ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 10 of 135
Description
Filtration for microfluidic sample-collection devices is desirable for sample selection, concentration, preprocessing, and downstream manipulation, but microfabricating the required sub-micrometer filtration structure is an elaborate process. This thesis presents a simple method to fabricate polydimethylsiloxane (PDMS) devices with an integrated membrane filter that will sample, lyse, and extract the DNA from microorganisms in aqueous environments. An off-the-shelf membrane filter disc was embedded in a PDMS layer and sequentially bound with other PDMS channel layers. No leakage was observed during filtration. This device was validated by concentrating a large amount of cyanobacterium Synechocystis in simulated sample water with consistent performance across devices. After accumulating sufficient biomass on the filter, a sequential electrochemical lysing process was performed by applying 5VDC across the filter. This device was further evaluated by delivering several samples of differing concentrations of cyanobacterium Synechocystis then quantifying the DNA using real-time PCR. Lastly, an environmental sample was run through the device and the amount of photosynthetic microorganisms present in the water was determined. The major breakthroughs in this design are low energy demand, cheap materials, simple design, straightforward fabrication, and robust performance, together enabling wide-utility of similar chip-based devices for field-deployable operations in environmental micro-biotechnology.
ContributorsLecluse, Aurelie (Author) / Meldrum, Deirdre (Thesis advisor) / Chao, Joseph (Thesis advisor) / Westerhoff, Paul (Committee member) / Arizona State University (Publisher)
Created2011
Description
With the rapid growth of mobile computing and sensor technology, it is now possible to access data from a variety of sources. A big challenge lies in linking sensor based data with social and cognitive variables in humans in real world context. This dissertation explores the relationship between creativity in teamwork, and team members' movement and face-to-face interaction strength in the wild. Using sociometric badges (wearable sensors), electronic Experience Sampling Methods (ESM), the KEYS team creativity assessment instrument, and qualitative methods, three research studies were conducted in academic and industry R&D; labs. Sociometric badges captured movement of team members and face-to-face interaction between team members. KEYS scale was implemented using ESM for self-rated creativity and expert-coded creativity assessment. Activities (movement and face-to-face interaction) and creativity of one five member and two seven member teams were tracked for twenty five days, eleven days, and fifteen days respectively. Day wise values of movement and face-to-face interaction for participants were mean split categorized as creative and non-creative using self- rated creativity measure and expert-coded creativity measure. Paired-samples t-tests [t(36) = 3.132, p < 0.005; t(23) = 6.49 , p < 0.001] confirmed that average daily movement energy during creative days (M = 1.31, SD = 0.04; M = 1.37, SD = 0.07) was significantly greater than the average daily movement of non-creative days (M = 1.29, SD = 0.03; M = 1.24, SD = 0.09). The eta squared statistic (0.21; 0.36) indicated a large effect size. A paired-samples t-test also confirmed that face-to-face interaction tie strength of team members during creative days (M = 2.69, SD = 4.01) is significantly greater [t(41) = 2.36, p < 0.01] than the average face-to-face interaction tie strength of team members for non-creative days (M = 0.9, SD = 2.1). The eta squared statistic (0.11) indicated a large effect size. The combined approach of principal component analysis (PCA) and linear discriminant analysis (LDA) conducted on movement and face-to-face interaction data predicted creativity with 87.5% and 91% accuracy respectively. This work advances creativity research and provides a foundation for sensor based real-time creativity support tools for teams.
ContributorsTripathi, Priyamvada (Author) / Burleson, Winslow (Thesis advisor) / Liu, Huan (Committee member) / VanLehn, Kurt (Committee member) / Pentland, Alex (Committee member) / Arizona State University (Publisher)
Created2011
Description
Atmospheric particulate matter has a substantial impact on global climate due to its ability to absorb/scatter solar radiation and act as cloud condensation nuclei (CCN). Yet, little is known about marine aerosol, in particular, the carbonaceous fraction. In the present work, particulate matter was collected, using High Volume (HiVol) samplers, onto quartz fiber substrates during a series of research cruises on the Atlantic Ocean. Samples were collected on board the R/V Endeavor on West–East (March–April, 2006) and East–West (June–July, 2006) transects in the North Atlantic, as well as on the R/V Polarstern during a North–South (October–November, 2005) transect along the western coast of Europe and Africa. The aerosol total carbon (TC) concentrations for the West–East (Narragansett, RI, USA to Nice, France) and East–West (Heraklion, Crete, Greece to Narragansett, RI, USA) transects were generally low over the open ocean (0.36±0.14 μg C/m3) and increased as the ship approached coastal areas (2.18±1.37 μg C/m3), due to increased terrestrial/anthropogenic aerosol inputs. The TC for the North–South transect samples decreased in the southern hemisphere with the exception of samples collected near the 15th parallel where calculations indicate the air mass back trajectories originated from the continent. Seasonal variation in organic carbon (OC) was seen in the northern hemisphere open ocean samples with average values of 0.45 μg/m3 and 0.26 μg/m3 for spring and summer, respectively. These low summer time values are consistent with SeaWiFS satellite images that show decreasing chlorophyll a concentration (a proxy for phytoplankton biomass) in the summer. There is also a statistically significant (p<0.05) decline in surface water fluorescence in the summer. Moreover, examination of water–soluble organic carbon (WSOC) shows that the summer aerosol samples appear to have a higher fraction of the lower molecular weight material, indicating that the samples may be more oxidized (aged). The seasonal variation in aerosol content seen during the two 2006 cruises is evidence that a primary biological marine source is a significant contributor to the carbonaceous particulate in the marine atmosphere and is consistent with previous studies of clean marine air masses.
ContributorsHill, Hansina Rae (Author) / Herckes, Pierre (Thesis advisor) / Westerhoff, Paul (Committee member) / Hartnett, Hilairy (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis deals with the analysis of interpersonal communication dynamics in online social networks and social media. Our central hypothesis is that communication dynamics between individuals manifest themselves via three key aspects: the information that is the content of communication, the social engagement i.e. the sociological framework emergent of the communication process, and the channel i.e. the media via which communication takes place. Communication dynamics have been of interest to researchers from multi-faceted domains over the past several decades. However, today we are faced with several modern capabilities encompassing a host of social media websites. These sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information, our modes of social engagement, and mechanisms of how the media characteristics impact our interactional behavior. The outcomes of this research are manifold. We present our contributions in three parts, corresponding to the three key organizing ideas. First, we have observed that user context is key to characterizing communication between a pair of individuals. However interestingly, the probability of future communication seems to be more sensitive to the context compared to the delay, which appears to be rather habitual. Further, we observe that diffusion of social actions in a network can be indicative of future information cascades; that might be attributed to social influence or homophily depending on the nature of the social action. Second, we have observed that different modes of social engagement lead to evolution of groups that have considerable predictive capability in characterizing external-world temporal occurrences, such as stock market dynamics as well as collective political sentiments. Finally, characterization of communication on rich media sites have shown that conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Based on all these outcomes, we believe that this research can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.
ContributorsDe Choudhury, Munmun (Author) / Sundaram, Hari (Thesis advisor) / Candan, K. Selcuk (Committee member) / Liu, Huan (Committee member) / Watts, Duncan J. (Committee member) / Seligmann, Doree D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.
ContributorsSun, Liang (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Liu, Huan (Committee member) / Mittelmann, Hans D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information mediators need to handle is the varying levels of incompleteness in the underlying databases in terms of missing attribute values. Existing approaches such as Query Processing over Incomplete Autonomous Databases (QPIAD) aim to mine and use Approximate Functional Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These approaches make independence assumptions about missing values--which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this thesis, I present a principled probabilis- tic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it stands for. I learn this distribution in terms of Bayes networks. My approach involves min- ing/"learning" Bayes networks from a sample of the database, and using it do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness on the query-constrained attributes, when the data sources are autonomous). I present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayes networks do provide a significantly higher classification accuracy and (ii) the relevant possible answers retrieved by the queries reformulated using Bayes networks provide higher precision and recall than AFDs while keeping query processing costs manageable.
ContributorsRaghunathan, Rohit (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2011
Description
Source selection is one of the foremost challenges for searching deep-web. For a user query, source selection involves selecting a subset of deep-web sources expected to provide relevant answers to the user query. Existing source selection models employ query-similarity based local measures for assessing source quality. These local measures are necessary but not sufficient as they are agnostic to source trustworthiness and result importance, which, given the autonomous and uncurated nature of deep-web, have become indispensible for searching deep-web. SourceRank provides a global measure for assessing source quality based on source trustworthiness and result importance. SourceRank's effectiveness has been evaluated in single-topic deep-web environments. The goal of the thesis is to extend sourcerank to a multi-topic deep-web environment. Topic-sensitive sourcerank is introduced as an effective way of extending sourcerank to a deep-web environment containing a set of representative topics. In topic-sensitive sourcerank, multiple sourcerank vectors are created, each biased towards a representative topic. At query time, using the topic of query keywords, a query-topic sensitive, composite sourcerank vector is computed as a linear combination of these pre-computed biased sourcerank vectors. Extensive experiments on more than a thousand sources in multiple domains show 18-85% improvements in result quality over Google Product Search and other existing methods.
ContributorsJha, Manishkumar (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Strong communities are important for society. One of the most important community builders, making friends, is poorly supported online. Dating sites support it but in romantic contexts. Other major social networks seem not to encourage it because either their purpose isn't compatible with introducing strangers or the prevalent methods of introduction aren't effective enough to merit use over real word alternatives. This paper presents a novel digital social network emphasizing creating friendships. Research has shown video chat communication can reach in-person levels of trust; coupled with a game environment to ease the discomfort people often have interacting with strangers and a recommendation engine, Zazzer, the presented system, allows people to meet and get to know each other in a manner much more true to real life than traditional methods. Its network also allows players to continue to communicate afterwards. The evaluation looks at real world use, measuring the frequency with which players choose the video chat game versus alternative, more traditional methods of online introduction. It also looks at interactions after the initial meeting to discover how effective video chat games are in creating sticky social connections. After initial use it became apparent a critical mass of users would be necessary to draw strong conclusions, however the collected data seemed to give preliminary support to the idea that video chat games are more effective than traditional ways of meeting online in creating new relationships.
ContributorsSorensen, Asael (Author) / VanLehn, Kurt (Thesis advisor) / Liu, Huan (Committee member) / Burleson, Winslow (Committee member) / Arizona State University (Publisher)
Created2011
Description
Lipids and free fatty acids (FFA) from cyanobacterium Synechocystis can be used for biofuel (e.g. biodiesel or renewable diesel) production. In order to utilize and scale up this technique, downstream processes including culturing and harvest, cell disruption, and extraction were studied. Several solvents/solvent systems were screened for lipid extraction from Synechocystis. Chloroform + methanol-based Folch and Bligh & Dyer methods were proved to be "gold standard" for small-scale analysis due to their highest lipid recoveries that were confirmed by their penetration of the cell membranes, higher polarity, and stronger interaction with hydrogen bonds. Less toxic solvents, such as methanol and MTBE, or direct transesterification of biomass (without pre-extraction step) gave only slightly lower lipid-extraction yields and can be considered for large-scale application. Sustained exposure to high and low temperature extremes severely lowered the biomass and lipid productivity. Temperature stress also triggered changes of lipid quality such as the degree of unsaturation; thus, it affected the productivities and quality of Synechocystis-derived biofuel. Pulsed electric field (PEF) was evaluated for cell disruption prior to lipid extraction. A treatment intensity > 35 kWh/m3 caused significant damage to the plasma membrane, cell wall, and thylakoid membrane, and it even led to complete disruption of some cells into fragments. Treatment by PEF enhanced the potential for the low-toxicity solvent isopropanol to access lipid molecules during subsequent solvent extraction, leading to lower usage of isopropanol for the same extraction efficiency. Other cell-disruption methods also were tested. Distinct disruption effects to the cell envelope, plasma membrane, and thylakoid membranes were observed that were related to extraction efficiency. Microwave and ultrasound had significant enhancement of lipid extraction. Autoclaving, ultrasound, and French press caused significant release of lipid into the medium, which may increase solvent usage and make medium recycling difficult. Production of excreted FFA by mutant Synechocystis has the potential of reducing the complexity of downstream processing. Major problems, such as FFA precipitation and biodegradation by scavengers, account for FFA loss in operation. Even a low concentration of FFA scavengers could consume FFA at a high rate that outpaced FFA production rate. Potential strategies to overcome FFA loss include high pH, adsorptive resin, and sterilization techniques.
ContributorsSheng, Chieh (Author) / Rittmann, Bruce E. (Thesis advisor) / Westerhoff, Paul (Committee member) / Vermaas, Willem (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation presents a systematic study of the sorption mechanisms of hydrophobic silica aerogel (Cabot Nanogel®) granules for oil and volatile organic compounds (VOCs) in different phases. The performance of Nanogel for removing oil from laboratory synthetic oil-in-water emulsions and real oily wastewater, and VOCs from their aqueous solution, in both packed bed (PB) and inverse fluidized bed (IFB) modes was also investigated. The sorption mechanisms of VOCs in the vapor, pure liquid, and aqueous solution phases, free oil, emulsified oil, and oil from real wastewater on Nanogel were systematically studied via batch kinetics and equilibrium experiments. The VOC results show that the adsorption of vapor is very slow due to the extremely low thermal conductivity of Nanogel. The faster adsorption rates in the liquid and solution phases are controlled by the mass transport, either by capillary flow or by vapor diffusion/adsorption. The oil results show that Nanogel has a very high capacity for adsorption of pure oils. However, the rate for adsorption of oil from an oil-water emulsion on the Nanogel is 5-10 times slower than that for adsorption of pure oils or organics from their aqueous solutions. For an oil-water emulsion, the oil adsorption capacity decreases with an increasing proportion of the surfactant added. An even lower sorption capacity and a slower sorption rate were observed for a real oily wastewater sample due to the high stability and very small droplet size of the wastewater. The performance of Nanogel granules for removing emulsified oil, oil from real oily wastewater, and toluene at low concentrations in both PB and IFB modes was systematically investigated. The hydrodynamics characteristics of the Nanogel granules in an IFB were studied by measuring the pressure drop and bed expansion with superficial water velocity. The density of the Nanogel granules was calculated from the plateau pressure drop of the IFB. The oil/toluene removal efficiency and the capacity of the Nanogel granules in the PB or IFB were also measured experimentally and predicted by two models based on equilibrium and kinetic batch measurements of the Nanogel granules.
ContributorsWang, Ding (Author) / Lin, Jerry Y.S. (Thesis advisor) / Pfeffer, Robert (Thesis advisor) / Westerhoff, Paul (Committee member) / Nielsen, David (Committee member) / Lind, Mary Laura (Committee member) / Arizona State University (Publisher)
Created2011