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This study aims to explore the formation mechanism of aerodynamic noise of a high-speed maglev train and understand the characteristics of dipole and quadrupole sound sources of the maglev train at different speed levels.

Based on large eddy simulation (LES) method and Kirchhoff–Ffowcs Williams and Hawkings (K-FWH) equations, the characteristics of dipole and quadrupole sound sources of maglev trains at different speed levels were simulated and analyzed by constructing reasonable penetrable integral surface.

The spatial disturbance resulting from the separation of the boundary layer in the streamlined area of the tail car is the source of aerodynamic sound of the maglev train. The dipole sources of the train are mainly distributed around the radio terminals of the head and tail cars of the maglev train, the bottom of the arms of the streamlined parts of the head and tail cars and the nose tip area of the streamlined part of the tail car, and the quadrupole sources are mainly distributed in the wake area. When the train runs at three speed levels of 400, 500 and 600 km·h⁻¹, respectively, the radiated energy of quadrupole source is 62.4%, 63.3% and 71.7%, respectively, which exceeds that of dipole sources.

This study can help understand the aerodynamic noise characteristics generated by the high-speed maglev train and provide a reference for the optimization design of its aerodynamic shape.

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.

Due to the rapid development and innovation in the Internet-based technology, conventional banks are under pressure and have to compete with Internet-based finance. This has made banks adopt measures to improve operational efficiency and reduce input and increase output.

The authors had proposed a two-stage fairness concern efficiency model based on the classical theory of data envelopment analysis (DEA) and performed an empirical study to measure agricultural loan efficiency in the 20 major Chinese banks.

The findings of the empirical analysis are as follows: (1) peer-induced fairness concern has no impact on deposit efficiency in a centralized bank supply chain; (2) The China Merchants Bank (CMB) has the third lowest deposit efficiency; (3) monotonicity of loan efficiency with input allocation depends on a bank's ownership structure; (4) efficiency ranks are strongly affected by the fairness concern; (5) most Chinese banks show a low agricultural loan efficiency.

This paper contributes to the literature in several ways. First, to the best of the authors’ knowledge, this is the first attempt to analyze agricultural loan efficiency for a bank supply chain system with the fairness concern. This work reveals the hidden factor that restricts loan efficiency of Chinese banks. Second, the proposed fairness concern two-stage DEA model has shown good ability for full ranking. It can provide a new perspective to the classical DEA literature for ranking decision-making units (DMUs). Third, the authors have demonstrated empirical bank efficiency for the 20 major Chinese banks.

The purpose of the paper is to predict the residual life of liquid-storage tank to ensure safety and long-term service life of the structure.

The paper carried out the stress analysis of the wall plate and bottom plate of the liquid-storage tank, and the influence of circumferential stress on the tank is considered. On the other hand, considering the influence of the tank wall surface on the tank life, based on the Paris law, the surface processing coefficient and surface roughness coefficient are introduced to improve the Paris law.

The effectiveness of the improved model is verified by comparing with theoretical and experimental data, which provide a new method for the prediction of the remaining service life of the tank. Combined with the fatigue crack data in the test report and the calculated circumferential stress, the residual life of the storage tank is predicted.

The improved model provides a new method for the prediction of the remaining service life of the tank.

In the academic field of business management, several potential theories were established during the last decades to explain companies' decisions, organizational behavior, consumer patterns, and internationalization, among others. As a result, businesses and scholars were able to analyze and decide based on theoretical approaches to explain the current conditions of the market. Secondary research was conducted to collect more than 36 management theories. This chapter aims to develop the most famous theories related to business applied in the international field. The novelty of this chapter relies on the compilation of recognized previous research studies from the academic literature and evidence in international business.

The purpose of this paper is to examine how customers’ self-control affects their purchase decisions and to discuss the pricing decisions of the retailer under different forms of contract.

The authors use the literature on hyperbolic discounting to model customers’ self-control problems. In this framework, the authors examine how the customers’ self-control affects the optimal pricing decision and the selection of the optimal contract form when there is a supplier and a retailer in the supply chain.

The study’s results show that when wholesale price contract is compared with buyback contract, buyback contract is better when customers’ self-control is weak; when quantity-discount contract is compared with wholesale price contract and buyback contract, although quantity discount can encourage customers to purchase more units of products, but both wholesale price contract and buyback contract can be better than quantity-discount contract in some cases. Additionally, the authors demonstrate that revenue sharing contract can increase the supply chain’s profit. The authors also find that sometimes customers’ preplan will lead to the result that the supplier produces more unhealthy products.

To the best of the authors’ knowledge, this is the first study to analyze the decision-making of the retailer by developing an analytical framework combining customer’s self-control and supply chain contract. These results have important implications for the supplier and the retailer that sell vice goods.

The purpose of this paper is to explore the social responsibility performance’s current situation, influencing factors and mechanisms of multinational companies (MNCs) in China. The paper is based on the MNC’s social responsibility performance factors to find some relevant reasons for unsatisfied performance and on the multiple perspectives such as institutional distance and stakeholders, to provide feasible solutions for MNCs to assume civic responsibility and the high-quality development of the host country.

The study uses general literature review and empirical analysis to analyze the way and degree of various factors which are affecting the social responsibility of MNC in China, and supplements the deficiencies of previous studies.

The paper reveals the influencing factors and mechanisms of MNCs’ social responsibility in China through the regression results. In addition, based on Research Report on Corporate Social Responsibility of China (Huang et al., 2013/2019), the paper also summarizes the performance and dynamic changes of MNCs social responsibility in the Chinese market in the past decade.

The paper enriches research on the social responsibility of MNCs in the background of non-western countries, dynamically describes the performance of MNCs’ responsibilities in a 10-year span and solves the problem of inconsistent research conclusions caused by too long time span. The paper also creates the “local enterprise responsibility level” to quantify the development of the host country’s responsibility, and identifies the “responsibility demonstration effect” of state-owned enterprises and private enterprises.

The purpose of this paper is to explore the potential of enriching the library subject headings with folksonomy for enhancing the visibility and usability of the library subject headings.

The WorldCat-million data set and SocialBM0311 are preprocessing and over 210,000 library catalog records and 124,482 non-repeating tags were adopted to construct the matrix to observe the semantic relation between library subject headings and folksonomy. The proposed system is compared with the state-of-the-art methods and the parameters are fixed to obtain effective performance.

The results demonstrate that by integrating different semantic relations from library subject headings and folksonomy, the system’s performance can be improved compared to the benchmark methods. The evaluation results also show that the folksonomy can enrich library subject headings through the semantic relationship.

The proposed method simultaneous weighted matrix factorization can integrate the semantic relation from the library subject headings and folksonomy into one semantic space. The observation of the semantic relation between library subject headings and social tags from folksonomy can help enriching the library subject headings and improving the visibility of the library subject headings.

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.

The current studies on remaining useful life (RUL) prediction mainly rely on convolutional neural networks (CNNs) and long short-term memories (LSTMs) and do not take full advantage of the attention mechanism, resulting in lack of prediction accuracy. To further improve the performance of the above models, this study aims to propose a novel end-to-end RUL prediction framework, called convolutional recurrent attention network (CRAN) to achieve high accuracy.

The proposed CRAN is a CNN-LSTM-based model that effectively combines the powerful feature extraction ability of CNN and sequential processing capability of LSTM. The channel attention mechanism, spatial attention mechanism and LSTM attention mechanism are incorporated in CRAN, assigning different attention coefficients to CNN and LSTM. First, features of the bearing vibration data are extracted from both time and frequency domain. Next, the training and testing set are constructed. Then, the CRAN is trained offline using the training set. Finally, online RUL estimation is performed by applying data from the testing set to the trained CRAN.

CNN-LSTM-based models have higher RUL prediction accuracy than CNN-based and LSTM-based models. Using a combination of max pooling and average pooling can reduce the loss of feature information, and in addition, the structure of the serial attention mechanism is superior to the parallel attention structure. Comparing the proposed CRAN with six different state-of-the-art methods, for the predicted results of two testing bearings, the proposed CRAN has an average reduction in the root mean square error of 57.07/80.25%, an average reduction in the mean absolute error of 62.27/85.87% and an average improvement in score of 12.65/6.57%.

This article provides a novel end-to-end rolling bearing RUL prediction framework, which can provide a reference for the formulation of bearing maintenance programs in the industry.