Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 10 of 124
Description
There is a lack of music therapy services for college students who have problems with depression and/or anxiety. Even among universities and colleges that offer music therapy degrees, there are no known programs offering music therapy to the institution's students. Female college students are particularly vulnerable to depression and anxiety symptoms compared to their male counterparts. Many students who experience mental health problems do not receive treatment, because of lack of knowledge, lack of services, or refusal of treatment. Music therapy is proposed as a reliable and valid complement or even an alternative to traditional counseling and pharmacotherapy because of the appeal of music to young women and the potential for a music therapy group to help isolated students form supportive networks. The present study recruited 14 female university students to participate in a randomized controlled trial of short-term group music therapy to address symptoms of depression and anxiety. The students were randomly divided into either the treatment group or the control group. Over 4 weeks, each group completed surveys related to depression and anxiety. Results indicate that the treatment group's depression and anxiety scores gradually decreased over the span of the treatment protocol. The control group showed either maintenance or slight worsening of depression and anxiety scores. Although none of the results were statistically significant, the general trend indicates that group music therapy was beneficial for the students. A qualitative analysis was also conducted for the treatment group. Common themes were financial concerns, relationship problems, loneliness, and time management/academic stress. All participants indicated that they benefited from the sessions. The group progressed in its cohesion and the participants bonded to the extent that they formed a supportive network which lasted beyond the end of the protocol. The results of this study are by no means conclusive, but do indicate that colleges with music therapy degree programs should consider adding music therapy services for their general student bodies.
ContributorsAshton, Barbara (Author) / Crowe, Barbara J. (Thesis advisor) / Rio, Robin (Committee member) / Davis, Mary (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data mining, machine learning, and geovisualization techniques. Three different types of spatiotemporal activity data were collected through different data collection approaches: (1) crowd sourced geo-tagged digital photos, representing people's travel activity, were retrieved from the website Panoramio.com through information retrieval techniques; (2) the same techniques were used to crawl crowd sourced GPS trajectory data and related metadata of their daily activities from the website OpenStreetMap.org; and finally (3) preschool children's daily activities and interactions tagged with time and geographical location were collected with a novel TabletPC-based behavioral coding system. The proposed methodology is applied to these data to (1) automatically recommend optimal multi-day and multi-stay travel itineraries for travelers based on discovered attractions from geo-tagged photos, (2) automatically detect movement types of unknown moving objects from GPS trajectories, and (3) explore dynamic social and socio-spatial patterns of preschool children's behavior from both geographic and social perspectives.
ContributorsLi, Xun (Author) / Anselin, Luc (Thesis advisor) / Koschinsky, Julia (Committee member) / Maciejewski, Ross (Committee member) / Rey, Sergio (Committee member) / Griffin, William (Committee member) / Arizona State University (Publisher)
Created2012
Description
This document presents a new implementation of the Smoothed Particles Hydrodynamics algorithm using DirectX 11 and DirectCompute. The main goal of this document is to present to the reader an alternative solution to the largely studied and researched problem of fluid simulation. Most other solutions have been implemented using the NVIDIA CUDA framework; however, the proposed solution in this document uses the Microsoft general-purpose computing on graphics processing units API. The implementation allows for the simulation of a large number of particles in a real-time scenario. The solution presented here uses the Smoothed Particles Hydrodynamics algorithm to calculate the forces within the fluid; this algorithm provides a Lagrangian approach for discretizes the Navier-Stockes equations into a set of particles. Our solution uses the DirectCompute compute shaders to evaluate each particle using the multithreading and multi-core capabilities of the GPU increasing the overall performance. The solution then describes a method for extracting the fluid surface using the Marching Cubes method and the programmable interfaces exposed by the DirectX pipeline. Particularly, this document presents a method for using the Geometry Shader Stage to generate the triangle mesh as defined by the Marching Cubes method. The implementation results show the ability to simulate over 64K particles at a rate of 900 and 400 frames per second, not including the surface reconstruction steps and including the Marching Cubes steps respectively.
ContributorsFigueroa, Gustavo (Author) / Farin, Gerald (Thesis advisor) / Maciejewski, Ross (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
In blindness research, the corpus callosum (CC) is the most frequently studied sub-cortical structure, due to its important involvement in visual processing. While most callosal analyses from brain structural magnetic resonance images (MRI) are limited to the 2D mid-sagittal slice, we propose a novel framework to capture a complete set of 3D morphological differences in the corpus callosum between two groups of subjects. The CCs are segmented from whole brain T1-weighted MRI and modeled as 3D tetrahedral meshes. The callosal surface is divided into superior and inferior patches on which we compute a volumetric harmonic field by solving the Laplace's equation with Dirichlet boundary conditions. We adopt a refined tetrahedral mesh to compute the Laplacian operator, so our computation can achieve sub-voxel accuracy. Thickness is estimated by tracing the streamlines in the harmonic field. We combine areal changes found using surface tensor-based morphometry and thickness information into a vector at each vertex to be used as a metric for the statistical analysis. Group differences are assessed on this combined measure through Hotelling's T2 test. The method is applied to statistically compare three groups consisting of: congenitally blind (CB), late blind (LB; onset > 8 years old) and sighted (SC) subjects. Our results reveal significant differences in several regions of the CC between both blind groups and the sighted groups; and to a lesser extent between the LB and CB groups. These results demonstrate the crucial role of visual deprivation during the developmental period in reshaping the structural architecture of the CC.
ContributorsXu, Liang (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sometimes difficult life events challenge our existing resources in such a way that routinized responses are inadequate to handle the challenge. Some individuals will persist in habitual, automatic behavior, regardless of environmental cues that indicate a mismatch between coping strategy and the demands of the stressor. Other individuals will marshal adaptive resources to construct new courses of action and reconceptualize the problem, associated goals and/or values. A mixed methods approach was used to describe and operationalize cognitive shift, a relatively unexplored construct in existing literature. The study was conducted using secondary data from a parent multi-year cross-sectional study of resilience with eight hundred mid-aged adults from the Phoenix metro area. Semi-structured telephone interviews were analyzed using a purposive sample (n=136) chosen by type of life event. Participants' beliefs, assumptions, and experiences were examined to understand how they shaped adaptation to adversity. An adaptive mechanism, "cognitive shift," was theorized as the transition from automatic coping to effortful cognitive processes aimed at novel resolution of issues. Aims included understanding when and how cognitive shift emerges and manifests. Cognitive shift was scored as a binary variable and triangulated through correlational and logistic regression analyses. Interaction effects revealed that positive personality attributes influence cognitive shift most when people suffered early adversity. This finding indicates that a certain complexity, self-awareness and flexibility of mind may lead to a greater capacity to find meaning in adversity. This work bridges an acknowledged gap in literature and provides new insights into resilience.
ContributorsRivers, Crystal T (Author) / Zautra, Alex (Thesis advisor) / Davis, Mary (Committee member) / Kurpius, Sharon (Committee member) / Arizona State University (Publisher)
Created2014
Description
Membrane proteins are a vital part of cellular structure. They are directly involved in many important cellular functions, such as uptake, signaling, respiration, and photosynthesis, among others. Despite their importance, however, less than 500 unique membrane protein structures have been determined to date. This is due to several difficulties with macromolecular crystallography, primarily the difficulty of growing large, well-ordered protein crystals. Since the first proof of concept for femtosecond nanocrystallography showing that diffraction patterns can be collected on extremely small crystals, thus negating the need to grow larger crystals, there have been many exciting advancements in the field. The technique has been proven to show high spatial resolution, thus making it a viable method for structural biology. However, due to the ultrafast nature of the technique, which allows for a lack of radiation damage in imaging, even more interesting experiments are possible, and the first temporal and spatial images of an undamaged structure could be acquired. This concept was denoted as time-resolved femtosecond nanocrystallography.
This dissertation presents on the first time-resolved data set of Photosystem II where structural changes can actually be seen without radiation damage. In order to accomplish this, new crystallization techniques had to be developed so that enough crystals could be made for the liquid jet to deliver a fully hydrated stream of crystals to the high-powered X-ray source. These changes are still in the preliminary stages due to the slightly lower resolution data obtained, but they are still a promising show of the power of this new technique. With further optimization of crystal growth methods and quality, injection technique, and continued development of data analysis software, it is only a matter of time before the ability to make movies of molecules in motion from X-ray diffraction snapshots in time exists. The work presented here is the first step in that process.
This dissertation presents on the first time-resolved data set of Photosystem II where structural changes can actually be seen without radiation damage. In order to accomplish this, new crystallization techniques had to be developed so that enough crystals could be made for the liquid jet to deliver a fully hydrated stream of crystals to the high-powered X-ray source. These changes are still in the preliminary stages due to the slightly lower resolution data obtained, but they are still a promising show of the power of this new technique. With further optimization of crystal growth methods and quality, injection technique, and continued development of data analysis software, it is only a matter of time before the ability to make movies of molecules in motion from X-ray diffraction snapshots in time exists. The work presented here is the first step in that process.
ContributorsKupitz, Christopher (Author) / Fromme, Petra (Thesis advisor) / Spence, John C. (Thesis advisor) / Redding, Kevin (Committee member) / Ros, Alexandra (Committee member) / Arizona State University (Publisher)
Created2014
Description
The utilization of solar energy requires an efficient means of its storage as fuel. In bio-inspired artificial photosynthesis, light energy can be used to drive water oxidation, but catalysts that produce molecular oxygen from water are required. This dissertation demonstrates a novel complex utilizing earth-abundant Ni in combination with glycine as an efficient catalyst with a modest overpotential of 0.475 ± 0.005 V for a current density of 1 mA/cm2 at pH 11. The production of molecular oxygen at a high potential was verified by measurement of the change in oxygen concentration, yielding a Faradaic efficiency of 60 ± 5%. This Ni species can achieve a current density of 4 mA/cm2 that persists for at least 10 hours. Based upon the observed pH dependence of the current amplitude and oxidation/reduction peaks, the catalysis is an electron-proton coupled process. In addition, to investigate the binding of divalent metals to proteins, four peptides were designed and synthesized with carboxylate and histidine ligands. The binding of the metals was characterized by monitoring the metal-induced changes in circular dichroism spectra. Cyclic voltammetry demonstrated that bound copper underwent a Cu(I)/Cu(II) oxidation/reduction change at a potential of approximately 0.32 V in a quasi-reversible process. The relative binding affinity of Mn(II), Fe(II), Co(II), Ni(II) and Cu(II) to the peptides is correlated with the stability constants of the Irving-Williams series for divalent metal ions. A potential application of these complexes of transition metals with amino acids or peptides is in the development of artificial photosynthetic cells.
ContributorsWang, Dong (Author) / Allen, James P. (Thesis advisor) / Ghirlanda, Giovanna (Committee member) / Redding, Kevin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Cyanovirin-N (CVN) is a cyanobacterial lectin with potent anti-HIV activity, mediated by binding to the N-linked oligosaccharide moiety of the envelope protein gp120. CVN offers a scaffold to develop multivalent carbohydrate-binding proteins with tunable specificities and affinities. I present here biophysical calculations completed on a monomeric-stabilized mutant of cyanovirin-N, P51G-m4-CVN, in which domain A binding activity is abolished by four mutations; with comparisons made to CVNmutDB, in which domain B binding activity is abolished. Using Monte Carlo calculations and docking simulations, mutations in CVNmutDB were considered singularly, and the mutations E41A/G and T57A were found to impact the affinity towards dimannose the greatest. 15N-labeled proteins were titrated with Manα(1-2)Manα, while following chemical shift perturbations in NMR spectra. The mutants, E41A/G and T57A, had a larger Kd than P51G-m4-CVN, matching the trends predicted by the calculations. We also observed that the N42A mutation affects the local fold of the binding pocket, thus removing all binding to dimannose. Characterization of the mutant N53S showed similar binding affinity to P51G-m4-CVN. Using biophysical calculations allows us to study future iterations of models to explore affinities and specificities. In order to further elucidate the role of multivalency, I report here a designed covalent dimer of CVN, Nested cyanovirin-N (Nested CVN), which has four binding sites. Nested CVN was found to have comparable binding affinity to gp120 and antiviral activity to wt CVN. These results demonstrate the ability to create a multivalent, covalent dimer that has comparable results to that of wt CVN.
WW domains are small modules consisting of 32-40 amino acids that recognize proline-rich peptides and are found in many signaling pathways. We use WW domain sequences to explore protein folding by simulations using Zipping and Assembly Method. We identified five crucial contacts that enabled us to predict the folding of WW domain sequences based on those contacts. We then designed a folded WW domain peptide from an unfolded WW domain sequence by introducing native contacts at those critical positions.
WW domains are small modules consisting of 32-40 amino acids that recognize proline-rich peptides and are found in many signaling pathways. We use WW domain sequences to explore protein folding by simulations using Zipping and Assembly Method. We identified five crucial contacts that enabled us to predict the folding of WW domain sequences based on those contacts. We then designed a folded WW domain peptide from an unfolded WW domain sequence by introducing native contacts at those critical positions.
ContributorsWoodrum, Brian William (Author) / Ghirlanda, Giovanna (Thesis advisor) / Redding, Kevin (Committee member) / Wang, Xu (Committee member) / Arizona State University (Publisher)
Created2014
Description
Crises or large-scale emergencies such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally, this process has been restricted to the information collected by first responders on the ground in the affected region or by official agencies such as local governments involved in the response. However, the ubiquity of mobile devices has empowered people to publish information during a crisis through social media, such as the damage reports from a hurricane. Social media has thus emerged as an important channel of information which can be leveraged to improve crisis response. Twitter is a popular medium which has been employed in recent crises. However, it presents new challenges: the data is noisy and uncurated, and it has high volume and high velocity. In this work, I study four key problems in the use of social media for crisis response: effective monitoring and analysis of high volume crisis tweets, detecting crisis events automatically in streaming data, identifying users who can be followed to effectively monitor crisis, and finally understanding user behavior during crisis to detect tweets inside crisis regions. To address these problems I propose two systems which assist disaster responders or analysts to collaboratively collect tweets related to crisis and analyze it using visual analytics to identify interesting regions, topics, and users involved in disaster response. I present a novel approach to detecting crisis events automatically in noisy, high volume Twitter streams. I also investigate and introduce novel methods to tackle information overload through the identification of information leaders in information diffusion who can be followed for efficient crisis monitoring and identification of messages originating from crisis regions using user behavior analysis.
ContributorsKumar, Shamanth (Author) / Liu, Huan (Thesis advisor) / Davulcu, Hasan (Committee member) / Maciejewski, Ross (Committee member) / Agarwal, Nitin (Committee member) / Arizona State University (Publisher)
Created2015