Matching Items (179)
Description资本市场开放是新兴市场国家经济发展和金融体系完善的重要举措，本研究探讨了中国沪深港通制度如何影响企业研发支出，以及在高管持股、境内机构持股、契约环境和行业竞争程度的不同水平下，沪深港通制度对企业创新投入影响效果的差异。基于DID双重差分模型和中国A股上市公司数据，本研究验证了沪深港通制度的实施能有效提高企业的研发支出水平，并且在控制企业资产规模和收入规模后，该正向影响依然显著。另外，对于高管持股比例较高、境内机构持股比例较低、契约环境水平较高和行业竞争程度较弱的企业，其研发支出受沪深港通制度的提升激励作用更强。因为高管持股比例高，企业内部管理者能获得更多的创新收益，创新意愿将更强。契约环境水平越高意味着创新资源越充足，公平竞争的市场环境越有效，也会激发企业的创新行为。行业竞争程度较弱的企业，沪深港通制度的引入能激励企业打造长期竞争优势，缓解由缺乏外部竞争而导致的创新动力不足。另外，本研究还进一步分析了由调节变量交互产生的双重调节效应。发现高管持股水平与契约环境水平正向调节沪深港通的积极作用，而与市场竞争程度负向调节。高管持股水平与境内金融机构持股、契约环境水平与行业竞争程度均正向调节沪深港通的积极作用。最重要的是，契约环境是其中最关键的影响因素，良好的契约环境水平有助于强化股权激励、金融机构持股以及市场竞争的作用。
总体来看，沪深港通制度引入了较为成熟的境外投资者，提高了监督作用的同时扩散了鼓励创新的经营理念，能有效缓解企业创新面临的融资约束、信息不对称、创新认知和意愿不足的问题，从而激励企业增加创新投入。本研究验证了沪深港通制度对企业研发支出的正向影响，并且分析了多种内外部情境因素下该影响的差异性。丰富了资本市场开放对企业微观行为影响与机制，一定程度拓展了资本市场开放与企业创新等研究的理论边界。
ContributorsXie, Mingru (Author) / Pei, Ker-Wei (Thesis advisor) / Sun, Jianfei (Thesis advisor) / Shi, Weilei (Committee member) / Arizona State University (Publisher)
Created2022
Description
Ultra-fast 2D/3D material microstructure reconstruction and quantitative structure-property mapping are crucial components of integrated computational material engineering (ICME). It is particularly challenging for modeling random heterogeneous materials such as alloys, composites, polymers, porous media, and granular matters, which exhibit strong randomness and variations of their material properties due to the hierarchical uncertainties associated with their complex microstructure at different length scales. Such uncertainties also exist in disordered hyperuniform systems that are statistically isotropic and possess no Bragg peaks like liquids and glasses, yet they suppress large-scale density fluctuations in a similar manner as in perfect crystals. The unique hyperuniform long-range order in these systems endow them with nearly optimal transport, electronic and mechanical properties. The concept of hyperuniformity was originally introduced for many-particle systems and has subsequently been generalized to heterogeneous materials such as porous media, composites, polymers, and biological tissues for unconventional property discovery. An explicit mixture random field (MRF) model is proposed to characterize and reconstruct multi-phase stochastic material property and microstructure simultaneously, where no additional tuning step nor iteration is needed compared with other stochastic optimization approaches such as the simulated annealing. The proposed method is shown to have ultra-high computational efficiency and only requires minimal imaging and property input data. Considering microscale uncertainties, the material reliability will face the challenge of high dimensionality. To deal with the so-called “curse of dimensionality”, efficient material reliability analysis methods are developed. Then, the explicit hierarchical uncertainty quantification model and efficient material reliability solvers are applied to reliability-based topology optimization to pursue the lightweight under reliability constraint defined based on structural mechanical responses. Efficient and accurate methods for high-resolution microstructure and hyperuniform microstructure reconstruction, high-dimensional material reliability analysis, and reliability-based topology optimization are developed. The proposed framework can be readily incorporated into ICME for probabilistic analysis, discovery of novel disordered hyperuniform materials, material design and optimization.
ContributorsGao, Yi (Author) / Liu, Yongming (Thesis advisor) / Jiao, Yang (Committee member) / Ren, Yi (Committee member) / Pan, Rong (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2021
Description人口的老龄化不仅对养老事业提出更高的要求，也对养老服务产业人才的培养提出要求。但是青年学生选择涉老服务专业的意愿却非常低。因此，为了探究职业学院如何增强涉老服务专业吸引力这一问题，本文以学生为主体视角，利用相关理论，对于影响青年学生选择涉老服务专业的因素进行全面的分析，并结合深度访谈和调查法，提出并建构了相关的理论模型。首先，通过深度访谈和焦点小组讨论，结合对现有的文献的分析，本文提出了影响青年学生选择职业院校涉老服务专业的各种因素，主要包括：个人未来风险感知、家庭经济资本、社会信息评价、校企合作水平、专业课程建设水平、学生激励水平、师资队伍建设水平。之后，本文通过调查法，基于社会认同理论构建了本文的研究模型，并通过结构方程模型对所构建的模型进行检查。
本文的研究结果表明：个人未来风险感知对学生专业认同度产生负面影响；家庭经济资本对学生专业认同度产生负面影响；社会信息评价对学生专业认同度产生正面影响；校企合作水平对学生专业认同度产生正面影；专业课程建设水平对学生专业认同度产生正面影响；学生激励水平对学生专业认同度产生正面影响；师资队伍建设水平对学生专业认同度产生正面影响；学生专业认同度对学生专业选择意愿产生正面影响。
基于上述研究结论，本文选取了个人未来风险感知、家庭经济资本、社会信息评价、校企合作水平、专业课程建设水平、学生激励水平、师资队伍建设水平等因素对于广东岭南职业技术学院涉老服务专业的现有吸引力进行了分析和评估，并从这些视角进一步了对如何提升招生吸引力问题进行探讨，为提高涉老服务专业对于青年学生的吸引力，得出了相关管理建议。
ContributorsZhou, Lanqing (Author) / Shen, Wei (Thesis advisor) / Wu, Fei (Thesis advisor) / Pei, Ker-Wei (Committee member) / Arizona State University (Publisher)
Created2021
Description
Advanced Polymer and Ceramic Matrix Composites (PMCs and CMCs) are currently employed in a variety of airframe and engine applications. This includes PMC jet engine fan cases and CMC hot gas path turbine components. In an impact event, such as a jet engine fan blade-out, PMCs exhibit significant deformation-induced temperature rises in addition to strain rate, temperature, and pressure dependence. CMC turbine components experience elevated temperatures, large thermal gradients, and sustained loading for long time periods in service, where creep is a major issue. However, the complex nature of woven and braided composites presents significant challenges for deformation, progressive damage, and failure prediction, particularly under extreme service conditions where global response is heavily driven by competing time and temperature dependent phenomena at the constituent level. In service, the constituents in these advanced composites experience history-dependent inelastic deformation, progressive damage, and failure, which drive global nonlinear constitutive behavior. In the case of PMCs, deformation-induced heating under impact conditions is heavily influenced by the matrix. The creep behavior of CMCs is a complex manifestation of time-dependent load transfer due to the differing creep rates of the constituents; simultaneous creep and relaxation at the constituent level govern macroscopic CMC creep. The disparity in length scales associated with the constituent materials, woven and braided tow architectures, and composite structural components therefore necessitates the development of robust multiscale computational tools. In this work, multiscale computational tools are developed to gain insight into the deformation, progressive damage, and failure of advanced PMCs and CMCs. This includes multiscale modeling of the impact response of PMCs, including adiabatic heating due to the conversion of plastic work to heat at the constituent level, as well as elevated temperature creep in CMCs as a result of time-dependent constituent load transfer. It is expected that the developed models and methods will provide valuable insight into the challenges associated with the design and certification of these advanced material systems.
ContributorsSorini, Christopher (Author) / Chattopadhyay, Adit (Thesis advisor) / Goldberg, Robert K (Committee member) / Liu, Yongming (Committee member) / Mignolet, Marc (Committee member) / Yekani-Fard, Masoud (Committee member) / Arizona State University (Publisher)
Created2021
Description
Damage and failure of advanced composite materials and structures are often manifestations of nonlinear deformation that involve multiple mechanisms and their interactions at the constituent length scale. The presence and interactions of inelastic microscale constituents strongly influence the macroscopic damage anisotropy and useful residual life. The mechano-chemical interactions between constituents at the atomistic length scale play a more critical role with nanoengineered composites. Therefore, it is desirable to link composite behavior to specific microscopic constituent properties explicitly and lower length scale features using high-fidelity multiscale modeling techniques.In the research presented in this dissertation, an atomistically-informed multiscale modeling framework is developed to investigate damage evolution and failure in composites with radially-grown carbon nanotube (CNT) architecture. A continuum damage mechanics (CDM) model for the radially-grown CNT interphase region is developed with evolution equations derived using atomistic simulations. The developed model is integrated within a high-fidelity generalized method of cells (HFGMC) micromechanics theory and is used to parametrically investigate the influence of various input micro and nanoscale parameters on the mechanical properties, such as elastic stiffness, strength, and toughness. In addition, the inter-fiber stresses and the onset of damage in the presence of the interphase region are investigated to better understand the energy dissipation mechanisms that attribute to the enhancement in the macroscopic out-of-plane strength and toughness. Note that the HFGMC theory relies heavily on the description of microscale features and requires many internal variables, leading to high computational costs. Therefore, a novel reduced-order model (ROM) is also developed to surrogate full-field nonlinear HFGMC simulations and decrease the computational time and memory requirements of concurrent multiscale simulations significantly.
The accurate prediction of composite sandwich materials' thermal stability and durability remains a challenge due to the variability of thermal-related material coefficients at different temperatures and the extensive use of bonded fittings. Consequently, the dissertation also investigates the thermomechanical performance of a complex composite sandwich space structure subject to thermal cycling. Computational finite element (FE) simulations are used to investigate the intrinsic failure mechanisms and damage precursors in honeycomb core composite sandwich structures with adhesively bonded fittings.
ContributorsVenkatesan, Karthik Rajan (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Yekani Fard, Masoud (Committee member) / Stoumbos, Tom (Committee member) / Arizona State University (Publisher)
Created2021
Description
Composite materials have gained interest in the aerospace, mechanical and civil engineering industries due to their desirable properties - high specific strength and modulus, and superior resistance to fatigue. Design engineers greatly benefit from a reliable predictive tool that can calculate the deformations, strains, and stresses of composites under uniaxial and multiaxial states of loading including damage and failure predictions. Obtaining this information from (laboratory) experimental testing is costly, time consuming, and sometimes, impractical. On the other hand, numerical modeling of composite materials provides a tool (virtual testing) that can be used as a supplemental and an alternate procedure to obtain data that either cannot be readily obtained via experiments or is not possible with the currently available experimental setup. In this study, a unidirectional composite (Toray T800-F3900) is modeled at the constituent level using repeated unit cells (RUC) so as to obtain homogenized response all the way from the unloaded state up until failure (defined as complete loss of load carrying capacity). The RUC-based model is first calibrated and validated against the principal material direction laboratory tests involving unidirectional loading states. Subsequently, the models are subjected to multi-directional states of loading to generate a point cloud failure data under in-plane and out-of-plane biaxial loading conditions. Failure surfaces thus generated are plotted and compared against analytical failure theories. Results indicate that the developed process and framework can be used to generate a reliable failure prediction procedure that can possibly be used for a variety of composite systems.
ContributorsKatusele, Daniel Mutahwa (Author) / Rajan, Subramaniam (Thesis advisor) / Mobasher, Barzin (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Structural/system health monitoring (SHM) and prognostic health management (PHM) are vital techniques to ensure engineering system reliability and safety during the service. As multi-functionality and enhanced performance are in demand, modern engineering systems including aerospace, mechanical, and civil applications have become more complex. The constituent and architectural complexity, and multisource sensing sources in modern engineering systems may limit the monitoring capabilities of conventional approaches and require more advanced SHM/PHM techniques. Therefore, a hybrid methodology that incorporates information fusion, nondestructive evaluation (NDE), machine learning (ML), and statistical analysis is needed for more effective damage diagnosis/prognosis and system safety management.This dissertation presents an automated aviation health management technique to enable proactive safety management for both aircraft and national airspace system (NAS). A real-time, data-driven aircraft safety monitoring technique using ML models and statistical models is developed to enable an early-stage upset detection capability, which can improve pilot’s situational awareness and provide a sufficient safety margin. The detection accuracy and computational efficiency of the developed monitoring techniques is validated using commercial unlabeled flight data recorder (FDR) and reported accident FDR dataset. A stochastic post-upset prediction framework is developed using a high-fidelity flight dynamics model to predict the post-impacts in both aircraft and air traffic system. Stall upset scenarios that are most likely occurred during loss of control in-flight (LOC-I) operation are investigated, and stochastic flight envelopes and risk region are predicted to quantify their severities.
In addition, a robust, automatic damage diagnosis technique using ultrasonic Lamb waves and ML models is developed to effectively detect and classify fatigue damage modes in composite structures. The dispersion and propagation characteristics of the Lamb waves in a composite plate are investigated. A deep autoencoder-based diagnosis technique is proposed to detect fatigue damage using anomaly detection approach and automatically extract damage sensitive features from the waves. The patterns in the features are then further analyzed using outlier detection approach to classify the fatigue damage modes. The developed diagnosis technique is validated through an in-situ fatigue tests with periodic active sensing.
The developed techniques in this research are expected to be integrated with the existing safety strategies to enhance decision making process for improving engineering system safety without affecting the system’s functions.
ContributorsLee, Hyunseong (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Fard, Masoud Yekani (Committee member) / Tang, Pingbo (Committee member) / Campbell, Angela (Committee member) / Arizona State University (Publisher)
Created2021
Description
Stiffness and flexibility are essential in many fields, including robotics, aerospace, bioengineering, etc. In recent years, origami-based mechanical metamaterials were designed for better mechanical properties including tunable stiffness and tunable collapsibility. However, in existing studies, the tunable stiffness is only with limited range and limited controllability. To overcome these challenges, two objectives were proposed and achieved in this dissertation: first, to design mechanical metamaterials with metamaterials with selective stiffness and collapsibility; second, to design mechanical metamaterials with in-situ tunable stiffness among positive, zero, and negative.In the first part, triangulated cylinder origami was employed to build deployable mechanical metamaterials through folding and unfolding along the crease lines. These deployable structures are flexible in the deploy direction so that it can be easily collapsed along the same way as it was deployed. An origami-inspired mechanical metamaterial was designed for on-demand deployability and selective collapsibility: autonomous deployability from the collapsed state and selective collapsibility along two different paths, with low stiffness for one path and substantially high stiffness for another path. The created mechanical metamaterial yields unprecedented load bearing capability in the deploy direction while possessing great deployability and collapsibility. The principle in this prospectus can be utilized to design and create versatile origami-inspired mechanical metamaterials that can find many applications.
In the second part, curved origami patterns were designed to accomplish in situ stiffness manipulation covering positive, zero, and negative stiffness by activating predefined creases on one curved origami pattern. This elegant design enables in situ stiffness switching in lightweight and space-saving applications, as demonstrated through three robotic-related components. Under a uniform load, the curved origami can provide universal gripping, controlled force transmissibility, and multistage stiffness response. This work illustrates an unexplored and unprecedented capability of curved origami, which opens new applications in robotics for this particular family of origami patterns.
ContributorsZhai, Zirui (Author) / Nian, Qiong (Thesis advisor) / Zhuang, Houlong (Committee member) / Huang, Huei-Ping (Committee member) / Zhang, Wenlong (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2021
Description应收账款（Accounts Receivable）是企业在正常的经营过程中因销售商品、产品、提供劳务等业务，应向购买单位收取的款项，包括应由购买单位或接受劳务单位负担的税金、代购买方垫付的各种运杂费等。国内的工业制造业，由于产能过剩带来的产需不平衡、市场信誉危机带来的市场不规范以及历史供给关系等诸多问题的影响，形成了以买方市场为主的供需结构关系。企业为了提高资金周转效率、增大市场占有率，往往会选择以信用赊销为主的结算方式，造成应收账款激增。但是，应收账款的激增在提高企业账面利润、降低存货储备的同时，也无形中减少了企业的现金流量，造成企业真实盈利能力下降、偿还债务能力减弱、资金周转效率降低，加大企业经营风险。所以，如何有效管理应收账款，从而增加企业竞争力是每个企业发展过程中的重要课题。应收账款的管理不仅要在形成应收账款之后加大催收力度，形成多种多样、真实有效的回款方式，同时也要在合同签订、执行过程中加强风险管理力度。应收账款的信用风险管理已经成为现代企业管理中不可或缺的一部分。分析卧龙公司应收账款的成因与管理可以发现，赊销形成的应收账款占据极高比例，卧龙公司对于赊销管理缺乏有效的组织结构与保障体系，本研究从销售合同、客户特征属性、客户财务数据角度出发，研究合同条款、客户特征属性、客户财务数据与应收账款是否逾期的关系。研究发现，应收账款逾期的客户，每年的逾期原因都不一样，但是影响逾期的显性因素是很少的，这与电机行业作为传统制造业，受宏观经济周期影响相关，行业形势每年都面临较大的变化，但是另一方面，影响逾期的因素相对少意味着其实还是有办法找到主要原因的，只要可以提前预判出主要因素，并有针对性的采取预防措施，可以一定程度上缓解应收账款逾期的问题。从整体的逾期与否预测的逻辑回归模型看，无论使用单变量筛选的logistic回归模型，还是使用层次分析法确定的logistic回归模型，分类正确率都可以达到70%以上，使用本研究中提炼出来的变量，在历史数据中，模拟预测过程的效果很好，为解决应收账款逾期问题提供了有力的辅助工具，而且，层次分析法充分采纳了团队管理公司多年的经验，变量最后控制在15个以内，能实现与50多个变量类似的效果，甚至更佳，降价了模型维护的经济成本、实际使用的难度，也客观上给带领团队进行尝试提供了信心。本文研究成果可以辅助卧龙集团管理应收账款，为控制风险提供指导，具有一定的实践价值。
ContributorsPang, Xinyuan (Author) / Pei, Ker-Wei (Thesis advisor) / Chang, Chun (Thesis advisor) / Zhu, Qigui (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation contributes to uncertainty-aware neural networks using multi-modality data, with a focus on industrial and aviation applications. Drawing from seminal works in recent years that have significantly advanced the field, this dissertation develops techniques for incorporating uncertainty estimation and leveraging multi-modality information into neural networks for tasks such as fault detection and environmental perception. The escalating complexity of data in engineering contexts demands models that predict accurately and quantify uncertainty in these predictions. The methods proposed in this document utilize various techniques, including Bayesian Deep Learning, multi-task regularization and feature fusion, and efficient use of unlabeled data. Popular methods of uncertainty quantification are analyzed empirically to derive important insights on their use in real world engineering problems. The primary objective is to develop and refine Bayesian neural network models for enhanced predictive accuracy and decision support in engineering. This involves exploring novel architectures, regularization methods, and data fusion techniques. Significant attention is given to data handling challenges in deep learning, particularly in the context of quality inspection systems. The research integrates deep learning with vision systems for engineering risk assessment and decision support tasks, and introduces two novel benchmark datasets designed for semantic segmentation and classification tasks. Additionally, the dissertation delves into RGB-Depth data fusion for pipeline defect detection and the use of semi-supervised learning algorithms for manufacturing inspection tasks with imaging data. The dissertation contributes to bridging the gap between advanced statistical methods and practical engineering applications.
ContributorsRathnakumar, Rahul (Author) / Liu, Yongming (Thesis advisor) / Yan, Hao (Committee member) / Jayasuriya, Suren (Committee member) / Zhuang, Houlong (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2024