Master of Arts, The Ohio State University, 2020, Sociology

Summary birth history (SBH) is a low-cost instrument widely used in developing countries lacking complete vital registration system for estimating demographic statistics. Indirect methods are utilized to estimate mortaliy rates the total number of children born and total number of children surviving data from SBH. However existing methods don't allow estimation for full detailed mortality age schedule with uncertainty. This paper introduces an innovative Singular Value Decomposition(SVD)-based method within the Bayesian framework, the SVD-Bayes model, to jointly estimate full age schedules of mortality for children and fertility for women from SBH data by single-month intervals along with uncertainty estimates. SVD model enables construction of full mortality and fertility age schedules with a few SVD-weight components. Posterior distributions for SVD-weight components are obtained using modified Approximate Bayesian Computation (ABC). Based on the results from simulation study, the SVD-Bayes model estimates full mortality age schedules by single-month age group from summary birth history data for children aged 0-20 years. The model also produces probability of giving birth by single-month age group for women of reproductive age. With SVD-Bayes model, SBH data from censuses and surveys could be used to produce mortality and fertility estimates for evidence-based policy-making and program monitoring and evaluation. The attempt of using ABC algorithm with SVD-Bayes model also shows the promising future of applying this advanced statistical technique in demographic research.

Committee: Samuel Clark (Advisor); Jon Wakefield (Committee Member); David Melamed (Committee Member) Subjects: Demography; Public Health; Sociology

Doctor of Philosophy, Case Western Reserve University, 2019, Systems Biology and Bioinformatics

The process of parturition is driven by multiple transcriptional pathways that work in concert to transform the quiescent myometrium (uterine smooth muscle) to the highly contractile laboring state. Here, important genes and pathways altered at the onset of labor were identified using three transcriptomic tissue studies and computational analyses including classification, singular value decomposition (SVD), pathway enrichment, and signaling network inference. The interplay between three important signaling processes (progesterone (P4), cyclic AMP (cAMP), and inflammation) identified from the tissue analysis was then characterized through comprehensive analysis of a transcriptional study involving a human myometrial cell line (hTERT-HM^A/B ) treated with P4, forskolin (FSK, induces cAMP), and interleukin-1B (IL-1B, mimics inflammation) alone and in all combinations. Gene set enrichment and regulatory network analyses was then performed to identify pathways commonly, differentially, or synergistically regulated by these treatments. Finally, results from cell line and tissue analysis were compared and used alongside Tissue Similarity Index (TSI) to further characterize the correspondence between cell lines and tissue phenotypes. In the tissue analysis, a high-confidence set (significant across all studies) of 126 labor-associated genes were identified, signatures associated with labor included inflammatory related genes and pathways (e.g. signaling by interleukins, NF-KB, JAK-STAT) while the non-labor phenotype was associated with relaxation pathways (e.g. cyclic AMP and muscle relaxation), and a parturition signaling network was constructed. From the cell line analysis, we observed that P4 was strongly anti-inflammatory (mainly through the JUN transcription factor), cAMP was partially anti-inflammatory and promoted myometrial relaxation, and P4 and cAMP synergistically blocked specific inflammatory pathways/regulators including STAT3/6, CEBPA/B, and OCT1/7, but not N (open full item for complete abstract)

Committee: Gurkan Bebek (Committee Chair); Mehmet Koyuturk (Advisor); Sam Mesiano (Committee Member); Mark Cameron (Committee Member) Subjects: Bioinformatics; Biology; Biomedical Research; Biostatistics; Cellular Biology; Computer Science; Endocrinology; Genetics; Immunology; Molecular Biology

Doctor of Philosophy, The Ohio State University, 2017, Chemistry

In this work, we outline the development, testing, and application of a novel electronic structure method for computing the properties of excited states of molecular aggregates. The method is an ab-inito realization of the molecular exciton model, proposed a long time ago by Frenkel and Davydov to describe excited states of molecular crystals, and is called the Ab-Initio Frenkel Davydov Exciton Model (AIFDEM). The AIFDEM ansatz follows the traditional exciton model by expanding the supersystem excited state wavefunction as a linear combination of excitations that are localized on the component molecules. Our method is a truly ab-inito implementation of this model as the requisite fragment excited states and the exciton Hamiltonian matrix are computed rigorously, including exact Coulomb and Hartree-Fock exchange interactions, without any neglect of overlap, nearest neighbor, or other common approximations. We have tested this method and found that it can reproduce excitation energies of water clusters, DNA bases, and organic chromophores within ~0.1 eV. A charge embedding scheme is able to reduce the scaling of this method to only quadratic with the number of fragments and provides near perfect parallel performance without reducing the accuracy, significantly outperforming traditional approaches. The method was utilized to investigate the excitation energy transfer dynamics of a napthalene-diimide nanotube where it was found that model systems beyond the scope of traditional methods are necessary for a fully detailed mechanistic picture, including the role of quantum coherence. Analytic derivatives of the AIFDEM Hamiltonian are derived and implemented and these provide access to non-adiabatic couplings as well as Holstein and Peierls electron-phonon coupling constants. This is applied to the challenging electronic structure of the singlet exciton fission process to identify vibrational modes key to the mechanism. Dynamics simulations, using parameters computed via th (open full item for complete abstract)

Committee: Sherwin Singer (Advisor); Heather Allen (Committee Member); Terry Gustafson (Committee Member) Subjects: Physical Chemistry

MS, University of Cincinnati, 2014, Engineering and Applied Science: Mechanical Engineering

The vast field of rotational systems – including measurement capabilities, analytical tools and observability – is still evolving. Spectral maps and order tracks are the most popular tools for analyzing the behavior of various components subject to a rotational characteristic. Although various forms of these tools are well researched and implemented, they are still susceptible to improper sensor location on the structure and to measurement noise. This thesis attempts to bridge the gap between properly located sensors and effective analysis based on their measurements. The concept of singular value decomposition (SVD), which is already well used in modal analysis, forms the basis of observability improvement. By using response data acquired from multiple sensors on a structure, it is possible to calculate and plot the singular values obtained from the entire frequency domain response data at each point in the spectral map graph. The resulting singular value plots will depict the magnitude of contribution of the sensor assembly which can form a noise-free and reliable basis for further analytical tools.

Committee: Randall Allemang Ph.D. (Committee Chair); David L. Brown Ph.D. (Committee Member); Allyn Phillips Ph.D. (Committee Member) Subjects: Engineering

MS, University of Cincinnati, 2014, Engineering and Applied Science: Mechanical Engineering

The main objective of this thesis work was to investigate different image classification and pattern recognition methods to try to develop a validation metric. A validation metric is a means of comparison between two sets of numerical information. The numerical information could represent a set of measurements made on a system or its internal characteristics derived from such measurements. A validation metric (v-metric) is used to determine the correctness with which one of the data-sets is able to describe the other and to quantify the extent of this correctness. A moment descriptor method has been identified from among the most widely used image classification and pattern recognition methods as the system most likely to give way to an effective validation metric for reasons discussed in subsequent chapters. Different sets of Orthogonal Polynomials have been investigated as kernel functions for the aforementioned method to generate descriptors that depict the most significant features of the data-sets being compared. The algorithms developed as such have been verified using standard gray-scale and color images to establish their ability to reconstruct the image intensity function using a subset of the features extracted. The above Orthogonal Polynomials have then been used to extract features from two measured data-sets and means to develop a v-metric from these descriptors have been explored. A study of algorithms thus developed using different Orthogonal Polynomials has been made to compare their effectiveness as well as shortcomings as kernel functions for developing a v-metric. An alternate form of the existing two dimensional moments has been proposed to generate features that are more conveniently compared against each other. This method has been examined to determine its efficiency in reducing the amount of information that needs to be used in the final comparison for multiple pairs of data-sets. A way to effect such a comparison using singular (open full item for complete abstract)

Committee: Randall Allemang Ph.D. (Committee Chair); David L. Brown Ph.D. (Committee Member); Allyn Phillips Ph.D. (Committee Member) Subjects: Mechanics

MS, University of Cincinnati, 2008, Engineering : Mechanical Engineering

Vibration based damage detection of engineering structures has become an important and difficult issue for the last couple of decades. Research in vibration based structural damage detection has been rapidly expanding from traditional modal parameter estimation based techniques to modern feature based, online monitoring techniques. However, there is still a need for a universal structural damage detection method that does not depend on modal parameter estimation, finite element model or specific structural type. This research outlines and validates a Unified Matrix Polynomial Approach (UMPA) and subspace analysis based structural damage detection method. UMPA presents a theoretical basis and a fundamental mathematical framework for experimental modal parameter estimation algorithms while Singular Value Decomposition (SVD) based subspace analysis provides an mechanism to extract and compare the characteristic features from this mathematical framework to detect structural damage. Simulations were performed on an analytical 15 Degree of Freedom (DOF) mass-spring-damper system and a lightly damped circular plate finite element model to validate and assess the proposed structural damage detection method. The results show that the proposed method successfully identifies structural damage under all test conditions. The proposed method has a significant resistance to measurement uncertainty, has a good consistency with the severity of the damage and is applicable to various structural damage locations.

Committee: Randall J. Allemang PhD (Committee Chair); Teik C. Lim PhD (Committee Member); Allyn W. Phillips PhD (Committee Member) Subjects: Mechanical Engineering

Master of Science (MS), Ohio University, 2006, Electrical Engineering & Computer Science (Engineering and Technology)

With the recent emphasis given to security, automatic human identification has received significant attention. In particular, iris based subject recognition has become especially important because of its high level of complexity which lends itself to high confidence recognition. In addition, the eye is well protected and generally does not change very much over extended periods of time. This thesis gives a review of some currently available methods that have already been investigated. A wide sense stationary approximation for gray scale values is explored as a possible means of feature extraction. The singular value decomposition (SVD) is discussed as a low bit rate tool for iris discrimination. The 2D principal component analysis (2DPCA) is explored as a method for feature extraction. It is determined experimentally that the SVD for iris recognition is a novel way to significantly reduce the storage requirements (133 bits) for iris recognition as compared to other methods (2048 bits). However, recognition accuracy has not reached a desirable level. The 2DPCA, on the other hand, significantly improves recognition accuracy on the same dataset, but at the cost of greater storage requirements.

Committee: Mehmet Celenk (Advisor) Subjects:

Master of Science (MS), Ohio University, 2006, Electrical Engineering & Computer Science (Engineering and Technology)

As the state of remote sensing technology improves, the acquisition of three-dimensional images and video will become more common in several different applications. However, the problem of protecting and authenticating three-dimensional data – in particular, three-dimensional video data – has been largely unexplored. An application of the singular value decomposition (SVD) and two-dimensional principal component analysis (2DPCA) to video data with an arbitrary number of channels for the purpose of watermarking is presented. It will be shown that it is possible to select parameters that preserve the visual quality of the video while effectively embedding the watermark in both the spatial and temporal domains. However, much processing time is required to embed and extract the watermark. Furthermore, it is unclear how robust the presented technique is to attack.

Committee: Mehmet Celenk (Advisor) Subjects:

Master of Science, The Ohio State University, 2013, Electrical and Computer Engineering

In spin echo electron paramagnetic resonance (SE-EPR) spectroscopy, traditional methods to estimate T2 relaxation time include fitting an exponential to the peaks or the integrated areas of multiple noisy echoes. These methods are suboptimal and result in lower estimation accuracy for a given acquisition time. Here, two data processing methods to estimate T2 for SE-EPR are proposed. The first method finds the maximum likelihood estimate (MLE) of T2 via parametric modeling of the spin echo and joint least-squares fitting of the collected data. The second method exploits the underlying rank-one structure in SE-EPR data via singular-value decomposition (SVD). The right singular vector corresponding to the largest singular value is then fitted with an exponential to find T2. This method bears strong similarity to a non-parametric MLE-based approach that does not assume a structure of an echo. The methods are validated using simulation and experimental data. The proposed methods provide 41-fold and 3-fold acquisition time savings over the traditional methods of fitting echo peaks and areas, respectively. Interestingly, the results also indicate that the SVD-based approach generates mean squared error nearly identical to that produced by the MLE based on parametric modeling for a wide range of SNR.

Committee: Lee Potter (Advisor); Bradley Clymer (Committee Member); Rizwan Ahmad (Committee Member) Subjects: Electrical Engineering; Medical Imaging