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Description
As mobile devices have risen to prominence over the last decade, their importance has been increasingly recognized. Workloads for mobile devices are often very different from those on desktop and server computers, and solutions that worked in the past are not always the best fit for the resource- and energy-constrained computing that characterizes mobile devices. While this is most commonly seen in CPU and graphics workloads, this device class difference extends to I/O as well. However, while a few tools exist to help analyze mobile storage solutions, there exists a gap in the available software that prevents quality analysis of certain research initiatives, such as I/O deduplication on mobile devices. This honors thesis will demonstrate a new tool that is capable of capturing I/O on the filesystem layer of mobile devices running the Android operating system, in support of new mobile storage research. Uniquely, it is able to capture both metadata of writes as well as the actual written data, transparently to the apps running on the devices. Based on a modification of the strace program, fstrace and its companion tool fstrace-replay can record and replay filesystem I/O of actual Android apps. Using this new tracing tool, several traces from popular Android apps such as Facebook and Twitter were collected and analyzed.
ContributorsMor, Omri (Author) / Zhao, Ming (Thesis director) / Zhao, Ziming (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Almost every form of cancer deregulates the expression and activity of anabolic glycosyltransferase (GT) enzymes, which incorporate particular monosaccharides in a donor acceptor as well as linkage- and anomer-specific manner to assemble complex and diverse glycans that significantly affect numerous cellular events, including tumorigenesis and metastasis. Because glycosylation is not template-driven, GT deregulation yields heterogeneous arrays of aberrant intact glycan products, some in undetectable quantities in clinical bio-fluids (e.g., blood plasma). Numerous glycan features (e.g., 6 sialylation, β-1,6-branching, and core fucosylation) stem from approximately 25 glycan “nodes:” unique linkage specific monosaccharides at particular glycan branch points that collectively confer distinguishing features upon glycan products. For each node, changes in normalized abundance (Figure 1) may serve as nearly 1:1 surrogate measure of activity for culpable GTs and may correlate with particular stages of carcinogenesis. Complementary to traditional top down glycomics, the novel bottom-up technique applied herein condenses each glycan node and feature into a single analytical signal, quantified by two GC-MS instruments: GCT (time-of-flight analyzer) and GCMSD (transmission quadrupole analyzers). Bottom-up analysis of stage 3 and 4 breast cancer cases revealed better overall precision for GCMSD yet comparable clinical performance of both GC MS instruments and identified two downregulated glycan nodes as excellent breast cancer biomarker candidates: t-Gal and 4,6-GlcNAc (ROC AUC ≈ 0.80, p < 0.05). Resulting from the activity of multiple GTs, t-Gal had the highest ROC AUC (0.88) and lowest ROC p‑value (0.001) among all analyzed nodes. Representing core-fucosylation, glycan node 4,6-GlcNAc is a nearly 1:1 molecular surrogate for the activity of α-(1,6)-fucosyltransferase—a potential target for cancer therapy. To validate these results, future projects can analyze larger sample sets, find correlations between breast cancer stage and changes in t-Gal and 4,6-GlcNAc levels, gauge the specificity of these nodes for breast cancer and their potential role in other cancer types, and develop clinical tests for reliable breast cancer diagnosis and treatment monitoring based on t-Gal and 4,6-GlcNAc.
ContributorsZaare, Sahba (Author) / Borges, Chad (Thesis director) / LaBaer, Joshua (Committee member) / School of Molecular Sciences (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
Popular Culture of today, particularly books and movies have begun to influence the way individ- uals and society as a whole, views specific concepts. In this case, the fairly recent phenomenon of the Sci- ence Fiction Drug Niche has produced significant thought among audiences as to both the benefits and costs of cognitive enhancers in our world. Through the use of both a thorough analysis of modern films and novels on the topic as well as focus groups of the average college students this study analyzes the influence that this niche has had on the perceptions that students have towards the use of such cognitive enhancements. Small groups of students were shown the same film: Limitless, and discussion after the film displayed the students thoughts and attitudes towards the ideas shown in the film. Limitless itself falls into this Science Fiction drug niche and discusses both benefits and harms of chemical cognitive enhancement. The study indicates that audiences have thought not only about the issues that may arise with the presence of cognitive enhancement in our world but also the possible benefits of this enhancement. The results go even further to preliminarily show that there are common thoughts that arise in such situations. These common ideas that arise show, at least on a very basic level, that the presence of these Science Fiction Drug-inspired works are influencing the way audiences perceive the use of cognitive enhancement as well as influencing what doubts, questions, hopes, and fears arise from these pharmaceuticals. This preliminary study could use further research to ana- lyze the effects of popular culture on perceptions of cognitive enhancement and pharmaceuticals to alter consciousness.
ContributorsSyed, Mariha Batool (Author) / Zachary, Gregg (Thesis director) / Hurlbut, Ben (Committee member) / School of Historical, Philosophical and Religious Studies (Contributor) / School of Molecular Sciences (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
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
Description
The goal of our study is to identify socio-economic risk factors for depressive disorder and poor mental health by statistically analyzing survey data from the CDC. The identification of risk groups in a particular demographic could aid in the development of targeted interventions to improve overall quality of mental health in the United States. In our analysis, we studied the influences and correlations of socioeconomic factors that regulate the risk of developing Depressive Disorders and overall poor mental health. Using the statistical software STATA, we ran a regression model of selected independent socio-economic variables with the dependent mental health variables. The independent variables of the statistical model include Income, Race, State, Age, Marital Status, Sex, Education, BMI, Smoker Status, and Alcohol Consumption. Once the regression coefficients were found, we illustrated the data in graphs and heat maps to qualitatively provide visuals of the prevalence of depression in the U.S. demography. Our study indicates that the low-income and under-educated populations who are everyday smokers, obese, and/or are in divorced or separated relationships should be of main concern. A suggestion for mental health organizations would be to support counseling and therapeutic efforts as secondary care for those in smoking cessation programs, weight management programs, marriage counseling, or divorce assistance group. General improvement in alleviating poverty and increasing education could additionally show progress in counter-acting the prevalence of depressive disorder and also improve overall mental health. The identification of these target groups and socio-economic risk factors are critical in developing future preventative measures.
ContributorsGrassel, Samuel (Co-author) / Choueiri, Alexi (Co-author) / Choueiri, Robert (Co-author) / Goegan, Brian (Thesis director) / Holter, Michael (Committee member) / Sandra Day O'Connor College of Law (Contributor) / School of Molecular Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05