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 125
Description
Mostly, manufacturing tolerance charts are used these days for manufacturing tolerance transfer but these have the limitation of being one dimensional only. Some research has been undertaken for the three dimensional geometric tolerances but it is too theoretical and yet to be ready for operator level usage. In this research, a new three dimensional model for tolerance transfer in manufacturing process planning is presented that is user friendly in the sense that it is built upon the Coordinate Measuring Machine (CMM) readings that are readily available in any decent manufacturing facility. This model can take care of datum reference change between non orthogonal datums (squeezed datums), non-linearly oriented datums (twisted datums) etc. Graph theoretic approach based upon ACIS, C++ and MFC is laid out to facilitate its implementation for automation of the model. A totally new approach to determining dimensions and tolerances for the manufacturing process plan is also presented. Secondly, a new statistical model for the statistical tolerance analysis based upon joint probability distribution of the trivariate normal distributed variables is presented. 4-D probability Maps have been developed in which the probability value of a point in space is represented by the size of the marker and the associated color. Points inside the part map represent the pass percentage for parts manufactured. The effect of refinement with form and orientation tolerance is highlighted by calculating the change in pass percentage with the pass percentage for size tolerance only. Delaunay triangulation and ray tracing algorithms have been used to automate the process of identifying the points inside and outside the part map. Proof of concept software has been implemented to demonstrate this model and to determine pass percentages for various cases. The model is further extended to assemblies by employing convolution algorithms on two trivariate statistical distributions to arrive at the statistical distribution of the assembly. Map generated by using Minkowski Sum techniques on the individual part maps is superimposed on the probability point cloud resulting from convolution. Delaunay triangulation and ray tracing algorithms are employed to determine the assembleability percentages for the assembly.
ContributorsKhan, M Nadeem Shafi (Author) / Phelan, Patrick E (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Farin, Gerald (Committee member) / Roberts, Chell (Committee member) / Henderson, Mark (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Natural photosynthesis features a complex biophysical/chemical process that requires sunlight to produce energy rich products. It is one of the most important processes responsible for the appearance and sustainability of life on earth. The first part of the thesis focuses on understanding the mechanisms involved in regulation of light harvesting, which is necessary to balance the absorption and utilization of light energy and in that way reduce the effect caused by photooxidative damage. In photosynthesis, carotenoids are responsible not only for collection of light, but also play a major role in protecting the photosynthetic system. To investigate the role of carotenoids in the quenching of the excited state of cyclic tetrapyrroles, two sets of dyads were studied. Both sets of dyads contain zinc phthalocyanine (Pc) covalently attached to carotenoids of varying conjugation lengths. In the first set of dyads, carotenoids were attached to the phthalocyanine via amide linkage. This set of dyads serves as a good model for understanding the molecular "gear-shift" mechanism, where the addition of one double bond can turn the carotenoid from a nonquencher to a very strong quencher of the excited state of a tetrapyrrole. In the second set of dyads, carotenoids were attached to phthalocyanine via a phenyl amino group. Two independent studies were performed on these dyads: femtosecond transient absorption and steady state fluorescence induced by two-photon excitation. In the transient absorption study it was observed that there is an instantaneous population of the carotenoid S1 state after Pc excitation, while two-photon excitation of the optically forbidden carotenoid S1 state shows 1Pc population. Both observations provide a strong indication of the existence of a shared excitonic state between carotenoid and Pc. Similar results were observed in LHC II complexes in plants, supporting the role of such interactions in photosynthetic down regulation. In the second chapter we describe the synthesis of porphyrin dyes functionalized with carboxylate and phosphonate anchoring groups to be used in the construction of photoelectrochemical cells containing a porphyrin-IrO2·nH2O complex immobilized on a TiO2 electrode. The research presented here is a step in the development of high potential porphyrin-metal oxide complexes to be used in the photooxidation of water. The last chapter focuses on developing synthetic strategies for the construction of an artificial antenna system consisting of porphyrin-silver nanoparticle conjugates, linked by DNA of varied length to study the distance dependence of the interaction between nanoparticles and the porphyrin chromophore. Preliminary studies indicate that at the distance of about 7-10 nm between porphyrin and silver nanoparticle is where the porphyrin absorption leading to fluorescence shows maximum enhancement. These new hybrid constructs will be helpful for designing efficient light harvesting systems.
ContributorsPillai, Smitha (Author) / Moore, Ana (Thesis advisor) / Moore, Thomas (Committee member) / Gould, Ian (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsChen, Jianhui (Author) / Ye, Jieping (Thesis advisor) / Kumar, Sudhir (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2011
Description
A potential new class of cancer chemotherapeutic agents has been synthesized by varying the 2 position of a benzimidazole based extended amidine. Compounds 6-amino-2-chloromethyl-4-imino-1-(2-methansulfonoxyethyl)-5-methyl-1H-benzimidazole-7-one (1A) and 6-amino-2-hydroxypropyl-4-imino-1-(2-methansulfonoxyethyl)-5-methyl-1H-benzimidazole-7-one (1B) were assayed at the National Cancer Institute's (NCI) Developmental Therapeutic Program (DTP) and found to be cytotoxic at sub-micromolar concentrations, and have shown between a 100 and a 1000-fold increase in specificity towards lung, colon, CNS, and melanoma cell lines. These ATP mimics have been found to correlate with sequestosome 1 (SQSTM1), a protein implicated in drug resistance and cell survival in various cancer cell lines. Using the DTP COMPARE algorithm, compounds 1A and 1B were shown to correlate to each other at 77%, but failed to correlate with other benzimidazole based extended amidines previously synthesized in this laboratory suggesting they operate through a different biological mechanism.
ContributorsDarzi, Evan (Author) / Skibo, Edward (Thesis advisor) / Gould, Ian (Committee member) / Francisco, Wilson (Committee member) / Arizona State University (Publisher)
Created2011
Description
Natural photosynthesis dedicates specific proteins to achieve the modular division of the essential roles of solar energy harvesting, charge separation and carrier transport within natural photosynthesis. The modern understanding of the fundamental photochemistry by which natural photosynthesis operates is well advanced and solution state mimics of the key photochemical processes have been reported previously. All of the early events in natural photosynthesis responsible for the conversion of solar energy to electric potential energy occur within proteins and phospholipid membranes that act as scaffolds for arranging the active chromophores. Accordingly, for creating artificial photovoltaic (PV) systems, scaffolds are required to imbue structure to the systems. An approach to incorporating modular design into solid-state organic mimics of the natural system is presented together with how conductive scaffolds can be utilized in organic PV systems. To support the chromophore arrays present within this design and to extract separated charges from within the structure, linear pyrazine-containing molecular ribbons were chosen as candidates for forming conductive linear scaffolds that could be functionalized orthogonally to the linear axis. A series of donor-wire-acceptor (D-W-A) compounds employing porphyrins as the donors and a C60 fullerene adduct as the acceptors have been synthesized for studying the ability of the pyrazine-containing hetero-aromatic wires to mediate photoinduced electron transfer between the porphyrin donor and fullerene acceptor. Appropriate substitutions were made and the necessary model compounds useful for dissecting the complex photochemistry that the series is expected to display were also synthesized. A dye was synthesized using a pyrazine-containing heteroaromatic spacer that features two porphyrin chromophores. The dye dramatically outperforms the control dye featuring the same porphyrin and a simple benzoic acid linker. A novel, highly soluble 6+kDa extended phthalocyanine was also synthesized and exhibits absorption out to 900nm. The extensive functionalization of the extended phthalocyanine core with dodecyl groups enabled purification and characterization of an otherwise insoluble entity. Finally, in the interest of incorporating modular design into plastic solar cells, a series of porphyrin-containing monomers have been synthesized that are intended to form dyadic and triadic molecular-heterojunction polymers with dedicated hole and electron transport pathways during electrochemical polymerization.
ContributorsWatson, Brian Lyndon (Author) / Gust, Devens (Thesis advisor) / Gould, Ian (Committee member) / Moore, Ana L (Committee member) / Arizona State University (Publisher)
Created2013
Description
The bleomycins are a family of glycopeptide-derived antibiotics isolated from various Streptomyces species and have been the subject of much attention from the scientific community as a consequence of their antitumor activity. Bleomycin clinically and is an integral part of a number of combination chemotherapy regimens. It has previously been shown that bleomycin has the ability to selectively target tumor cells over their non-malignant counterparts. Pyrimidoblamic acid, the N-terminal metal ion binding domain of bleomycin is known to be the moiety that is responsible for O2 activation and the subsequent chemistry leading to DNA strand scission and overall antitumor activity. Chapter 1 describes bleomycin and related DNA targeting antitumor agents as well as the specific structural domains of bleomycin. Various structural analogues of pyrimidoblamic acid were synthesized and subsequently incorporated into their corresponding full deglycoBLM A6 derivatives by utilizing a solid support. Their activity was measured using a pSP64 DNA plasmid relaxation assay and is summarized in Chapter 2. The specifics of bleomycin—DNA interaction and kinetics were studied via surface plasmon resonance and are presented in Chapter 3. By utilizing carefully selected 64-nucleotide DNA hairpins with variable 16-mer regions whose sequences showed strong binding in past selection studies, a kinetic profile was obtained for several BLMs for the first time since bleomycin was discovered in 1966. The disaccharide moiety of bleomycin has been previously shown to be a specific tumor cell targeting element comprised of L-gulose-D-mannose, especially between MCF-7 (breast cancer cells) and MCF-10A ("normal" breast cells). This phenomenon was further investigated via fluorescence microscopy using multiple cancerous cell lines with matched "normal" counterparts and is fully described in Chapter 4.
ContributorsBozeman, Trevor C (Author) / Hecht, Sidney M. (Thesis advisor) / Chaput, John (Committee member) / Gould, Ian (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
The biological and chemical diversity of protein structure and function can be greatly expanded by position-specific incorporation of non-natural amino acids bearing a variety of functional groups. Non-cognate amino acids can be incorporated into proteins at specific sites by using orthogonal aminoacyl-tRNA synthetase/tRNA pairs in conjunction with nonsense, rare, or 4-bp codons. There has been considerable progress in developing new types of amino acids, in identifying novel methods of tRNA aminoacylation, and in expanding the genetic code to direct their position. Chemical aminoacylation of tRNAs is accomplished by acylation and ligation of a dinucleotide (pdCpA) to the 3'-terminus of truncated tRNA. This strategy allows the incorporation of a wide range of natural and unnatural amino acids into pre-determined sites, thereby facilitating the study of structure-function relationships in proteins and allowing the investigation of their biological, biochemical and biophysical properties. Described in Chapter 1 is the current methodology for synthesizing aminoacylated suppressor tRNAs. Aminoacylated suppressor tRNACUAs are typically prepared by linking pre-aminoacylated dinucleotides (aminoacyl-pdCpAs) to 74 nucleotide (nt) truncated tRNAs (tRNA-COH) via a T4 RNA ligase mediated reaction. Alternatively, there is another route outlined in Chapter 1 that utilizes a different pre-aminoacylated dinucleotide, AppA. This dinucleotide has been shown to be a suitable substrate for T4 RNA ligase mediated coupling with abbreviated tRNA-COHs for production of 76 nt aminoacyl-tRNACUAs. The synthesized suppressor tRNAs have been shown to participate in protein synthesis in vitro, in an S30 (E. coli) coupled transcription-translation system in which there is a UAG codon in the mRNA at the position corresponding to Val10. Chapter 2 describes the synthesis of two non-proteinogenic amino acids, L-thiothreonine and L-allo-thiothreonine, and their incorporation into predetermined positions of a catalytically competent dihydrofolate reductase (DHFR) analogue lacking cysteine. Here, the elaborated proteins were site-specifically derivitized with a fluorophore at the thiothreonine residue. The synthesis and incorporation of phosphorotyrosine derivatives into DHFR is illustrated in Chapter 3. Three different phosphorylated tyrosine derivatives were prepared: bis-nitrobenzylphosphoro-L-tyrosine, nitrobenzylphosphoro-L-tyrosine, and phosphoro-L-tyrosine. Their ability to participate in a protein synthesis system was also evaluated.
ContributorsNangreave, Ryan Christopher (Author) / Hecht, Sidney M. (Thesis advisor) / Yan, Hao (Committee member) / Gould, Ian (Committee member) / Arizona State University (Publisher)
Created2013
Description
Nowadays product reliability becomes the top concern of the manufacturers and customers always prefer the products with good performances under long period. In order to estimate the lifetime of the product, accelerated life testing (ALT) is introduced because most of the products can last years even decades. Much research has been done in the ALT area and optimal design for ALT is a major topic. This dissertation consists of three main studies. First, a methodology of finding optimal design for ALT with right censoring and interval censoring have been developed and it employs the proportional hazard (PH) model and generalized linear model (GLM) to simplify the computational process. A sensitivity study is also given to show the effects brought by parameters to the designs. Second, an extended version of I-optimal design for ALT is discussed and then a dual-objective design criterion is defined and showed with several examples. Also in order to evaluate different candidate designs, several graphical tools are developed. Finally, when there are more than one models available, different model checking designs are discussed.
ContributorsYang, Tao (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Borror, Connie (Committee member) / Rigdon, Steve (Committee member) / Arizona State University (Publisher)
Created2013