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This study's purpose is to examine benevolent leadership's effect on employee silence, as moderated by perceived employee agreement on leader behaviors and cultural value orientations.

Two-wave survey data were collected from 240 Chinese employees working in various industries. Hierarchical regression and simple slope analysis were used to test the hypotheses.

Benevolent leadership was negatively related to employee silence. When perceived employee agreement on leader behaviors was high, employees with high power-distance orientation or low vertical individualism were more sensitive to benevolent leadership and engaged in less silence.

Managers are advised to exhibit benevolent behaviors to mitigate employees' tendency to remain silence. Organizations and managers can also design interventions to encourage employees with low power distance or high vertical individualism to speak up.

This study advances the understanding of the relationship between benevolent leadership and employee silence. By highlighting the moderating role of employees' perception of leader behaviors and their cultural value orientations, this study helps explain the conditions that when employees choose to keep silence or not.

The authors investigate the extent to which online talk can influence contemporaneous and future stock trading, especially when market news is unpresented.

The authors propose an improved sentiment formula incorporating online hype, neutral sentiment and poster reputation. In addition, they conduct event study, OLS regression analyses and probit models.

First, investors tend to be more talkative in relation to firms that are (1) smaller size, (2) more growth-like, (3) with lower prices and higher short interests and (4) of higher beta. Second, the bullish tone of investors positively affects the abnormal returns of small-capitalization stocks. However, online talk has little impact on large-capitalization stocks, except that more postings boost trading liquidity. Third, online talk predicts the presence of future news regardless of firm size, with stronger predictive power found for small-capitalization stocks.

It is of interest to practitioners and researchers to study online talk so as to better understand the trading psychology of retail investors and the effects on the stock market. Furthermore, policymakers are interested in tracking activities on stock message boards in order to prevent security fraud and protect investors' interests.

The results are robust and suggest that online talk has significant impacts on stock trading exploiting an information asymmetry. This study of stock message board posting activities helps researchers to understand whether message contents contain valuable and unique content compared with information available via more traditional media channels.

Helicopter fuselage vibration prediction is important to keep a safety and comfortable flight process. The helicopter vibration mechanism model is difficult to meet of demand for accurate vibration prediction. Thus, the purpose of this paper is to develop an intelligent algorithm for accurate helicopter fuselage vibration analysis.

In this research, a novel weighted variational mode decomposition (VMD)- extreme gradient boosting (xgboost) helicopter fuselage vibration prediction model is proposed. The vibration data is decomposed and reconstructed by the signal clustering results. The vibration response is predicted by xgboost algorithm based on the reconstructed data. The information transfer order between the controllable flight data and flight attitude are analyzed.

The mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) of the proposed weighted VMD-xgboost model are decreased by 6.8%, 31.5% and 32.8% compared with xgboost model. The established weighted VMD-xgboost model has the highest prediction accuracy with the lowest mean MAPE, RMSE and MAE of 4.54%, 0.0162, and 0.0131, respectively. The attitude of horizontal tail and cycle pitch are the key factors to vibration.

A novel weighted VMD-xgboost intelligent prediction methods is proposed. The prediction effect of xgboost model is highly improved by using the signal-weighted reconstruction technique. In addition, the data set used is collected from actual helicopter flight process.

A series of environmental pollution issues and economic improvement go hand in hand. Since financial listed companies contribute significantly to the national economic development, China has been paying increasing attention to the development of the financial industry. The purpose of this paper is to explore the relationship among the development level of the financial industry, over-investment of the listed companies and environmental pollution through a macro-level and micro-level mechanism.

In this study, we adopt the 2011–2017 panel data of listed companies in the manufacturing industry to study the impacts of the financial industry on environmental pollution. Meanwhile, the paper uses a mediator model, and over-investment is introduced to the econometric model as a mediator to explore whether the development of the financial industry can affect the environmental pollution through over-investment.

A U-shaped relationship between financial industry development and environmental pollution was observed through a macro-perspective; additionally, over-investment of the listed companies significantly increased environmental pollution, along with a significant mediator effect of over-investment. A significant positive correlation was observed between financial industry development and environmental pollution in the East region of China, while the correlation was negative in the Central and West regions. The mitigation effect of financial industry development on environmental pollution was more significant in the low-end manufacturing industry, compared with basic and high-end manufacturing industries.

Strengthening the incentives and supervision toward company managers, reducing over-investment behaviors, encouraging suitable financial industry development to reduce financial risks, improving environmental conservation laws and regulations, and implementing stringent penalty mechanisms for environmental conservation are necessary.

Recently, China has been paying increasing attention to how to improve the efficiency of the marine environment and realize a green and sustainable development of the marine economy. Consequently, the industrial structure is crucial to improving efficiency. The purpose of this paper is to introduce environmental factors into the efficiency analysis framework and explore the relationship between marine industrial structure and marine environmental efficiency.

This paper uses marine economic data under the DEA-BBC model to measure the marine environmental efficiency of provinces and cities and classifies them by cluster analysis. Then, the marine industrial structure and marine environmental efficiency are studied by an econometric model with human capital, ownership structure, land economic development level, scientific research input and government intervention degree as control variables.

The overall level of marine environmental efficiency is relatively low in China, increasing and then decreasing over the research period. The rationalization of industrial structure and scientific research input have significant promoting effects on marine environmental efficiency, while the degree of government intervention has a significant inhibiting effect. The positive effect of human capital on efficiency depends on whether it can be successfully converted into productivity. The effects of industrial structure advancement, ownership structure and land economic development level of on the marine environmental efficiency are mixed.

The results provide a theoretical and decision-making basis for China to transform and upgrade its marine industrial structure and sustainably develop the marine economy.

Currently, few academics agree on a standard and scientific way to measure risk tolerance. This paper aims to create a unique model for empirically measuring risk tolerance and to make a strong contribution to the growing literature in risk tolerance and risk management.

The authors use factor analysis and regression analysis to identify relevant factors for measuring risk tolerance.

The risk tolerance model is based on the acronymed model riskTRACK, which includes the five significant factors this paper identifies for measuring risk tolerance: traditional risk factor, reflective risk factor, allocation risk factor, capacity risk factor and knowledge risk factor.

Uses for future research streams devoted to risk tolerance and risk management.

The results also have practical applications for the financial services industry, particularly risk management, portfolio management and financial planning.

In sum, this research expands previous research in risk tolerance and also adds to the growing literature in risk management. Once again, this paper is unique in that the authors develop a valid and reliable risk tolerance model based on five specific factors for measuring risk tolerance.

The purpose of this paper is to examine the effects of energy technological innovation on carbon emissions in China from 2001 to 2016.

Conditional mean (CM) methods are first applied to implement our investigation. Then, considering the tremendous heterogeneity in China, quantile regression is further employed to comprehensively investigate the potential heterogeneous effect between energy technological innovation and carbon emission intensity.

The results suggest that renewable energy technological innovation has a significantly positive effect on carbon emission intensity in lower quantile areas and a negative effect in higher quantile areas. Contrarily, fossil energy technological innovation exerts a negative correlation with carbon emission intensity in lower quantile areas and a positive effect on carbon emission intensity in higher quantiles areas.

Considering that energy consumption is the main source of CO₂ emissions, it is of great importance to study the impact of energy technological innovation on carbon emissions. However, the previous studies mainly focus on the impact of integrated technological innovation on carbon emissions, ignoring the impact of energy technological innovation on carbon emissions mitigation. To fill this gap, we construct an extended STIRPAT model to examine the effects of renewable energy technological innovation and fossil energy technological innovation on carbon emissions in this paper. The results can provide a reference for the government to formulate carbon mitigation policies.

This paper aims to study the friction layer and tribological property of polyimide (PI)–matrix composites under different friction speeds.

Friction tests were conducted under friction speeds ranging from 20-120 km/h and pressure of 0.57 MPa by a pin-on-disk tribometer.

The results indicate that the friction coefficient decreases with the increasing of the friction speed. Under different friction speeds, the structure of the friction layer and debris are different, which affects the actual tribological performance of the composites. At low friction speed, the morphology of the friction layer is mainly particulate. The higher level of clenching action between the friction pair leads to a high friction coefficient, and the morphology of the particles in the particulate zone and the wear debris are mostly equiaxial particles. At high friction speed, the morphology of the friction layer is mainly a compact zone. The reduction of the surface roughness leads to a low friction coefficient. The debris collected on the counter surface at high friction speeds are mostly big sheets, and the morphology of the particles in the particulate zone is mostly rod-like. Controlling the conditions of the disk and the pin can reveal the influence of friction speed on the friction layer. The wear mechanisms at different friction speeds are also discussed.

By controlling the conditions of the disk and the pin to reveal the influence of friction speed on the friction layer, and the evolutions of the friction layer, wear debris were carefully inspected with the aim of demonstrating the relationship between friction speed and wear mechanism of PI–matrix composites.

The purpose of this paper is to construct a new algorithm of program procedure for medical grey relational method based on SAS software.

Based on the SAS environment, the authors construct a new algorithm of program procedure through the following methods: the construction data set, confirmation of the comparison sequence and reference sequence, the original data transformation, calculation of the grey relational coefficient of reference sequence and comparison sequence and calculating the correlation.

The results show that the novel algorithm of program procedure for medical grey relational method based on SAS software satisfies the properties properly. It also fully confirmed the biggest advantage of the grey relational analysis is that its requirements are not too high for the amount of data, and it does not need to follow the typical distribution.

The paper succeeds in constructing a novel algorithm of program procedures for medical grey relational method and providing a valuable tool for solving similar problems.

Porous titanium is used in many bioimplant and electrode applications because of its interconnected pore structure and good corrosion resistance. The purpose of this paper is to study the anodic polarization behavior of porous titanium in different electrolytes and clarify the influences of the porosity and macro‐pore size on the corrosion resistance.

The porous titanium with 10‐70% porosities and average macro‐pore sizes in the range of 100‐500 μm was prepared by the powder metallurgy method using polymethyl methacrylate (PMMA) as a space holder. Electrochemical corrosion tests were performed on porous titanium as well as solid titanium (with the same irregular and isolated micro‐pore structures as that on the interconnected spheroidal macro‐pore walls of porous titanium) in the 0.1 M H₂SO₄, 1 M NaOH and 0.9% NaCl (37 °C) solutions.

It was found that porous titanium exhibited an active‐passive transition behavior in the 1 M NaOH and 0.1 M H₂SO₄ solutions. In contrast, a self‐passivation transition behavior was observed in the 0.9% NaCl solution (37 °C).

The paper demonstrates that both the porosity and macro‐pore size of porous titanium play an important role in determining the corrosion rate, rather than the corrosion potential.