Search results

Effective safety supervision plays a crucial role in ensuring safe production within coal mines. Conventional coal mine safety supervision (CMSS) in China has suffered from the problems of power-seeking, excessive resource consumption and poor timeliness. This paper aims to explore the Internet+ CMSS mode being emerged in China.

The evolution of CMSS systems underwent comprehensive scrutiny through a blend of qualitative and quantitative approaches. First, evolutionary game theory was used to analyze the necessity of incorporating Internet+ technology. Second, a system dynamics model of Internet+ CMSS was crafted, encompassing a system flow diagram and equations for various variables. The model was subsequently simulated by taking the W coal mine in Shanxi Province as a representative case study.

It was revealed that the expected safety profit from the Internet+ mode is 296.03% more than that from the conventional mode. The precise dissemination of law enforcement information was identified as a pivotal approach through which the Internet+ platform served as a conduit to foster synergistic collaboration among diverse elements within the system.

The outcomes of this study not only raise awareness about the potential of Internet+ technology in safety supervision but also establish a vital theoretical foundation for enhancing the efficacy of the Internet+ CMSS mode. The significance of these findings extends to fostering the wholesome and sustainable progress of the coal mining industry.

This research stands out as one of the limited studies that delve into the influence of Internet+ technology on CMSS. Building upon the pivotal approach identified, to the best of authors’ knowledge, a novel “multi-blind” working mechanism for Internet+ CMSS is introduced for the first time.

To enrich the research on the economic consequences of enterprise digital development from the perspective of capacity utilization.

Using a sample of listed firms from 2010 to 2020, this paper exploits text analysis of annual reports to construct a proxy for enterprise digital development.

Results show that enterprise digital development not only improves their own capacity utilization but also generates a positive spillover effect on the capacity utilization of peer firms and firms in the supply chain. Next, based on the incomplete information about market demand and potential competitors when making capacity-building decisions, the mechanism tests show that improving the accuracy of market forecasts and reducing investment surges are potential channels behind the baseline results. Cross-sectional tests show the baseline result is more pronounced when industries are highly homogeneous and when firms have access to less information.

This paper contributes to the research related to the economic consequences of digital development. With the development of the digital economy, the real effects of enterprise digital development have also triggered extensive interest and exploration. Existing studies mainly examine the impact on physical operations, such as specialization division of labor, innovation activities, business performance or total factor productivity (Huang, Yu, & Zhang, 2019; Yuan, Xiao, Geng, & Sheng, 2021; Wang, Kuang, & Shao, 2017; Li, Liu, & Shao, 2021; Zhao, Wang, & Li, 2021). These studies measure the economic benefits from the perspective of the supply (output) side but neglect the importance of the supply system to adapt to the actual market demand. In contrast, this paper focuses on capacity utilization, aimed at estimating the net economic effect of digital development by considering the supply-demand fit scenario. Thus, our findings enrich the relevant studies on the potential consequences of digital development.

This study aims to investigate the relationship between corporate environmental responsibility (CER) and risk-taking in Chinese A-share listed companies from 2011 to 2020. It seeks to understand the influence of CER on risk-taking behavior and explore potential moderating factors.

A quantitative approach is used, using data from Chinese A-share listed companies over the specified period. Regression analysis is used to examine the relationship between CER and risk-taking, while considering moderating variables such as performance aspiration, environmental enrichment and contextual factors.

The findings indicate that CER positively influences corporate risk-taking, with significant impacts on information asymmetry and corporate reputation. Moreover, positive performance aspiration strengthens the effect of CER on risk-taking, while negative performance aspiration and environmental enrichment weaken this effect. Cross-sectional analysis shows that the positive association between CER and risk-taking is more prominent for firms located in areas with strict environmental regulation, for nonstate-owned firms, and for firms with higher levels of internal control.

This research contributes to the literature by providing insights into the dynamics between CER and risk-taking in the Chinese market context. It expands existing knowledge by considering the influence of performance aspiration on this relationship, offering practical implications for firms seeking to enhance corporate performance through strategic management of environmental responsibilities.

Firms have already begun integrating artificial intelligence (AI) as a replacement for conventional performance management systems owing to its technological superiority. This transition has sparked a growing interest in determining how employees perceive and respond to performance feedback provided by AI as opposed to human supervisors.

A 2 x 2 between-subject experimental design was employed that was manipulated into four experimental conditions: AI algorithms, AI data, highly experienced human supervisors and low-experience human supervisor conditions. A one-way ANOVA and Welch t-test were used to analyze data.

Our findings revealed that with a predefined fixed formula employed for performance feedback, employees exhibited higher levels of trust in AI algorithms, had greater performance expectations and showed stronger intentions to seek performance feedback from AI algorithms than highly experienced human supervisors. Conversely, when performance feedback was provided by human supervisors, even those with less experience, in a discretionary manner, employees' perceptions were higher compared to similar feedback provided by AI data. Moreover, additional analysis findings indicated that combined AI-human performance feedback led to higher levels of employees' perceptions compared to performance feedback solely by AI or humans.

The findings of our study advocate the incorporation of AI in performance management systems and the implementation of AI-human combined feedback approaches as a potential strategy to alleviate the negative perception of employees, thereby increasing firms' return on AI investment.

Our study represents one of the initial endeavors exploring the integration of AI in performance management systems and AI-human collaboration in providing performance feedback to employees.

The rising occurrence of digitally driven public consumer complaints has made it necessary for enterprises to obtain consumer forgiveness. However, existing research has provided little understanding regarding how to obtain consumer forgiveness effectively. Thus, the present study examined how brand avatars can improve consumer forgiveness in the context of public apology.

This study tested the mechanism of a brand avatar on consumer forgiveness using three studies. Specifically, we explored the direct and mediating effect of empathy toward a brand (Study 1); we identified the moderating mediating effect of humorous responses (Study 2) and product type (Study 3). Data for these studies were collected on Credamo. We analyzed the data using SPSS (26.0) for the primary analysis and PROCESS (3.5) for the mediating and moderating mediating analysis.

The results indicate that brand avatars enhance consumer forgiveness. Moreover, empathy toward a brand plays a mediating role in the effect of brand avatars on consumer forgiveness. Additionally, when a humorous response is present, a brand avatar can enhance customer forgiveness through empathy toward that brand. Compared to utilitarian products, hedonic products can also increase the impact of a brand avatar on empathy toward the brand, thus enhancing consumers' forgiveness.

From the perspective of emotion, this study explored the impact of brand avatars on consumer forgiveness via empathy toward a brand. It augments the research on brand avatars and consumer forgiveness. The study also verified the moderating mediating effect of humor response and product type while expanding the brand avatar research boundary.

Due to the harsh underground environment in coal mining, the surface of hydraulic support columns corrodes severely, resulting in significant economic losses. Therefore, a highly corrosion-resistant coatings is needed to extend the service life of the columns.

This study aims to compare the corrosion resistance of ST-Cr₃C₂-NiCr (sealed treatment Cr₃C₂-NiCr) coatings with industrially applied chromium plating. The corrosion failure mechanism of the coatings was investigated.

The results demonstrated that the ST-Cr₃C₂-NiCr coatings exhibited excellent corrosion resistance. After sealing treatment, the corrosion potential of Cr₃C₂-NiCr coatings was −0.215 V, and the corrosion current density of Cr₃C₂-NiCr coatings was lower than that of the plated parts.

ST-Cr₃C₂-NiCr coatings prepared by supersonic atmospheric plasma spraying could provide excellent corrosion resistance in the coal industry.

The low porosity and the presence of the NiCr phase were crucial factors contributing to the preferable corrosion resistance exhibited by the ST-Cr₃C₂-NiCr coatings. The corrosive process of the coatings involved layer-by-layer delamination of surface oxide film, sub-surface pitting, formation and degradation of sub-surface passive film, as well as severe block-like delamination.

This study aims to examine how women on board influence quality and quantity disclosure of emissions discharge by the listed non-financial firms for the period of six years (2016–2021), with institutional ownership as a moderator.

The study obtained data from a sample of 83 listed non-financial firms. A content analysis technique was employed to compute emissions disclosure indexes using Global Reporting Initiatives standards from the sampled firms. Random and fixed effect regression analyses were run for both direct and moderation models. Based on the results of the Hausman tests, random results were adopted and used in examining the relationship.

The result reveals that women on board are significantly related to emission disclosure. The study also documented that institutional owners have not influenced the relationship between women directors and emissions disclosure.

The study's findings have practical implications for emerging economies, corporations and other business organizations seeking to actively involve the emissions control and reduction issues toward sustainable development goals 5, 7 and 13 in their business models and successfully communicate these efforts to stakeholders.

Listed firms in emerging economies would gain sincerity through the women directors’ knowledge, skills, demographics and ethnicity in the society. Therefore, corporate bodies in emerging economies can successfully contribute toward improving the social welfare of various segments of society by controlling current and future climate issues. Additionally, society will surely benefit when firms control the pollution discharges within the community.

This is the first study, to the best of the authors’ knowledge, that provides empirical evidence on the effect of the presence of women on board on emissions disclosure using institutional ownership as a moderator in Nigeria.

Stiffness adjusting ability is essential for soft robotic arms to perform complex tasks. A soft state enables dexterous operation and safe interaction, while a rigid state enables large force output or heavy weight carrying. However, making a compact integration of soft actuators with powerful stiffness adjusting mechanisms is challenging. This study aims to develop a piston-like particle jamming mechanism for enhanced stiffness adjustment of a soft robotic arm.

The arm has two pairs of differential tendons for spatial bending, and a jamming core consists of four jamming units with particles sealed inside braided tubes for stiffness adjustment. The jamming core is pushed and pulled smoothly along the tendons by a piston, which is then driven by a motor and a ball screw mechanism.

The tip displacement of the arm under 150 N jamming force and no more than 0.3 kg load is minimal. The maximum stiffening ratio measured in the experiment under 150 N jamming force is up to 6–25 depends on the bending direction and added load of the arm, which is superior to most of the vacuum powered jamming method.

The proposed robotic arm makes an innovative compact integration of tendon-driven robotic arm and motor-driven piston-like particle jamming mechanism. The jamming force is much larger compared to conventional vacuum-powered systems and results in a superior stiffening ability.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.