Matching Items (21)
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Description
he accurate simulation of many-body quantum systems is a challenge for computational physics. Quantum Monte Carlo methods are a class of algorithms that can be used to solve the many-body problem. I study many-body quantum systems with Path Integral Monte Carlo techniques in three related areas of semiconductor physics: (1)

he accurate simulation of many-body quantum systems is a challenge for computational physics. Quantum Monte Carlo methods are a class of algorithms that can be used to solve the many-body problem. I study many-body quantum systems with Path Integral Monte Carlo techniques in three related areas of semiconductor physics: (1) the role of correlation in exchange coupling of spins in double quantum dots, (2) the degree of correlation and hyperpolarizability in Stark shifts in InGaAs/GaAs dots, and (3) van der Waals interactions between 1-D metallic quantum wires at finite temperature. The two-site model is one of the simplest quantum problems, yet the quantitative mapping from a three-dimensional model of a quantum double dot to an effective two-site model has many subtleties requiring careful treatment of exchange and correlation. I calculate exchange coupling of a pair of spins in a double dot from the permutations in a bosonic path integral, using Monte Carlo method. I also map this problem to a Hubbard model and find that exchange and correlation renormalizes the model parameters, dramatically decreasing the effective on-site repulsion at larger separations. Next, I investigated the energy, dipole moment, polarizability and hyperpolarizability of excitonic system in InGaAs/GaAs quantum dots of different shapes and successfully give the photoluminescence spectra for different dots with electric fields in both the growth and transverse direction. I also showed that my method can deal with the higher-order hyperpolarizability, which is most relevant for fields directed in the lateral direction of large dots. Finally, I show how van der Waals interactions between two metallic quantum wires change with respect to the distance between them. Comparing the results from quantum Monte Carlo and the random phase approximation, I find similar power law dependance. My results for the calculation in quasi-1D and exact 1D wires include the effect of temperature, which has not previously been studied.
ContributorsZhang, Lei (Author) / Shumway, John (Thesis advisor) / Schmidt, Kevin (Committee member) / Bennet, Peter (Committee member) / Menéndez, Jose (Committee member) / Drucker, Jeff (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Human-robot interactions can often be formulated as general-sum differential games where the equilibrial policies are governed by Hamilton-Jacobi-Isaacs (HJI) equations. Solving HJI PDEs faces the curse of dimensionality (CoD). While physics-informed neural networks (PINNs) alleviate CoD in solving PDEs with smooth solutions, they fall short in learning discontinuous solutions due

Human-robot interactions can often be formulated as general-sum differential games where the equilibrial policies are governed by Hamilton-Jacobi-Isaacs (HJI) equations. Solving HJI PDEs faces the curse of dimensionality (CoD). While physics-informed neural networks (PINNs) alleviate CoD in solving PDEs with smooth solutions, they fall short in learning discontinuous solutions due to their sampling nature. This causes PINNs to have poor safety performance when they are applied to approximate values that are discontinuous due to state constraints. This dissertation aims to improve the safety performance of PINN-based value and policy models. The first contribution of the dissertation is to develop learning methods to approximate discontinuous values. Specifically, three solutions are developed: (1) hybrid learning uses both supervisory and PDE losses, (2) value-hardening solves HJIs with increasing Lipschitz constant on the constraint violation penalty, and (3) the epigraphical technique lifts the value to a higher-dimensional state space where it becomes continuous. Evaluations through 5D and 9D vehicle and 13D drone simulations reveal that the hybrid method outperforms others in terms of generalization and safety performance. The second contribution is a learning-theoretical analysis of PINN for value and policy approximation. Specifically, by extending the neural tangent kernel (NTK) framework, this dissertation explores why the choice of activation function significantly affects the PINN generalization performance, and why the inclusion of supervisory costate data improves the safety performance. The last contribution is a series of extensions of the hybrid PINN method to address real-time parameter estimation problems in incomplete-information games. Specifically, a Pontryagin-mode PINN is developed to avoid costly computation for supervisory data. The key idea is the introduction of a costate loss, which is cheap to compute yet effectively enables the learning of important value changes and policies in space-time. Building upon this, a Pontryagin-mode neural operator is developed to achieve state-of-the-art (SOTA) safety performance across a set of differential games with parametric state constraints. This dissertation demonstrates the utility of the resultant neural operator in estimating player constraint parameters during incomplete-information games.
ContributorsZhang, Lei (Author) / Ren, Yi (Thesis advisor) / Si, Jennie (Committee member) / Berman, Spring (Committee member) / Zhang, Wenlong (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2024
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Description
Small and medium-sized enterprises (SMEs) have emerged as a vital force in the economic development of our country, significantly influencing national economic growth, employment rates, market competition, and innovation. Consequently, the ability of SMEs to achieve stable performance growth is crucial for socio-economic development. Previous research has identified factors such

Small and medium-sized enterprises (SMEs) have emerged as a vital force in the economic development of our country, significantly influencing national economic growth, employment rates, market competition, and innovation. Consequently, the ability of SMEs to achieve stable performance growth is crucial for socio-economic development. Previous research has identified factors such as political connections, cash flow, capital, and information technology as significant contributors to business growth. However, SMEs often lack these resources or cannot control them, making existing research inadequate for guiding the performance growth of SMEs.To address this issue, this study is grounded in the resource-based view and constructs a theoretical model on the impact of SME strategic completeness on performance growth. This model is based on literature research and in-depth interviews. It highlights the mediating roles of market adaptability, operational agility, and employee proactivity. Additionally, the model examines the moderating effects of competitive intensity, strategic implementation capability, and management digitalization. The study collected 203 valid SME samples for analysis and hypothesis testing, yielding the following conclusions: (1)The more comprehensive the business strategy, the better the adaptability to external markets, the higher the operational agility, and the stronger the initiative of employees. (2)Better market adaptability leads to faster performance growth. (3)Higher operational agility accelerates performance growth. (4)Greater employee initiative enhances performance growth. (5)The stronger the external competitive intensity faced by a business, the more effective a well-developed strategy is in improving market adaptability, operational agility, and employee initiative. (6)Stronger strategic implementation capabilities further enhance the effectiveness of a well-developed strategy in improving market adaptability and employee initiative. (7)A higher level of management digitalization enhances the effects of market adaptability, operational agility, and employee initiative, thereby boosting business performance. This study offers theoretical and practical value by clarifying the paths, mechanisms, and boundary conditions through which strategic completeness promotes business performance growth, enriching the theoretical system of SME performance growth. Furthermore, by integrating the cutting-edge concept of management digitalization into the research on strategic completeness and performance growth, this study contributes to the development and refinement of the research framework. Practically, it provides strategic recommendations for SMEs on how to enhance business performance growth through strategic completeness.
ContributorsZhang, Lei (Author) / Wang, Lili (Thesis advisor) / Guo, Qigui (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2024
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Description
This thesis introduces new techniques for clustering distributional data according to their geometric similarities. This work builds upon the optimal transportation (OT) problem that seeks global minimum cost for matching distributional data and leverages the connection between OT and power diagrams to solve different clustering problems. The OT formulation is

This thesis introduces new techniques for clustering distributional data according to their geometric similarities. This work builds upon the optimal transportation (OT) problem that seeks global minimum cost for matching distributional data and leverages the connection between OT and power diagrams to solve different clustering problems. The OT formulation is based on the variational principle to differentiate hard cluster assignments, which was missing in the literature. This thesis shows multiple techniques to regularize and generalize OT to cope with various tasks including clustering, aligning, and interpolating distributional data. It also discusses the connections of the new formulation to other OT and clustering formulations to better understand their gaps and the means to close them. Finally, this thesis demonstrates the advantages of the proposed OT techniques in solving machine learning problems and their downstream applications in computer graphics, computer vision, and image processing.
ContributorsMi, Liang (Author) / Wang, Yalin (Thesis advisor) / Chen, Kewei (Committee member) / Karam, Lina (Committee member) / Li, Baoxin (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2020
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Description
Modular multilevel converters (MMCs) have become an attractive technology for high power applications. One of the main challenges associated with control and operation of the MMC-based systems is to smoothly precharge submodule (SM) capacitors to the nominal voltage during the startup process. The existing closed-loop methods require additional effort to

Modular multilevel converters (MMCs) have become an attractive technology for high power applications. One of the main challenges associated with control and operation of the MMC-based systems is to smoothly precharge submodule (SM) capacitors to the nominal voltage during the startup process. The existing closed-loop methods require additional effort to analyze the small-signal model of MMC and tune control parameters. The existing open-loop methods require auxiliary voltage sources to charge SM capacitors, which add to the system complexity and cost. A generalized precharging strategy is proposed in this thesis.

For large-scale MMC-embedded power systems, it is required to investigate dynamic performance, fault characteristics, and stability. Modeling of the MMC is one of the challenges associated with the study of large-scale MMC-based power systems. The existing models of MMC did not consider the various configurations of SMs and different operating conditions. An improved equivalent circuit model is proposed in this thesis.

The solid state transformer (SST) has been investigated for the distribution systems to reduce the volume and weight of power transformer. Recently, the MMC is employed into the SST due to its salient features. For design and control of the MMC-based SST, its operational principles are comprehensively analyzed. Based on the analysis, its mathematical model is developed for evaluating steady-state performances. For optimal design of the MMC-based SST, the mathematical model is modified by considering circuit parameters.

One of the challenges of the MMC-based SST is the balancing of capacitor voltages. The performances of various voltage balancing algorithms and different modulation methods have not been comprehensively evaluated. In this thesis, the performances of different voltage-balancing algorithms and modulation methods are analyzed and evaluated. Based on the analysis, two improved voltage-balancing algorithms are proposed in this thesis.

For design of the MMC-based SST, existing references only focus on optimal design of medium-frequency transformer (MFT). In this thesis, an optimal design procedure is developed for the MMC under medium-frequency operation based on the mathematical model of the MMC-based SST. The design performance of MMC is comprehensively evaluated based on free system parameters.
ContributorsZhang, Lei (Author) / Qin, Jiangchao (Thesis advisor) / Ayyanar, Raja (Committee member) / Weng, Yang (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2020
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Description

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI)

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.

ContributorsShi, Jie (Author) / Stonnington, Cynthia M. (Author) / Thompson, Paul M. (Author) / Chen, Kewei (Author) / Gutman, Boris (Author) / Reschke, Cole (Author) / Baxter, Leslie C. (Author) / Reiman, Eric M. (Author) / Caselli, Richard J. (Author) / Wang, Yalin (Author) / Ira A. Fulton Schools of Engineering (Contributor)
Created2015-01-01
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Description

Conformal deposition of platinum as ultrathin shells on facet-controlled palladium nanocrystals offers a great opportunity to enhance the catalytic performance while reducing its loading. Here we report such a system based on palladium icosahedra. Owing to lateral confinement imposed by twin boundaries and thus vertical relaxation only, the platinum overlayers

Conformal deposition of platinum as ultrathin shells on facet-controlled palladium nanocrystals offers a great opportunity to enhance the catalytic performance while reducing its loading. Here we report such a system based on palladium icosahedra. Owing to lateral confinement imposed by twin boundaries and thus vertical relaxation only, the platinum overlayers evolve into a corrugated structure under compressive strain. For the core-shell nanocrystals with an average of 2.7 platinum overlayers, their specific and platinum mass activities towards oxygen reduction are enhanced by eight- and sevenfold, respectively, relative to a commercial catalyst. Density functional theory calculations indicate that the enhancement can be attributed to the weakened binding of hydroxyl to the compressed platinum surface supported on palladium. After 10,000 testing cycles, the mass activity of the core-shell nanocrystals is still four times higher than the commercial catalyst. These results demonstrate an effective approach to the development of electrocatalysts with greatly enhanced activity and durability.

ContributorsWang, Xue (Author) / Choi, Sang-Il (Author) / Roling, Luke T. (Author) / Luo, Ming (Author) / Ma, Cheng (Author) / Zhang, Lei (Author) / Chi, Miaofang (Author) / Liu, Jingyue (Author) / Xie, Zhaoxiong (Author) / Herron, Jeffrey A. (Author) / Mavrikakis, Manos (Author) / Xia, Younan (Author) / College of Liberal Arts and Sciences (Contributor)
Created2015-07-01
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Description

Background: Carriers of the APOE ε4 allele are at increased risk of developing Alzheimer’s disease (AD), and have been shown to have reduced cerebral metabolic rate of glucose (CMRgl) in the same brain areas frequently affected in AD. These individuals also exhibit reduced plasma levels of apolipoprotein E (apoE) attributed to

Background: Carriers of the APOE ε4 allele are at increased risk of developing Alzheimer’s disease (AD), and have been shown to have reduced cerebral metabolic rate of glucose (CMRgl) in the same brain areas frequently affected in AD. These individuals also exhibit reduced plasma levels of apolipoprotein E (apoE) attributed to a specific decrease in the apoE4 isoform as determined by quantification of individual apoE isoforms in APOE ε4 heterozygotes. Whether low plasma apoE levels are associated with structural and functional brain measurements and cognitive performance remains to be investigated.

Methods: Using quantitative mass spectrometry we quantified the plasma levels of total apoE and the individual apoE3 and apoE4 isoforms in 128 cognitively normal APOE ε3/ε4 individuals included in the Arizona APOE cohort. All included individuals had undergone extensive neuropsychological testing and 25 had in addition undergone FDG-PET and MRI to determine CMRgl and regional gray matter volume (GMV).

Results: Our results demonstrated higher apoE4 levels in females versus males and an age-dependent increase in the apoE3 isoform levels in females only. Importantly, a higher relative ratio of apoE4 over apoE3 was associated with GMV loss in the right posterior cingulate and with reduced CMRgl bilaterally in the anterior cingulate and in the right hippocampal area. Additional exploratory analysis revealed several negative associations between total plasma apoE, individual apoE isoform levels, GMV and CMRgl predominantly in the frontal, occipital and temporal areas. Finally, our results indicated only weak associations between apoE plasma levels and cognitive performance which further appear to be affected by sex.

Conclusions: Our study proposes a sex-dependent and age-dependent variation in plasma apoE isoform levels and concludes that peripheral apoE levels are associated with GMV, CMRgl and possibly cognitive performance in cognitively healthy individuals with a genetic predisposition to AD.

ContributorsNielsen, Henrietta M. (Author) / Chen, Kewei (Author) / Lee, Wendy (Author) / Chen, Yinghua (Author) / Bauer, Robert (Author) / Reiman, Eric (Author) / Caselli, Richard (Author) / Bu, Guojun (Author) / College of Liberal Arts and Sciences (Contributor)
Created2016-12-21
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Description

The unicellular microalga Haematococcus pluvialis has emerged as a promising biomass feedstock for the ketocarotenoid astaxanthin and neutral lipid triacylglycerol. Motile flagellates, resting palmella cells, and cysts are the major life cycle stages of H. pluvialis. Fast-growing motile cells are usually used to induce astaxanthin and triacylglycerol biosynthesis under stress

The unicellular microalga Haematococcus pluvialis has emerged as a promising biomass feedstock for the ketocarotenoid astaxanthin and neutral lipid triacylglycerol. Motile flagellates, resting palmella cells, and cysts are the major life cycle stages of H. pluvialis. Fast-growing motile cells are usually used to induce astaxanthin and triacylglycerol biosynthesis under stress conditions (high light or nutrient starvation); however, productivity of biomass and bioproducts are compromised due to the susceptibility of motile cells to stress. This study revealed that the Photosystem II (PSII) reaction center D1 protein, the manganese-stabilizing protein PsbO, and several major membrane glycerolipids (particularly for chloroplast membrane lipids monogalactosyldiacylglycerol and phosphatidylglycerol), decreased dramatically in motile cells under high light (HL). In contrast, palmella cells, which are transformed from motile cells after an extended period of time under favorable growth conditions, have developed multiple protective mechanisms - including reduction in chloroplast membrane lipids content, downplay of linear photosynthetic electron transport, and activating nonphotochemical quenching mechanisms - while accumulating triacylglycerol. Consequently, the membrane lipids and PSII proteins (D1 and PsbO) remained relatively stable in palmella cells subjected to HL. Introducing palmella instead of motile cells to stress conditions may greatly increase astaxanthin and lipid production in H. pluvialis culture.

ContributorsWang, Baobei (Author) / Zhang, Zhen (Author) / Hu, Qiang (Author) / Sommerfeld, Milton (Author) / Lu, Yinghua (Author) / Han, Danxiang (Author) / College of Liberal Arts and Sciences (Contributor)
Created2014-09-15
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Description

Background: We introduced a hypometabolic convergence index (HCI) to characterize in a single measurement the extent to which a person’s fluorodeoxyglucose positron emission tomogram (FDG PET) corresponds to that in Alzheimer’s disease (AD). Apolipoprotein E ε4 (APOE ε4) gene dose is associated with three levels of risk for late-onset AD. We

Background: We introduced a hypometabolic convergence index (HCI) to characterize in a single measurement the extent to which a person’s fluorodeoxyglucose positron emission tomogram (FDG PET) corresponds to that in Alzheimer’s disease (AD). Apolipoprotein E ε4 (APOE ε4) gene dose is associated with three levels of risk for late-onset AD. We explored the association between gene dose and HCI in cognitively normal ε4 homozygotes, heterozygotes, and non-carriers.

Methods: An algorithm was used to characterize and compare AD-related HCIs in cognitively normal individuals, including 36 ε4 homozygotes, 46 heterozygotes, and 78 non-carriers.

Results: These three groups differed significantly in their HCIs (ANOVA, p = 0.004), and there was a significant association between HCIs and gene dose (linear trend, p = 0.001).

Conclusions: The HCI is associated with three levels of genetic risk for late-onset AD. This supports the possibility of using a single FDG PET measurement to help in the preclinical detection and tracking of AD.

ContributorsSchraml, Frank (Author) / Chen, Kewei (Author) / Ayutyanont, Napatkamon (Author) / Auttawut, Roontiva (Author) / Langbaum, Jessica B. S. (Author) / Lee, Wendy (Author) / Liu, Xiaofen (Author) / Bandy, Dan (Author) / Reeder, Stephanie Q. (Author) / Alexander, Gene E. (Author) / Caselli, Richard J. (Author) / Fleisher, Adam S. (Author) / Reiman, Eric M. (Author) / Alzheimer's Disease Neuroimaging Initiative (Project) (Contributor)
Created2013-06-26