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Description
The timescale of a simulation, determined by its size of time step, is an incredibly important consideration for resolving quickly varying dynamics. Indeed, if one chooses an observation timescale larger than the fastest forces in a dynamical system, the intricate details produced by that force’s fluctuations are lost. As such, for large time steps, we require integrators that are capable of capturing the effect of these missed forces, perhaps in a statistical sense if not exactly. This problem is difficult to solve in general. As such, for simulations where these high frequency details do not need to be resolved, lower order models must be employed that resolve the overall effect of these high frequency details by construction. A representative example of this is the modeling of molecular dynamics and Brownian motion. High order models of Brownian motion, such as the generalized Langevin equations, are computationally infeasible when one wants to resolve the dynamics of, for example, a pollen particle in water. A more reasonable choice of model might be the Langevin equations or even further, the overdamped Langevin equations. As a case study, by posing the generalized Langevin equations as a relaxation system, we show how in choosing the size of time step, we automatically reduce to the effective model, without
ever explicitly choosing the model used.
ContributorsRistich, Eron (Author) / Kobayashi, Yoshihiro (Thesis director) / Seyler, Sean (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
DescriptionBuck-it is a budgeting application designed to meet the unique needs of college students. As financial literacy is crucial for developing good long-term financial habits, Buck-it aims to promote budgeting among college students through an appealing user interface, robust customization, and effective categorization.
ContributorsVemuri, Rajeev (Author) / Davitt, Ryan (Co-author) / Doyle, Michael (Co-author) / Walle, Andrew (Co-author) / Baptista, Asher (Co-author) / Byrne, Jared (Thesis director) / Lee, Peggy (Committee member) / Barrett, The Honors College (Contributor) / Economics Program in CLAS (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
As the use of Artificial Intelligence (AI) continues to expand and improve across numerous industries, the success with which it has been integrated into the medical sector stands out. Physicians and researchers now utilize AI in many situations. In particular, advancements within the field of detection AI have had a significant impact on the diagnosis and treatment of scoliosis.
Detection AI has been developed to recognize important features within an image, such as malignant tumors in adults. For a scoliosis patient, a detection model can manipulate radiograph images to create masks and highlight important features that could be missed by the human eye, such as minute changes in a cell, and create binary masks of a spine. Using a popular convolution neural network (CNN) to examine datasets of scoliosis x-rays, provided by Hanger Inc [6], this paper examines the capabilities of machine learning to effectively differentiate the spine from other bones in x-ray imagery and to identify scoliosis in affected patients.
Based on the results of the project, several issues were discovered that, if resolved, could improve the overall accuracy of the model, which would allow it to potentially find its own place within medical workflows to expedite the scoliosis design process.
ContributorsCooney, Sloan (Author) / Kerner, Hannah (Thesis director) / Clark, Geoffrey (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Music festivals are a vibrant celebration of art, culture, and community, attracting global audiences and creating memorable experiences. However, the environmental footprint associated with these events from waste production, energy consumption, transportation, and water usage, poses significant sustainability challenges. This thesis proposes the development of a sustainable festival event management software designed to enhance and support sustainability practices at music festivals. The software enables real-time monitoring and analysis of key environmental strategies in waste management, energy use, transportation modes, and water management, assisting organizers in making informed decisions towards reducing ecological impacts. The research encompasses a detailed review of existing sustainable practices in the festival industry, identification of critical monitoring areas, and the integration of relevant algorithms for data analysis within the software. By facilitating better management through technology, this software aims to set a new standard for eco-friendly festival operations, promoting a balance between operational needs and environmental mindfulness.
ContributorsGulaya, Ashwin (Author) / Kuhn, Anthony (Thesis director) / Hedges, Craig (Committee member) / Barrett, The Honors College (Contributor) / Arts, Media and Engineering Sch T (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
The Business Design Labs Thesis Pathways, run by the Center for Entrepreneurship and New Business Design within ASU’s W. P. Carey School of Business, helps students develop entrepreneurial skills outside of the classroom. As a team, the students are challenged with solving complex problems that affect local businesses. The students have worked as a team and independently depending on the project to come up with solutions for these problems.
ContributorsFlores, Lea (Author) / Byrne, Jared (Thesis director) / Lee, Christopher (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05