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Given the significance of innovation in enabling firms to maintain a long-term competitive edge and secure excess profits, this paper aims to investigate whether and how stakeholders’ attention to innovation (SATI) influences corporate innovation.

This paper introduces a novel variable, SATI, which is achieved by segmenting stakeholders’ attention into two categories: attention to innovation and attention to other facets, using textual analysis methods. Subsequently, this paper empirically examines the influence of SATI on corporate innovation.

This paper finds that SATI positively affects corporate innovation input, and the result remains true after addressing possible endogeneity issues using instrumental variable regression. Furthermore, the positive effect of SATI on corporate innovation is stronger in firms facing greater financing constraints, thus verifying the financing constraints hypothesis. The positive effect is also stronger in firms with lower risk-taking levels, thus confirming the innovation failure tolerance hypothesis. Further analysis suggests that SATI increases both corporate innovation output and efficiency, thus ruling out the catering hypothesis.

This paper highlights the importance of SATI in driving corporate innovation. It enriches the literature on the repercussions of stakeholders’ attention and determinants of corporate innovation. In addition, it provides practical suggestions for further implementing China’s national innovation-driven development strategy.

The purpose of this paper is to propose a new grey system model used for prediction.

It had been proven that the GM(1,1) model is a biased exponential model, the model is fit for non‐negative raw data, which accord with or basically accord with the exponential form and do not have a quick growth rate. Based on the results, an unbiased GM(1,1) model was proposed. With the method of transforming every datum of raw data sequence into its 2‐th root, a new data sequence from the raw data sequence can be produced. The new data sequence is used to establish an unbiased GM(1,1) model and statistical experiments and a practical example in load forecasting are given in the paper.

The results of statistical experiments and a practical example in load forecasting show the proposed method is effective in increasing the accuracy of the model.

The model exposed in the paper can be used for constructing models of prediction in many fields such as agriculture, electric power, IT, transportation, economics, management, etc.

The paper succeeds in proposing a modified unbiased GM(1,1) model that has high accuracy. The model is applied to the field of load forecasting and the results show the model is better than the unbiased GM(1,1) model. The model proposed has great theoretical and practical value.

In this paper, microbial transglutaminase (MTG) was applied to process silk fabric for improving its crease resistance under the prerequisite of maintaining other performances. Not only was the effect of MTG on silk fabric investigated through the Fourier transform infrared spectroscopy (FR), but analysis was also undertaken in the microcosmic structure of fibroin through the scanning electron microscope (SEM). Solo MTG treatment as well as compound treatments of MTG followed by hydrogen peroxide, protease and ultrasonic, all showed that MTG can improve the crease resistance of silk fabric. It also enhanced its tensile breaking strength or amended damage in the tensile breaking strength caused by pretreatments.

Simultaneously, comparison with other treatments showed that compound treatment of MTG followed by ultrasonic exerted a better coordinated effect and conferred better performances, which made the wrinkle recovery angle (WRA) increase by 17.4% and tensile breaking strength improve by 11.2% respectively. At the same time, other performances were still maintained well.

This study aims to explore the relationship between non-market strategies and organizational resilience, using a Chinese private enterprise as an example.

This study collected data through semi-structured interviews and analyzed them through grounded theory, using a three-step approach of open coding, axial coding and selective coding to analyze and construct a model of the mechanism of the impact of non-market strategies on organizational resilience.

The following conclusions were drawn from this study. (1) Stakeholders, internal and external environment and entrepreneurship are important motivations that influence private firms to implement non-market strategies to enhance organizational resilience, with entrepreneurship being the key driver. (2) Non-market strategies contain three dimensions, and different non-market behaviors have different mechanisms of action on the organizational resilience of firms. (3) Non-market strategies and organizational resilience form an interactive spiral relationship. This mutually reinforcing effect promotes firm growth and sustainable corporate development. The research results enrich the theoretical connotation of non-market strategies, construct a model of the mechanism of influence of non-market strategies on organizational resilience, and describe three explanatory paths for the relationship between the two–incentive mechanism, functional mechanism and transformation mechanism.

This study's single case is unique and based on the Chinese context. In addition, this study adopts a rooted qualitative research approach and although the coding and model construction strictly follow the steps of grounded theory research, a degree of subjectivity is inevitable. On this basis, future research can adopt quantitative analysis methods to test and improve the model.

This paper explores the important role of non-market strategies in the Chinese context under the impact of traditional market mechanisms, based on the perspective of Chinese private enterprises, and provides new insights and revelations for private enterprises to achieve sustainable development.

This study innovatively explores the formation mechanism of organizational resilience from the perspective of non-market strategies, adding a new perspective to the literature. Additionally, it examines the mechanisms between long-term non-market strategy and organizational resilience, particularly their relationship in times of crisis, utilizing a rooted approach that goes beyond static analysis.

The purpose of this paper is to study the resonance failure sensitivity analysis of straight-tapered assembled pipe conveying nonuniform axial fluid by an active learning Kriging (ALK) method.

In this study, first, the motion equation of straight-tapered assembled pipe conveying nonuniform fluid is built. Second, the Galerkin method is used for calculating the natural frequency of assembled pipe conveying nonuniform fluid. Third, the ALK method based on expected risk function (ERF) is used to calculate the resonance failure probability and moment independent global sensitivity analysis.

The findings of this paper highlight that the eigenfrequency and critical velocity of uniform fluid-conveying pipe are less than the reality and the error is biggest in first-order natural frequency. The importance ranking of input variables affecting the resonance failure can be obtained. The importance ranking is different for a different velocity and mode number. By reducing the uncertainty of variables with a high index, the resonance failure probability can be reduced maximally.

There are no experiments on the eigenfrequency and critical velocity. There is no experiments about natural frequency and critical velocity of straight tapered assembled pipe to verify the theory in this paper.

The originality of this paper lies as follows: the motion equation of straight-tapered pipe conveying nonuniform fluid is first obtained. The eigenfrequency of nonuniform fluid and uniform fluid inside the assembled pipe are compared. The resonance reliability analysis of straight-tapered assembled pipe is first proposed. From the results, it is observed that the resonance failure probability can be reduced efficiently.

The purpose of this paper is to build a transient wear prediction model of surface topography of textured work roll, and then to investigate the wear performance of different original textured surfaces. The surface topography of steel sheets is one of the most important surface quality indexes, which is inherited from the textured work rolls in cold rolling. Surface topography of work roll is obviously changing in the cold rolling process. However, surface topography is difficult to measure in the industry production process.

This paper presents a numerical approach to simulate the wear process based on the mixed lubrication model of cold rolling interface developed by Wilson and Sheu (Sheu and Wilson, 1994). It is assumed that wear takes place at locations where the surfaces are in direct contact, and the volume is removed by an abrasive particle which is an abstract concept based on the wear phenomenon of textured work roll. At each simulation cycle, the distribution of the contact pressure is calculated by the lubrication model. The material is removed by an abstract abrasive particle and the surface topography is modified correspondingly. The renewed surface topography is then used for the next cycle.

Through comparative analysis, it can be found that the simulation results possess similar statistical characteristic with the measured data. A set of roughness parameters such as the amplitude, spacing and frequency-domain characteristics are introduced to analyze the wear performance of different textured surfaces. Numerical examples show that the surface topography has a significant effect on the wear performance of work roll in cold rolling.

The proposed model can accurately predict the wear process of the surface topography in the cold rolling process, which provides the foundation for optimization of original surface topography of textured work roll. The model can also be considered as a tool applicable for research on control of the surface topography of steel strip in the cold rolling process.

This paper aims to provide an effective method so that the ultrasonic technique can be applied to the online debris particle detection. It proposes utilizing the waveshape features in discriminating the debris particle in lubricant.

The finite element model has been developed to investigate the scattering of the ultrasonic waves in lubricant containing single scatterer, such as the debris particle and the air bubble. The simulation results show that the results verify that different scatterers differ in the waveshape features. The static experiments were carried out on a specially fixture. The single spherical debris, long debris and air bubble were measured. The fast Fourier transform (FFT) method was applied to the analysis of the echo signals to obtain the features implicated in the waveshape.

The research of this paper verifies that different scatterers differ both in their shape features and in the FFT analysis features.

The rapid movement of the debris particles as well as the lubricant temperature may influence the measuring signals. Besides, the measuring signals are usually corrupted by noise, especially for the tiny debris. Therefore, researchers are encouraged to solve those problems further.

The paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection.

The paper provides a promising way that the ultrasonic waveshape features can be utilized to the identify debris particle online.

This paper aims to obtain optimal specialization mode and level for complex network or system structures. In the e-commerce system, this paper studies the changes of each transaction subject in the process of ecological structure based on the income level of each transaction subject.

This paper aims to research the change of transaction efficiency evolution process of intermediaries. With the improvement of transaction efficiency, intermediaries interact with other transaction subjects at given modes in e-commerce systems. This paper analyzes the relationship between the factors of production and trade and explains the quantitative relationship between them in the form of mathematical modeling. An evolution simulation framework is established to elaborate the simulation process and method of crowd network in e-commerce ecosystem and then sets up the simulation experiment.

During simulation processes, the changes of data are observed and analyzed to obtain the optimal evolution paths and specialization modes. Furthermore, this paper provides solid supports for the research of the quantitative analysis of ecological structure evolutions.

Evolution simulation of ecological structure is first proposed in the topic of crowd network. It is with the aid of the concept of ecology, the theory and method, simulation of complex network structure and system structure. This paper analyses and researches the evolution process of optimal specialization modes and intelligent level of crowd networks with transaction efficiency changing. The ecological structure optimal evolution paths can be obtained by trend of simulations.

Collaboration is a common phenomenon in human society. The best way of collaborations can make the group achieve the best interests. Because of the low cost and high repeatability of simulation, it is a good method to explore the best way of collaborations by means of simulation. The traditional simulation is difficult to adapt to the crowd intelligence network simulation, so the crowd collaborations simulation is proposed.

In this paper, the atomic swarm intelligence unit and collective swarm intelligence unit are proposed to represent the behavior of individuals and groups in physical space and the interaction between them.

To explore the best collaboration mode of the group, a framework of crowd collaborations simulation is proposed, which decomposes the big goal into the small goals by constructing the cooperation chain and analyzes the cooperation results and feeds them back to the next simulation.

Two kinds of swarm intelligence units are used to represent the simulated individuals in the group, and the pattern is used to represent individual behavior. It is suitable for the simulation of collaboration problems in various types and situations.

The objective of this chapter is to provide a first assessment on the evolution of spatial distribution of foreign firms in China.

We examine the overall changes in the location of foreign firms in China over the period 1999–2009. Then, we distinguish two time periods, 1998–2001 and 2002–2009 so as to analyze whether foreign firms’ agglomeration across regions has changed significantly after the China’s entry into the WTO (2001) and the first launch of the Chinese government policies to develop western internal areas.

Our analysis suggests that foreign-invested enterprises (FIEs) with higher foreign capital shares are more geographically clustered in coastal regions than other enterprises with lower foreign capital shares. This group with the highest intensity of foreign involvement in firm capital also experienced the most relevant changes over the decade of our analysis becoming more localized between the core-periphery divide (coastal provinces and the rest of mainland China).

The main limitation refers to poor data availability, data matching problems, and measurement errors in the database used, as highlighted by Nie, Jiang, and Yang (2012).

A general analysis of location patterns and the role of public policies may inform foreign companies in their entry strategy in the Chinese market.

Very few studies have explored location patterns with detailed geographical data and, at the same time, with data disaggregated by foreign ownership shares.