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.
This project is a critical analysis of the works of 6 American war veterans and how they demonstrate trauma in their narratives. The texts covered here are Philip Red Eagle’s Red Earth (2007), John A. Williams’ Captain Blackman (1972), Roy Scranton’s War Porn (2016), Tim O’Brien’s The Things They Carried (1990), Kurt Vonnegut’s Slaughterhouse-Five (1969), and Joseph Heller’s Catch-22 (1961).
The United States is an empire. It was founded as such and continues to be one to this day. However, during the most prominent periods of imperial expansion, anti-imperialist organizations and politicians often rise up to oppose these further imperialist actions. This thesis paper examines the rhetoric used by these organizations and politicians, particularly through their speeches and platforms. The primary focus is on the role of American exceptionalism in this rhetoric, and what American anti-imperialism not rooted in this concept looks like. This analysis will be done by looking at a few key specific texts from these organizations and politicians, including (but not limited to) the platform of the Anti-Imperialist League and the speech Representative Barbara Lee gave to explain her lone no vote on the Authorization for Use of Military Force in Afghanistan in 2001.
High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.
The goal of this experiment was to examine the energy absorption properties of origami-inspired honeycomb and standard honeycomb structures. These structures were 3D printed with two different materials: thermoplastic polyurethane (TPU) and acrylonitrile butadiene styrene (ABS). Quasi-static compression testing was performed on these structures for both types and materials at various wall thicknesses. The energy absorption and other material properties were analyzed for each structure. Overall, the results indicate that origami-inspired structures perform best at energy absorption at a higher wall thickness with a rigid material. The results also indicated that standard honeycomb structures perform better with lower wall thickness, and also perform better with a rigid, rather than a flexible material. Additionally, it was observed that a flexible material, like TPU, better demonstrates the folding and recovery properties of origami-inspired structures. The results of this experiment have applications wherever honeycomb structures are used, mostly on aircraft and spacecraft. In vehicles with structures of a sufficiently high wall thickness with a rigid material, origami-inspired honeycomb structures could be used instead of current honeycomb structures in order to better protect the passengers or payload through improved energy absorption.
