Matching Items (244)
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Description本文对中国制药企业并购溢价影响因素进行了研究,提出了对制药企业并购非常重要的两个新的影响因素:可生产药品批文和在研新药批文。本文以2011年1月—2019年12月间我国制药行业上市公司并购事件为样本,对在研新药和可生产药品批文的价值从四个维度度量:是否有在研新药和可生产药品批文;在研新药数量及可生产药品批文数量;根据创新药和仿制药两个类别进行细分;标的企业所拥有的在研新药和可生产药品批文的市场价值。论文发现药品批文对企业并购溢价的影响不是很显著。进一步的,本文探究了药品批文对主并企业的对被并购公司的估值的影响。实证结果表明,我国制药企业在并购估值时确实会考虑到在研新药和可生产药品批文的价值。本文还发现对于可生产药品来说,相对创新药,被并购公司持有的仿制药批文影响更显著。而对于在研新药来说,主并企业更看重在研的创新药,在研仿制药对并购估值的影响不大。最后,本文选取了两个代表性案例进一步分析和探讨药品批文对企业并购的影响。
ContributorsYe, Tao (Author) / Shen, Wei (Thesis advisor) / Chang, Chun (Thesis advisor) / Jiang, Zhan (Committee member) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Disordered many-body systems are ubiquitous in condensed matter physics, materials science and biological systems. Examples include amorphous and glassy states of matter, granular materials, and tissues composed of packings of cells in the extra-cellular matrix (ECM). Understanding the collective emergent properties in these systems is crucial to improving the capability

Disordered many-body systems are ubiquitous in condensed matter physics, materials science and biological systems. Examples include amorphous and glassy states of matter, granular materials, and tissues composed of packings of cells in the extra-cellular matrix (ECM). Understanding the collective emergent properties in these systems is crucial to improving the capability for controlling, engineering and optimizing their behaviors, yet it is extremely challenging due to their complexity and disordered nature. The main theme of the thesis is to address this challenge by characterizing and understanding a variety of disordered many-body systems via unique statistical geometrical and topological tools and the state-of-the-art simulation methods. Two major topics of the thesis are modeling ECM-mediated multicellular dynamics and understanding hyperuniformity in 2D material systems. Collective migration is an important mode of cell movement for several biological processes, and it has been the focus of a large number of studies over the past decades. Hyperuniform (HU) state is a critical state in a many-particle system, an exotic property of condensed matter discovered recently. The main focus of this thesis is to study the mechanisms underlying collective cell migration behaviors by developing theoretical/phenomenological models that capture the features of ECM-mediated mechanical communications in vitro and investigate general conditions that can be imposed on hyperuniformity-preserving and hyperuniformity-generating operations, as well as to understand how various novel transport physical properties arise from the unique hyperuniform long-range correlations.
ContributorsZheng, Yu (Author) / Jiao, Yang (Thesis advisor) / Zhuang, Houlong (Committee member) / Beckstein, Oliver (Committee member) / Ros, Robert (Committee member) / Arizona State University (Publisher)
Created2022
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Description汽车行业属于国家支柱型产业,创造了高额的产值,增加了就业岗位。随着汽车生产行业竞争日趋激烈的趋势影响,汽车经销商在未来会出现明显的分化,并且逐步向头部集中。基于这样的行业背景,本项研究开展汽车经销商整体经营和盈利能力等方面的详细深入分析,即系统整合汽车经销商业务运营层面和财务层面数据,结合统计研究方法,对经销商盈利能力进行系统且详实归因分析,从而试别驱动盈利能力的关键业务要素。其研究成果能够完善对行业发展规律和经营模式系统性理解,从而进一步指导该领域的相关业务实践,提高经销商整体经营业绩。本课题通过四个阶段来开展经销商整体经营与盈利归因的相关研究。首先,本课题梳理了中国汽车消费行业发展的历史,同时阐述样本期内(2018-2020年)国内宏观经济和汽车消费市场的特征进行,并介绍X品牌汽车经销商的地理分布、资质和业绩评级体系、自身经营特征以及汽车生产商对经销商扶持政策等方面。在第二阶段,本课题聚焦研究假设、模型与方法,通过对X品牌汽车经销商的业务结构和运营管理开展分析,并逐步识别影响经销商盈利的关键指标变量,并提出研究假设和相关模型(即时间序列模型和面板回归模型)。在第三阶段,本课题首先开展经销商相关信息整体性统计分析,获得关键业务指标在样本期内动态特征,并结合时间序列回归模型探讨各项业务指标对经销商整体盈利能力的影响程度。在第四阶段,本课题采用(个体)固定效应的面板回归模型来研究不同组别(控制)条件下经销商盈利能力的影响因素以及其盈利能力对这些因素的敏感程度,从而更深入和全面地揭示影响经销商盈利能力的潜在因素。 基于上述四阶段的研究结果,本研究进一步就提升经销商盈利能力展开讨论,并提出相应对策。本课题相关结论仅从X品牌汽车经销商经营和财务数据进行定性和定量分析获得,但衷心希望本研究的成果能够对汽车经销商改善经营业务方面能起到实践上的借鉴和指导意义。
ContributorsPan, Guangxiong (Author) / Shen, Wei (Thesis advisor) / Wu, Fei (Thesis advisor) / Zhu, Qigui (Committee member) / Arizona State University (Publisher)
Created2022
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Description
The increasing availability of data and advances in computation have spurred the development of data-driven approaches for modeling complex dynamical systems. These approaches are based on the idea that the underlying structure of a complex system can be discovered from data using mathematical and computational techniques. They also show promise

The increasing availability of data and advances in computation have spurred the development of data-driven approaches for modeling complex dynamical systems. These approaches are based on the idea that the underlying structure of a complex system can be discovered from data using mathematical and computational techniques. They also show promise for addressing the challenges of modeling high-dimensional, nonlinear systems with limited data. In this research expository, the state of the art in data-driven approaches for modeling complex dynamical systems is surveyed in a systemic way. First the general formulation of data-driven modeling of dynamical systems is discussed. Then several representative methods in feature engineering and system identification/prediction are reviewed, including recent advances and key challenges.
ContributorsShi, Wenlong (Author) / Ren, Yi (Thesis advisor) / Hong, Qijun (Committee member) / Jiao, Yang (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Applications such as heat exchangers, surface-based cellular structures, rotating blades, and waveguides rely on thin metal walls as crucial constituent elements of the structure. The design freedom enabled by laser powder bed fusion has led to an interest in exploiting this technology to further the performance of these components, many

Applications such as heat exchangers, surface-based cellular structures, rotating blades, and waveguides rely on thin metal walls as crucial constituent elements of the structure. The design freedom enabled by laser powder bed fusion has led to an interest in exploiting this technology to further the performance of these components, many of which retain their as-built surface morphologies on account of their design complexity. However, there is limited understanding of how and why mechanical properties vary by wall thickness for specimens that are additively manufactured and maintain an as-printed surface finish. Critically, the contributions of microstructure and morphology to the mechanical behavior of thin wall laser powder bed fusion structures have yet to be systematically identified and decoupled. This work focuses on elucidating the room temperature quasi-static tensile and high cycle fatigue properties of as-printed, thin-wall Inconel 718 fabricated using laser powder bed fusion, with the aim of addressing this critical gap in the literature. Wall thicknesses studied range from 0.3 - 2.0 mm, and the effects of Hot Isostatic Pressing are also examined, with sheet metal specimens used as a baseline for comparison. Statistical analyses are conducted to identify the significance of the dependence of properties on wall thickness and Hot Isostatic Pressing, as well as to examine correlations of these properties to section area, porosity, and surface roughness. A thorough microstructural study is complemented with a first-of-its-kind study of surface morphology to decouple their contributions and identify underlying causes for observed changes in mechanical properties. This thesis finds that mechanical properties in the quasi-static and fatigue framework do not see appreciable declines until specimen thickness is under 0.75 mm in thickness. The added Hot Isostatic Pressing heat treatment effectively closed pores, recrystallized the grain structure, and provided a more homogenous microstructure that benefits the modulus, tensile strength, elongation, and fatigue performance at higher stresses. Stress heterogeneities, primarily caused by surface defects, negatively affected the thinner specimens disproportionately. Without the use of the Hot Isostatic Pressing, the grain structure remained much more refined and benefitted the yield strength and fatigue endurance limit.
ContributorsParadise, Paul David (Author) / Bhate, Dhruv (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Azeredo, Bruno (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Tire blowout often occurs during driving, which can suddenly disturb vehicle motions and seriously threaten road safety. Currently, there is still a lack of effective methods to mitigate tire blowout risks in everyday traffic, even for automated vehicles. To fundamentally study and systematically resolve the tire blowout issue for automated

Tire blowout often occurs during driving, which can suddenly disturb vehicle motions and seriously threaten road safety. Currently, there is still a lack of effective methods to mitigate tire blowout risks in everyday traffic, even for automated vehicles. To fundamentally study and systematically resolve the tire blowout issue for automated vehicles, a collaborative project between General Motors (GM) and Arizona State University (ASU) has been conducted since 2018. In this dissertation, three main contributions of this project will be presented. First, to explore vehicle dynamics with tire blowout impacts and establish an effective simulation platform for close-loop control performance evaluation, high-fidelity tire blowout models are thoroughly developed by explicitly considering important vehicle parameters and variables. Second, since human cooperation is required to control Level 2/3 partially automated vehicles (PAVs), novel shared steering control schemes are specifically proposed for tire blowout to ensure safe vehicle stabilization via cooperative driving. Third, for Level 4/5 highly automated vehicles (HAVs) without human control, the development of control-oriented vehicle models, controllability study, and automatic control designs are performed based on impulsive differential systems (IDS) theories. Co-simulations Matlab/Simulink® and CarSim® are conducted to validate performances of all models and control designs proposed in this dissertation. Moreover, a scaled test vehicle at ASU and a full-size test vehicle at GM are well instrumented for data collection and control implementation. Various tire blowout experiments for different scenarios are conducted for more rigorous validations. Consequently, the proposed high-fidelity tire blowout models can correctly and more accurately describe vehicle motions upon tire blowout. The developed shared steering control schemes for PAVs and automatic control designs for HAVs can effectively stabilize a vehicle to maintain path following performance in the driving lane after tire blowout. In addition to new research findings and developments in this dissertation, a pending patent for tire blowout detection is also generated in the tire blowout project. The obtained research results have attracted interest from automotive manufacturers and could have a significant impact on driving safety enhancement for automated vehicles upon tire blowout.
ContributorsLi, Ao (Author) / Chen, Yan (Thesis advisor) / Berman, Spring (Committee member) / Kannan, Arunachala Mada (Committee member) / Liu, Yongming (Committee member) / Lin, Wen-Chiao (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2023
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Description
In this research, the chemical and mineralogical compositions, physical and mechanical properties, and failure mechanisms of two ordinary chondrite (OCs) meteorites Aba Panu (L3) and Viñales (L6), and the iron meteorite called Gibeon (IVA) were studied. OCs are dominated by anhydrous silicates with lesser amounts of sulfides and native Fe-Ni

In this research, the chemical and mineralogical compositions, physical and mechanical properties, and failure mechanisms of two ordinary chondrite (OCs) meteorites Aba Panu (L3) and Viñales (L6), and the iron meteorite called Gibeon (IVA) were studied. OCs are dominated by anhydrous silicates with lesser amounts of sulfides and native Fe-Ni metals, while Gibeon is primarily composed of Fe-Ni metals with scattered inclusions of graphite and troilite. The OCs were investigated to understand their response to compressive loading, using a three-dimensional (3-D) Digital Image Correlation (DIC) technique to measure full-field deformation and strain during compression. The DIC data were also used to identify the effects of mineralogical and structural heterogeneity on crack formation and growth. Even though Aba Panu and Viñales are mineralogically similar and are both classified as L ordinary chondrites, they exhibit differences in compressive strengths due to variations in chemical compositions, microstructure, and the presence of cracks and shock veins. DIC data of Aba Panu and Viñales show a brittle failure mechanism, consistent with the crack formation and growth from pre-existing microcracks and porosity. In contrast, the Fe-Ni phases of the Gibeon meteorite deform plastically without rupture during compression, whereas during tension, plastic deformations followed by necking lead to final failure. The Gibeon DIC results showed strain concentration in the tensile gauge region along the sample edge, resulting in the initiation of new damage surfaces that propagated perpendicular to the loading direction. Finally, an in-situ low-temperature testing method of iron meteorites was developed to study the response of their unique microstructure and failure mechanism.
ContributorsRabbi, Md Fazle (Author) / Chattopadhyay, Aditi (Thesis advisor) / Garvie, Laurence A.J. (Thesis advisor) / Liu, Yongming (Committee member) / Fard, Masoud Yekani (Committee member) / Cotto-Figueroa, Desiree (Committee member) / Arizona State University (Publisher)
Created2023
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Description
This thesis presents a study of Boron Nitride (BN) and Copper (Cu)/BN multilayer thin films in terms of synthesis, chemical, structural, morphological, and mechanical properties characterization. In this study, the influence of Ar/N₂ flow rate in synthesizing stoichiometric BN thin films via magnetron sputtering was investigated initially. Post magnetron

This thesis presents a study of Boron Nitride (BN) and Copper (Cu)/BN multilayer thin films in terms of synthesis, chemical, structural, morphological, and mechanical properties characterization. In this study, the influence of Ar/N₂ flow rate in synthesizing stoichiometric BN thin films via magnetron sputtering was investigated initially. Post magnetron sputtering, the crystalline nature and B:N stoichiometric ratio of deposited thin films were investigated by X-ray diffraction (XRD) and X-ray Photoelectron Spectroscopy (XPS) respectively. Thicknesses revealed by ellipsometry analysis for nearly stoichiometric B:N thin films and their corresponding deposition times were used for estimating BN interlayer deposition times during the deposition of Cu/BN multilayer thin films. To characterize the microstructure of the synthesized Cu/BN multilayer thin films, XRD and scanning electron microscopy (SEM) have been used. Finally, a comparison of nanoindentation measurements on pure Cu and Cu/BN multilayer thin films having different number of BN interlayers were used for studying the influence of BN interlayers on improving mechanical properties such as hardness and elastic modulus. The results show that the stoichiometry of BN thin films is dependent on the Ar/N₂ flow rate during magnetron sputtering. An optimal Ar/N₂ flow rate of 13:5 during deposition was required to achieve an approximately 1:1 B:N stoichiometry. Grazing incidence and powder XRD analysis on these stoichiometric BN thin films deposited at room temperature did not reveal a phase match when compared to hexagonal boron nitride (h-BN) and cubic boron nitride (c-BN) reference XRD patterns. For a BN thin film deposition time of 5 hours, a thickness of approximately 40 nm was achieved, as revealed by ellipsometry. XRD and microstructure analysis using scanning electron microscopy (SEM) on pure Cu and Cu/BN thin films showed that the Cu grain size in Cu/BN thin films is much finer than pure Cu thin films. Interestingly, nanoindentation measurements on pure Cu and Cu/BN thin films having a similar overall thickness demonstrated that hardness and Young’s modulus of the films were improved significantly when BN interlayers are present.
ContributorsCaner, Sumeyye (Author) / Rajagopalan, Jagannathan (Thesis advisor) / Oswald, Jay (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2023
Description
Heusler alloys were discovered in 1903, and materials with half-metallic characteristics have drawn more attention from researchers since the advances in semiconductor industry [1]. Heusler alloys have found application as spin-filters, tunnel junctions or giant magnetoresistance (GMR) devices in technological applications [1]. In this work, the electronic structures, phonon

Heusler alloys were discovered in 1903, and materials with half-metallic characteristics have drawn more attention from researchers since the advances in semiconductor industry [1]. Heusler alloys have found application as spin-filters, tunnel junctions or giant magnetoresistance (GMR) devices in technological applications [1]. In this work, the electronic structures, phonon dispersion, thermal properties, and electrical conductivities of PdMnSn and six novel alloys (AuCrSn, AuMnGe, Au2MnSn, Cu2NiGe, Pd2NiGe and Pt2CoSn) along with their magnetic moments are studied using ab initio calculations to understand the roots of half-metallicity in these alloys of Heusler family. From the phonon dispersion, the thermodynamic stability of the alloys in their respective phases is assessed. Phonon modes were also used to further understand the electrical transport in the crystals of these seven alloys. This study evaluates the relationship between materials' electrical conductivity and minority-spin bandgap in the band structure, and it provides suggestions for selecting constituent elements when designing new half-metallic Heusler alloys of C1b and L21 structures.
ContributorsPatel, Deep (Author) / Zhuang, Houlong (Thesis advisor) / Solanki, Kiran (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2023
Description
The study aims to develop and evaluate failure prediction models that accurately predict crack initiation sites, fatigue life in additively manufactured Ti-6Al-4V, and burst pressure in relevant applications.The first part proposes a classification model to identify crack initiation sites in AM-built Ti-6Al-4V alloy. The model utilizes surface and pore-related parameters

The study aims to develop and evaluate failure prediction models that accurately predict crack initiation sites, fatigue life in additively manufactured Ti-6Al-4V, and burst pressure in relevant applications.The first part proposes a classification model to identify crack initiation sites in AM-built Ti-6Al-4V alloy. The model utilizes surface and pore-related parameters and achieves high accuracy (0.97) and robustness (F1 score of 0.98). Leveraging CT images for characterization and data extraction from the CT-images built STL files, the model effectively detects crack initiation sites while minimizing false positives and negatives. Data augmentation techniques, including SMOTE+Tomek Links, are employed to address imbalanced data distributions and improve model performance. This study proposes the Probabilistic Physics-guided Neural Network 2.0 (PPgNN) for probabilistic fatigue life estimation. The presented approach overcomes the limitations of classical regression machine models commonly used to analyze fatigue data. One key advantage of the proposed method is incorporating known physics constraints, resulting in accurate and physically consistent predictions. The efficacy of the model is demonstrated by training the model with multiple fatigue S-N curve data sets from open literature with relevant morphological data and tested using the data extracted from CT-built STL files. The results illustrate that PPgNN 2.0 is a flexible and robust model for predicting fatigue life and quantifying uncertainties by estimating the mean and standard deviation of the fatigue life. The loss function that trains the proposed model can capture the underlying distribution and reduce the prediction error. A comparison study between the performance of neural network models highlights the benefits of physics-guided learning for fatigue data analysis. The proposed model demonstrates satisfactory learning capacity and generalization, providing accurate fatigue life predictions to unseen examples. An elastic-plastic Finite Element Model (FEM) is developed in the second part to assess pipeline integrity, focusing on burst pressure estimation in high-pressure gas pipelines with interactive corrosion defects. The FEM accurately predicts burst pressure and evaluates the remaining useful life by considering the interaction between corrosion defects and neighboring pits. The FEM outperforms the well-known ASME-B31G method in handling interactive corrosion threats.
ContributorsBalamurugan, Rakesh (Author) / Liu, Yongming (Thesis advisor) / Zhuang, Houlong (Committee member) / Bhate, Dhruv (Committee member) / Arizona State University (Publisher)
Created2023