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Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

Molecular dynamics is an emerging simulation technique in the field of machining in recent years. Many researchers have tried to simulate different processing methods of various materials with the theory of molecular dynamics (MD), and some preliminary conclusions have been obtained. However, the application of MD simulation is more limited compared with traditional finite element model (FEM) simulation technique due to the complex modeling approach and long computation time. Therefore, more studies on the MD simulations are required to provide a reliable theoretical basis for the nanoscale interpretation of grinding process. This study investigates the crystal structures, dislocations, force, temperature and subsurface damage (SSD) in the grinding of iron-nickel alloy using MD analysis.

In this study the simulation model is established on the basis of the workpiece and single cubic boron nitride (CBN) grit with embedded atom method and Morse potentials describing the forces and energies between different atoms. The effects of grinding parameters on the material microstructure are studied based on the simulation results.

When CBN grit goes through one of the grains, the arrangement of atoms within the grain will be disordered, but other grains will not be easily deformed due to the protection of the grain boundaries. Higher grinding speed and larger cutting depth can cause greater impact of grit on the atoms, and more body-centered cubic (BCC) structures will be destroyed. The dislocations will appear in grain boundaries due to the rearrangement of atoms in grinding. The increase of grinding speed results in the more transformation from BCC to amorphous structures.

This study is aimed to study the grinding of Fe-Ni alloy (maraging steel) with single grit through MD simulation method, and to reveal the microstructure evolution within the affected range of SSD layer in the workpiece. The simulation model of polycrystalline structure of Fe-Ni maraging steel and grinding process of single CBN grit is constructed based on the Voronoi algorithm. The atomic accumulation, transformation of crystal structures, evolution of dislocations as well as the generation of SSD are discussed according to the simulation results.

With the rapid development of the digital economy, an increasing number of digitalized two-sided platforms have deployed the tying strategy to leverage their market power from the core two-sided product to other two-sided products in the competitive market, which transforms the competition among single platforms into that among platform ecological networks. To clarify the mechanism of the formation of the digital platform ecological networks, this paper aims to analyze the expansion and stability of platform ecology by exploring the impacts of network externalities and sellers’ heterogeneity on the tying strategy of two-sided platforms.

This paper develops a game model of two-sided platforms based on Choi and Jeon (2021), which highlights the decisive influence of non-negative price constraints (NPC) on platforms’ tying motivation. Taking the operating systems market as an example, we expand from the perspective of platform service differences to relax the NPC and explore the internal logic of platform ecosystem expansion.

Platforms have an incentive to charge lower prices or even subsidize buyers when the network externalities on the sellers’ side are relatively strong. When the product is highly differentiated and heterogenous, platforms are motivated to tie to capture more buyers with a lower price and grab excess profits from sellers. Eventually, tying is able to consolidate the two-sided platform ecological networks by excluding competitors, capturing user value and deterring entry.

In order to describe the characteristics of platform ecological network more generally, this paper extends the research based on the analyses of Choi and Jeon (2021) by (1) allowing horizontal differences between tied products and (2) relaxing the NPC. Unlike Choi and Jeon (2021), this paper allows platforms to charge users of two-sided platforms at negative prices (or to subsidize them). (3) Setting simultaneous pricing in two-sided platforms. Classical two-sided market theory stresses that the presence of cross-network externalities can give rise to a “chicken and egg” problem.

This study aims to examine the role of authenticity in tourists’ destination selection, analyze the factors that influence tourists to form their initial opinions and explore how tourists construct the authenticity of traditional villages. The authors selected Chengkan village in Huizhou district, Huangshan city, as a case. In the study, the authors constructed an attribute-hardware-software research framework and analyzed tourists’ authentic emic experiences from the perspective of constructivism. The findings of this study suggest that tourists’ destination selection is influenced by authenticity. The destination culture brokers who interact with tourists play an essential role in forming authentic experiences. According to differences in how tourists construct authenticity, the study divided tourists into three types: primitive imagination, aesthetic reality and rational cognition. The results of this study provide a deeper understanding of various viewpoints about authenticity research and contribute to the academic discussion on how to understand the authenticity of unique cultural heritage sites such as traditional villages in the context of tourism development.

The purpose of this paper is to identify staff skills and competencies on which Chinese open universities should focus their professional development activities in order to enhance the quality of open and distance learning (ODL) in China.

Data were collected from surveys of 220 academic and academic-related Chinese staff, each of whom participated in one of seven 3-day Learning Design and Course Creation (LDCC) workshops at three large regional open universities in China between 2017 and 2019. The workshops were based on the UK Open University (UKOU) approach to learning design (LD) and course creation. Using content analysis methodology, textual responses were analysed and compared against the Instructional Design Competencies framework provided by the International Board of Standards for Training, Performance and Instruction (IBSTPI).

(1) Designing instructional interventions, (2) keeping up to date with design theories, (3) communicating in order to manage stakeholders, teams and projects were the main competencies identified from the participants responses. However, these three identified competencies differed in emphasis between the institutions that took part.

In Western higher education institutions (HEIs), LD has developed as an important approach to improving quality. A need has been identified for robust approaches to quality and professional development opportunities to enhance teaching and learning standards in open and distance learning (ODL) in China. This paper identifies and discusses specific LD skills and competencies that could be targeted to improve the quality of ODL in China.

The purpose of the work is to provide a comprehensive review of the digital image correlation (DIC) technique for those who are interested in performing the DIC technique in the area of manufacturing.

No methodology was used because the paper is a review article.

no fundings.

Herein, the historical development, main strengths and measurement setup of DIC are introduced. Subsequently, the basic principles of the DIC technique are outlined in detail. The analysis of measurement accuracy associated with experimental factors and correlation algorithms is discussed and some useful recommendations for reducing measurement errors are also offered. Then, the utilization of DIC in different manufacturing fields (e.g. cutting, welding, forming and additive manufacturing) is summarized. Finally, the current challenges and prospects of DIC in intelligent manufacturing are discussed.

Beijing rail transit can actively control the density of rail transit passenger flow, ensure travel facilities and provide a safe and comfortable riding atmosphere for rail transit passengers during the epidemic. The purpose of this paper is to efficiently monitor the flow of rail passengers, the first method is to regulate the flow of passengers by means of a coordinated connection between the stations of the railway line; the second method is to objectively distribute the inbound traffic quotas between stations to achieve the aim of accurate and reasonable control according to the actual number of people entering the station.

This paper analyzes the rules of rail transit passenger flow and updates the passenger flow prediction model in time according to the characteristics of passenger flow during the epidemic to solve the above-mentioned problems. Big data system analysis restores and refines the time and space distribution of the finely expected passenger flow and the train service plan of each route. Get information on the passenger travel chain from arriving, boarding, transferring, getting off and leaving, as well as the full load rate of each train.

A series of digital flow control models, based on the time and space composition of passengers on trains with congested sections, has been designed and developed to scientifically calculate the number of passengers entering the station and provide an operational basis for operating companies to accurately control flow.

This study can analyze the section where the highest full load occurs, the composition of passengers in this section and when and where passengers board the train, based on the measured train full load rate data. Then, this paper combines the full load rate control index to perform reverse deduction to calculate the inbound volume time-sharing indicators of each station and redistribute the time-sharing indicators for each station according to the actual situation of the inbound volume of each line during the epidemic. Finally, form the specified full load rate index digital time-sharing passenger flow control scheme.

This study aims to examine the impact of controversial governance practices on media coverage under a specific context. Based on the attribution theory, this study develops a theoretical framework to explore how antecedent factors can influence attribution process under a particular cultural context.

This paper presents a behavioral view of the media and corporate governance to demonstrate how media attributes different reasons for the same controversial governance practice in Chinese-specific context. Using 1,198 non-state-owned listed company observations in China as the study sample, cross-section data are used to build a multiple linear regression mode to test hypotheses.

The analysis indicates that the media imposes fewer penalties on founder-CEO firms than on non-founder-CEO firms for engaging in controversial governance practices, such as CEO compensation. CEO tenure negatively moderates the effect of CEO compensation on negative media coverage in non-founder-CEO firms. The positive media bias evidence for founder-CEO firms exists only when the firm is better performed.

This study’s contribution to the governance literature starts with its logical reasoning of basic assumptions in the agency theory, and that media penalty will arise when managers impose actions that against interests of shareholders or other stakeholders. This study shows that the rule is not always true. The findings also bridge the connection of governance literature and reputation literature to better explain how media can act as a social arbitration role.

This study provides insights into how belief and information of reputational evaluators affect attribution consequences on controversial governance practices. Moreover, this study looks beyond the internal elements and focuses on China’s traditional cultural context as well. Specifically, the authors concentrate on the attribution process by showing the importance of evaluators’ framing tendency with regard to controversial practices. The results extend the knowledge about how conformity makes media coverage shows a bias effect on interactions during the evaluation process.