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Description
Sex trafficking is an issue that is prevalent in the United States, including in Arizona. The Catholic Charities Diversion Program in Phoenix seeks to rehabilitate adults who have been involved in prostitution. The aim of this paper was to test three pilot interventions to address stress experienced by the program clients through three different techniques that were given in workshop format. Cognitive Behavior Therapy (CBT), Dance Movement Therapy, and yoga and meditation are the three types of stress reduction techniques that have been studied in previous research and were pilot tested with adult sex trafficking victims. The results of the pilot studies and all three techniques reduced levels of stress significantly, and they warrant future testing.
ContributorsSatapathy, Nikita (Co-author) / Khanal, Garima (Co-author) / Somayaji, Vallari (Co-author) / Roe-Sepowitz, Dominique (Thesis director) / Graff, Sarah (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Sex trafficking is an issue that is prevalent in the United States, including in Arizona. The Catholic Charities Diversion Program in Phoenix seeks to rehabilitate women who were involved in prostitution. The aim of this paper was to treat Post Traumatic Stress Disorder (PTSD) through three different techniques, which will be given in the format of workshops. Cognitive Behavior Therapy (CBT), Dance Movement Therapy (DMT), and yoga and meditation are the three types of stress reduction techniques that have been studied in previous papers and will be used for sex-trafficked victims. It is important to combat stress and find stress reducing techniques to improve health and quality of life in an individual. This specific section of the study focuses on yoga and meditation therapy and how it helps to reduce stress. The results showed that all three techniques, including yoga and meditation, reduced levels of stress significantly, thereby giving them a tool to use on their own to improve their health, and that they can be further tested in a longitudinal study to examine the reduction of PTSD symptoms over time.
ContributorsSomayaji, Vallari (Co-author) / Khanal, Garima (Co-author) / Satapathy, Nikita (Co-author) / Roe-Sepowitz, Dominique (Thesis director) / Graff, Sarah (Committee member) / School of Life Sciences (Contributor) / School of Social Transformation (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
A specific species of the genus Geobacter exhibits useful electrical properties when processing a molecule often found in waste water. A team at ASU including Dr Cèsar Torres and Dr Sudeep Popat used that species to create a special type of solid oxide fuel cell we refer to as a microbial fuel cell. Identification of possible chemical processes and properties of the reactions used by the Geobacter are investigated indirectly by taking measurements using Electrochemical Impedance Spectroscopy of the electrode-electrolyte interface of the microbial fuel cell to obtain the value of the fuel cell's complex impedance at specific frequencies. Investigation of the multiple polarization processes which give rise to measured impedance values is difficult to do directly and so examination of the distribution function of relaxation times (DRT) is considered instead. The DRT is related to the measured complex impedance values using a general, non-physical equivalent circuit model. That model is originally given in terms of a Fredholm integral equation with a non-square integrable kernel which makes the inverse problem of determining the DRT given the impedance measurements an ill-posed problem. The original integral equation is rewritten in terms of new variables into an equation relating the complex impedance to the convolution of a function based upon the original integral kernel and a related but separate distribution function which we call the convolutional distribution function. This new convolutional equation is solved by reducing the convolution to a pointwise product using the Fourier transform and then solving the inverse problem by pointwise division and application of a filter function (equivalent to regularization). The inverse Fourier transform is then taken to get the convolutional distribution function. In the literature the convolutional distribution function is then examined and certain values of a specific, less general equivalent circuit model are calculated from which aspects of the original chemical processes are derived. We attempted to instead directly determine the original DRT from the calculated convolutional distribution function. This method proved to be practically less useful due to certain values determined at the time of experiment which meant the original DRT could only be recovered in a window which would not normally contain the desired information for the original DRT. This limits any attempt to extend the solution for the convolutional distribution function to the original DRT. Further research may determine a method for interpreting the convolutional distribution function without an equivalent circuit model as is done with the regularization method used to solve directly for the original DRT.
ContributorsBaker, Robert Simpson (Author) / Renaut, Rosemary (Thesis director) / Kostelich, Eric (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
A problem of interest in theoretical physics is the issue of the evaporation of black holes via Hawking radiation subject to a fixed background. We approach this problem by considering an electromagnetic analogue, where we have substituted Hawking radiation with the Schwinger effect. We treat the case of massless QED in 1+1 dimensions with the path integral approach to quantum field theory, and discuss the resulting Feynman diagrams from our analysis. The results from this thesis may be useful to find a version of the Schwinger effect that can be solved exactly and perturbatively, as this version may provide insights to the gravitational problem of Hawking radiation.
ContributorsDhumuntarao, Aditya (Author) / Parikh, Maulik (Thesis director) / Davies, Paul C. W. (Committee member) / Department of Physics (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The Community Action Research Experiences (CARE) Program collaborated with a local non-profit organization called Arizona Facts of Life to design a program evaluation for their Youth Leadership Development Training Program, FACTS Curriculum. The purpose of this study was to identify targeted program outcomes, design an assessment to address these outcomes, and recommend possible evaluation designs. Arizona Facts of Life will implement the assessment using one of the recommended evaluation designs, and use the results to measure their Youth Leadership Development Training Program's outcomes and demonstrate efficacy.
ContributorsVan Vianen, Olivia Leigh (Author) / Updegraff, Kimberly (Thesis director) / Dumka, Larry (Committee member) / School of Life Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor)
Created2016-05
Description
The purpose of this study is to analyze the stereotypes surrounding four wind instruments (flutes, oboes, clarinets, and saxophones), and the ways in which those stereotypes propagate through various levels of musical professionalism in Western culture. In order to determine what these stereotypes might entail, several thousand social media and blog posts were analyzed, and direct quotations detailing the perceived stereotypical personality profiles for each of the four instruments were collected. From these, the three most commonly mentioned characteristics were isolated for each of the instrument groups as follows: female gender, femininity, and giggliness for flutists, intelligence, studiousness, and demographics (specifically being an Asian male) for clarinetists, quirkiness, eccentricity, and being seen as a misfit for oboists, and overconfidence, attention-seeking behavior, and coolness for saxophonists. From these traits, a survey was drafted which asked participating college-aged musicians various multiple choice, opinion scale, and short-answer questions that gathered how much they agree or disagree with each trait describing the instrument from which it was derived. Their responses were then analyzed to determine how much correlation existed between the researched characteristics and the opinions of modern musicians. From these results, it was determined that 75% of the traits that were isolated for a particular instrument were, in fact, recognized as being true in the survey data, demonstrating that the stereotypes do exist and seem to be widely recognizable across many age groups, locations, and levels of musical skill. Further, 89% of participants admitted that the instrument they play has a certain stereotype associated with it, but only 38% of people identify with that profile. Overall, it was concluded that stereotypes, which are overwhelmingly negative and gendered by nature, are indeed propagated, but musicians do not appear to want to identify with them, and they reflect a more archaic and immature sense that does not correlate to the trends observed in modern, professional music.
ContributorsAllison, Lauren Nicole (Author) / Bhattacharjya, Nilanjana (Thesis director) / Ankeny, Casey (Committee member) / School of Life Sciences (Contributor) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The Clean Power Plan seeks to reduce CO2 emissions in the energy industry, which is the largest source of CO2 emissions in the United States. In order to comply with the Clean Power Plan, electric utilities in Arizona will need to meet the electricity demand while reducing the use of fossil fuel sources in generation. The study first outlines the organization of the power sector in the United States and the structural and price changes attempted in the industry during the period of restructuring. The recent final rule of the Clean Power Plan is then described in detail with a narrowed focus on Arizona. Data from APS, a representative utility of Arizona, is used for the remainder of the analysis to determine the price increase necessary to cut Arizona's CO2 emissions in order to meet the federal goal. The first regression models the variables which affect total demand and thus generation load, from which we estimate the marginal effect of price on demand. The second regression models CO2 emissions as a function of different levels of generation. This allows the effect of generation on emissions to fluctuate with ranges of load, following the logic of the merit order of plants and changing rates of emissions for different sources. Two methods are used to find the necessary percentage increase in price to meet the CPP goals: one based on the mass-based goal for Arizona and the other based on the percentage reduction for Arizona. Then a price increase is calculated for a projection into the future using known changes in energy supply.
ContributorsHerman, Laura Alexandra (Author) / Silverman, Daniel (Thesis director) / Kuminoff, Nicolai (Committee member) / Department of Economics (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
A semi-implicit, fourth-order time-filtered leapfrog numerical scheme is investigated for accuracy and stability, and applied to several test cases, including one-dimensional advection and diffusion, the anelastic equations to simulate the Kelvin-Helmholtz instability, and the global shallow water spectral model to simulate the nonlinear evolution of twin tropical cyclones. The leapfrog scheme leads to computational modes in the solutions to highly nonlinear systems, and time-filters are often used to damp these modes. The proposed filter damps the computational modes without appreciably degrading the physical mode. Its performance in these metrics is superior to the second-order time-filtered leapfrog scheme developed by Robert and Asselin.
ContributorsBurke, Lee Matthew Moore (Author) / Moustaoui, Mohamed (Thesis director) / Kostelich, Eric (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Memory CD8+ T-cells can persist in the absence of antigen, primed for immediate activation and proliferation if later exposed to the same antigen. These cytotoxic lymphocytes provide long-term immunity following an acute infection. Studies have observed that intermediate levels of general T cell transfer prior to infection may cause an inappropriate response resulting in increased pathology rather than prevention. Therefore, our study focused on a memory CD8 T-cell therapy using lymphocytic choriomeningitis virus (LCMV) specific splenocytes, which activate and proliferate at an accelerated pace compared to that of naive T-cells. LCMV is a natural murine pathogen which also poses a zoonotic infection threat to humans, and the effect of immune cell vaccination therapies for LCMV is not fully understood. We observed the effect of multiple memory CD8 T cell dosage levels on overall disease and memory CD8 T-cell response to the virus. Infection by exposure to a carrier was shown to have a reduced impact on mice receiving higher doses of memory T cells prior to infection compared to mice receiving less or no memory cells. Higher presence of activated memory cells were shown to correlate with less disease-related weight loss and accelerated recovery times. Survival rate after exposure to carriers was not shown to be affected by dosage level, warranting further research regarding the prevalence of the immunopathology observed in other studies in natural murine transmission models.
ContributorsMiller, Charles (Author) / Blattman, Joseph (Thesis director) / Holechek, Susan (Committee member) / Carmen, Joshua (Committee member) / W. P. Carey School of Business (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Preventive maintenance is a practice that has become popular in recent years, largely due to the increased dependency on electronics and other mechanical systems in modern technologies. The main idea of preventive maintenance is to take care of maintenance-type issues before they fully appear or cause disruption of processes and daily operations. One of the most important parts is being able to predict and foreshadow failures in the system, in order to make sure that those are fixed before they turn into large issues. One specific area where preventive maintenance is a very big part of daily activity is the automotive industry. Automobile owners are encouraged to take their cars in for maintenance on a routine schedule (based on mileage or time), or when their car signals that there is an issue (low oil levels for example). Although this level of maintenance is enough when people are in charge of cars, the rise of autonomous vehicles, specifically self-driving cars, changes that. Now instead of a human being able to look at a car and diagnose any issues, the car needs to be able to do this itself. The objective of this project was to create such a system. The Electronics Preventive Maintenance System is an internal system that is designed to meet all these criteria and more. The EPMS system is comprised of a central computer which monitors all major electronic components in an autonomous vehicle through the use of standard off-the-shelf sensors. The central computer compiles the sensor data, and is able to sort and analyze the readings. The filtered data is run through several mathematical models, each of which diagnoses issues in different parts of the vehicle. The data for each component in the vehicle is compared to pre-set operating conditions. These operating conditions are set in order to encompass all normal ranges of output. If the sensor data is outside the margins, the warning and deviation are recorded and a severity level is calculated. In addition to the individual focus, there's also a vehicle-wide model, which predicts how necessary maintenance is for the vehicle. All of these results are analyzed by a simple heuristic algorithm and a decision is made for the vehicle's health status, which is sent out to the Fleet Management System. This system allows for accurate, effortless monitoring of all parts of an autonomous vehicle as well as predictive modeling that allows the system to determine maintenance needs. With this system, human inspectors are no longer necessary for a fleet of autonomous vehicles. Instead, the Fleet Management System is able to oversee inspections, and the system operator is able to set parameters to decide when to send cars for maintenance. All the models used for the sensor and component analysis are tailored specifically to the vehicle. The models and operating margins are created using empirical data collected during normal testing operations. The system is modular and can be used in a variety of different vehicle platforms, including underwater autonomous vehicles and aerial vehicles.
ContributorsMian, Sami T. (Author) / Collofello, James (Thesis director) / Chen, Yinong (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05