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This paper aims to take International SPOrt (ISPO) as a typical case to study how exhibition organizers can reshape their relationship with users through business model innovation to answer the question that how enterprises can help the exhibition industry to upgrade and develop through business model innovation in the internet environment.

Faced with the development of internet technology, the impact of online platforms, the relationship between exhibition organizers and their customers are facing unprecedented challenges. On the basis of the literature review, this study analyzed the innovation of exhibitors’ business model from three modules: value proposition, revenue logic and cost base and how to reshape their interaction with users through innovation. This study systematically analyzed the innovation of the ISPO business model and the process of reshaping its relationship with users and dynamic interaction with a single case study method.

The main conclusions are as follows: the starting point of reshaping the relationship between exhibition organizer and users in the internet era is to re-understand the needs of customers, the key point of reshaping the relationship is to further cultivate the industrial value and the sustainability of the relationship lies in the customer life cycle management.

From the perspective of exhibition organizers filling the gap of case study in the field of the exhibition. In the area of the exhibition, previous studies rarely started from the perspective of exhibition organizers, but, this paper discusses the interaction between exhibition organizers, exhibitors and visitors from this perspective in this study.

The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms.

Interpreting user behavior from the probabilistic perspective of hidden variables is helpful to improve robustness and over-fitting problems. Constructing a recommendation network by variational inference can effectively solve the complex distribution calculation in the probabilistic recommendation model. Based on the aforementioned analysis, this paper uses variational auto-encoder to construct a generating network, which can restore user-rating data to solve the problem of poor robustness and over-fitting caused by large-scale data. Meanwhile, for the existing KL-vanishing problem in the variational inference deep learning model, this paper optimizes the model by the KL annealing and Free Bits methods.

The effect of the basic model is considerably improved after using the KL annealing or Free Bits method to solve KL vanishing. The proposed models evidently perform worse than competitors on small data sets, such as MovieLens 1 M. By contrast, they have better effects on large data sets such as MovieLens 10 M and MovieLens 20 M.

This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic model effect. Because the variational inference training denotes the probability distribution of the hidden vector, the problem of poor robustness and overfitting is alleviated. When the amount of data is relatively large in the actual application scenario, the probability distribution of the fitted actual data can better represent the user and the item. Therefore, using variational inference for collaborative filtering recommendation is of practical value.

We examine the maturity structure in private placements of debt and relate it to contracting, signaling, tax, and liquidity risk considerations for firms. We find that firms with higher tax rates issue private placements of debt with longer maturities, consistent with the tax hypothesis. However, our results do not support the contracting, signaling, and liquidity risk hypotheses. In addition, the results are confined to the smaller firms in the sample, firms without a public debt rating, and debt issues not pursuant to Rule 144A. The evidence is consistent with smaller firms issuing private placements of debt to avoid monopoly rent extraction from banks.

This paper aims to propose a four-point measurement model for directly measuring the pose (i.e. position and orientation) of industrial robot and reducing its calculating error and measurement uncertainty.

A four-point measurement model is proposed for directly measuring poses of industrial robots. First, this model consists of a position measurement model and an orientation model gotten by the position of spherically mounted reflector (SMR). Second, an influence factor analysis, simulated by Monte Carlo simulation, is performed to investigate the influence of certain factors on the accuracy and uncertainty. Third, comparisons with the common method are carried out to verify the advantage of this model. Finally, a test is carried out for evaluating the repeatability of five poses of an industrial robot.

In this paper, results show that the proposed model is better than the three-SMRs model in measurement accuracy, measurement uncertainty and computational efficiency. Moreover, both measurement accuracy and measurement uncertainty can be improved by using the proposed influence laws of its key parameters on the proposed model.

The proposed model can measure poses of industrial robots directly, accurately and effectively. Additionally, influence laws of key factors on the accuracy and uncertainty of the proposed model are given to provide some guidelines for improving the performance of the proposed model.

Existing studies have paid less attention to the impact of knowledge accumulation on digital transformation and its boundary conditions. Hence, this study aims to investigate the effects of ambidextrous knowledge accumulation on manufacturing digital transformation under the moderation of dynamic capability.

This study divides knowledge accumulation into exploratory and exploitative knowledge accumulation and divides dynamic capability into alliance management capability and new product development capability. To clarify the relationship among ambidextrous knowledge accumulation, dynamic capability and manufacturing digital transformation, the authors collect data from 421 Chinese listed manufacturing enterprises from 2016 to 2020 and perform analysis by multiple hierarchical regression method, heterogeneity test and robustness analysis.

The empirical results show that both exploratory and exploitative knowledge accumulation can significantly promote manufacturing digital transformation. Keeping ambidextrous knowledge accumulation in parallel is more conducive than keeping single-dimensional knowledge accumulation. Besides, dynamic capability positively moderates the relationship between ambidextrous knowledge accumulation and manufacturing digital transformation. Moreover, the heterogeneity test shows that the impact of ambidextrous knowledge accumulation and dynamic capabilities on manufacturing digital transformation varies widely across different industry segments or different regions.

First, this paper shifts attention to the role of ambidextrous knowledge accumulation in manufacturing digital transformation and expands the connotation and extension of knowledge accumulation. Second, this study reveals that dynamic capability is a vital driver of digital transformation, which corroborates the previous findings of dynamic capability as an important driver and contributes to enriching the knowledge management literature. Third, this paper provides a comprehensive micro measurement of ambidextrous knowledge accumulation and digital transformation based on the development characteristics of the digital economy era, which provides a theoretical basis for subsequent research.

This paper aims to analyze the friction heat conduction and entransy of two friction linings in the high‐speed slide accident of a mine friction hoist.

Firstly, the dynamic thermophysical properties were analyzed and their fitting equations were also obtained. Additionally, the dynamic heat partition ratio was obtained according to the dynamic thermophysical properties. Then, a simple method was developed to solve the temperature rise of friction lining. Finally, based on the theoretical model of temperature rise, the entransy of friction lining with respect to T and t were gained.

The error of temperature rise between simulation result and experiment result is less than 7 per cent, which proves that the theoretical model is correct. The entransy decreases with the temperature below 40°C and it increases after 40°C. The entransy of lining K is a little higher than that of lining G within 19 s, but the entransy of lining G is much higher than lining K after 19 s and the entransy difference gets great gradually. It is indicated that the lining K has good heat‐resistant property which is of great benefit to the tribological property of friction lining.

The authors' study provides a fundamental basis for developing a new friction lining with good heat‐resistant property, and it also brings forward a new quantitative method to evaluate the heat‐transfer capability of friction materials.

A simple method was introduced to calculate the temperature rise of friction lining with the consideration of dynamic thermophysical properties and dynamic heat partition ratio. And the entransy of friction lining was obtained to evaluate the heat‐transfer capability of friction linings quantitatively.