types or frequencies of speech disfluencies that are produced, however further research is recommended on this topic with a larger sample size of participants.
ContributorsKhakhanova, Anastasiya (Author) / Gradoville, Michael (Thesis director) / Daliri, Ayoub (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / School of Life Sciences (Contributor)
Created2024-05
Description
Little is known about the state of Arctic sea ice at any given instance
in time. The harshness of the Arctic naturally limits the amount of in situ data that can be collected, resulting in gathered data being limited in both location and time. Remote sensing modalities such as satellite Synthetic Aperture Radar (SAR) imaging and laser altimetry help compensate for the lack of data, but suffer from uncertainty because of the inherent indirectness. Furthermore, precise remote sensing modalities tend to be severely limited in spatial and temporal availability, while broad methods are more accessible at the expense of precision. This thesis focuses on the intersection of these two problems and explores the possibility of corroborating remote sensing methods to create a precise, accessible source of data that can be used to
examine sea ice at local scale.
ContributorsBaker, John (Author) / Cochran, Douglas (Thesis director) / Wei, Hua (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
I study some comparative statics implications of disappointment-averse preferences
for optimal portfolios. Specifically, I find that risk-averse disappointment-averse investors
increase investment in a risky asset as a result of a monotone likelihood ratio improvement in
the asset’s distribution, a subset of First Order Stochastic improvements. This gives a testable
implication between the disappointment aversion model, and alternatives, including expected
utility. I also discuss previously noted implications for disappointment aversion in helping
explain the equity premium puzzle.
ContributorsWarrier, Raghav (Author) / Schlee, Edward (Thesis director) / Almacen, Christopher (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Manually determining the health of a plant requires time and expertise from a human. Automating this process utilizing machine learning could provide significant benefits to the agricultural field. The detection and classification of health defects in crops by analyzing visual data using computer vision tools can accomplish this. In this paper, the task is completed using two different types of existing machine learning algorithms, ResNet50 and CapsNet, which take images of crops as input and return a classification that denotes the health defect the crop suffers from. Specifically, the models analyze the images to determine if a nutritional deficiency or disease is present and, if so, identify it. The purpose of this project is to apply the proven deep learning architecture, ResNet50, to the data, which serves as a baseline for comparison of performance with the less researched architecture, CapsNet. This comparison highlights differences in the performance of the two architectures when applied to a complex dataset with a multitude of classes. This report details the data pipeline process, including dataset collection and validation, as well as preprocessing and application to the model. Additionally, methods of improving the accuracy of the models are recorded and analyzed to provide further insights into the comparison of the different architectures. The ResNet-50 model achieved an accuracy of 100% after being trained on the nutritional deficiency dataset. It achieved an accuracy of 88.5% on the disease dataset. The CapsNet model achieved an accuracy of 90% on the nutritional deficiency dataset but only 70% on the disease dataset. In comparing the performance of the two models, the ResNet model outperformed the other; however, the CapsNet model shows promise for future implementations. With larger, more complete datasets as well as improvements to the design of capsule networks, they will likely provide exceptional performance for complex image classification tasks.
ContributorsChristner, Drew (Author) / Carter, Lynn (Thesis director) / Ghayekhloo, Samira (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
This study evaluates the level of human papillomavirus (HPV) awareness and knowledge among students and healthcare workers at Arizona State University (ASU). The significance of understanding HPV awareness and knowledge is paramount in the context of global public health as HPV is the most commonly sexually transmitted infection (STI) in the United States and can cause various health conditions, most notably cervical cancer. This study employed an anonymous, cross-sectional survey design to test for HPV awareness and knowledge among ASU students and healthcare workers. It most notably found that while approximately 90% of students are aware of HPV as a potential STI, only 54% are aware that HPV can cause various warts, only 30% are aware that HPV can cause six types of cancers, only 61% have been and/or know someone who has been vaccinated against HPV, and 80% have never been tested for HPV. Data was unable to be collected from healthcare workers on campus due to logistical conflicts with ASU Health Services. Overall, the study showed general awareness of HPV among ASU students but less functional knowledge about HPV, such as the health conditions it can cause, that could play a part in prevention behaviors, most notably with getting vaccinated and tested. While this study is a good start and baseline for ASU, it should be noted that it may not be entirely reflective of the university as a whole due to the majority of participants being white, cis, and female, so future work for data collection and subsequent outreach prevention programs is recommended to be complete.
ContributorsEsquerra, Sebastian (Author) / Quezada, Blanca (Thesis director) / Leaver, Jill (Committee member) / Ackerley, Hunter (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2024-05
Description
The social determinants of drug use are critical factors that influence patterns of substance use across urban and rural populations. This paper explores these determinants by comparing the prevalence and types of drug use within these diverse environments, drawing upon data and insights from recent studies. In urban areas, the variety and availability of drugs, coupled with socioeconomic disparities and cultural diversity, lead to distinct patterns of drug use. Conversely, rural areas face challenges like limited healthcare access and higher rates of prescription opioid misuse, often exacerbated by economic and social isolation. Significant differences in the social fabric between urban and rural settings influence drug use behaviors. Urban environments, characterized by dense populations and a wide array of available substances, report higher incidences of cocaine, heroin, and synthetic drug use. These areas often benefit from more extensive healthcare services but also face challenges such as higher levels of homelessness and crime, influencing substance use patterns. In contrast, rural areas deal with issues such as limited healthcare services and greater social stigma, which can deter individuals from seeking help, thus perpetuating cycles of substance abuse. This study underscores the importance of understanding the social determinants of health as they pertain to drug use. Factors like socioeconomic status, education, age, religion, gender, and sexual orientation play significant roles in shaping these patterns. By addressing these root causes through targeted public health interventions and policies, it is possible to effectively mitigate the impact of drug use across both urban and rural landscapes. This approach not only aids in substance abuse prevention but also ensures that interventions are equitable and culturally sensitive, thereby improving public health outcomes for diverse populations. The findings highlight the need for comprehensive strategies that consider both the unique and shared challenges faced by urban and rural communities in managing drug use. This nuanced understanding is crucial for developing interventions that are not only effective but also inclusive, addressing the broader social and economic dynamics that contribute to substance abuse.
ContributorsBahl, Shivani (Author) / Smith, Lisa (Thesis director) / Daniulaityte, Raminta (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2024-05