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The purpose of this paper is to identify the reasons for the differences of executives' compensation across industries from the managerial discretion perspective.

Based on the data from 37 manufacturing industries from 2002 to 2007 in China, managerial discretion for each industry is calculated regarding to the conception raised by Hambrick and Finkelstein which is further clustered into three groups. Then, regression model is used to testify the relation between managerial discretion and executives' compensation.

The executives' compensation is positively related to managerial discretion that is determined by the industrial environment. In the faster growing or higher competing industries, the executives tend to have more managerial discretion, thus they will be better paid due to the extensive latitude of their decision making.

To a certain extent, managerial discretion can be taken to measure the uncertainty or marginal productivity of the executives' work. From the industrial perspective, there are actually some factors far beyond the control of executives but influencing their pay.

When designing the compensation system for the executives, the industrial factors surely should be taken into consideration, to work out a fair and competitive incentive plan.

The paper proves a very important point in the issue of the decisive factors for executives' compensation. Managerial discretion raises the uncertainty and complexity to executives' work, thus it determines the compensation.

Drawing attention to the significant number of unsuccessful (abandoned) cross-border merger and acquisition (M&A) transactions in recent years, the purpose of this paper is to analyze three litigated cross-border inbound acquisitions that associated with an emerging economy – India, such as Vodafone-Hutchison and Bharti Airtel-MTN deals in the telecommunications industry, and Vedanta-Cairn India deal in the oil and gas exploration industry. The study intends to explore how do institutional and political environments in the host country affect the completion likelihood of cross-border acquisition negotiations.

Nested within the interdisciplinary framework, the study adopts a legitimate method in qualitative research, that is, case study method, and performs a unit of analysis and cross-case analysis of sample cases.

The critical analysis suggests that government officials’ erratic nature and ruling political party intervention have detrimental effects on the success of Indian-hosted cross-border deals with higher bid value, listed target firm, cash payment, and stronger government control in the target industry. The findings emerge from the cross-case analysis of sample cases contribute to the Lucas paradox – why does not capital flow from rich to poor countries and interdisciplinary M&A literature on the completion likelihood of international takeovers.

The findings have several implications for multinational managers who typically involve in cross-border negotiations. The causes and consequences of sample cases would help develop economy firms who intend to invest in emerging economies. The study also offers some implications of M&A for telecommunications and extractive industries.

Although a huge amount of extant research investigates why M&A fail to create value to the shareholders during the public announcement and post-merger stages, there is a significant dearth of research on the causes and consequences of delayed or abandoned national and international deals. The paper fills this knowledge gap by discussing an in-depth cross-case analysis of Indian-hosted cross-border acquisitions.

The intelligent Central Traffic Control (CTC) system plays a vital role in establishing an intelligent high-speed railway (HSR) system. As the core of HSR transportation command, the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching. This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.

This paper first briefly introduces the functions and configuration of the intelligent CTC system. Some new servers, terminals and interfaces are introduced, which are plan adjustment server/terminal, interface for automatic train operation (ATO), interface for Dynamic Monitoring System of Train Control Equipment (DMS), interface for Power Supervisory Control and Data Acquisition (PSCADA), interface for Disaster Monitoring, etc.

The key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans, safety control of train routes and commands, traffic information data platform, integrated simulation of traffic dispatching and ATO function. These technologies have been applied in the Beijing-Zhangjiakou HSR, which commenced operations at the end of 2019. Implementing these key intelligent functions has improved the train dispatching command capacity, ensured the safe operation of intelligent HSR, reduced the labor intensity of dispatching operators and enhanced the intelligence level of China's dispatching system.

This paper provides further challenges and research directions for the intelligent dispatching command of HSR. To achieve the objectives, new measures need to be conducted, including the development of advanced technologies for intelligent dispatching command, coping with new requirements with the development of China's railway signaling system, the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.

This research examines whether anthropomorphizing artificial intelligence (AI) chatbots alters consumers' risk preferences toward financial investment options involving differential risks.

An experimental approach has been adopted with three studies, all featuring a between-subjects design.

Through three studies, the findings document that, in a financial decision-making context, anthropomorphizing AI leads to significantly greater risk aversion in investment decision-making (Study 1). This occurs because AI-enabled chatbot anthropomorphization activates greater psychological risk attachment, which enacts consumers to manifest stronger risk aversion tendency (Studies 2 and 3).

Anthropomorphizing AI has undeniable relevance in the contemporary marketing landscape, such as humanoid robotics and emotion AI algorithms. Despite of anthropomorphism's significance and relevance, the downstream impact of anthropomorphism remains unfortunately underexplored.

Wool, mainly composed of keratin, is a relatively high-grade clothing material. Although woollen textile has the advantages of high wearing comfort and excellent warmth retention property as we have known, its inherent disadvantage of easy pilling has easy puzzled people for a long time. Most of the existing technologies for pilling resistance are not eco-friendly or severely damaged the internal structure of wool.

In this work, a controlled and effective surface treatment method was proposed to controllable micro-dissolution the scale layers of wool with minor damage to its internal structure, thereby improving the anti-pilling property of wool. Thiourea dioxide (TD) is used as a dissolving agent to swell and dissolve wool surface flakes. After TD treatment, the morphology changes of wool fibers were observed by scanning electron microscope (SEM) and methylene blue staining. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) were used to characterize the structural changes of TD wool. At the same time, the anti-pilling properties and wettability of wool fabrics were tested.

The results show that the wool scale layer is destroyed after TD treatment, which reduces the friction between fibers and improves the anti-pilling performance of wool fabrics. The methylene blue-stained images further demonstrate that low concentrations of TD can damage the superficial scale layer of wool without significant loss of strength.

This method is simple, eco-friendly and economical, and opens up a new direction for the surface treatment of wool fabrics.

The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.

This paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.

The global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.

Unlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.

Detection of hidden defects of aluminum alloy plate with damping coating is a challenging problem. At present, only a few non-destructive testing methods exist to address this engineering problem. Without the restriction of skin effect, remote field eddy current (RFEC) overcomes the interference caused by the damping coating. The RFEC, which has potential advantages for detecting the hidden defects of aluminum plate with damping coating, can penetrate the metal plate to detect buried depth defects. This study aims to test how thick the RFEC sensor can penetrate the metal plate to detect the buried defects.

The magnetic field distribution characteristics are analyzed, the magnetic field intensity distribution is calculated, and the structure and parameters of the coil, magnetic circuit and shielding damping are determined through the two- and three-dimensional finite element simulation methods. Optimal excitation frequency is obtained, and the distance between the excitation coil and detection coil is determined by analyzing the relationship between excitation frequency and remote field points.

Simulation and experimental results verify the feasibility of applying the RFEC detection technology in detecting the hidden defects of aluminum alloy plate with damping coating.

In this paper, the RFEC testing model of hidden defects in aluminum plate sample with damping coating is established by using the finite element method.

The purpose of this paper is to develop a novel multivariate fractional grey model termed GM(a, n) based on the classical GM(1, n) model. The new model can provide accurate prediction with more freedom, and enrich the content of grey theory.

The GM(α, n) model is systematically studied by using the grey modelling technique and the forward difference method. The optimal fractional order a is computed by the genetic algorithm. Meanwhile, a stochastic testing scheme is presented to verify the accuracy of the new GM(a, n) model.

The recursive expressions of the time response function and the restored values of the presented model are deduced. The GM(1, n), GM(a, 1) and GM(1, 1) models are special cases of the model. Computational results illustrate that the GM(a, n) model provides accurate prediction.

The GM(a, n) model is used to predict China’s total energy consumption with the raw data from 2006 to 2016. The superiority of the GM(a, n) model is more freedom and better modelling by fractional derivative, which implies its high potential to be used in energy field.

It is the first time to investigate the multivariate fractional grey GM(α, n) model, apply it to study the effects of China’s economic growth and urbanization on energy consumption.

This study aims to examine whether corporate stconcerns on social responsibility issues pressurize corporate managers to act in a socially responsible way and how this affects corporate value.

This study follows previous studies such as Wu et al. (2008) and Jiang and Huang (2011) in using the research model.

The analysis shows that in regions where large attention paid to demolitions, corporate managers are under great pressure from corporate stakeholders and they thus act in more socially responsible ways; this, in turn, results in higher corporate values. Furthermore, in regions in which demolition events attract more attention, the increase in corporate value is relatively small for state-owned enterprises and unprofitable listed enterprises.

The findings demonstrate that internet governance heightens corporate stakeholders’ awareness of social responsibility, and this leads to pressure on corporate relative behaviors to satisfy the requests of corporate stakeholders, leading to an increase in corporate value and improvement in social welfare.