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Description
Recently, the use of zinc oxide (ZnO) nanowires as an interphase in composite materials has been demonstrated to increase the interfacial shear strength between carbon fiber and an epoxy matrix. In this research work, the strong adhesion between ZnO and carbon fiber is investigated to elucidate the interactions at the interface that result in high interfacial strength. First, molecular dynamics (MD) simulations are performed to calculate the adhesive energy between bare carbon and ZnO. Since the carbon fiber surface has oxygen functional groups, these were modeled and MD simulations showed the preference of ketones to strongly interact with ZnO, however, this was not observed in the case of hydroxyls and carboxylic acid. It was also found that the ketone molecules ability to change orientation facilitated the interactions with the ZnO surface. Experimentally, the atomic force microscope (AFM) was used to measure the adhesive energy between ZnO and carbon through a liftoff test by employing highly oriented pyrolytic graphite (HOPG) substrate and a ZnO covered AFM tip. Oxygen functionalization of the HOPG surface shows the increase of adhesive energy. Additionally, the surface of ZnO was modified to hold a negative charge, which demonstrated an increase in the adhesive energy. This increase in adhesion resulted from increased induction forces given the relatively high polarizability of HOPG and the preservation of the charge on ZnO surface. It was found that the additional negative charge can be preserved on the ZnO surface because there is an energy barrier since carbon and ZnO form a Schottky contact. Other materials with the same ionic properties of ZnO but with higher polarizability also demonstrated good adhesion to carbon. This result substantiates that their induced interaction can be facilitated not only by the polarizability of carbon but by any of the materials at the interface. The versatility to modify the magnitude of the induced interaction between carbon and an ionic material provides a new route to create interfaces with controlled interfacial strength.
ContributorsGalan Vera, Magdian Ulises (Author) / Sodano, Henry A (Thesis advisor) / Jiang, Hanqing (Committee member) / Solanki, Kiran (Committee member) / Oswald, Jay (Committee member) / Speyer, Gil (Committee member) / Arizona State University (Publisher)
Created2013
Description
Coccidioidomycosis, also known as Valley Fever, is a disease caused by the dimorphic soil-dwelling fungus, Coccidioides sp. Coccidioidomycosis is difficult to diagnose because symptoms are similar to community-acquired pneumonia. Current diagnostic tests rely on antibody responses, but immune responses can be delayed and aberrant, resulting in false negative diagnoses. Unlike serology, detection of coccidioidal proteins or other fungal components in blood could distinguish valley fever from other pulmonary infections and provide a definitive diagnosis. Using mass spectrometry (LC-MS/MS) we examined the plasma peptidome from patients with serologically confirmed coccidioidomycosis. Mass spectra were searched using the protein database from the Coccidioides species, generated and annotated by the Broad Institute. 15 of 20 patients with serologically confirmed coccidioidomycosis demonstrated the presence of a peptide in plasma, "PGLDSKSLACTFSQV" (PGLD). The peptide is derived from an open reading frame from a "conserved hypothetical protein" annotated with 2 exons, and to date, found only in the C. posadasii strain Silviera RMSCC 3488 genomic sequence. In this thesis work, cDNA sequence analysis from polyadenylated RNA confirms the peptide sequence and genomic location of the peptide, but does not indicate that the intron in the gene prediction of C. posadasii strain Silviera RMSCC 3488 is present. A monoclonal antibody generated against the peptide bound to a 16kDa protein in T27K coccidioidal lysate. Detecting components of the fungus plasma could be a useful diagnostic tool, especially when serology does not provide a definitive diagnosis.
ContributorsDuffy, Stacy Leigh (Author) / Lake, Douglas (Thesis advisor) / Magee, Dewey Mitch (Committee member) / Antwi, Kwasi (Committee member) / Arizona State University (Publisher)
Created2013
Description
Wind measurements are fundamental inputs for the evaluation of potential energy yield and performance of wind farms. Three-dimensional scanning coherent Doppler lidar (CDL) may provide a new basis for wind farm site selection, design, and control. In this research, CDL measurements obtained from multiple wind energy developments are analyzed and a novel wind farm control approach has been modeled. The possibility of using lidar measurements to more fully characterize the wind field is discussed, specifically, terrain effects, spatial variation of winds, power density, and the effect of shear at different layers within the rotor swept area. Various vector retrieval methods have been applied to the lidar data, and results are presented on an elevated terrain-following surface at hub height. The vector retrieval estimates are compared with tower measurements, after interpolation to the appropriate level. CDL data is used to estimate the spatial power density at hub height. Since CDL can measure winds at different vertical levels, an approach for estimating wind power density over the wind turbine rotor-swept area is explored. Sample optimized layouts of wind farm using lidar data and global optimization algorithms, accounting for wake interaction effects, have been explored. An approach to evaluate spatial wind speed and direction estimates from a standard nested Coupled Ocean and Atmosphere Mesoscale Prediction System (COAMPS) model and CDL is presented. The magnitude of spatial difference between observations and simulation for wind energy assessment is researched. Diurnal effects and ramp events as estimated by CDL and COAMPS were inter-compared. Novel wind farm control based on incoming winds and direction input from CDL's is developed. Both yaw and pitch control using scanning CDL for efficient wind farm control is analyzed. The wind farm control optimizes power production and reduces loads on wind turbines for various lidar wind speed and direction inputs, accounting for wind farm wake losses and wind speed evolution. Several wind farm control configurations were developed, for enhanced integrability into the electrical grid. Finally, the value proposition of CDL for a wind farm development, based on uncertainty reduction and return of investment is analyzed.
ContributorsKrishnamurthy, Raghavendra (Author) / Calhoun, Ronald J (Thesis advisor) / Chen, Kangping (Committee member) / Huang, Huei-Ping (Committee member) / Fraser, Matthew (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2013
Description
Speciation is the fundamental process that has generated the vast diversity of life on earth. The hallmark of speciation is the evolution of barriers to gene flow. These barriers may reduce gene flow either by keeping incipient species from hybridizing at all (pre-zygotic), or by reducing the fitness of hybrids (post-zygotic). To understand the genetic architecture of these barriers and how they evolve, I studied a genus of wasps that exhibits barriers to gene flow that act both pre- and post-zygotically. Nasonia is a genus of four species of parasitoid wasps that can be hybridized in the laboratory. When two of these species, N. vitripennis and N. giraulti are mated, their offspring suffer, depending on the generation and cross examined, up to 80% mortality during larval development due to incompatible genic interactions between their nuclear and mitochondrial genomes. These species also exhibit pre-zygotic isolation, meaning they are more likely to mate with their own species when given the choice. I examined these two species and their hybrids to determine the genetic and physiological bases of both speciation mechanisms and to understand the evolutionary forces leading to them. I present results that indicate that the oxidative phosphorylation (OXPHOS) pathway, an essential pathway that is responsible for mitochondrial energy generation, is impaired in hybrids of these two species. These results indicate that this impairment is due to the unique evolutionary dynamics of the combined nuclear and mitochondrial origin of this pathway. I also present results showing that, as larvae, these hybrids experience retarded growth linked to the previously observed mortality and I explore possible physiological mechanisms for this. Finally, I show that the pre-mating isolation is due to a change in a single pheromone component in N. vitripennis males, that this change is under simple genetic control, and that it evolved neutrally before being co-opted as a species recognition signal. These results are an important addition to our overall understanding of the mechanisms of speciation and showcase Nasonia as an emerging model for the study of the genetics of speciation.
ContributorsGibson, Joshua D (Author) / Gadau, Jürgen (Thesis advisor) / Harrison, Jon (Committee member) / Pratt, Stephen (Committee member) / Verrelli, Brian (Committee member) / Willis, Wayne (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Modified and artificial water sources can be used as a management tool for game and non-game wildlife species. State, federal, and private agencies allocate significant resources to install and maintain artificial water sources (AWS) annually. Capture mark recapture methods were used to sample small mammal communities in the vicinity of five AWS and five paired control sites (treatments) in the surrounding Sonoran desert from October 2011 to May 2012. I measured plant species richness, density, and percent cover in the spring of 2012. A Multi-response Permutation Procedure was used to identify differences in small mammal community abundance, biomass, and species richness by season and treatment. I used Principle Component Analysis to reduce 11 habitat characteristics to five habitat factors. I related rodent occurrence to habitat characteristics using multiple and logistic regression. A total of 370 individual mammals representing three genera and eight species of rodents were captured across 4800 trap nights. Desert pocket mouse (Chaetodipus penicillatus) was the most common species in both seasons and treatments. Whereas rodent community abundance, biomass, and richness were similar between seasons, community variables of AWS were greater than CS. Rodent diversity was similar between treatments. Desert pocket mouse abundance and biomass were twice as high at AWS when compared to controls. Biomass of white-throated woodrat (Neotoma albigula) was five times greater at AWS. Habitat characteristics were similar between treatments. Neither presence of water nor distance to water explained substantial habitat variation. Occurrence of rodent species was associated with habitat characteristics. Desert rodent communities are adapted for arid environments (i.e. Heteromyids) and are not dependent on "free water". Higher abundances of desert pocket mouse at AWS were most likely related to increased disturbance and debris and not the presence of water. The results of this study and previous studies suggest that more investigation is needed and that short term studies may not be able to detect interactions (if any) between AWS and desert small mammal communities.
ContributorsSwitalski, Aaron (Author) / Bateman, Heather L (Thesis advisor) / Miller, William (Committee member) / Alford, Eddie (Committee member) / Arizona State University (Publisher)
Created2013
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
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
One of the main challenges in planetary robotics is to traverse the shortest path through a set of waypoints. The shortest distance between any two waypoints is a direct linear traversal. Often times, there are physical restrictions that prevent a rover form traversing straight to a waypoint. Thus, knowledge of the terrain is needed prior to traversal. The Digital Terrain Model (DTM) provides information about the terrain along with waypoints for the rover to traverse. However, traversing a set of waypoints linearly is burdensome, as the rovers would constantly need to modify their orientation as they successively approach waypoints. Although there are various solutions to this problem, this research paper proposes the smooth traversability of the rover using splines as a quick and easy implementation to traverse a set of waypoints. In addition, a rover was used to compare the smoothness of the linear traversal along with the spline interpolations. The data collected illustrated that spline traversals had a less rate of change in the velocity over time, indicating that the rover performed smoother than with linear paths.
ContributorsKamasamudram, Anurag (Author) / Saripalli, Srikanth (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Continuous Delivery, as one of the youngest and most popular member of agile model family, has become a popular concept and method in software development industry recently. Instead of the traditional software development method, which requirements and solutions must be fixed before starting software developing, it promotes adaptive planning, evolutionary development and delivery, and encourages rapid and flexible response to change. However, several problems prevent Continuous Delivery to be introduced into education world. Taking into the consideration of the barriers, we propose a new Cloud based Continuous Delivery Software Developing System. This system is designed to fully utilize the whole life circle of software developing according to Continuous Delivery concepts in a virtualized environment in Vlab platform.
ContributorsDeng, Yuli (Author) / Huang, Dijiang (Thesis advisor) / Davulcu, Hasan (Committee member) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2013