The purpose of this thesis is to accurately simulate in 3D the HH901 jet in the Mystic Mountain Formation of the Carina Nebula. Astronomers present a narrow-band Wide Field Camera image of Carina and the morphology of some astrophysical jets, including HH901. The simulation attempts to replicate features of the jet, among which are pulses, bow shock, terminal Mach disk, and Kelvin-Helmholtz rollup. We use the gas dynamical equations to solve for density, velocity, and temperature. The numerical methods used to solve the equations are third-order WENO (weighted essentially non-oscillatory) and third-order Runge-Kutta. Graphs of density and radiative cooling demonstrate the effect of adding wind (nonzero ambient velocity). The paper discusses the altering of the ambient velocity and final time to fit the shape of the jet in the Hubble image. The suggested next steps are simulating the other HH901 jet and comparing the jets’ atomic makeups to advance understanding of astrophysical jets.

Applying a classical theorem due to Macbeath applied to a suitably sized horoball, we calculate novel group presentations for singly-cusped Bianchi groups. We find new presentations for Bianchi groups with d = -43, -67, -163. With previously known presentations for d = -1, -2, -3, -7, -11, -19, this constitutes a complete set of presentations for singly-cusped Bianchi groups.

This paper addresses echo chambers, an online phenomena wherein social media users can "only hear their own voice". In this paper I will examine the history and recent proliferation of online echo chambers. I will outline a comprehensive theory of echo chamber generation and maintenance, intended for educational value. I then conduct my own experiment based on previous echo chamber detection work.

Molecular pathology makes use of estimates of tumor content (tumor percentage) for pre-analytic and analytic purposes, such as molecular oncology testing, massive parallel sequencing, or next-generation sequencing (NGS), assessment of sample acceptability, accurate quantitation of variants, assessment of copy number changes (among other applications), determination of specimen viability for testing (since many assays require a minimum tumor content to report variants at the limit of detection) may all be improved with more accurate and reproducible estimates of tumor content. Currently, tumor percentages of samples submitted for molecular testing are estimated by visual examination of Hematoxylin and Eosin (H&E) stained tissue slides under the microscope by pathologists. These estimations can be automated, expedited, and rendered more accurate by applying machine learning methods on digital whole slide images (WSI).

Find My College is an app to help people who are interested in pursuing a collegiate degree; find a college/s that is right for them. This app is designed using the Ionic Framework, to allow access across all operating systems such as Android and MacOS. We wanted to create an app that people using Android or Apple can use, and this framework allows us to do that. The app is very user friendly and straightforward, which makes it usable to all types of people. It will be a free to use app that can be improved and adjusted if changes are needed/wanted.

Find My College is an app to help people who are interested in pursuing a collegiate degree; find a college/s that is right for them. This app is designed using the Ionic Framework, to allow access across all operating systems such as Android and MacOS. We wanted to create an app that people using Android or Apple can use, and this framework allows us to do that. The app is very user friendly and straightforward, which makes it usable to all types of people. It will be a free to use app that can be improved and adjusted if changes are needed/wanted.

As threats emerge, change, and grow, the life of a police officer continues to intensify. To help support police training curriculums and police cadets through this critical career juncture, this study proposes a state of the art approach to stress prediction and intervention through wearable devices and machine learning models. As an integral first step of a larger study, the goal of this research is to provide relevant information to machine learning models to formulate a correlation between stress and police officers’ physiological responses on and off on the job. Fitbit devices were leveraged for data collection and were complemented with a custom built Fitbit application, called StressManager, and study dashboard, termed StressWatch. This analysis uses data collected from 15 training cadets at the Phoenix Police Regional Training Academy over a 13 week span. Close collaboration with these participants was essential; the quality of data collection relied on consistent “syncing” and troubleshooting of the Fitbit devices. After the data were collected and cleaned, features related to steps, calories, movement, location, and heart rate were extracted from the Fitbit API and other supplemental resources and passed through to empirically chosen machine learning models. From the results of these models, we formulate that events of increased intensity combined with physiological spikes contribute to the overall stress perception of a police training cadet