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 86
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Developing a system capable of using solar energy to drive the conversion of an abundant and available precursor to fuel would profoundly impact humanity's energy use and thereby the condition of the global ecosystem. Such is the goal of artificial photosynthesis: to convert water to hydrogen using solar radiation as the sole energy input and ideally do so with the use of low cost, abundant materials. Constructing photoelectrochemical cells incorporating photoanodes structurally reminiscent of those used in dye sensitized photovoltaic solar cells presents one approach to establishing an artificial photosynthetic system. The work presented herein describes the production, integration, and study of water oxidation catalysts, molecular dyes, and metal oxide based photoelectrodes carried out in the pursuit of developing solar water splitting systems.
ContributorsSherman, Benjamin D (Author) / Moore, Thomas (Thesis advisor) / Moore, Ana (Committee member) / Buttry, Daniel (Committee member) / Arizona State University (Publisher)
Created2013
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
The work presented in this thesis covers the synthesis and characterization of an ionomer that is applicable to zinc-air batteries. Polysulfone polymer is first chloromethylated and then quaternized to create an ion-conducting polymer. Nuclear magnetic resonance (NMR) spectra indicates that the degree of chloromethylation was 114%. The chemical and physical properties that were investigated include: the ionic conductivity, ion exchange capacity, water retention capacity, diameter and thickness swelling ratios, porosity, glass transition temperature, ionic conductivity enhanced by free salt addition, and the concentration and diffusivity of oxygen within the ionomer. It was found that the fully hydrated hydroxide form of the ionomer had a room temperature ionic conductivity of 39.92mS/cm while the chloride form had a room temperature ionic conductivity of 11.80mS/cm. The ion exchange capacity of the ionomer was found to be 1.022mmol/g. The water retention capacity (WRC) of the hydroxide form was found to be 172.6% while the chloride form had a WRC of 67.9%. The hydroxide form of the ionomer had a diameter swelling ratio of 34% and a thickness swelling ratio of 55%. The chloride form had a diameter swelling ratio of 32% and a thickness swelling ratio of 28%. The largest pore size in the ionomer was found to be 32.6nm in diameter. The glass transition temperature of the ionomer is speculated to be 344°C. A definite measurement could not be made. The room temperature ionic conductivity at 50% relative humidity was improved to 12.90mS/cm with the addition of 80% free salt. The concentration and diffusivity of oxygen in the ionomer was found to be 1.3 ±0.2mMol and (0.49 ±0.15)x10-5 cm2/s respectively. The ionomer synthesized in this research had material properties and performance that is comparable to other ionomers reported in the literature. This is an indication that this ionomer is suitable for further study and integration into a zinc-air battery. This thesis is concluded with suggestions for future research that is focused on improving the performance of the ionomer as well as improving the methodology.
ContributorsPadilla, Manuel (Author) / Friesen, Cody A (Thesis advisor) / Buttry, Daniel (Committee member) / Sieradzki, Karl (Committee member) / Arizona State University (Publisher)
Created2012
Description
Dealloying, the selective dissolution of an elemental component from an alloy, is an important corrosion mechanism and a technological significant means to fabricate nanoporous structures for a variety of applications. In noble metal alloys, dealloying proceeds above a composition dependent critical potential, and bi-continuous structure evolves "simultaneously" as a result of the interplay between percolation dissolution and surface diffusion. In contrast, dealloying in alloys that show considerable solid-state mass transport at ambient temperature is largely unexplored despite its relevance to nanoparticle catalysts and Li-ion anodes. In my dissertation, I discuss the behaviors of two alloy systems in order to elucidate the role of bulk lattice diffusion in dealloying. First, Mg-Cd alloys are chosen to show that when the dealloying is controlled by bulk diffusion, a new type of porosity - negative void dendrites will form, and the process mirrors electrodeposition. Then, Li-Sn alloys are studied with respect to the composition, particle size and dealloying rate effects on the morphology evolution. Under the right condition, dealloying of Li-Sn supported by percolation dissolution results in the same bi-continuous structure as nanoporous noble metals; whereas lattice diffusion through the otherwise "passivated" surface allows for dealloying with no porosity evolution. The interactions between bulk diffusion, surface diffusion and dissolution are revealed by chronopotentiometry and linear sweep voltammetry technics. The better understanding of dealloying from these experiments enables me to construct a brief review summarizing the electrochemistry and morphology aspects of dealloying as well as offering interpretations to new observations such as critical size effect and encased voids in nanoporous gold. At the end of the dissertation, I will describe a preliminary attempt to generalize the morphology evolution "rules of dealloying" to all solid-to-solid interfacial controlled phase transition process, demonstrating that bi-continuous morphologies can evolve regardless of the nature of parent phase.
ContributorsChen, Qing (Author) / Sieradzki, Karl (Thesis advisor) / Friesen, Cody (Committee member) / Buttry, Daniel (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2013
Description
Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering is applied to find projects which involve resources of similar skillsets and which involve similar complexities and size. This results in "resource utilization templates" for groups of related projects from a resource consumption perspective. Then project characteristics are identified which generate this diversity in headcounts and skillsets. These characteristics are not currently contained in the data base and are elicited from the managers of historical projects. This represents an opportunity to improve the usefulness of the data collection system for the future. The ultimate goal is to match the product technical features with the resource requirement for projects in the past as a model to forecast resource requirements by skill set for future projects. The forecasting model is developed using linear regression with cross validation of the training data as the past project execution are relatively few in number. Acceptable levels of forecast accuracy are achieved relative to human experts' results and the tool is applied to forecast some future projects' resource demand.
ContributorsBhattacharya, Indrani (Author) / Sen, Arunabha (Thesis advisor) / Kempf, Karl G. (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question. In this thesis, I develop a framework to personalize social signals for users to guide their activities on an online platform. As the result, we gradually nudge the activity distribution on the platform from the initial distribution p to the target distribution q. My work is particularly applicable to guiding collaborations, guiding collective actions, and online advertising. In particular, I first propose a probabilistic model on how users behave and how information flows on the platform. The main part of this thesis after that discusses the Influence Individuals through Social Signals (IISS) framework. IISS consists of four main components: (1) Learner: it learns users' interests and characteristics from their historical activities using Bayesian model, (2) Calculator: it uses gradient descent method to compute the intermediate activity distributions, (3) Selector: it selects users who can be influenced to adopt or drop specific activities, (4) Designer: it personalizes social signals for each user. I evaluate the performance of IISS framework by simulation on several network topologies such as preferential attachment, small world, and random. I show that the framework gradually nudges users' activities to approach the target distribution. I use both simulation and mathematical method to analyse convergence properties such as how fast and how close we can approach the target distribution. When the number of activities is 3, I show that for about 45% of target distributions, we can achieve KL-divergence as low as 0.05. But for some other distributions KL-divergence can be as large as 0.5.
ContributorsLe, Tien D (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014
Description
This work demonstrated a novel microfluidic device based on direct current (DC) insulator based dielectrophoresis (iDEP) for trapping individual mammalian cells in a microfluidic device. The novel device is also applicable for selective trapping of weakly metastatic mammalian breast cancer cells (MCF-7) from mixtures with mammalian Peripheral Blood Mononuclear Cells (PBMC) and highly metastatic mammalian breast cancer cells, MDA-MB-231. The advantage of this approach is the ease of integration of iDEP structures in microfliudic channels using soft lithography, the use of DC electric fields, the addressability of the single cell traps for downstream analysis and the straightforward multiplexing for single cell trapping. These microfluidic devices are targeted for capturing of single cells based on their DEP behavior. The numerical simulations point out the trapping regions in which single cell DEP trapping occurs. This work also demonstrates the cell conductivity values of different cell types, calculated using the single-shell model. Low conductivity buffers are used for trapping experiments. These low conductivity buffers help reduce the Joule heating. Viability of the cells in the buffer system was studied in detail with a population size of approximately 100 cells for each study. The work also demonstrates the development of the parallelized single cell trap device with optimized traps. This device is also capable of being coupled detection of target protein using MALDI-MS.
ContributorsBhattacharya, Sanchari (Author) / Ros, Alexandra (Committee member) / Ros, Robert (Committee member) / Buttry, Daniel (Committee member) / Arizona State University (Publisher)
Created2013
Description
Most data cleaning systems aim to go from a given deterministic dirty database to another deterministic but clean database. Such an enterprise pre–supposes that it is in fact possible for the cleaning process to uniquely recover the clean versions of each dirty data tuple. This is not possible in many cases, where the most a cleaning system can do is to generate a (hopefully small) set of clean candidates for each dirty tuple. When the cleaning system is required to output a deterministic database, it is forced to pick one clean candidate (say the "most likely" candidate) per tuple. Such an approach can lead to loss of information. For example, consider a situation where there are three equally likely clean candidates of a dirty tuple. An appealing alternative that avoids such an information loss is to abandon the requirement that the output database be deterministic. In other words, even though the input (dirty) database is deterministic, I allow the reconstructed database to be probabilistic. Although such an approach does avoid the information loss, it also brings forth several challenges. For example, how many alternatives should be kept per tuple in the reconstructed database? Maintaining too many alternatives increases the size of the reconstructed database, and hence the query processing time. Second, while processing queries on the probabilistic database may well increase recall, how would they affect the precision of the query processing? In this thesis, I investigate these questions. My investigation is done in the context of a data cleaning system called BayesWipe that has the capability of producing multiple clean candidates per each dirty tuple, along with the probability that they are the correct cleaned version. I represent these alternatives as tuples in a tuple disjoint probabilistic database, and use the Mystiq system to process queries on it. This probabilistic reconstruction (called BayesWipe–PDB) is compared to a deterministic reconstruction (called BayesWipe–DET)—where the most likely clean candidate for each tuple is chosen, and the rest of the alternatives discarded.
ContributorsRihan, Preet Inder Singh (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Titanium oxide (TiO2), an abundant material with high photocatalytic activity and chemical stability is an important candidate for photocatalytic applications. The photocatalytic activity of the TiO2 varies with its phase. In the current project, phase and morphology changes in TiO2 nanotubes were studied using ex-situ and in-situ transmission electron microscopy (TEM). X-ray diffraction and scanning electron microscopy studies were also performed to understand the phase and morphology of the nanotubes. As prepared TiO2 nanotubes supported on Ti metal substrate were amorphous, during the heat treatment in the ex-situ furnace nanotubes transform to anatase at 450 oC and transformed to rutile when heated to 800 oC. TiO2 nanotubes that were heat treated in an in-situ environmental TEM, transformed to anatase at 400 oC and remain anatase even up to 800 oC. In both ex-situ an in-situ case, the morphology of the nanotubes drastically changed from a continuous tubular structure to aggregates of individual nanoparticles. The difference between the ex-situ an in-situ treatments and their effect on the phase transformation is discussed. Metal doping is one of the effective ways to improve the photocatalytic performance. Several approaches were performed to get metal loading on to the TiO2 nanotubes. Mono-dispersed platinum nanoparticles were deposited on the TiO2 nanopowder and nanotubes using photoreduction method. Photo reduction for Ag and Pt bimetallic nanoparticles were also performed on the TiO2 powders.
ContributorsSantra, Sanjitarani (Author) / Crozier, Peter A. (Thesis advisor) / Carpenter, Ray (Committee member) / Buttry, Daniel (Committee member) / Arizona State University (Publisher)
Created2014