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This paper aims to solve the problem of the inability to apply learning methods for robot motion skills based on dynamic movement primitives (DMPs) in tasks with explicit environmental constraints, while ensuring the reliability of the robot system.

The authors propose a novel DMP that takes into account environmental constraints to enhance the generality of the robot motion skill learning method. First, based on the real-time state of the robot and environmental constraints, the task space is divided into different regions and different control strategies are used in each region. Second, to ensure the effectiveness of the generalized skills (trajectories), the control barrier function is extended to DMP to enforce constraint conditions. Finally, a skill modeling and learning algorithm flow is proposed that takes into account environmental constraints within DMPs.

By designing numerical simulation and prototype demonstration experiments to study skill learning and generalization under constrained environments. The experimental results demonstrate that the proposed method is capable of generating motion skills that satisfy environmental constraints. It ensures that robots remain in a safe position throughout the execution of generation skills, thereby avoiding any adverse impact on the surrounding environment.

This paper explores further applications of generalized motion skill learning methods on robots, enhancing the efficiency of robot operations in constrained environments, particularly in non-point-constrained environments. The improved methods are applicable to different types of robots.

This study aims to understand the influence of raceway surface topography on the temperature rise characteristics of silicon nitride (Si₃N₄) full ceramic ball bearing and improve its service life.

The arithmetic average height S_a, skewness S_sk and kurtosis S_ku in the three-dimensional surface roughness parameters are used to quantitatively characterize the surface topography of the raceway after superfinishing. The bearing life testing machine is used to test the Si₃N₄ full ceramic ball bearing using polytetrafluoroethylene (PTFE) cage under dry friction conditions, and the self-lubricating full ceramic ball bearing heat generation model is established.

With the decrease of S_a and S_sk on the raceway surface and the increase of S_ku, the average height of the raceway surface decreases, and the peaks and valleys tend to be symmetrically distributed on the average surface, and the surface texture becomes tighter. This kind of raceway surface topography is beneficial to form a thin and uniform filamentous PTFE transfer film with a wide coverage area on the raceway surface based on consuming less cage materials and improving the temperature rise characteristics of hot isostatic pressing silicon nitride full ceramic ball bearings.

The research results provide a theoretical basis for the reasonable selection of Si₃N₄ ring raceway processing technology and have important significance for improving the working characteristics and service life of Si₃N₄ full ceramic ball bearings under dry friction conditions.

Over the past three decade, China has established a housing finance system that borrows from the collective experiences of advanced economies. After examining the evolution of China’s housing finance system, the paper focuses on analyzing its challenges and recent changes. The paper argues that China’s highly-centralized financial system prefers financial stability but neglects financial liberalization, and then resulted in severe financial repression, which hurts the efficiency and equality of the housing finance service. After recovering from the 2008 financial crisis via high-cost financial intervention, China took some policy innovations to promote a decentralized finance mechanism, expand finance resources, and support affordable housing financing, through which China hopes to provide a more stable, affordable, and equal housing finance service to help more households own homes.

We review and synthesize the existing research on directors' and officers’ (D&O) liability insurance. Our objectives are (1) to examine the institutional forces and regulatory requirements that have influenced the development of D&O liability insurance; (2) to identify the factors that influence firms to purchase D&O liability insurance and explore the consequences associated with its usage and (3) to identify gaps in the current literature and provide recommendations for future research on D&O liability insurance.

We perform a systematic literature review (SLR) using the Preferred Reporting Items for a Systematic Review of Meta-Analysis (PRISMA) guidelines to examine archival studies that investigate the determinants and consequences of D&O liability insurance. Using a Boolean search strategy on the “Web of Science” (WoS) and PRISMA selection criteria, we review 64 published archival research articles and three working papers from 1987 to October 2023.

Our review reveals that disclosing detailed information regarding D&O liability insurance, such as total insurance premiums and coverage limit, is predominantly voluntary, except in Taiwan. Our findings suggest that the decision to purchase D&O liability insurance is influenced by litigation risk, which is determined by factors such as firm size, complexity and corporate governance variables. We also find that D&O liability insurance has implications for financial reporting, audit outcomes, investment behavior and capital market performance.

In the post-COVID era, where firms face pressure due to financial constraints, our research emphasizes the practical importance of carefully considering and understanding the impact of D&O liability insurance, particularly as it concerns the demand for such insurance.

To the best of our knowledge, this study represents the first systematic review of previous research on D&O liability insurance. Our review highlights some research gaps, particularly in relation to the implications for financial reporting practices, auditing outcomes, firm investment behavior and capital market consequences.

In a structural optimization design-based single-level response surface, the number of optimal variables is too much, which not only increases the number of experiment times, but also reduces the fitting accuracy of the response surface. In addition, the uncertainty of the optimal variables and their boundary conditions makes the optimal solution difficult to obtain. The purpose of this paper is to propose a method of fuzzy optimization design-based multi-level response surface to deal with the problem.

The main optimal variables are determined by Monte Carlo simulation, and are classified into four levels according to their sensitivity. The linear membership function and the optimal level cut set method are applied to deal with the uncertainties of optimal variables and their boundary conditions, as well as the non-fuzzy processing is carried out. Based on this, the response surface function of the first-level design variables is established based on the design of experiments. A combinatorial optimization algorithm is developed to compute the optimal solution of the response surface function and bring the optimal solution into the calculation of the next level response surface, and so on. The objective value of the fourth-level response surface is an optimal solution under the optimal design variables combination.

The results show that the proposed method is superior to the traditional method in computational efficiency and accuracy, and improves 50.7 and 5.3 percent, respectively.

Most of the previous work on optimization was based on single-level response surface and single optimization algorithm, without considering the uncertainty of design variables. There are very few studies which discuss the optimization efficiency and accuracy of multiple design variables. This research illustrates the importance of uncertainty factors and hierarchical surrogate models for multi-variable optimization design.

Given a product or service, the number of its installed user base has a significant positive effect on the existing users’ loyalty and new users’ conversion. This effect is conceptualized as network externalities in economics. Network externalities are supposed to be particularly striking in nowadays online business-to-business (B2B) platforms, but yet the mystery behind their effects on user loyalty to online B2B platforms remains to be delicately unraveled. The purpose of this paper is to discover the factors driving users’ loyalty, especially buyers’ loyalty, to online B2B platforms, by highlighting the impacts of network externalities on loyalty and other mediating factors.

A conceptual model of buyer loyalty under network externalities is elaborated. The reliability and validity of the instruments of the latent model constructs are assessed by confirmatory factor analysis, and the hypothesized causal relationships among the constructs are tested by structural equation modeling, on 710 valid buyer samples collected from a famous online B2B platform in China.

The analysis demonstrates that: perceived value, user satisfaction and switching costs are the major predictors of buyer loyalty to online B2B platforms characterized by network externalities; network externalities positively account for buyer loyalty by contributing to perceived value, user satisfaction and switching costs; and direct network externality (measured by perceived network size and perceived external prestige) has a significant effect on indirect network externality (measured by perceived compatibility and perceived complementarity).

The findings allow the authors to conclude meaningful managerial implications for online B2B service providers to build up loyal user bases through improving users’ perceptions of network externalities, switching costs and value.

The purpose of this study is to investigate the effects of load and rotation speed on dry sliding of silicon nitride, including a series of tribological behaviors (friction coefficient, wear rate, temperature rise, etc.) and wear mechanism. Through the analysis of the above characteristics, the influence law of load and speed on them and the internal relationship between them are determined, and then the best comprehensive performance parameters of silicon nitride full-ceramic spherical plain bearings in dry sliding are predicted, which can provide guidance for the operation condition of silicon nitride full-ceramic spherical plain bearings in dry sliding.

The experimental study of different loads and rotation speeds under dry friction conditions was carried out by the using ball-disk sliding test method.

With the increase of load, the friction coefficient of silicon nitride friction pair and the wear rate of silicon nitride ball decrease continuously. With the increase of rotation speed, the friction coefficient of silicon nitride friction pair first increases and then decreases, and the wear of silicon nitride ball first increases and then decreases. With the increase of load and rotation speed, the wear mechanism eventually changes to adhesive wear.

Because of the low timeliness and inefficiency of bearing experiments, this work adopts a simple ball-disk model to comprehensively explore the influence rules of different conditions, which provides a theoretical basis for the subsequent practical application of silicon nitride full-ceramic spherical plain bearings.

This study aims to research the influence of non-probabilistic design variables on interval robust optimization of electric multiple units (EMU) brake module, therefore obtain the reasonable of design variables of the EMU brake module.

A robust optimization model of the EMU brake module based on interval analysis is established. This model also considers the dimension tolerance of design variables, and it uses symmetric tolerance to describe the uncertainty of design variables. The interval order relation and possibility degree of interval number are employed to deal with the uncertainty of objective function and constraint condition, respectively. On this basis, a multiobjective robust optimization model in view of interval analysis is established and applied to the robust optimization of the EMU brake module.

Compared with the traditional method and the method proposed in the reference, the maximum stress fluctuation of the EMU brake module structure is smaller after using the method proposed in this paper, which indicates that the robustness of the maximum stress of the structure has been improved. In addition, the weight and strength of the structure meet the design requirements. It shows that this method and model introduced in this research have certain feasibility.

This study is the first attempt to apply the robust optimization model based on interval analysis to the optimization of EMU structure and obtain the optimal solution set that meets the design requirements. Therefore, this study provides an idea for nonprobabilistic robust optimization of the EMU structure.

The purpose of this paper is to obtain more design freedom and realize fast fabrication of mechanism which is the core subsystem of many machines and always consists of several parts with assigned relative motion.

The mechanism is digitally assembled and later directly fabricated by selective laser melting (SLM) without post‐assembly. The joint is re‐designed to facilitate powdered material removal; the displays and the corresponding support additions are discussed to avoid too many supports within the clearances. Then, a series of universal joint are directly fabricated using SLM machine and a minimum clearance of 0.1 mm is obtained; a crank rocker mechanism is also fabricated and it can achieve the required performances.

The digitally assembled mechanism can be successfully fabricated by SLM technique using metal powdered material.

It is well known that the components fabricated by SLM have good mechanical properties. Therefore, it can be expected that more mechanisms with more design freedom will be developed and be used in some practical fields with improvement of fabrication quality.