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This paper attempts to select the leading industry concerning uncertain information and grey interval numbers.

This paper combines grey system theory and the input‐output method, which describe uncertain information as interval numbers in the input‐output table. Additionally, in transforming the grey interval numbers to functions with time, the original input‐output table can be considered as a function of functions, in which functional analysis can be employed to determine important parameters. Neumann series is utilized to reverse the Leontief matrix having grey interval numbers. Based on the result, one can calculate the influence and induced coefficients of industries, which can help the decision maker to select the leading industry by comparing the overall effects of industries. A case in Wuxi city is conducted to show the feasibility of the above method.

The results of this paper show that selecting the leading industry is not only based on regional the input‐output table, but also depends on the algorithm dealing with and comparing uncertain values.

The method proposed in the paper can help people to choose the leading industry under uncertain information, which provides a novel approach for analyzing local economy development.

The paper is shows a functional analysis perspective and contributes to regional economy development by solving selecting the leading industry without exact input‐output tables.

The purpose of this paper is to realize scientific reasoning and prediction in economic catastrophe, which occurs in the short‐term and leads to invalidation of most classical prediction models through lacking basic sample data.

Based on functional theory, grey number algebra theory, Bayesian network theory and interval grey number theory, the authors established GFAM (1,1), which is grey function analysis model (1,1), to excavate and utilize the existing data sufficiently.

This paper proved least squares parameters theorem and prediction theorem and the process of GFAM (1,1). A case was established and demonstrated the utility and good prediction of this model.

This paper established GFAM (1,1), which overcomes the hysteretic defect of classical prediction model and provides a preferable solution in system prediction in economic catastrophe.

This paper aims to introduce a high-temperature grease design method assisted by back propagation neural network (BPNN) and verify its application value.

First, the grease data sets were built by sorting out the base data of greases in a large number of literatures and textbooks. Second, the BPNN model was built, trained and tested. Then, the optimized BPNN model was used to search the unknown data space and find the composition of greases with excellent high-temperature performance. Finally, a grease was prepared according to the selected composition predicted by the model and the high-temperature physicochemical performance, high-temperature stability and tribological properties under different friction conditions were investigated.

Through high temperature tribology experiments, thermal gravimetric analysis and differential scanning calorimetry experiments, it is proved that the high temperature grease prepared based on BPNN has good high-temperature performance.

To the best of the authors’ knowledge, a new method of designing and exploring high-temperature greases is successfully proposed, which is useful and important for the industrial applications.

The purpose of this paper is to improve the antifriction and antiabrasive behavior of the used oil through the addition of a lubricant.

The author selected 85W-90 used oil with three kinds of 4,758, 10,507 and 16,223 km mileages, which may represent run-in wear period, steady-state wear period and rapid wear for used oil, respectively. Nano copper, molybdenum dithiocarbamate (MoDTC) and copper dioctyl dithiocarbamate (CuDTC) of lubricant additive are added to the used oil to improve its antifriction performances and service life. The influence of lubricant additive on the tribological properties of used oil is investigated by the friction tests.

An abnormal phenomenon has been observed by the friction test under high mileage used oil with CuDTC in presence of MoDTC lubrication, and superlow friction coefficient of 0.04 has been achieved after a running-in period for the first time. It is found that CuDTC additive is beneficial to improve greatly the antifriction behavior of used oil, especially when MoDTC is present. The results indicate that the dissoluble of CuDTC and the tribochemical reaction of MoDTC play an important role in superlow friction of high mileage used oil. Moreover, the superlow friction is also closely related to the viscosity of used oil.

The possible mechanism of superlow friction is attributed to the additive thinning effect and the synergistic effect of the dissoluble of CuDTC and the tribochemical reaction of MoDTC binary lubricant additives in high mileage used oil. This work will extend the application of CuDTC additive widely and explore a new method to the reutilization and the life extension of used lubricating oil.

The aim of the paper is to investigate the effect of labor strength on stock price crash risk and related moderating mechanisms.

To examine the relationship between labor unions and stock price crash risk and, more importantly, whether corporate governance moderates this relationship. Ordinary least squares (OLS), two-stage least squares, cross-sectional analyses, industry-level regressions and firm-level regressions are conducted.

The results suggest a negative impact of labor union strength on stock price crash risk. Further analysis suggests strong corporate governance mechanisms may mitigate the increased stock price crash risk in less-unionized firms.

Labor unions have a long-term horizon in the firm and have strong incentives to monitor managerial opportunism. However, labor unions may also increase financial reporting opacity and collude with managers to gain bargaining power in labor negotiations. The authors’ finding suggests that labor union strength is negatively associated with stock price crash risk. This finding is consistent with the notion that labor unions curb managerial opportunism in information disclosure, resulting in reduced crash risk. More importantly, the authors find corporate governance mitigates the negative impact of reduced unionization on crash risk, providing empirical support for recent regulatory efforts to strengthen corporate governance to prevent stock market crash.

The purpose of this paper is to design an oil-air lubrication system with low temperature rise, vibration and noise simplifies the spindle configuration. The oil-air lubrication unit is a key component for high-speed grinding machine tools. The development of oil-air lubrication unit suitable for high/ultrahigh rotational speed is a daunting task owing to the lubrication challenges.

This paper emphasizes three main issues: the analysis of oil-air two-phase flow for tradition oil-air lubrication unit with the simulation method; the design of new oil-air lubrication unit for the high/ultrahigh-speed grinding machine tools and the comparative experiment research of tradition and new oil-air lubrication unit. The optimum structure parameters that create the optimum flow pattern and operating conditions resulting in low temperature increase, vibration and noise of oil-air lubricated spindle can be achieved by the simulation method and experiments.

The simulation and experimental results show that new oil-air lubrication unit lubricating a high speed electric spindle has a better performance with a small temperature increase and vibration, which means that our proposed method is an effective design method for oil-air lubrication system.

A design method suitable for high-speed oil-air lubrication unit is proposed. New oil-air lubrication unit is expected to apply for high/ultrahigh rotational speed grinding machine tools.

This paper examines the impact of audit quality on the managerial preferences between real and accrual earnings management (REM and AEM, respectively) in oil and gas firms operating in the Gulf Cooperation Council (GCC) member countries.

The study relies on the modified Jones model’s (Dechow et al., 1995) to capture AEM and employs Roychowdhury (2006) approach to examine the use of REM through abnormal cash flows, abnormal production and abnormal discretionary expenditures. Audit quality is measured by auditor-industry specialization. The analyses are based on a sample of 30 oil and gas firms from 2008 to 2019.

The findings highlight that sample companies may substitute between earnings management strategies and tend to shift from AEM to REM when audited by an industry expert. Further analysis points out that the trade-off decision of the pooled sample stems from both upstream and downstream sectors.

This study is subject to two main limitations. First, the narrowed scope of audit quality related factors due to the scarcity of corporate governance reports of companies. Second, the sample size is reduced.

The regulators and users of financial statements should be aware that REM strategy is used by oil and gas firms even when scrutinized by a high quality auditor, calling for extra caution when auditing or analyzing the financial information.

The current research is the first, unveiling the association between audit quality and the trade-off between AEM and REM in a less inspected sector and a unique institutional setting.