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The purpose of this paper is to investigate how revelations of corporate misconduct are associated with trade credit. Specifically, it investigates how this association varies in different regions, in different types of industries and in response to companies’ subsequent charitable donations.

The authors empirically tested various hypotheses using a sample of 2,725 Chinese A-share listed companies from 2009 to 2014 based on signaling theory. Fixed effect models underpinned the methods used.

The authors found that corporate misconduct has a significant negative impact on an irresponsible company’s trade credit received and granted, and the negative impact is heterogeneous for different regions and industries. There is no evidence that charitable donations mitigate the effect on the trade credit of irresponsible companies following revelations of corporate misconduct.

The results suggest that listed companies in China should obey national and local laws and regulations if they wish to avoid the risk of significant trade credit loss. If a company’s violation of these laws and regulations is disclosed, making charitable donations is not an effective strategy for safeguarding trade credit.

This study enriches understanding on the consequences of corporate misconduct and extends the literature on trade credit. It fills a research gap by identifying the impact of corporate misconduct on trade credit.

Since megaproject social responsibility (MSR) has received increasing attention in megaproject management and plays critical roles in megaproject practices, the purpose of this paper is to explore how MSR facilitates an improved sustainability of the construction industry.

By integrating multiple theoretical perspectives of transaction cost theory, institutionalism and attention- and resource-based views, and by using survey data of Chinese megaprojects and construction enterprises, this paper offers a theoretical elaboration of and an empirical investigation into the impacts that MSR’s four dimensions exert on industrial improvement in economic sustainability and social responsibility.

The study’s empirical results indicate that MSR has positive impacts on improving the sustainability of the construction industry, and that such positive impacts are weakened by the interactions of primary stakeholders in the megaprojects but are strengthened by the interactions of secondary stakeholders.

This paper suggests that managers and policymakers make efforts to governmental guidance, media monitoring and public participation in megaprojects, so as to limit the potential unethical behaviors in megaproject management and enhance the sociopolitical legitimacy that are essential for the sustainability of the construction industry.

By analyzing the industrial outcomes of MSR, this paper extends studies on the topic beyond the current literature’s focus on the antecedents of MSR, and it enriches the research on MSR stakeholders by elaborating on the contingent roles of the various stakeholders in megaproject management.

This study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor (BR) onboard the vehicle, which overcomes the vulnerability of having conventional temperature sensor.

In this study, the energy model based sensorless estimation method is developed. By analyzing the structure and the convection dissipation process of the BR onboard the vehicle, the energy-based operational temperature model of the BR and its cooling domain is established. By adopting Newton's law of cooling and the law of conservation of energy, the energy and temperature dynamic of the BR can be stated. To minimize the use of all kinds of sensors (including both thermal and electrical), a novel regenerative braking power calculation method is proposed, which involves only the voltage of DC traction network and the duty cycle of the chopping circuit; both of them are available for the traction control unit (TCU) of the vehicle. By utilizing a real-time iterative calculation and updating the parameter of the energy model, the operational temperature of the BR can be obtained and monitored in a sensorless manner.

In this study, a sensorless estimation/monitoring method of the operational temperature of BR is proposed. The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR, instead of adding dedicated thermal sensors. The results also validate the effectiveness of the proposal is acceptable for the engineering practical.

The proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks. The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.

The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm.

This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm.

Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments.

The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents.

This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.

The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently.

In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices.

Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level.

This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.