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.

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In thesis we will build up our operator theory for finite and infinite dimensional systems. We then prove that Heisenberg and Schrodinger representations are equivalent for systems with finite degrees of freedom. We will then make a case to switch to a C*-algebra formulation of quantum mechanics as we will

In thesis we will build up our operator theory for finite and infinite dimensional systems. We then prove that Heisenberg and Schrodinger representations are equivalent for systems with finite degrees of freedom. We will then make a case to switch to a C*-algebra formulation of quantum mechanics as we will prove that the Schrodinger and Heisenberg pictures become inadequate to full describe systems with infinitely many degrees of freedom because of inequivalent representations. This becomes important as we shift from single particle systems to quantum field theory, statistical mechanics, and many other areas of study. The goal of this thesis is to introduce these mathematical topics rigorously and prove that they are necessary for further study in particle physics.

ContributorsPerleberg, Sarah (Author) / Quigg, John (Thesis director) / Lebed, Richard (Committee member) / Barrett, The Honors College (Contributor) / Department of Physics (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05
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Description
In the age of information, collecting and processing large amounts of data is an integral part of running a business. From training artificial intelligence to driving decision making, the applications of data are far-reaching. However, it is difficult to process many types of data; namely, unstructured data. Unstructured data is

In the age of information, collecting and processing large amounts of data is an integral part of running a business. From training artificial intelligence to driving decision making, the applications of data are far-reaching. However, it is difficult to process many types of data; namely, unstructured data. Unstructured data is “information that either does not have a predefined data model or is not organized in a pre-defined manner” (Balducci & Marinova 2018). Such data are difficult to put into spreadsheets and relational databases due to their lack of numeric values and often come in the form of text fields written by the consumers (Wolff, R. 2020). The goal of this project is to help in the development of a machine learning model to aid CommonSpirit Health and ServiceNow, hence why this approach using unstructured data was selected. This paper provides a general overview of the process of unstructured data management and explores some existing implementations and their efficacy. It will then discuss our approach to converting unstructured cases into usable data that were used to develop an artificial intelligence model which is estimated to be worth $400,000 and save CommonSpirit Health $1,200,000 in organizational impact.
ContributorsBergsagel, Matteo (Author) / De Waard, Jan (Co-author) / Chavez-Echeagaray, Maria Elena (Thesis director) / Burns, Christopher (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
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Description
This thesis explores the relationship between the performance of beauty and Potential New Member (PNM) success across various formats of formal sorority recruitment at ASU. It builds off of existing scholarship in economics of beauty premiums in labor markets, as well as sociological research on the intersection of beauty and

This thesis explores the relationship between the performance of beauty and Potential New Member (PNM) success across various formats of formal sorority recruitment at ASU. It builds off of existing scholarship in economics of beauty premiums in labor markets, as well as sociological research on the intersection of beauty and human interaction. Through interviews of women who went through formal recruitment across three different modalities (in-person, virtual, and hybrid), themes emerged that suggest the current policies in place by ASU Panhellenic make it so that the performance of beauty hinders the facilitation of a recruitment process that is truly values-based.
Created2022-05
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Description
Game theory, the mathematical study of mathematical models and simulations that often play out like a game, is applicable to a plethora of disciplines, one of which is infrastructure security. This is a rather new and niche subject area, and our aim is to perform a bibliographic analysis to analyze

Game theory, the mathematical study of mathematical models and simulations that often play out like a game, is applicable to a plethora of disciplines, one of which is infrastructure security. This is a rather new and niche subject area, and our aim is to perform a bibliographic analysis to analyze the thematic makeup of a selected body of publications in this area, as well as analyze trends in paper publication, journal contributions, country contributions, and trends in the authorship of the publications.
ContributorsChandra, Varun (Author) / Jevtic, Petar (Thesis director) / Gall, Melanie (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05
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Description

Bdellovibrio bacteriovorus (BB) is a gram negative predatory bacteria that uses other gram negative bacteria to proliferate non-binarily. Due to the predatory nature of BB researchers have proposed to use it as a potential biocontrol agent against other gram negative bacteria. The in vivo effect of predatory bacteria on a

Bdellovibrio bacteriovorus (BB) is a gram negative predatory bacteria that uses other gram negative bacteria to proliferate non-binarily. Due to the predatory nature of BB researchers have proposed to use it as a potential biocontrol agent against other gram negative bacteria. The in vivo effect of predatory bacteria on a living host lacks thorough investigation. This paper explores BB inside and outside of the C. elegans. BB acts internally by pre- infecting C. elegans with E. coli and then treating the worms with BB. After BB treatment worm survivavbility increased and morbidity decreased. Ex- ternally, BB modulated the environment around the nematode which reduced infection rates and increased nematode lifespan and survivability. Together, the internal and external results suggest BB has the capability to act as a living antibiotic acting topically and internally to reduce infection rates.

ContributorsStambolic, Milena (Author) / Presse, Steve (Thesis director) / Vlcek, Jessi (Committee member) / Barrett, The Honors College (Contributor) / Department of Physics (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05
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Description
Malaria is a deadly, infectious, parasitic disease which is caused by Plasmodium parasites and transmitted between humans via the bite of adult female Anopheles mosquitoes. The primary insecticide-based interventions used to control malaria are indoor residual spraying (IRS) and long-lasting insecticide nets (LLINs). Larvicides are another insecticide-based intervention which is

Malaria is a deadly, infectious, parasitic disease which is caused by Plasmodium parasites and transmitted between humans via the bite of adult female Anopheles mosquitoes. The primary insecticide-based interventions used to control malaria are indoor residual spraying (IRS) and long-lasting insecticide nets (LLINs). Larvicides are another insecticide-based intervention which is less commonly used. In this study, a mathematical model for malaria transmission dynamics in an endemic region which incorporates the use of IRS, LLINS, and larvicides is presented. The model is rigorously analyzed to gain insight into the asymptotic stability of the disease-free equilibrium. Simulations of the model show that individual insecticide-based interventions will not realistically control malaria in regions with high endemicity, but an integrated vector management strategy involving the use of multiple interventions could lead to the effective control of the disease. This study suggests that the use of larvicides alongside IRS and LLINs in endemic regions may be more effective than using only IRS and LLINs.
ContributorsJameson, Leah (Author) / Gumel, Abba (Thesis director) / Huijben, Silvie (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Civic & Economic Thought and Leadership (Contributor)
Created2022-05
Description
In this work, we explore the potential for realistic and accurate generation of hourly traffic volume with machine learning (ML), using the ground-truth data of Manhattan road segments collected by the New York State Department of Transportation (NYSDOT). Specifically, we address the following question– can we develop a ML algorithm

In this work, we explore the potential for realistic and accurate generation of hourly traffic volume with machine learning (ML), using the ground-truth data of Manhattan road segments collected by the New York State Department of Transportation (NYSDOT). Specifically, we address the following question– can we develop a ML algorithm that generalizes the existing NYSDOT data to all road segments in Manhattan?– by introducing a supervised learning task of multi-output regression, where ML algorithms use road segment attributes to predict hourly traffic volume. We consider four ML algorithms– K-Nearest Neighbors, Decision Tree, Random Forest, and Neural Network– and hyperparameter tune by evaluating the performances of each algorithm with 10-fold cross validation. Ultimately, we conclude that neural networks are the best-performing models and require the least amount of testing time. Lastly, we provide insight into the quantification of “trustworthiness” in a model, followed by brief discussions on interpreting model performance, suggesting potential project improvements, and identifying the biggest takeaways. Overall, we hope our work can serve as an effective baseline for realistic traffic volume generation, and open new directions in the processes of supervised dataset generation and ML algorithm design.
ContributorsOtstot, Kyle (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
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This report attempts to understand the effects of the many aspects that pertain to a woman’s path into the construction industry and their role in limiting women’s overall representation in the construction industry. More specifically, it aims to understand how upbringing, background, and culture impact women that do pursue careers

This report attempts to understand the effects of the many aspects that pertain to a woman’s path into the construction industry and their role in limiting women’s overall representation in the construction industry. More specifically, it aims to understand how upbringing, background, and culture impact women that do pursue careers in the construction industry. This paper presents some of the current and prominent issues being faced by women in in the construction industry, including those in the trades. These issues then contribute to their lack of representation and forceful exit. Additionally, it assesses personal narratives from a localized group of women who are currently employed at a large construction company. This information and these narratives are analyzed jointly to try and gain a better understanding of the current challenges being faced by women in comparison to those reported previously. This joint comparison allows for a deeper understanding of women’s perception of the construction industry as a whole.

ContributorsContreras, Marisa (Author) / Lou, Yingyan (Thesis director) / Parrish, Kristen (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Social Transformation (Contributor) / Civil, Environmental and Sustainable Eng Program (Contributor)
Created2022-05
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Description

Of the many retirement savings options available, defined benefit pension plans were once a retirement income staple. Due to the highs and lows of the economic cycle, defined benefit pension plans have become severely underfunded. A series of inadequate contributions, enabled by weak funding and risk management policies, poses uncertainty

Of the many retirement savings options available, defined benefit pension plans were once a retirement income staple. Due to the highs and lows of the economic cycle, defined benefit pension plans have become severely underfunded. A series of inadequate contributions, enabled by weak funding and risk management policies, poses uncertainty for the retirement of many. The cost of paying pension benefits rises as defined benefit pension plans become increasingly underfunded, burdening the employers who continue to pay them. However, without increasing these already unaffordable pension benefits alongside inflation, they become less valuable to retirees. As pension benefits lose their value and the costs of retirement, such as healthcare and assisted living, increase, defined benefit pension plans may not provide the retirement security that was once promised.

ContributorsCliatt, Charlotte (Author) / Milovanovic, Jelena (Thesis director) / Zicarelli, John (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05
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Description
Basketball has evolved and is continuing to evolve in parallel with media and communication. The 21st century bears witness to the digitization of basketball, media, and communication with the advent of social media. Arguably the most esteemed professional basketball league in the world, the National Basketball Association (NBA) observes fans

Basketball has evolved and is continuing to evolve in parallel with media and communication. The 21st century bears witness to the digitization of basketball, media, and communication with the advent of social media. Arguably the most esteemed professional basketball league in the world, the National Basketball Association (NBA) observes fans and players alike conversing about the game through social media platforms available across the world. One of the most popular platforms, Twitter, enables anyone with a computer to write a textual post known as a “tweet” that can be made viewable to the public. The Twitter landscape holds a trove of data and information including “sentiment” for NBA teams to analyze with the goal of improving the success of their team from a managerial perspective. Two aspects this paper will examine are fan engagement and revenue generation from the perspective of several franchises in the NBA. The purpose of this research is to explore and discover if key measures of performance including both the number of points scored in a game and the game outcome either being a win or a loss, and the location of a game being won either at home or away on the road influence fan Twitter sentiment and if there is a correlation between fan Twitter sentiment and game attendance. The statistical computing tool RStudio in combination with data compiled from online databases and websites including Basketball Reference, Wikipedia, ESPN, and Statista are employed to execute two t-tests, two analysis of variance (ANOVA) tests, and one correlation test. The results indicate there is a significant difference in fan Twitter sentiment between high-scoring games and low-scoring games, between game wins and losses, among games being won at home versus away on the road, and there is no conclusion that can be made regarding any existing correlation between fan Twitter sentiment and game attendance.
ContributorsKwan, Matthew (Author) / McIntosh, Daniel (Thesis director) / Eaton, John (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Information Systems (Contributor)
Created2022-05