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.
Filtering by
- All Subjects: Supply Chain
- All Subjects: Simulation
The creative project of this thesis showcases various wardrobes that have solely been purchased second-hand. The purpose of the creative presentation is to show that no matter one’s style preference, occupation, or age, second hand shopping can appeal to every type of customer. Second hand shopping is not only for “thrifty” millennials, it it for everyone, and can encompass anyone’s clothing needs.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
To understand the role communication and effective management play in the project management field, virtual work was analyzed in two phases. Phase one consisted of gaining familiarity within the field of project management by interviewing three project managers who discussed their field of work, how it has changed due to Covid-19, approaches to communication and virtual team management, and strategies that allow for effective project management. Phase two comprised a simulation in which 8 ASU student volunteers were put into scenarios that required completing and executing a given project. Students gained project experience through the simulation and had an opportunity to reflect on their project experience.
Currently, autonomous vehicles are being evaluated by how well they interact with humans without evaluating how well humans interact with them. Since people are not going to unanimously switch over to using autonomous vehicles, attention must be given to how well these new vehicles signal intent to human drivers from the driver’s point of view. Ineffective communication will lead to unnecessary discomfort among drivers caused by an underlying uncertainty about what an autonomous vehicle is or isn’t about to do. Recent studies suggest that humans tend to fixate on areas of higher uncertainty so scenarios that have a higher number of vehicle fixations can be reasoned to be more uncertain. We provide a framework for measuring human uncertainty and use the framework to measure the effect of empathetic vs non-empathetic agents. We used a simulated driving environment to create recorded scenarios and manipulate the autonomous vehicle to include either an empathetic or non-empathetic agent. The driving interaction is composed of two vehicles approaching an uncontrolled intersection. These scenarios were played to twelve participants while their gaze was recorded to track what the participants were fixating on. The overall intent was to provide an analytical framework as a tool for evaluating autonomous driving features; and in this case, we choose to evaluate how effective it was for vehicles to have empathetic behaviors included in the autonomous vehicle decision making. A t-test analysis of the gaze indicated that empathy did not in fact reduce uncertainty although additional testing of this hypothesis will be needed due to the small sample size.
A novel CFD algorithm called LEAP is currently being developed by the Kasbaoui Research Group (KRG) using the Immersed Boundary Method (IBM) to describe complex geometries. To validate the algorithm, this research project focused on testing the algorithm in three dimensions by simulating a sphere placed in a moving fluid. The simulation results were compared against the experimentally derived Schiller-Naumann Correlation. Over the course of 36 trials, various spatial and temporal resolutions were tested at specific Reynolds numbers between 10 and 300. It was observed that numerical errors decreased with increasing spatial and temporal resolution. This result was expected as increased resolution should give results closer to experimental values. Having shown the accuracy and robustness of this method, KRG will continue to develop this algorithm to explore more complex geometries such as aircraft engines or human lungs.
This thesis looks at the digitalization process holistically. It recognizes that for a digitalization initiative to be successful, it takes input from multiple departments and experts from diverse backgrounds. This paper will be evaluating the interconnectivity needed between the supply chain and human resources departments to spearhead the creation of a digitalization team. Both sectors must have a firm understanding of the other’s needs, in order to acquire, train, and maintain people who will have the necessary hard and soft skills to develop the digital processes. After conducting extensive research around hiring and training, the researchers identified several best practices that companies can utilize to build a successful digital logistics team. Regarding hiring, companies can improve their current practices by collaborating with universities to create synergy between enterprise needs and college curriculum, as well as utilizing talent acquisition data analytics. They must also employ targeted recruiting strategies to attract high-quality talent and create explicit and attractive job postings. In addition to hiring, companies must also continuously improve their training initiatives to ensure their team’s success. In order to do so, firms should conduct training needs analysis, personalize training using technology, offer non-traditional learning modalities, provide holistic supply chain training, and create a learning culture.