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Focusing on internal corporate governance, the purpose of this paper is to apply the shareholder activism perspective to consider how large shareholder participation behaviors might influence firm innovation performance. Specifically, “confrontationally strategic intervention” and “cooperatively strategic consensus” participation behaviors are examined and hypothesized to have different effects on managers’ risk-taking and firm innovation performance.

Drawing on 182 Chinese firm samples, this paper applies hierarchical ordinary least-squares regression analysis to test the proposed hypotheses.

The results show that strategic intervention was negatively associated with managers’ risk-taking and firm innovation performance, while strategic consensus positively affected managers’ risk-taking and firm innovation performance. Moreover, managers’ risk-taking fully mediated the influence of strategic intervention on firm innovation performance, whereas it partially mediated the influence of strategic consensus on firm innovation performance.

The study extends research on shareholder participation by construing that large shareholders’ participation behaviors can significantly influence managers’ risk-taking and corporate innovation performance, further deepening the understanding of the influences of large shareholders on the firm-level outcomes. The theoretical and practical implications of this finding are also discussed.

The purpose of this paper is to investigate whether the Boardroom heterogeneity affects IPO underpricing for entrepreneurial firms, where Boardroom heterogeneity was classified in terms of functional background, educational background, age and length of tenure.

A national research design was conducted using data collected from 355 firms listed on China’s Growth Enterprise Market from its start in 2009 to 2012.

The author found that IPO underpricing has a significant negative correlation with functional heterogeneity, a positive correlation with educational heterogeneity, a significant negative correlation with age heterogeneity, but it does not show significant correlation with heterogeneity in tenure. Board heterogeneity affects IPO underpricing of entrepreneurial firms partially, which means functional, educational and age heterogeneity conveys signals to potential investors regarding a firm’s quality.

More entrepreneurial firms in more years for data and long-term performance research design in future research would be required for further understanding of the relationships among the variables in this study.

This paper suggests that IPO firms may make use of such an influencing mechanism to determine the issue price or to control the IPO underpricing by showing the Boardroom heterogeneity.

This paper revealed the influence of the characteristics of board members of such firms on IPO underpricing, which is rare in recent studies comparing to the study for the top management team; also this study provides empirical support for such effect.

The advanced synthetic and natural materials, such as piezoelectric ceramics, electroactive polymers and biological soft tissues, exhibit the multi‐physical or physicochemical coupling behaviors. The coupling behavior involves the thermal‐mechanical, electric‐mechanical and electrochemicalmechanical interactions. The coupling phenomena can be modeled in the microscopic and macroscopic levels. In the microscale, the material consists of the solid, fluid and ions. The domain FE technique can be used to model the deformation of the solid and the flow of the fluid. In the macroscale, the mixture theory can be applied to description of the coupled response of the continuum under coupled thermal, electrical, chemical and mechanical loadings. A weak form of the governing equations is established by means of variational principle and a multi‐field finite element (MFE) method is developed for numerical modeling of the coupling behavior of advanced materials.

This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed.

First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions.

Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future.

This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.

A direct power control (DPC) of the doubly-fed induction generator (DFIG) is presented. A new method, which is based on the rotation of the space sector, clockwise or vice versa, is proposed to improve the performance of the switching table. Then, it is combined with a fuzzy system to have advantages of both rotation sector and fuzzy controller. The paper aims to discuss these issues.

In this paper, a new DPC of the DFIG is presented. To improve the performance of the switching table, a new method is proposed. The method is based on the rotation of the space sector, clockwise or vice versa. The excellence of the proposed method is proven. Then, it is shown that the performance of the system can be enhanced by using a fuzzy logic controller. The rotation method is combined with a fuzzy system.

Simulation shows that although sector rotation and fuzzy controller can improve the performance of the DFIG, a combination of both demonstrates a smoother response in order that reactive and active power ripples and THD of the injected current decrease in different speeds. Also, it is demonstrated that the proposed method is robust against parameters variations. However, a hardware experiment should be performed to be practically verified.

A sector rotation is proposed and its effect on the performance of the DFIG is considered. A simple method to write rules table is presented and the performance of sector rotation and fuzzy controller on the DFIG is analysed.

Shows how the banking world too is adjusting to the needs of the socialist market economy. As a valuable adjunct to and tool for internal auditing, demonstrates the development of a bank surveillance system, ideally suited to the needs of bank management and internal audit. Such a system has required extensive development, necessitating probing into all aspects of banking activities. Details the approach taken to project control and reports that the bank concerned is now in a much more favourable position to determine risk assessment.

The purpose of this paper is to theoretically and empirically explore the effects of attention levels on individual investors' investment return.

By introducing the heterogeneous attention, the authors first expand the theoretical model of Barber and Odean. The authors use graphical analysis, univariate analysis, multiple regression analysis and construct a portfolio to carry out an empirical study.

The authors first find evidence in support of Barber and Odean's price pressure hypothesis. By theoretical and empirical study, the authors conclude that attention negatively affects individual investors' investment return.

By introducing the heterogeneous attention, the paper provides a theoretical basis for empirical study. Baidu abnormal search volume was used as a proxy for individual investors' attention, and analysts' neutral ratings were used to empirically verify the theoretical theorem.

Power‐electronic systems have been playing a significant role in the integration of large‐scale wind turbines into power systems due to the fact that during the past three decades power‐electronic technology has experienced a dramatic evolution. This second part of the paper aims to focus on a comprehensive survey of power converters and their associated control systems for high‐power wind energy generation applications.

Advanced control strategies, i.e. field‐oriented vector control and direct power control, are initially reviewed for wind‐turbine driven doubly fed induction generator (DFIG) systems. Various topologies of power converters, comprising back‐to‐back (BTB) connected two‐ and multi‐level voltage source converters (VSCs), BTB current source converters (CSCs) and matrix converters, are identified for high‐power wind‐turbine driven PMSG systems, with their respective features and challenges outlined. Finally, several control issues, viz., basic control targets, active damping control and sensorless control schemes, are elaborated for the machine‐ and grid‐side converters of PMSG wind generation systems.

For high‐power PMSG‐based wind turbines ranging from 3 MW to 5 MW, parallel‐connected 2‐level LV BTB VSCs are the most cost‐effective converter topology with mature commercial products, particularly for dual 3‐phase stator‐winding PMSG generation systems. For higher‐capacity wind‐turbine driven PMSGs rated from 5 MW to 10 MW, medium voltage multi‐level converters, such as 5‐level regenerative CHB, 3‐ and 4‐level FC BTB VSC, and 3‐level BTB VSC, are preferred. Among them, 3‐level BTB NPC topology is the favorite with well‐proven technology and industrial applications, which can also be extensively applicable with open‐end winding and dual stator‐winding PMSGs so as to create even higher voltage/power wind generation systems. Sensorless control algorithms based on fundamental voltages/currents are suggested to be employed in the basic VC/DPC schemes for enhancing the robustness in the entire PMSG‐based wind power generation system, due to that the problems related with electromagnetic interferences in the position signals and the failures in the mechanical encoders can be avoided.

This second part of the paper for the first time systematically reviews the latest state of arts with regard to power converters and their associated advanced control strategies for high‐power wind energy generation applications. It summarizes a variety of converter topologies with pros and cons highlighted for different power ratings of wind turbines.

This study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.

In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.

By comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.

Although a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.

Mechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.

The MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.

The prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.

The MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.