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The purposes of this paper are to review the progress of and conclude the trend for paint thickness simulation for painting robot trajectory planning.

This paper compares the explicit function-based method and computational fluid dynamics (CFD)-based method used for paint thickness simulation. Previous research is considered, and conclusions with the outlook are drawn.

The CFD-based paint deposition simulation is the trend for paint thickness simulation for painting robot trajectory planning. However, the calculation of paint thickness resulting from dynamically painting complex surface remains to be researched, which needs to build an appropriate CFD model, study approaches to dynamic painting simulation and investigate the simulation with continuously changing painting parameters.

This paper illustrates that the CFD-based method is the trend for the paint thickness simulation for painting robot trajectory planning. Current studies have been analyzed, and techniques of CFD modeling have also been summarized, which is vital for future study.

Based on virtual maintenance, this paper aims to propose a time prediction method of assembly and disassembly (A&D) actions of product maintenance process to enhance existing methods’ prediction accuracy, applicability and efficiency.

First, a framework of A&D time prediction model is constructed, which describes the time prediction process in detail. Then, basic maintenance motions which can comprise a whole A&D process are classified into five categories: body movement, working posture change, upper limb movement, operation and grasp/placement. A standard posture library is developed based on the classification. Next, according to motion characteristics, different time prediction methods for each motion category are proposed based on virtual maintenance simulation, modular arrangement of predetermined time standard theory and the statistics acquired from motion experiment. Finally, time correction based on the quantitative evaluation method of motion time influence factors is studied so that A&D time could be predicted with more accuracy.

Case study of time prediction of products’ various A&D processes is conducted by implementing the proposed method. The prediction process of diesel cooling fan disassemble time is presented in detail. Through comparison, the advantages and effectiveness of the method are demonstrated.

This paper proposes a more accurate, efficient and applicable product A&D time prediction method. It can help designers predict A&D time of a product maintenance accurately in early design phases without a physical prototype. It can also provide basis for the verification of maintainability, the balance of the design of product structure and system layout.

The purpose of this paper is to design a special automatic redundant robot painting system (RRPS), which can automatically navigate and paint in the long non-regular duct.

The RRPS is designed with three subsystems: a redundant robot, a spraying system and a control and safety system. Based on the modular design theory, the robot falls naturally into a mobile platform, a 4-DOF location mechanism and a 10-DOF manipulator. The restriction of the distance between the links and the duct axis is used to plan the trajectory of the manipulator so that it would not collide with the duct. The restriction model is constructed by minimizing the sum of the weighed distances between the duct axis and the special points.

A fully working prototype system has been developed. Test results show that the minimal distance between the robot joints and duct is 18 mm, and it can finish painting long non-regular ducts at the speed of 12.5 cm/s and the spraying distance of 16 cm. The quality of coating layers is good.

The RRPS was used to paint non-regular rectangular ducts, cylindrical ducts and long non-regular ducts. The feasibility of painting long non-regular duct is proved with the prototype implementation and successful test results.

The RRPS shows a novel solution that is based on the 14-DOF redundant robot design for painting long non-regular ducts which is used in airplane.

Risks resulted from asymmetric information have become crucial barriers for commercial banks to implement supply chain finance (SCF) – mainly the inventory pledge financing (IPF). At the same time, online financial service providers (OFSPs) are emerging as strong competitors in the SCF market. As a result, commercial banks need to update their traditional SCF business models and alleviate their over-dependence on OFSPs.

The authors employ a multi-case-study method to investigate how the Internet of things (IoT) and blockchain technologies can be jointly leveraged to mitigate SCF risks. In-depth interviews were conducted to depict the business models and their novel ecosystem to reinforce traditional banks' ability in SCF services.

From the perspective of information asymmetry, the authors categorize IPF risks into three groups based on the principal-agent theory: collateral, warehousing and liquidity risk. The findings suggest that IoT can primarily improve traditional banks' information acquisition ability, and blockchain can facilitate credible information transformation, enabling banks to acquire knowledge from collaterals. Besides, the e-platform in the new architecture increases banks' involvement in the supply chain and builds a fair network to curtail warehousing risks. The employment of smart contracts and collaborative mechanism ensure process and outcome control in mitigating liquidity risks.

The research contributes to the literature by confirming the role of emerging technologies in reducing information asymmetry risks. Besides, the findings provide valuable insights for practitioners to promote effective practices and approaches in IPF.

Excessive borrowing significantly contributes to pushing businesses towards default and their transition into zombie enterprises. Despite government efforts to implement deleveraging policies and guide bank credit flows, it’s essential to delve into the internal dynamics that steer the borrowing behavior of these zombie enterprises at a micro level. To gain a comprehensive understanding of the issue, this study focuses on examining the incentives that drive corporate executives of zombie enterprises to consistently engage in large-scale borrowing from banks.

In this study, panel data analysis is utilized, incorporating firm-, industry- and year-fixed effects. Drawing from data pertaining to listed companies in China spanning from 2007 to 2020, we employ a one-by-one identification method to pinpoint zombie enterprises. Ultimately, a total of 2,533 samples of zombie enterprises were obtained.

The results indicate that as bank loans to zombie enterprises increase, executive monetary compensation decreases, while on-the-job consumption by executives increases, and they are less likely to be forced into rotation. Mechanism testing reveals that corporate performance partially mediates the relationship between bank loans and executive monetary compensation, but this mediation is ineffective for on-the-job consumption and job rotation. Further investigation suggests that the property rights nature of central enterprises and modified audit opinions can exacerbate the adverse impact of bank loans on the monetary compensation of zombie corporate executives, without significantly affecting on-the-job consumption or job rotation. Conversely, executive power does not enhance the positive effects of bank loans on monetary compensation or on-the-job consumption, but it diminishes the negative impact of bank loans on the forced rotation of zombie executives.

These results indicate that while bank loans may have a negative impact on corporate value, they function as safeguards for the positions and interests of executives. As a result, bank loans serve as incentives for executives of zombie enterprises.

This study holds theoretical significance as it explores the motivations behind non-performing loans in high-borrowing enterprises, sheds light on corporate governance challenges encountered by zombie enterprises and provides policy insights aimed at addressing the underlying causes of persistent non-performing loans in high-borrowing enterprises, including zombie enterprises.

The aim of this study was to analyze the effects of carbon reduction efforts and preservation efforts on system benefits in the cold chain industry of fresh products.

This study develops an optimal decision game model for the fresh products in the cold chain, incorporating the retailer's preservation effort and the supplier's carbon emission reduction effort. It quantifies the relationship between carbon emission reduction effort, preservation effort and system profit. The model considers parameters like carbon trading price, consumer low-carbon preference and consumer freshness preference, reflecting real-world conditions and market trends. Numerical simulations are conducted by varying these parameters to observe their impact on system profit.

Under the carbon cap-and-trade policy, the profit of the fresh cold chain system is higher than that of the fresh cold chain system without carbon constraints, and the profit of the supplier under decentralized decision-making is increased by nine times in the simulation results. The increase in carbon trading prices can effectively improve the freshness level of fresh products cold chain, carbon emission reduction level and system profit.

This study comprehensively considers the factors of freshness and carbon emission reduction, provides the optimal low-carbon production decision-making reference for the fresh food cold chain and promotes the sustainable development of the fresh food cold chain.

This paper aims to discuss major influencing factors causing users’ mobile social media fatigue and divides them into three hierarchies, including causal factors, intermediary factors and outcome factors. The study also sorts out connections between different levels of factors, thus providing effective guidance for the sustained development of social media.

Based on the grounded theory and by collecting data through in-depth interviews, the authors use open coding, axial coding and selective coding to analyze major influencing factors of users’ mobile social media fatigue, build a model using the software NVivo 11, organize and analyze mobile social media fatigue behavior and identify the relationships by combining the interpretive structural model and explore connections among the factors.

The influencing factors of mobile social media fatigue behavior conform with the stressors-strains-outcomes (SSO) theoretical framework, where stressors (S) include the five factors of fear of missing out, perceived overload, compulsive use, time cost and privacy concerns; strains (S) include the five factors of a low sense of achievement, emotional anxiety, reduced interest, social concerns and emotional exhaustion; outcomes (O) include the six factors of neglect behavior, diving behavior, avoidance behavior, tolerance behavior, withdrawal behavior and substitution behavior.

It focuses on the discussion of the interactions between users’ stressors, strains and outcomes without fully considering the impact of social environment and educational background on social media fatigue behavior. This study only focuses on one social media platform in the Chinese context, namely, WeChat. We reply on the qualitative research method to construct the relationships between social media fatigue factors because we were mainly interested in how users would respond psychologically and emotionally to social media fatigue behavior.

The study has extended the application of the SSO theory. Additionally, the research method and model used in this paper may serve as guidelines to other interested scholars who intend to explore relevant variables and conduct further research on the influencing factors of social media fatigue. In analyzing the causality of social media fatigue, the study has integrated the intermediary factor strain to display users’ strains from social media stress with a more detailed path discussion on the causality of social media fatigue, which has not received broad attention in previous research literature on social networking services users’ use.

In this study, text data are collected in a diversity of forms combined, allowing respondents to answer questions without being limited by the questions in the questionnaire, which helped us to identify new variables of social media fatigue. As a result, we were able to dig out the fundamental causes of social media fatigue and potential connections between the factors. Relevant scholars, users and businesses may analyze, manage and forecast users’ social media fatigue behavior by analyzing the type of social media stress and users’ state, providing guidance for the proposal of corresponding management strategies.

Most relevant studies focus on the sustained use of social media, and there is a scarcity of studies on social media fatigue in China. There is very limited research that conducts model analysis of social media fatigue through the integration of stressors, strains and outcomes.

Employees must receive proper cybersecurity training so that they can recognize the threats to their organizations and take the appropriate actions to reduce cyber risks. However, many cybersecurity awareness training (CSAT) programs fall short due to their misaligned training focuses.

To help organizations develop effective CSAT programs, we have developed a theoretical framework for conducting a cost–benefit analysis of those CSAT programs. We differentiate them into three types of CSAT programs (constant, complementary and compensatory) by their costs and into four types of CSAT programs (negligible, consistent, increasing and diminishing) by their benefits. Also, we investigate the impact of CSAT programs with different costs and the benefits on a company's optimal degree of security.

Our findings indicate that the benefit of a CSAT program with different types of cost plays a disparate role in keeping, upgrading or lowering a company's existing security level. Ideally, a CSAT program should spend more of its expenses on training employees to deal with the security threats at a lower security level and to reduce more losses at a higher security level.

Our model serves as a benchmark that will help organizations allocate resources toward the development of successful CSAT programs.

This study aims to provide a comprehensive review of the existing literature on uncertainty in underground mining operations, using a bibliometric and systematic analysis covering the period from 1975 to 2024.

To achieve this, the following questions were addressed using a mixed-method approach involving bibliometrics, text mining and content analysis: How has the field of uncertainty research in underground mining operations evolved? What are the most prominent research topics and trends in uncertainty in underground mining operations? and What are the possible directions for future research on uncertainty in underground mining operations?

As a result, bibliometric networks of 327 journal articles from the Scopus database were created and examined, the main research topics were underground mining management; rock mechanics; operational optimization; and stochastic systems. Finally, the inclusive investigation of uncertainty in underground mining operations and its prominent patterns can serve as a basis for real-time direction for new research and as a tool to improve underground mining activities by implementing advanced technology for innovative practices and optimizing operational efficiency. This is fundamental to identify unknown variables that impair the planning, operation, safety and economic viability of underground mines.

This research is 100% original because there is no review research on the uncertainty present in underground mining operations.