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Pages
 Title
 The mathematical models of nutritional plasticity and the bifurcation in a nonlocal diffusion equation
 Creator
 Liang, Yu, Ph. D.
 Date
 2016
 Collection
 Electronic Theses & Dissertations
 Description

The thesis consists of two parts. In the first part, I investigate the developmental mechanisms that regulate the nutritional plasticity of organ sizes in Drosophila melanganster, fruit fly. Here I focus on the insulinlike signalling pathway through which the developmental nutrition is signalled to growing organs. Two mathematical models, an ODE model and a PDE model, are established based on the IIS pathway. In the ODE model, the circulating gene expression of each components in IIS pathway...
Show moreThe thesis consists of two parts. In the first part, I investigate the developmental mechanisms that regulate the nutritional plasticity of organ sizes in Drosophila melanganster, fruit fly. Here I focus on the insulinlike signalling pathway through which the developmental nutrition is signalled to growing organs. Two mathematical models, an ODE model and a PDE model, are established based on the IIS pathway. In the ODE model, the circulating gene expression of each components in IIS pathway is considered as model variables. By analyzing the steady states of the ODE model under different parameter settings, the hypothesis that the difference of the nutritional plasticity among all organs of Drosophila is due to the variation of the total gene expressions of components in IIS pathway is verified. Furthermore, the forkhead transcription factor FOXO, a negative growth regulator that is activated when nutrition and insulin signaling are low is a key factor to maintain organspecific differences in nutritionalplasticity and insulinsensitivity. In the PDE model, I focus more on the molecule structure within each individual cell. The transportation of proteins between nucleus and cell membrane is modelled in the system. In simulations of the PDEs system, the hypothesis that the concentration of FOXO decrease as the concentration of insulin increase is verified.In the second part of the thesis, I study the bifurcation properties of the nonlocal diffusion equation:\[ L_{\epsilon} u + \lambda (u  u^3) = 0. \]where $L_{\epsilon} u$ is an integral defined as \[ L_{\epsilon} u = \int_{0}^{\pi} \epsilon^{3} J( \frac{yx}{\epsilon} ) ( u(y)  u(x) ) dy. \]and $J(x)$ is a nonnegative radially symmetric function with $J(0) > 0$. It is shown that as the scaling parameter $\epsilon$ is small enough the equation has the pitchfork bifurcations at the spectrum of the operator $L_{\epsilon} u$. A concrete example is considered. The bifurcations result is verified in the concrete example by solving the equation with Newton's Method.
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 Title
 A cybernetic model of the U.S. defense expenditure policymaking process
 Creator
 Marra, Robin Frank
 Date
 1984
 Collection
 Electronic Theses & Dissertations
 Title
 Dynamics of horizontal axis wind turbines and systems with parametric stiffness
 Creator
 Acar, Gizem Dilber
 Date
 2017
 Collection
 Electronic Theses & Dissertations
 Description

"The dynamics of a wind turbine blade under bendbendtwist coupled vibrations is investigated. The potential and kinetic energy expressions for a straight nonuniform blade are written in terms of beam parameters. Then, the energies are expressed in terms of modal coordinates by using the assumed modes method, and the equations of motion are found by applying Lagrange's formula. The bendbendtwist equations are coupled with each other, and have stiffness variations due to centrifugal effects...
Show more"The dynamics of a wind turbine blade under bendbendtwist coupled vibrations is investigated. The potential and kinetic energy expressions for a straight nonuniform blade are written in terms of beam parameters. Then, the energies are expressed in terms of modal coordinates by using the assumed modes method, and the equations of motion are found by applying Lagrange's formula. The bendbendtwist equations are coupled with each other, and have stiffness variations due to centrifugal effects and gravitational parametric terms which vary cyclicly with the hub angle. To determine the natural frequencies and mode shapes of the system, a modal analysis is applied on the linearized coupled equations of constant angle snapshots of a blade with effects of constant speed rotation. Lower modes of the coupled bendbendtwist model are dominantly inplane or outofplane modes. To investigate the parametric effects, several blade models are analyzed at different angular positions. The stiffness terms involving centrifugal and gravitational effects can be significant for long blades. To further see the effect of blade length on relative parametric stiffness change, the blade models are scaled in size, and analyzed at constant rotational speeds, at horizontal and vertical orientations. Bladehub dynamics of a horizontalaxis wind turbine is also studied. Blade equations are coupled through the hub equation, and have parametric terms due to cyclic aerodynamic forces, centrifugal effects and gravitational forces. The modal inertia of a single blade is defined by the linear mass density times the square of transverse displacements from blade's undeflected axis. For reasonable transverse displacements, the modal inertia of a blade is usually small compared to the rotor inertia which is the combined inertia of the hub plus all three blades about the shaft. This enables us to treat the effect of blade motion as a perturbation on the rotor motion. The rotor speed is not constant, and the cyclic variations cannot be expressed as explicit functions of time. By casting the rotor angle as the independent variable, and assuming small variations in rotor speed, the leading order blade equations are decoupled from the rotor equation. The interdependent blade equations constitute a threedegreeoffreedom system with periodic parametric and direct excitation. The response is analyzed by using the method of multiple scales. The system has superharmonic and subharmonic resonances due to direct and parametric effects introduced by gravity. Amplitudefrequency relations and stabilities of these resonances are studied. The Mathieu equation represents the transient dynamics of a singlemode blade model. Approximate solutions to the linear unforced Mathieu equation, and their stabilities, are investigated. Floquet theory shows that the solution can be written as a product between an exponential part and a periodic part at the same frequency or half the frequency of excitation. An approach combining Floquet theory with the harmonic balance method is investigated. A Floquet solution having an exponential part with an unknown exponential argument and a periodic part consisting of a truncated series of harmonics is assumed. Then, performing harmonic balance, the Floquet exponents and and harmonic coefficients are found. From this frequencies of the response and stability of the solution are determined. The truncated solution is consistent with an existing infinite series solution for the undamped case. The truncated solution is then applied to the damped Mathieu equation and to parametric excitation with two harmonics. Solutions and stability of multidegreeoffreedom Mathieutype systems are also investigated. A procedure similar to the one applied for the Mathieu equation is used to find the initial conditions response, frequency content, and stability characteristics. The approach is applied to two and threedegreesoffreedom examples. For a few parameter sets, the results obtained from this method are compared to the numerical solutions. This study provides a framework for a transient analysis of threeblade turbine equations."Pages iiiii.
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 Title
 Reconstruction algorithms for limited angular diffraction tomography
 Creator
 Roy Paladhi, Pavel
 Date
 2016
 Collection
 Electronic Theses & Dissertations
 Description

Tomography is a ubiquitous imaging modality applied to numerous fields like medical imaging, geophysical imaging, structural health monitoring etc. It is a process to view crosssectional profile of an object or region of interest through solving an inverse problem. When diffracting sources are used as the interrogating energy, the specific tomographic reconstruction is termed `Diffraction Tomography' as the algorithm accounts for diffraction effects. As a result, these algorithms are more...
Show moreTomography is a ubiquitous imaging modality applied to numerous fields like medical imaging, geophysical imaging, structural health monitoring etc. It is a process to view crosssectional profile of an object or region of interest through solving an inverse problem. When diffracting sources are used as the interrogating energy, the specific tomographic reconstruction is termed `Diffraction Tomography' as the algorithm accounts for diffraction effects. As a result, these algorithms are more complex than straight ray tomography algorithms viz. computed tomography which has been very successfully implemented for Xray tomography, PET etc. Ideally, projection data covering full 360o around the region of interest is necessary for accurate reconstruction. However, in many practical applications, this is not always feasible. As a result, reconstruction is performed on limited datasets, essentially making the process a recovery from an underdetermined system. This thesis focuses on development of novel and efficient methods to handle the challenges on limited data for image reconstruction under diffraction tomography. For a moderately limited coverage, some inherent redundancies in Diffraction Tomography projection data can be used to reduce the effect of limited coverage. For highly limited angular coverage, these redundancies are no longer available and cannot be exploited. Recently, however, optimization techniques involving l1norm minimization schemes under the so called `compressed sensing' regime have shown promise. These algorithms are capable of almost exact reconstruction of the object even with highly limited number of projections. This research has explored both techniques for moderate and highly limited angular tomography. In the first part of the thesis, for moderate angular access limitations, an optimum method for exploiting redundancy within projection data has been formulated. In the second part, for highly limited coverage with further limitations on the number of available projections, reconstruction schemes under compressed sensing regime have been examined. Further, this research demonstrates reconstruction of complexvalued objective function under the regime of compressed sensing. This generalizes the application of tomographic reconstruction for newer applications such as examining new complex structures (such as metamaterial and other smart material based structures) where knowledge of complex permittivity values is essential in evaluating structural integrity or morphological aberrations. The compressed sensing method heavily relies on sparsity of the reconstructed signal in some transformation domain. In this research, the sparsity has mainly been exploited through gradient magnitude of images. In a variety of applications, gradient magnitude of images are highly sparse, even if the images themselves are not. So the gradient magnitude of images can be effectively used as the sparse domain. Further, incorporation of multiple sparse domains into the compressed sensing framework has been explored. Using Haar wavelets in addition to gradient magnitude of images as the sparse domain has successfully been employed showing potential for significant improvements in image reconstruction from highly limited data through further research.
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 Title
 Merging activespace and renormalized coupledcluster methods via the CC(P;Q) formalism, with applications to chemical reaction profiles and singlettriplet gaps
 Creator
 Bauman, Nicholas P.
 Date
 2016
 Collection
 Electronic Theses & Dissertations
 Description

The development of accurate and computationally efficient wave function methods that can capture and balance dynamical and nondynamical manyelectron correlation effects to describe multireference problems, such as potential energy surfaces involving bond breaking, biradicals, and excited states characterized by dominant manyelectron excitations, is one of the main goals of quantum chemistry. Among the promising approaches in this endeavor are the completely renormalized and activespace...
Show moreThe development of accurate and computationally efficient wave function methods that can capture and balance dynamical and nondynamical manyelectron correlation effects to describe multireference problems, such as potential energy surfaces involving bond breaking, biradicals, and excited states characterized by dominant manyelectron excitations, is one of the main goals of quantum chemistry. Among the promising approaches in this endeavor are the completely renormalized and activespace coupledcluster (CC) and equationofmotion (EOM) CC methods. While the completely renormalized and activespace CC and EOMCC approaches have been very successful in many applications, there are some cases where they do not capture the dynamical or nondynamical manyelectron correlation effects in a satisfactory manner. In this dissertation, we introduce the CC(P;Q) formalism, which alleviates this concern by combining the completely renormalized and activespace together. The CC(P;Q) scheme provides a systematic approach to correcting energies obtained in the activespace CC and EOMCC calculations that recover much of the nondynamical and some dynamical manyelectron correlation effects for the remaining, mostly dynamical, correlation effects missing in the activespace CC and EOMCC considerations. We discuss the development of the CC(t;3), CC(t,q;3), CC(t,q;3,4), and CC(q;4) methods, which use the CC(P,Q) formalism to correct energies obtained with the CC and EOMCC approaches with singles, doubles, and activespace triples (CCSDt/EOMCCSDt) for missing triple excitations (CC(t;3)), or to correct energies obtained with the CC and EOMCC approaches with singles, doubles, and activespace triples and quadruples (CCSDtq/EOMCCSDtq) for missing triples (CC(t,q;3)) or missing triples and quadruples (CC(t,q;3,4)), or even to correct energies obtained with the CC and EOMCC approaches with singles, doubles, triples, and activespace quadruples (CCSDTq/EOMCCSDTq) for correlation effects due to the missing quadruple excitations (CC(q;4)). By examining the double dissociation of water, the Be + H2 > HBeH insertion, and the singlettriplet gaps in the strongly biradical (HFH) system and the BN molecule, we demonstrate that the CC(t;3), CC(t,q;3), and CC(t,q;3,4) methods reproduce the total and relative energies obtained with the parent full CC/EOMCC approaches with singles, doubles, and triples or singles, doubles, triples, and quadruples to within fractions of a millihartree at the tiny fraction of the computer cost, even when the electronic quasidegeneracies become substantial.The CC(P;Q) formulation prompted the development of efficient CCSDt, CCSDtq, and CCSDTq programs. In this dissertation, we describe the technique of spinintegration for both closed and open shells, and how the resulting equations for CCSDTQ were automatically derived and implemented in a factorized form. We also discuss how the efficiency of the code was improved by removing unnecessary operations through, in particular, the reorganization of the relevant loops. Finally, we explain how the CCSDTQ code was transformed to obtain the activespace CCSDtq and CCSDTq approaches, which are the most essential parts of the CC(t,q;3), CC(t,q;3,4), and CC(q;4) calculations.
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 Title
 Modeling and simulation of strongly coupled plasmas
 Creator
 Chowdhury, Rahnuma Rifat
 Date
 2016
 Collection
 Electronic Theses & Dissertations
 Description

The objective of this work is to develop new modeling and simulation tools for studying strongly coupled plasmas (SCP). Strongly coupled plasmas are different from traditional plasmas as potential energy is larger than the kinetic energy. The standard plasma model does not account for some major effects in SCP: 1) the change in the permittivity 2) the impact on relaxation of the charged particles undergoing Coulomb collisions in a system with weakly shielded long range interactions3) the...
Show moreThe objective of this work is to develop new modeling and simulation tools for studying strongly coupled plasmas (SCP). Strongly coupled plasmas are different from traditional plasmas as potential energy is larger than the kinetic energy. The standard plasma model does not account for some major effects in SCP: 1) the change in the permittivity 2) the impact on relaxation of the charged particles undergoing Coulomb collisions in a system with weakly shielded long range interactions3) the impact of statistical fluctuations in strongly coupled plasmas that leads to nonMarkovian effects. Proper modeling of such systems through consideration of Lévy flight processes gives rise to fractional derivatives in time that result in an incorporation of time history in the model. A Lévy flight is a random walk in which the steps are defined in terms of the steplengths, which have a certain probability distribution, with the directions of the steps being isotropic and random. Lévy processes in the plasma give rise to fluctuations in medium through which the electromagnetic waves are propagating. Averaging over the Lévy processes will allow us to relate to other important parameters in the plasma.
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 Title
 Understanding nonradiative recombination processes of the optoelectronic materials from first principles
 Creator
 Shu, Yinan
 Date
 2017
 Collection
 Electronic Theses & Dissertations
 Description

"The annual potential of the solar energy hit on the Earth is several times larger than the total energy consumption in the world. This huge amount of energy source makes it appealing as an alternative to conventional fuels. Due to the problems, for example, global warming, fossil fuel shortage, etc. arising from utilizing the conventional fuels, a tremendous amount of efforts have been applied toward the understanding and developing cost effective optoelectrical devices in the past decades....
Show more"The annual potential of the solar energy hit on the Earth is several times larger than the total energy consumption in the world. This huge amount of energy source makes it appealing as an alternative to conventional fuels. Due to the problems, for example, global warming, fossil fuel shortage, etc. arising from utilizing the conventional fuels, a tremendous amount of efforts have been applied toward the understanding and developing cost effective optoelectrical devices in the past decades. These efforts have pushed the efficiency of optoelectrical devices, say solar cells, increases from 0% to 46% as reported until 2015. All these facts indicate the significance of the optoelectrical devices not only regarding protecting our planet but also a large potential market. Empirical experience from experiment has played a key role in optimization of optoelectrical devices, however, a deeper understanding of the detailed electronbyelectron, atombyatom physical processes when material upon excitation is the key to gain a new sight into the field. It is also useful in developing the next generation of solar materials. Thanks to the advances in computer hardware, new algorithms, and methodologies developed in computational chemistry and physics in the past decades, we are now able to 1).model the real size materials, e.g. nanoparticles, to locate important geometries on the potential energy surfaces(PESs); 2).investigate excited state dynamics of the cluster models to mimic the real systems; 3).screen large amount of possible candidates to be optimized toward certain properties, so to help in the experiment design. In this thesis, I will discuss the efforts we have been doing during the past several years, especially in terms of understanding the nonradiative decay process of silicon nanoparticles with oxygen defects using ab initio nonadiabatic molecular dynamics as well as the accurate, efficient multireference electronic structure theories we have developed to fulfill our purpose. The new paradigm we have proposed in understanding the nonradiative recombination mechanisms is also applied to other systems, like water splitting catalyst. Besides in gaining a deeper understanding of the mechanism, we applied an evolutionary algorithm to optimize promising candidates towards specific properties, for example, organic light emitting diodes (OLED)."Pages iiiii.
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 Title
 Design and simulation of singlecrystal diamond diodes for high voltage, high power and high temperature applications
 Creator
 Suwanmonkha, Nutthamon
 Date
 2016
 Collection
 Electronic Theses & Dissertations
 Description

ABSTRACTDESIGN AND SIMULATION OF SINGLECRYSTAL DIAMOND DIODES FOR HIGH VOLTAGE, HIGH POWER AND HIGH TEMPERATURE APPLICATIONSByNutthamon SuwanmonkhaDiamond has exceptional properties and great potentials for making highpower semiconducting electronic devices that surpass the capabilities of other common semiconductors including silicon. The superior properties of diamond include wide bandgap, high thermal conductivity, large electric breakdown field and fast carrier mobilities. All of these...
Show moreABSTRACTDESIGN AND SIMULATION OF SINGLECRYSTAL DIAMOND DIODES FOR HIGH VOLTAGE, HIGH POWER AND HIGH TEMPERATURE APPLICATIONSByNutthamon SuwanmonkhaDiamond has exceptional properties and great potentials for making highpower semiconducting electronic devices that surpass the capabilities of other common semiconductors including silicon. The superior properties of diamond include wide bandgap, high thermal conductivity, large electric breakdown field and fast carrier mobilities. All of these properties are crucial for a semiconductor that is used to make electronic devices that can operate at high power levels, high voltage and high temperature.Twodimensional semiconductor device simulation software such as Medici assists engineers to design device structures that allow the performance requirements of device applications to be met. Most physical material parameters of the wellknown semiconductors are already compiled and embedded in Medici. However, diamond is not one of them. Material parameters of diamond, which include the models for incomplete ionization, temperatureandimpuritydependent mobility, and impact ionization, are not readily available in software such as Medici. Models and data for diamond semiconductor material have been developed for Medici in the work based on results measured in the research literature and in the experimental work at Michigan State University. After equipping Medici with diamond material parameters, simulations of various diamond diodes including Schottky, PNjunction and merged Schottky/PNjunction diode structures are reported. Diodes are simulated versus changes in doping concentration, drift layer thickness and operating temperature. In particular, the diode performance metrics studied include the breakdown voltage, turnon voltage, and specific onresistance. The goal is to find the designs which yield low power loss and provide high voltage blocking capability. Simulation results are presented that provide insight for the design of diamond diodes using the various diode structures. Results are also reported on the use of field plate structures in the simulations to control the electric field and increase the breakdown voltage.
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 Title
 A theory model : an analysis and integration of the factors and forces influencing curricular decisions
 Creator
 Coleman, Paul Robert, 1926
 Date
 1974
 Collection
 Electronic Theses & Dissertations
 Title
 Minimum mandatory sentences and plea bargaining : an economic perspective
 Creator
 Chester, Thomas Paul
 Date
 1979
 Collection
 Electronic Theses & Dissertations
 Title
 Advances in metal ion modeling
 Creator
 Li, Pengfei (Chemist)
 Date
 2016
 Collection
 Electronic Theses & Dissertations
 Description

Metal ions play fundamental roles in geochemistry, biochemistry and materials science.With the tremendous increasing power of the computational resources and largelyinventions of the computational tools, computational chemistry became a more and moreimportant tool to study various chemical processes. Force field modeling strategy, whichis built on physical background, offered a fast way to study chemical systems at atomiclevel. It could offer considerable accuracy when combined with the Monte...
Show moreMetal ions play fundamental roles in geochemistry, biochemistry and materials science.With the tremendous increasing power of the computational resources and largelyinventions of the computational tools, computational chemistry became a more and moreimportant tool to study various chemical processes. Force field modeling strategy, whichis built on physical background, offered a fast way to study chemical systems at atomiclevel. It could offer considerable accuracy when combined with the Monte Carlo orMolecular Dynamics simulation protocol. However, there are various metal ions and it isstill challenging to model them using available force field models. Generally there areseveral models available for modeling metal ions using the force field approach such asthe nonbonded model, the bonded model, the cationic dummy atom model, the combinedmodel, and the polarizable models. Our work concentrated on the nonbonded and bondedmodels, which are widely used nowadays. Firstly, we focused on filling in the blanks ofthis field. We proposed a noble gas curve, which was used to describe the relationshipbetween the van der Waals radius and well depth parameters in the 126 LennardJonespotential. By using the noble gas curve and multiple target values (the hydration freeenergy, ionoxygen distance, coordination number values), we have consistentlyparameterized the 126 LennardJones nonbonded model for 63 different ions (including11 monovalent cations, 4 monovalent anions, 24 divalent cations, 18 trivalent cations,and 6 tetravalent cations) combined with three widely used water models (TIP3P, SPC/E, and TIP4PEW). Secondly, we found there is limited accuracy of the 126 model, whichmakes it hard to simulate different properties simultaneously for ions with formal chargeequal or larger than +2. By considering the physical origins of the 126 model, weproposed a new nonbonded model, named the 1264 LJtype nonbonded model. Wehave systematically parameterized the 1264 model for 55 different ions (including 11monovalent cations, 4 monovalent anions, 16 divalent cations, 18 trivalent cations, and 6tetravalent cations) in the three water models. It was shown that the 1264 model couldreproduce several properties at the same time, showing remarkable improvement over the126 model. Meanwhile, through the usage of a proposed combining rule, the 1264model showed excellent transferability to mixed systems. Thirdly, we have developed theMCPB.py program to facilitate building of the bonded model for metal ion containingsystems, which can largely reduce human efforts. Finally, an application case of ametallochaperone  CusF was shown, and based on the simulations we hypothesized anion transfer mechanism.
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 Title
 Mathematical modeling and computation of molecular solvation and binding
 Creator
 Wang, Bao
 Date
 2016
 Collection
 Electronic Theses & Dissertations
 Description

This dissertation contains a couple of results on biophysics modeling and computation, ranging from solvated molecular conformation modeling to molecular solvation and binding modeling in the solvent environment.We study the solvent excluded surface in Eulerian representation, provide the surface area and enclosed volume calculation, the molecular topological analysis is also addressed. We further analyze the electrostatic for the solvated molecules with the Eulerian solvent excluded surface....
Show moreThis dissertation contains a couple of results on biophysics modeling and computation, ranging from solvated molecular conformation modeling to molecular solvation and binding modeling in the solvent environment.We study the solvent excluded surface in Eulerian representation, provide the surface area and enclosed volume calculation, the molecular topological analysis is also addressed. We further analyze the electrostatic for the solvated molecules with the Eulerian solvent excluded surface. We show that our surface is analytical without any numerical approximation.We study the coarse grid Poisson Boltzmann solver. Our software enables extremely accurate numerical solution to the Poisson Boltzmann equation even at very large grid spacing. As a consequence, our software provides a reliable electrostatic calculation for the solvation and protein ligand binding related problem.We study the blind solvation free energy prediction problem. A hybrid of physical and statistical protocol is proposed for highly accurate solvation free energy prediction. Furthermore, to mediate the force field parametrization influence on the solvation free energy prediction, we propose a learning to rank based solvation free energy prediction paradigm.We explore the protein ligand binding free energy prediction and docking scoring via the learning to rank approach. In which a learn to rank based scoring function is proposed for accurate protein ligand binding scoring.
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 Title
 Matrix completion with side information for effective recommendation
 Creator
 Barjasteh, Iman
 Date
 2016
 Collection
 Electronic Theses & Dissertations
 Description

The massive number of online choices, ecommerce items, and related data available on the web makes it profoundly challenging for users to extract insightful information that can help them decide on what to select among a vast multitude of choices. In recent years, recommender systems have played an important role in reducing the overwhelming impact of such information overload. In particular, a specific form of recommender system, which is known as collaborative filtering, is the most...
Show moreThe massive number of online choices, ecommerce items, and related data available on the web makes it profoundly challenging for users to extract insightful information that can help them decide on what to select among a vast multitude of choices. In recent years, recommender systems have played an important role in reducing the overwhelming impact of such information overload. In particular, a specific form of recommender system, which is known as collaborative filtering, is the most popular approach to building these systems, and it has been successfully employed in many applications. More importantly, the matrix completion paradigm provides an appealing solution to the collaborative filtering problem in recommendation systems. However, collaborative filtering based approaches perform poorly for sparse data and specifically for the socalled cold start users.Recently, there has been an upsurge interest in utilizing other rich sources of side infor mation about items/users to compensate for the insufficiency of rating information. Such information is of more importance to be aggregated when a single view of the data is sparse or even incomplete. Due to the advent and popularity of online social networks and ecommerce websites, many different types of side information are available that can be taken into account in addition to traditional rating matrices in order to improve the recommendation.The overarching goal of this thesis is to propose a novel and general algorithmic framework based on matrix factorization that simultaneously exploits the similarity information among users and items to alleviate the data sparsity issue and specifically the coldstart problem. Weextend matrix factorization and propose a model that takes into account the side information as well as the rating matrix. Therefore, by modeling different types of side information, such as social or trust/distrust relationships between users and metadata about items, as a constraint similarity/dissimilarity graph, we propose an effective recommendation framework that is able to boost the recommendation accuracy and overcome the challenges in existing recommendation systems such as coldstart users/items and data sparsity problems. The proposed modeling framework is capable of performing both rating and link prediction.Based on the proposed framework, a key objective of this thesis is to develop novel algo rithms and derive theoretical guarantees for their performance. The algorithms we developed so far have been experimentally evaluated and compared against existing stateoftheart methods on real life datasets (such as MovieLens, NIPS, Epinions and etc.). Our experi mental results show that our proposed modeling framework and related algorithms achieve substantial quality gains when compared to with existing methods. Our experimental results also illustrate how our framework and algorithms can overcome the shortcomings of other stateoftheart recommendation techniques.
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 Title
 I. amhb : (anti)aromaticitymodulated hydrogen bonding. ii. evaluation of implicit solvation models for predicting hydrogen bond free energies
 Creator
 Kakeshpour, Tayeb
 Date
 2019
 Collection
 Electronic Theses & Dissertations
 Description

My doctoral research under Professor James E. Jackson focused on hydrogen bonding (Hbonding) using physical organic chemistry tools. In the first chapter, I present how I used quantum chemical simulations, synthetic organic chemistry, NMR spectroscopy, and Xray crystallography to provide robust theoretical and experimental evidence for an interplay between (anti)aromaticity and Hbond strength of heterocycles, a concept that we dubbed (Anti)aromaticityModulated Hydrogen Bonding (AMHB). In...
Show moreMy doctoral research under Professor James E. Jackson focused on hydrogen bonding (Hbonding) using physical organic chemistry tools. In the first chapter, I present how I used quantum chemical simulations, synthetic organic chemistry, NMR spectroscopy, and Xray crystallography to provide robust theoretical and experimental evidence for an interplay between (anti)aromaticity and Hbond strength of heterocycles, a concept that we dubbed (Anti)aromaticityModulated Hydrogen Bonding (AMHB). In the second chapter, I used accurately measured hydrogen bond energies for a range of substrates and solvents to evaluate the performance of implicit solvation models in combination with density functional methods for predicting solution phase hydrogen bond energies. This benchmark study provides useful guidelines for a priori modeling of hydrogen bondingbased designs.Coordinates of the optimized geometries and crystal structures are provided as supplementary materials.
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 Title
 A containerattachable inertial sensor for realtime hydration tracking
 Creator
 Griffith, Henry
 Date
 2019
 Collection
 Electronic Theses & Dissertations
 Description

The underconsumption of fluid is associated with multiple adverse health outcomes, including reduced cognitive function, obesity, and cancer. To aid individuals in maintaining adequate hydration, numerous sensing architectures for tracking fluid intake have been proposed. Amongst the various approaches considered, containerattachable inertial sensors offer a nonwearable solution capable of estimating aggregate consumption across multiple drinking containers. The research described herein...
Show moreThe underconsumption of fluid is associated with multiple adverse health outcomes, including reduced cognitive function, obesity, and cancer. To aid individuals in maintaining adequate hydration, numerous sensing architectures for tracking fluid intake have been proposed. Amongst the various approaches considered, containerattachable inertial sensors offer a nonwearable solution capable of estimating aggregate consumption across multiple drinking containers. The research described herein demonstrates techniques for improving the performance of these devices.A novel sip detection algorithm designed to accommodate the variable duration and sparse occurrence of drinking events is presented at the beginning of this dissertation. The proposed technique identifies drinks using a twostage segmentation and classification framework. Segmentation is performed using a dynamic partitioning algorithm which spots the characteristic inclination pattern of the container during drinking. Candidate drinks are then distinguished from handling activities with similar motion patterns using a support vector machine classifier. The algorithm is demonstrated to improve true positive detection rate from 75.1% to 98.8% versus a benchmark approach employing static segmentation. Multiple strategies for improving drink volume estimation performance are demonstrated in the latter portion of this dissertation. Proposed techniques are verified through a largescale data collection consisting of 1,908 drinks consumed by 84 individuals over 159 trials. Support vector machine regression models are shown to improve perdrink estimation accuracy versus the prior stateoftheart for a single inertial sensor, with mean absolute percentage error reduced by 11.1%. Aggregate consumption accuracy is also improved versus previously reported results for a containerattachable device.An approach for computing aggregate consumption using fill level estimates is also demonstrated. Fill level estimates are shown to exhibit superior accuracy with reduced intersubject variance versus volume models. A heuristic fusion technique for further improving these estimates is also introduced herein. Heuristic fusion is shown to reduce root mean square error versus direct estimates by over 30%. The dissertation concludes by demonstrating the ability of the sensor to operate across multiple containers.
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 Title
 Ab initio molecular dynamics : applications to defective silicon nanocrystals and developments toward dense manifold systems
 Creator
 Peng, WeiTao
 Date
 2019
 Collection
 Electronic Theses & Dissertations
 Description

Ab initio molecular dynamics (AIMD) methods consider the nuclear motions under the potential generated by electronic wavefunctions which are determined from ab initio quantum mechanical calculations onthefly. AIMD methods allow researchers to investigate chemical processes without prior knowledge or assumptions about the shape of the potential energy surface (PES). In this thesis, we applied AIMD methods to study silicon nanocrystals with dangling bond defects (DBSiNCs). DB defects on...
Show moreAb initio molecular dynamics (AIMD) methods consider the nuclear motions under the potential generated by electronic wavefunctions which are determined from ab initio quantum mechanical calculations onthefly. AIMD methods allow researchers to investigate chemical processes without prior knowledge or assumptions about the shape of the potential energy surface (PES). In this thesis, we applied AIMD methods to study silicon nanocrystals with dangling bond defects (DBSiNCs). DB defects on SiNCs have been known as nonradiative (NR) decay centers. However, the atomistic mechanism for the decay process is unclear. Previously, researchers considered a pyramidalization mode surrounding the DB site involved in the process. Based on our AIMD calculations on the first excited state and the static analysis of the PESs of SiNC systems, we discovered that asymmetrical SiSi bond stretching modes surrounding DB sites are important, in addition to pyramidalization. Most importantly, we found a lowlying defectinduced conical intersection (DICI) in the neutral DB system. The minimum energy conical intersection (MECI) is estimated to be 1.74 eV above the ground state minimum energy geometry by application of multistate complete active space secondorder perturbation theory (MSCASPT2) to a small cluster model system. In addition, the roles of charged DBs on NR decay process are investigated. We found DICIs for both positively and negatively charged DB systems. The MECI energies are 2.10 eV and 2.65 eV respectively. The rationalization of the existence of conical intersections and detailed dynamics after excitation of these systems are discussed in the thesis. Additionally, to study the possible defectdefect interactions during the NR recombination process, we considered slab models with two DB defects at short (4 0303A) and long (100303 A) separations. According to our simulations, the NR recombination process is localized on a single DB site, regardless the defectdefect distances. However, energy transfer between defect sites with short separations is possible.For the defective SiNC systems, we demonstrated the power of the AIMD method to investigate the dynamics after excitations. However, the applications of AIMD to highlying states are much more challenging, due to the dense manifold of states that cause immense computational effort. In the thesis, we developed several methods toward the application to such systems. First, we developed a timedependent configuration interaction (TDCI) method that can simulate the electron dynamics under a strong field efficiently. The method is based on the direct scheme to form the vector, , which can be accelerated by a graphical processing unit. A TDCI calculation with 853776 determinants requires only 20.1 hours to propagate to 100 fs with 1 attosecond (1018 second) time steps. On the other hand, when the field is strong enough, the electrons can be driven to the boundary of the basis set, which would cause unphysical effects such as reflection. To account for this, we developed an analytical expression for a moleculecentered complex absorbing potential which can be evaluated efficiently to remove the unwanted effects. Finally, for the nuclear dynamics, we developed an Ehrenfest dynamics method based on the TDCI wavefunction. In this approach, the nuclear motions are propagated under the averaged potential generated by TDCI wave function, thus the approach is promising for application to systems with dense manifolds of states.
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 Title
 Investigation of the possibility of efficient Lband SRF cavities for mediumbeta heavy ion multichargestate beams
 Creator
 Shanab, Safwan
 Date
 2020
 Collection
 Electronic Theses & Dissertations
 Description

The possibility of using 1288 MHz (Lband) SRF elliptical cavities to accelerate heavy ion multichargestate (up to 5 charge states) beams is being investigated for accelerating energy higher than 200 MeV/u. This cavity can be a potential energy upgrade for heavy ions linac accelerators such as the Facility for Rare Isotope Beams (FRIB) in the United States of America and Rare Isotope Science Project (RISP) in Korea. One of possible disadvantages of the Lband frequency structure is its...
Show moreThe possibility of using 1288 MHz (Lband) SRF elliptical cavities to accelerate heavy ion multichargestate (up to 5 charge states) beams is being investigated for accelerating energy higher than 200 MeV/u. This cavity can be a potential energy upgrade for heavy ions linac accelerators such as the Facility for Rare Isotope Beams (FRIB) in the United States of America and Rare Isotope Science Project (RISP) in Korea. One of possible disadvantages of the Lband frequency structure is its small longitudinal acceptance. It should be sufficiently large for transporting the ions with the limited beam loss ensuring accelerator handson maintenance.A first simple analytic study was performed and it showed a promising result with 1288 MHz linac. In addition, from beam loss point of view, the study deduced that 1288 MHz linac frequency could be the limit for mediumbeta heavy ion up to five charge states beam accelerators. This work is the result of the detailed beam dynamics simulation for the linac performance using TRACK code to confirm the analytic result. The result shows that the longitudinal acceptance is large enough for mediumbeta heavy ion with five charge state beams. Usually accelerator upgrade projects have limitations such as environment, space, and cryogenic cooling plant. Thus, high frequency and high gradient cavity is demanded to resolve such limitations. Exploiting cavity nitrogen doping technology would be beneficial for efficient cryogenics.Nitrogen doping technology has shown that it is more beneficial for higher frequencies cavities. This impact on BCS surface resistance (RBCS). RBCS is proportional to the frequency squared, nitrogen doping technology benefit is larger in the higher frequencies. For instance, the benefit is larger at 1300 MHz cavity than low frequency cavities such as 650 MHz cavity. That is due to the fact that BCS surface resistance, RBCS (depends on temperature and electron mean free path of the niobium material) is higher than the residual surface resistance, Rres (Temperature independent) in higher frequencies. The Research and Development (R&D) of nitrogen doping technology is still on going. However our neon doping proposal to give us more insight on the physics of nitrogen doping of RF surfaces and confirm or refute the assumption that the interstitial nitrogen atoms play the role of improving the cavity intrinsic quality factor not the nitrogen nitride (NbN) chemical compositions hasn't been implemented yet, it is still an open question.
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 Title
 Variable selection in varying multiindex coefficient models with applications to geneenvironmental interactions
 Creator
 Guan, Shunjie
 Date
 2017
 Collection
 Electronic Theses & Dissertations
 Description

Variable selection is an important topic in modern statistics literature. And varying multiindex coefficient model(VMICM) is a promising tool to study the synergistic interaction effects between genes and multiple environmental exposures. In this dissertation, we proposed a variable selection approach for VMICM, we also generalized such approach to generalized and quantile regression settings. Their theoretical properties, simulation performance and application in genetic research were...
Show moreVariable selection is an important topic in modern statistics literature. And varying multiindex coefficient model(VMICM) is a promising tool to study the synergistic interaction effects between genes and multiple environmental exposures. In this dissertation, we proposed a variable selection approach for VMICM, we also generalized such approach to generalized and quantile regression settings. Their theoretical properties, simulation performance and application in genetic research were studied.Complicated diseases have both environmental and genetic risk factors, and large amount of research have been devoted to identify geneenvironment (G×E) interaction. Defined as different effect of a genotype on disease risk in persons with different environmental exposures (Ottman (1996)), we can view environmental exposures as the modulating factors in the effect of a gene. Based on this idea, we derived a three stage variable selection approach to estimate different effects of gene variables: varying, constant and zero which respectively correspond to nonlinear G$\times$E effect, no G$\times$E effect and no genetic effect. For multiple environmental exposure variables, we also select and estimate important environmental variables that contribute to the synergistic interaction effect. We theoretically evaluated the oracle property of the three step estimation method. We conducted simulation studies to further evaluate the finite sample performance of the method, considering both continuous and discrete predictors. Application to a real data set demonstrated the utility of the method.In Chapter 3, we generalized such variable selection approach to binary response setting. Instead of minimizing penalized squared error loss, we chose to maximize penalized loglikelihood function. We also theoretically evaluated the oracle property of the proposed selection approach in binary response setting. We demonstrated the performance of the model via simulation. At last, we applied our model to a Type II diabetes data set.Compared to conditional mean regression, conditional quantile regression could provide a more comprehensive understanding of the distribution of the response variable at different quantile. Even if the center of distribution is our only interest, median regression (special case of quantile regression) could offer a more robust estimator. Hence, we extended our three stage variable selection approach to a quantile regression setting in Chapter 4. We demonstrated the finite sample performance of the model via extensive simulation. And we applied our model to a birth weight data set.
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 Title
 Secure and private access control for systems of smart devices
 Creator
 Le, Tam Dan
 Date
 2019
 Collection
 Electronic Theses & Dissertations
 Description

With the emergence of Internet of Things (IoT) technologies and the invasion of smart devices in almost every aspect of our lives, access control that allows only authorized users to access IoT devices becomes an important problem. The limited capabilities of the devices and the distributed nature of IoT environments have presented unique challenges to the design of an effective access control mechanism. First, it should be lightweight enough for the IoT devices to handle due to their...
Show moreWith the emergence of Internet of Things (IoT) technologies and the invasion of smart devices in almost every aspect of our lives, access control that allows only authorized users to access IoT devices becomes an important problem. The limited capabilities of the devices and the distributed nature of IoT environments have presented unique challenges to the design of an effective access control mechanism. First, it should be lightweight enough for the IoT devices to handle due to their resource constraints. Second, the variety of devices and applications and the arbitrary manners of users require the support of finedgrain, flexible access control policies. Last but not least, traditional access control models that are often centralized may not be suitable for distributed IoT. Therefore, a decentralized approach should be considered.In this dissertation, we propose access control solutions that are not only secure and private but also scalable to meet IoT requirements. Our first design is an authorization protocol that supports flexible delegation for smart home applications. The protocol allows users to create and share various permissions within their authorities to other users. In addition, since simple computation operations are used, the protocol is lightweight and supports fast validation at resourceconstrained devices. Next, the need to support larger environments and the open problem with the exchange of access keys without a central authority motivate us to seek a decentralized solution from blockchain technology, which is originated from the famous cryptocurrency Bitcoin. The advantages of blockchain, which lie in an immutable distributed ledger that is maintained by a peertopeer network of untrusted nodes, can bring decentralization to IoT applications. However, applying blockchain to IoT is not straightforward as it was not originally designed for IoT requirements. We address two main issues in blockchainbased access control for IoT systems. First, since blockchain is a public platform, user privacy is one of the top priorities. Second, resourceconstrained IoT devices are often not powerful enough to interact directly with the blockchain but need to rely on certain trusted nodes to retrieve blockchain data.The first issue of user privacy leads to our design of CapChain, a blockchainbased privacypreserving access control framework that enables the sharing of access capabilities to multiple devices in a secure and private manner. Then, applying similar techniques to CapChain but also extending the use of blockchain by smart contracts, we design a privacypreserving service that allows users to create IoT automated tasks by defining one of multiple conditional statements that need to be satisfied before a task can be performed. We set up strict privilege at the triggering party, such that it may not trigger the task any time except only when the conditions are satisfied.To address the second issue of resource constrained devices, we propose a method for IoT devices to validate blockchain data without solely being dependent on a central server. In our approach, several witnesses on the network can be selected randomly by the devices to validate access control information. Our method is aided by Bloom filters, which are shown to be lightweight for resourceconstrained devices.
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 Title
 Recalibration of rigid pavement performance models and development of traffic inputs for PavementME design in Michigan
 Creator
 Musunuru, Gopi Krishna
 Date
 2019
 Collection
 Electronic Theses & Dissertations
 Description

The mechanisticempirical pavement design guide (AASHTOWARE PavementME) incorporates mechanistic models to estimate stresses, strains, and deformations in pavement layers using sitespecific climatic, material, and traffic characteristics. These structural responses are used to predict pavement performance using empirical models (i.e., transfer functions). The transfer functions need to be calibrated to improve the accuracy of the performance predictions, reflecting the unique field...
Show moreThe mechanisticempirical pavement design guide (AASHTOWARE PavementME) incorporates mechanistic models to estimate stresses, strains, and deformations in pavement layers using sitespecific climatic, material, and traffic characteristics. These structural responses are used to predict pavement performance using empirical models (i.e., transfer functions). The transfer functions need to be calibrated to improve the accuracy of the performance predictions, reflecting the unique field conditions and design practices. The existing local calibrations of the performance models were performed by using version 2.0 of the PavementME software. However, AASHTO has released versions 2.2 and 2.3 of the software since the completion of the last study. In the revised versions of the software, several bugs were fixed.Consequently, some performance models were modified in the newer software versions. As a result, the concrete pavement IRI predictions and the resulting PCC slab thicknesses have been impacted. The performance predictions varied significantly from the observed structural and function distresses, and hence, the performance models were recalibrated to enhance the confidence in pavement designs. Linear and nonlinear mixedeffects models were used for calibration to account for the nonindependence among the data measured on the same sections over time. Also, climate data, material properties, and design parameters were used to develop a model for predicting permanent curl for each location to address some limitations of the PavementME. This model can be used at the design stage to estimate permanent curl for a given location in Michigan.PavementME also requires specific types of traffic data to design new or rehabilitated pavement structures. The traffic inputs include monthly adjustment factors (MAF), hourly distribution factors (HDF), vehicle class distributions (VCD), axle groups per vehicle (AGPV), and axle load distributions for different axle configurations. During the last seven years, new traffic data were collected, which reflect the recent economic growth, additional, and downgraded WIM sites. Hence it was appropriate to reevaluate the current traffic inputs and incorporate any changes. Weight and classification data were obtained from 41 WeighinMotion (WIM) sites located throughout the State of Michigan to develop Level 1 (sitespecific) traffic inputs. Cluster analyses were conducted to group sites for the development of Level 2A inputs. Classification models such as decision trees, random forests, and Naive Bayes classifier were developed to assign a new site to these clusters; however, this proved difficult. An alternative simplified method to develop Level 2B inputs by grouping sites with similar attributes was also adopted. The optimal set of attributes for developing these Level 2B inputs were identified by using an algorithm developed in this study. The effects of the developed hierarchical traffic inputs on the predicted performance of rigid and flexible pavements were investigated using the PavementME. Based on the statistical and practical significance of the life differences, appropriate levels were established for each traffic input. The methodology for developing traffic inputs is intuitive and practical for future updates. Also, there is a need to identify the change in traffic patterns to update the traffic inputs so that the pavement sections would not be overdesigned or underdesigned. Models were developed where the shortterm counts from the PTR sites can be used as inputs to check if the new traffic patterns cause any substantial differences in design life predictions.
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