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This study aims to investigate the impact of mandatory audit partner rotation (MAPR) on Chinese listed firms’ insider trading, as well as the moderating effects of firm characteristics on this impact. The economic mechanism behind this impact is also explored.

This study conducts a regression analysis on firms associated with mandatory and voluntary audit partner rotation based on 2009–2019 firm data and examines whether corporate insiders of these two types of firms increase their share sales within 12 months before their financial statements are submitted to a new rotated auditor.

Client firms’ corporate insiders increase their share sales within 12 months before their financial statements are submitted to a new mandatory rotated auditor. In addition, such an association is less pronounced for client firms that changed from Big 4 auditors to those with higher financial constraints. This is more pronounced for client firms with higher information asymmetry. The economic mechanism of the finding is that is the MAPR implementation reduces earnings management activities from client firms. Moreover, client firms’ buy-and-hold stock returns decline in the first year after MAPR.

This study should assist investors, corporate shareholders and Chinese policymakers. Investors can be well protected through the adoption of MAPR because upcoming auditors enhance the audit quality of clients by restraining managers’ manipulation of reported earnings and declining firms’ insider trading afterwards. Investors, Chinese policymakers and corporate shareholders should pay more attention to firms’ financial report quality, auditor selection, financial situation, corporate governance and the information environment. Explicitly, firms with less transparent financial report quality, non-big 4 auditors and fewer financial constraints are more likely to be involved in insider trading.

To the best of the authors’ knowledge, none of the extant studies have examined the impact of MAPR on insider sales. This study extends the research on the effect of the audit process on firm market performance by investigating the impact of audit partner rotation policy on insider trading behaviors.

The purpose of this paper is to combine the entropy weight method with the cloud model and establish a fire risk assessment method for airborne lithium battery.

In this paper, the fire risk assessment index system is established by fully considering the influence of the operation process of airborne lithium battery. Then, the cloud model based on entropy weight improvement is used to analyze the indexes in the system, and the cloud image is output to discuss the risk status of airborne lithium batteries. Finally, the weight, expectation, entropy and hyperentropy are analyzed to provide risk prevention measures.

In the risk system, bad contact of charging port, mechanical extrusion and mechanical shock have the greatest impact on the fire risk of airborne lithium battery. The fire risk of natural factors is at a low level, but its instability is 25% higher than that of human risk cases and 150% higher than that of battery risk cases.

The method of this paper can evaluate any type of airborne lithium battery and provide theoretical support for airborne lithium battery safety management.

After the fire risk assessment is completed, the risk cases are ranked by entropy weight. By summarizing the rule, the proposed measures for each prevention level are given.

This research is aimed to examine how environmental pollution affects consumers' preference for self-improvement products.

Through a series of three experimental studies, this research substantiates our hypotheses by employing various manipulations of environmental pollution and examining different types of self-improvement products.

The research demonstrates that environmental pollution enhances consumers' preference for self-improvement products via the mediation of perceived environmental responsibility. And the effect is negatively moderated by social equity sensitivity.

The recurrent incidence of environmental pollution has elicited significant concern among the general public and academic scholars. An overwhelming majority of research examining the impact of pollution on consumer behavior has concentrated on its influence on environmentally friendly and healthy consumption patterns. Nevertheless, the current research proposes that pollution fosters a preference for products associated with self-improvement, mediated by perceived environmental responsibility, with the effects being moderated by social equity sensitivity.

The purpose of this paper was to use visual and arc sensors to simultaneously obtain the underwater wet welding information, and a weld seam-forming model was made to predict the weld seam's geometric parameters. It is difficult to obtain a fine welding quality in underwater welding because of the intense disturbances of the water environment. To automatically control the welding quality, the weld seam-forming model should first be established. Thus, the foundation was laid for automatically controlling the underwater welding seam-forming quality.

Visual and arc sensors were used simultaneously to obtain the weld seam image, current and voltage information; then signal algorithms were used to process the information, and the back propagation (BP) neural network was used to model the process.

Experiment results showed that the BP neural network model could precisely predict the weld seam-forming parameters of underwater wet welding.

A weld seam-forming model of underwater wet welding process was made; this laid the foundation for establishing a controller for controlling the underwater wet welding process automatically.