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Bioscience High School, a small magnet high school located in Downtown Phoenix and a STEAM (Science, Technology, Engineering, Arts, Math) focused school, has been pushing to establish a computer science curriculum for all of their students from freshman to senior year. The school's Mision (Mission and Vision) is to: "..provide a rigorous, collaborative, and relevant academic program emphasizing an innovative, problem-based curriculum that develops literacy in the sciences, mathematics, and the arts, thus cultivating critical thinkers, creative problem-solvers, and compassionate citizens, who are able to thrive in our increasingly complex and technological communities." Computational thinking is an important part in developing a future problem solver Bioscience High School is looking to produce. Bioscience High School is unique in the fact that every student has a computer available for him or her to use. Therefore, it makes complete sense for the school to add computer science to their curriculum because one of the school's goals is to be able to utilize their resources to their full potential. However, the school's attempt at computer science integration falls short due to the lack of expertise amongst the math and science teachers. The lack of training and support has postponed the development of the program and they are desperately in need of someone with expertise in the field to help reboot the program. As a result, I've decided to create a course that is focused on teaching students the concepts of computational thinking and its application through Scratch and Arduino programming.
ContributorsLiu, Deming (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Electromyography (EMG) and Electroencephalography (EEG) are techniques used to detect electrical activity produced by the human body. EMG detects electrical activity in the skeletal muscles, while EEG detects electrical activity from the scalp. The purpose of this study is to capture different types of EMG and EEG signals and to determine if the signals can be distinguished between each other and processed into output signals to trigger events in prosthetics. Results from the study suggest that the PSD estimates can be used to compare signals that have significant differences such as the wrist, scalp, and fingers, but it cannot fully distinguish between signals that are closely related, such as two different fingers. The signals that were identified were able to be translated into the physical output simulated on the Arduino circuit.
ContributorsJanis, William Edward (Author) / LaBelle, Jeffrey (Thesis director) / Santello, Marco (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-12
Description
Aortic aneurysms and dissections are life threatening conditions addressed by replacing damaged sections of the aorta. Blood circulation must be halted to facilitate repairs. Ischemia places the body, especially the brain, at risk of damage. Deep hypothermia circulatory arrest (DHCA) is employed to protect patients and provide time for surgeons to complete repairs on the basis that reducing body temperature suppresses the metabolic rate. Supplementary surgical techniques can be employed to reinforce the brain's protection and increase the duration circulation can be suspended. Even then, protection is not completely guaranteed though. A medical condition that can arise early in recovery is postoperative delirium, which is correlated with poor long term outcome. This study develops a methodology to intraoperatively monitor neurophysiology through electroencephalography (EEG) and anticipate postoperative delirium. The earliest opportunity to detect occurrences of complications through EEG is immediately following DHCA during warming. The first observable electrophysiological activity after being completely suppressed is a phenomenon known as burst suppression, which is related to the brain's metabolic state and recovery of nominal neurological function. A metric termed burst suppression duty cycle (BSDC) is developed to characterize the changing electrophysiological dynamics. Predictions of postoperative delirium incidences are made by identifying deviations in the way these dynamics evolve. Sixteen cases are examined in this study. Accurate predictions can be made, where on average 89.74% of cases are correctly classified when burst suppression concludes and 78.10% when burst suppression begins. The best case receiver operating characteristic curve has an area under its convex hull of 0.8988, whereas the worst case area under the hull is 0.7889. These results demonstrate the feasibility of monitoring BSDC to anticipate postoperative delirium during burst suppression. They also motivate a further analysis on identifying footprints of causal mechanisms of neural injury within BSDC. Being able to raise warning signs of postoperative delirium early provides an opportunity to intervene and potentially avert neurological complications. Doing so would improve the success rate and quality of life after surgery.
ContributorsMa, Owen (Author) / Bliss, Daniel W (Thesis advisor) / Berisha, Visar (Committee member) / Kosut, Oliver (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2020