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The key purpose of this study is to systematically examine the landscape of education research funded by the National Plan of Educational Research Funding (NPERF) in China. The study aims to: (1) identify the thematic focus areas that reflect the national education agenda, (2) analyze the general funding patterns of education research projects and (3) gain insights into the distinctive nature of the research agenda in China. The study employs a rigorous data-driven approach to offer valuable insights into the dynamic discourses within the field of education research in China, which has received relatively little attention despite its potential significance.

In this study, we utilized word co-occurrence analysis and corpus-based frequency analysis to analyze the research projects funded by the National Plan of Educational Research Funding (NPERF) from 2011 to 2020.

The key characteristics of these projects highlight the focus on higher education research, addressing the interests of specific cohorts of students, teachers and disadvantaged populations. Furthermore, these projects demonstrate a remarkable responsiveness to the policy needs of the country and a robust inclination toward an international comparative framework.

The findings offer valuable insights into the landscape and features of funded education research in China, revealing a strong emphasis on addressing practical needs and enhancing the capacity of the education system in the country.

This paper presents a systematic examination of the topics covered in funded research under the National Plan of Educational Research Funding (NPERF) scheme from 2011 to 2020. It contributes to the advancement of understanding regarding knowledge traditions and practices in the Chinese context. Methodologically, this paper is the first in the literature to be prototyped with a word co-occurrence analysis approach to systematically investigate the funded education research in China. Additionally, it includes the development of a comprehensive corpus list to uncover the key characteristics of the funded projects. The analysis provides unique insights into the priorities and directions of education research supported by the Chinese government, which are of potential interest to international readers.

This paper aims to introduce an imitation learning framework for a wheeled mobile manipulator based on dynamical movement primitives (DMPs). A novel mobile manipulator with the capability to learn from demonstration is introduced. Then, this study explains the whole process for a wheeled mobile manipulator to learn a demonstrated task and generalize to new situations. Two visual tracking controllers are designed for recording human demonstrations and monitoring robot operations. The study clarifies how human demonstrations can be learned and generalized to new situations by a wheel mobile manipulator.

The kinematic model of a mobile manipulator is analyzed. An RGB-D camera is applied to record the demonstration trajectories and observe robot operations. To avoid human demonstration behaviors going out of sight of the camera, a visual tracking controller is designed based on the kinematic model of the mobile manipulator. The demonstration trajectories are then represented by DMPs and learned by the mobile manipulator with corresponding models. Another tracking controller is designed based on the kinematic model of the mobile manipulator to monitor and modify the robot operations.

To verify the effectiveness of the imitation learning framework, several daily tasks are demonstrated and learned by the mobile manipulator. The results indicate that the presented approach shows good performance for a wheeled mobile manipulator to learn tasks through human demonstrations. The only thing a robot-user needs to do is to provide demonstrations, which highly facilitates the application of mobile manipulators.

The research fulfills the need for a wheeled mobile manipulator to learn tasks via demonstrations instead of manual planning. Similar approaches can be applied to mobile manipulators with different architecture.

Robotic systems for laparoscopic minimally invasive surgery (MIS) always end up with highly sophisticated mechanisms and control schemes – making it a long and hard development process with a steep price. This paper aims to propose and realize a new, efficient and convenient strategy for building effective control systems for surgical and even other complex robotic systems.

A novel method that takes advantage of the modularization concept by integrating two middleware technologies (robot operating system and robotic technology middleware) into a common architecture based on the strengths of both was designed and developed.

Tests of the developed control system showed very low time-delay between the master and slave sides; good movement representation on the slave manipulator; and high positional and operational accuracy. Moreover, the new development strategy trial came with much higher efficiency and lower costs.

This method results in a modularized and distributed control system that is amenable to collaboratively develop; convenient to modify and update; componentized and easy to extend; mutually independent among subsystems; and practicable to be running and communicating across multiple operating systems. However, experiments show that surgical training and updates of the robotic system are still required to achieve better proficiency for completing complex minimally invasive surgical operations with the proposed and developed system.

This research proposed and developed a novel modularization design method and a novel architecture for building a distributed teleoperation control system for laparoscopic MIS.

The purpose of this paper is to examine the difference of credit enhancement of variously secured bonds issued by local government financing platform bond (LGFPB).

The approaches to secure the bonds usually include mortgage, collateral, guarantee, etc.

Using a sample of LGFPBs issued during the 2007-2013 period, the authors find that all of the approaches to secure the bonds would increase the bond rating and that compounded approaches have a higher credit enhancement effect than single approaches. Among these approaches, the requirement of collateral has the strongest enhancement effect. Moreover, the authors find that the guarantee provided by a state-owned bank or enterprise increases the bond rating more than the guarantee provided by other local government financing platforms.

The findings in this study suggest that the credit enhancement would be deeply affected by the approach used to secure the bond.

These results can help the local government make better decisions when issuing bond.

This study empirically analyzes the different credit enhancement approaches for securing LGPFBs for the first time and contributes to the literature regarding credit ratings of local government bonds.

This paper aims to unveil the greenwashing intention of green bonds issuing in Chinese enterprises through the lens of stock pricing efficiency.

Drawing on data of Chinese listed companies during 2012–2021, this study uses a difference-in-differences method to study how and through what mechanisms issuing green bonds impacts stock pricing efficiency.

Issuing green bonds lowers stock pricing efficiency, verifying the greenwashing intention of green bonds in China. Potential mechanisms underlie the increased investor attention and sentiment resulting from the information disclosures about corporate green and low-carbon development. This greenwashing issue is more pronounced in firms facing lower financing constraints, having stronger relations with the government, and located in highly marketized regions. In the context of uncertainty surrounding economic policies, especially trade policies, issuing green bonds can signal a weakening of the greenwashing effect.

The quality of information disclosure should be emphasized to ensure a substantive commitment to environmental responsibility signaled by green bond issuance, thereby mitigating greenwashing concerns.

Regulators and standard-setters should improve the issuance system for green bonds and promote the sustainable development of the green bond market through formulating unified certification criteria for green bonds and implementing a stringently periodic reporting system.

First, to the best of the authors’ knowledge, it is the first study to draw on the quality of information disclosure and the perspective of stock pricing efficiency to identify whether firms issuing green bonds engage in greenwashing. Second, the study uncovers the black-box underlying this greenwashing issue through investor attention and sentiment and examines further the moderating role of economic policy uncertainties.

This paper aimed to propose a novel fused-coating-based additive manufacturing (FCAM); the study of key process parameters and mechanical tests are performed to determine the proper parameters when building metal components.

Sn63Pb37 alloy is deposited in an induction heating furnace with a fused-coating nozzle to build metal parts on a copper-clad substrate. The process parameters including nozzle pressure, nozzle and substrate temperature and nozzle gap between substrate are analyzed and found to have great influence on parts quality. The mechanical property tests between the fused-coating and casting parts are performed in horizontal and vertical directions. Also, the optical microscopy images are used to ascertain under which conditions good bonding can be achieved.

A FCAM method is proposed, and the exploration study about the manufacturing process is carried out. The critical parameters are analyzed, and microscopy images prove the suitable temperature range that requires to fabricate metal parts. The mechanical tests confirm that tensile strength of printing parts is improved by 20.4 and 11.9 per cent in horizontal and vertical direction than casting parts. The experimental results indicate that there is a close relationship between process parameters and mechanical properties.

This paper proves that FCAM provides an alternative way to quickly make functional metal parts with good quality and flexibility compared with other additive manufacturing methods. Moreover, good mechanical property is achieved than conventional casting parts.

The purpose of this paper is to detect wound infection by electronic nose (Enose) and to improve the performance of Enose.

Mice are used as experimental subjects. Orthogonal signal correction (OSC) is applied to preprocess the response of Enose. Radical basis function (RBF) network is used for discrimination, and the parameters in RBF are optimized by particle swarm optimization.

OSC is very suitable for eliminating interference and improving the performance of Enose in wound infection detection.

Further research is required to sample wound infection dataset of human beings and to demonstrate that the Enose with proper algorithms can be used to detect wound infection.

In this paper, Enose is used to detect wound infection, and OSC is used to improve the performance of the Enose. This widens the application area of Enose and OSC.

The innovative concept paves the way for the application of Enose.

The purpose of this study is to investigate whether the corporate environmental, social and governance (ESG) performance of enterprise is influenced by the enterprise digital transformation. In addition, this study explains how enterprise digital transformation affects ESG performance.

The sample covers 4,646 nonfinancial companies listed on China’s A-share market from 2009 to 2021. The study adopts the fixed-effects multiple linear regression to perform the data analysis.

The study finds that enterprise digital transformation has a significant inverted U-shaped impact on ESG performance. Moderate digital transformation can improve enterprise ESG performance, whereas excessive digital transformation will bring new organizational conflicts and increase enterprise costs, which is detrimental to ESG performance. This inverted U-shaped effect is more pronounced in industrial cities, manufacturing industries and enterprises with less financing constraints and executives with financial backgrounds. Enterprise digital transformation mainly affects ESG performance by affecting the level of internal information communication and disclosure, the level of internal control and the principal-agent cost.

The government should take multiple measures to encourage enterprises to choose appropriate digital transformation based on their own production behaviors and development strategies, encourage them to innovate and upgrade their organizational management and development models in conjunction with digital transformation and guide them to use digital technology to improve ESG performance.

This study shows that irrational digital transformation cannot effectively improve the ESG performance of enterprises and promote the sustainable development of the country. Enterprises should carry out reasonable digital transformation according to their own development needs and finally improve the green and sustainable development ability of enterprises and promote the sustainable development of society.

This study examines the relationship between enterprise digital transformation and ESG performance. Different from the linear relationship between the two in previous major studies, this study proves the inverse U-shaped relationship between enterprise digital transformation and ESG performance through mathematical theoretical model derivation and empirical test. This study also explores in detail how corporate digital transformation affects ESG performance, as well as discusses heterogeneity at the city, industry and firm levels. It is proposed that enterprises should take into account their own characteristics and carry out reasonable digital transformation according to their development needs.

The purpose of this paper is to identify the energy absorption characteristics of arch micro-strut (ARCH) lattice structure (different from traditional straight micro-strut lattice structure) under high-speed impact, and promote the development of special-shaped micro-strut lattice structure.

The study serves to study the anti-impact and energy absorption characteristics of ARCH lattice structure under different strain rates and different unit layers of lattice structure. In this paper, quasi-static compression and Hopkinson compression bar experiments are used for comparative analysis.

The results show that the ARCH lattice structure has obvious strain rate effect. When the strain rate is low, the number of layers of lattice structure has a great influence on the mechanical properties. With the increase of strain rate, the influence of the number of layers on the mechanical properties gradually weakens. So the ARCH lattice structure with fewer layers (less than five layers) should be selected as the impact energy absorbing materials at lower impact rate, while at higher impact rate, the number of layers can be selected according to the actual requirements of components or devices space size.

This study shows that Arch lattice structure has excellent energy absorption performance, and provides a theoretical reference for the application of ARCH lattice structure in energy-absorbing materials. ARCH lattice structure is expected to be applied to a variety of energy absorption and anti-impact components or devices, such as aircraft black box fall buffer components, impact resistant layer of bulletproof and landing buffer device.

With respect to the multiple-attribute decision-making problem with subjective preference for a certain attribute whose weight-value range have been given over other attributes whose weight values are unknown, a method based on the mean value of the grey number is proposed to analyse the decision-making problem. This method is used to choose a supply-chain partner under the condition that the decision makers have a preference for a certain attribute of various alternatives. The paper aims to discuss these issues.

First, the middle value of the preferred attribute’s weight-value range is supposed to be its weight value according to the content of the mean value of the grey number. Second, to reflect the decision maker’s subjective preference information, an improved optimisation model that requests the minimum deviation between the actual and expected numerical value of each attribute is constructed to assess the attributes’ weights. Third, the correlated degree and the correlation matrix, which are determined by the weight values of all attributes, are used to rank all the alternatives.

This paper provides a method for making a decision when decision makers have a preference for a certain attribute from an array of various alternatives, and the range of the certain attribute’s weight value is given but the weight value of the other attributes is unknown. When applied to supply-chain partner selection, this method proves feasible and effective.

This method is feasible and effective when applied to supply-chain partner selection, and can be applied to other kinds of decision-making problems. This means it has significant theoretical importance and extensive practical value.

Based on the mean value of the grey number, an optimisation model is built to determine the importance degree of each attribute, then the correlated degree of each alternative is combined to rank all the alternatives. This method can suit the decision makers’ subjective preference for a certain attribute well.