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The research aims to explore the influence mechanism of peer feedback and users' knowledge contribution behavior. This study draws on the social identity theory and considers social identity as a mediating factor into the research framework.

This paper collected users' activity data of 142,191 ideas submitted by 76,647 users from the MIUI community between October 2010 and May 2018 via Python software, and data were processed using Stata 16.0.

The results indicate that knowledge feedback and social feedback positively influence users' knowledge contribution (quantity and quality), respectively. User's cognitive identity positively mediates the relationship between peer feedback and knowledge contribution behavior, affective identity positively mediates the relationship between peer feedback and knowledge contribution behavior, while evaluative identity positively mediates the relationship between peer feedback and knowledge contribution quality, but there is no mediating effect between peer feedback and knowledge contribution quantity.

This study advances knowledge management by highlighting peer feedback on online innovation communities. By demonstrating the significant mediating effect of social identity, this study empirically clarifies the relationships of peer feedback (knowledge feedback and social feedback) to specific dimensions of knowledge contribution, thereby providing managerial guidance to the online innovation community on incentivizing and managing user interaction to foster the innovation development of firms.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.

This paper aims to analyze the role of influential nodes on other users on Facebook social media sites by social and behavioral characteristics of users. Hence, a new centrality for user is defined, applying susceptible-infected recovered (SIR) model to identify influence of users. Results show that the combination of behavioral and social characteristics would be determined the most influential users that influence majority of nodes on social networks.

In this paper, the authors define a new centrality for users, considering node status and behaviors. Thus, this node has a high level of influence. Node social status includes node degree, clustering coefficient and average neighbors’ node, and social status of node refers to user activities on Facebook social media website such as sending posts and receiving likes from other users. According to social status and user activity, the new centrality is defined. Finally, through the SIR model, the authors explore infection power of nodes and their influences of other node in the network.

Results show that the proposed centrality is more effective than other centrality approaches, infecting more nodes in social network. Another significant point in this research is that users who have high social status and activities on Facebook are more influential than users who have only high social status on the Facebook social media.

The influence of user on others in social media includes two key factors. The first factor is user social status such as node degree and clustering coefficient in social media graph and the second factor is related to user social activities in social media sites. Most centralities focused on node social status without considering node behavior. This paper analyzes the role of influential nodes on other users on Facebook social media site by social and behavioral characteristics of users.

Government cash forecasting is central to achieving effective government cash management but research in this area is scarce. The purpose of this paper is to address this shortcoming by developing a government cash forecasting model with an accuracy acceptable to the cash manager in emerging economies.

The paper follows “top-down” approach to develop a government cash forecasting model. It uses the Indonesian Government expenditure data from 2008 to 2015 as an illustration. The study utilises ARIMA, neural network and hybrid models to investigate the best procedure for predicting government expenditure.

The results show that the best method to build a government cash forecasting model is subject to forecasting performance measurement tool and the data used.

The study uses the data from one government only as its sample, which may limit the ability to generalise the results to a wider population.

This paper is novel in developing a government cash forecasting model in the context of emerging economies.

The purpose of this paper is to develop a vision-based dual-arm cyclic motion method, focusing on solving the problems of an uncertain grasp position of the object and the dual-arm joint-angle-drift phenomenon.

A novel cascade control structure is proposed which associates an adaptive neural network with kinematics redundancy optimization. A radial basis function (RBF) neural network in conjunction with a conventional proportional–integral (PI) controller is applied to compensate for the uncertainty of the image Jacobian matrix which includes the estimated grasp position. To avoid the joint-angle-drift phenomenon, a dual neural network (DNN) solver in conjunction with a PI controller and dual-arm-coordinated constraints is applied to optimize the closed-chain kinematics redundancy.

The proposed method was implemented on an industrial robotic MOTOMAN with two 7-degrees of freedom robotic arms. Two experiments of carrying a tray repeatedly and turning a steering wheel were carried out, and the results indicate that the closed-trajectories tracking is achieved successfully both in the image plane and the joint spaces with the uncertain grasp position, which validates the accuracy and realizability of the proposed PI-RBF-DNN control strategy.

The adaptive neural network visual servoing method is applied to the dual-arm cyclic motion with the uncertain grasp position of the object. The proposed method enhances the environmental adaptability of a dual-arm robot in a practical manipulation task.

The purpose of this paper is to track the changes of leadership attributes during the process of social development from 1998 to 2008 in China, and then to explore whether the significant events have potential impacts on the changes of leadership attributes during this time.

The authors conducted two studies through qualitative approach, based on data collected from Chinese official newspapers (People's Daily and Guangming Daily) covering 216 stories (90 stories in Study 1 and 126 stories in Study 2). The first study focuses on comparing the leadership attributes in 2008 with those in 1998 and 1988 presented in previous research to find out the changes. In the second study, the most significant events are selected during the period of 1998‐2008 and their effects on the changes of leadership attributes are examined.

The findings show that leadership attributes changed during the ten years and some new attributes are advocated in China. Furthermore, the changes of leadership attributes, especially for non‐business leaders, relate to the occurrence of significant events and reflect the changes of government policies.

Although some literature has explored leadership attributes in China, this study contributes to the extant research in two ways. First, it promotes the use of existing documents as data sources of longitudinal study to track the changes of leadership attributes. Second, it advances the line of inquiry of leadership attributes in China by concentrating on the factors driving those changes.

This paper aims to explore true technical efficiency in order to select the most competitive manufacturing industries in China. And the paper intends to discuss how environmental variables measured by energy consumption affect performance in different industrial sectors under the restriction of low-carbon economy.

In order to measure the calculated efficiency of industrial sectors more accurately, Three-stage DEA model is presented in the empirical analysis using data from 2007 to 2010 covering 29 manufacturing industries in China. The advantage of using this method is enabling us to separate the managerial factor from external environmental factors and random errors factors on the technical efficiency.

The results using this Three-stage DEA model show that textile manufacturing sector has the highest technical efficiency, and when environment variables are not considered, efficiencies in machinery and electronics manufacturing industries have a significant increase. Moreover, this empirical model enables us to evaluate the technical performance in various manufacturing sectors more accurately.

This study provides a useful efficiency measurement tool (Three-stage DEA model) to calculate technical efficiency among different industrial sectors. Technical efficiency plays a key role in building the competitiveness of manufacturing industry. Based on the objective efficiency evaluation, the paper can make a better selection of the most competitive industries.

The paper contributes to the existing literature by developing a Three-stage DEA to examine the technical efficiency and competitive power of manufacturing sectors in China. This study has great policy implications for the research of China's manufacturing in both ideas and methodology.

The purpose of this paper is to examine the changes of brain functional network after electroconvulsive therapy (ECT) treatment in major depressive disorder (MDD).

In this study, resting electroencephalography (EEG) is used to explore the changes in spectral power density, functional connectivity and network topology elicited by an acute open-label course of ECT in a group of 19 MDD subjects. The brain functional network based on Pearson correlation is constructed in a continuous threshold space (0.38–0.59). Complex network theory is used to analyze the network characteristic such as the length of the characteristic path, clustering coefficient, degree, betweenness centrality, global efficiency and small-world architecture.

The results show that ECT increased the spectral power density of Delta, Theta and Alpha1 bands and the full frequency. ECT increases the functional connectivity in Delta and full frequency and reduces the functional connectivity in Alpha2 band. In the selected threshold space, the clustering coefficient, global efficiency and small-world attributes of the network are changed significantly after ECT.

The findings indicate that resting EEG could effectively characterize the changes of brain functional networks following ECT in MDD. The results provide a theoretical basis to explore the neurophysiological mechanism of ECT in the field of MDD treatment.

The purpose of this paper is to describe and discuss the use of Chinese medicine in the prevention and treatment of coronavirus disease 2019 (COVID-19) in China and Asia.

This paper provides a brief overview of the COVID-19. Based on the syndrome differentiation (辨證論治), the concept of clearing heat and detoxifying lung in traditional Chinese medicine is used to prevent and treat COVID-19 through restoring the vital qi (正氣) in human body and regulating the lung as well as spleen to strengthen the immune system. Traditional Chinese medicine has been used as a complementary therapy for the possible intervention of COVID-19 including traditional Chinese herbal decoctions, Chinese traditional patent medicines, acupuncture and moxibustion as well as the traditional health exercises in China and parts of Asia.

Traditional Chinese medicine plays a significant role in the prevention and treatment of COVID-19 pandemic. The infection cases of China are around 80,000 and a steady decline compared with the USA which has 5,000,000 infection cases and continuous increases. It is shown that more than 90% of patients recovered after the treatment of traditional Chinese herbal decoctions and Chinese traditional patent medicines without any side-effect compared to the use of Remdesivir (GS-5734). Acupuncture (針灸) and moxibustion (艾灸) stimulate the immune and nervous systems for preventing infectious diseases. Taichi (太極) and Baduanjin (八段錦) as the auxiliary aerobic exercise under the theory of Chinese medicine can enhance the immune system and improve the lung function. Thus, an integration of traditional Chinese Medicine and Western medicine is the best strategy for the prevention, treatment and control of COVID-19 pandemic in the future.

This paper describes traditional Chinese medicine as an effective way for the prevention and treatment of COVID-19.

Mechatronics is a very important area of research in industrial applications. The purpose of this study is to find some of the most important components of the literature on this subject.

The analysis is based on the use of the database Compendex; it was searched in the broadest way for documents related to mechatronics. In addition, subject guides from libraries of universities with mechatronics programs were studied to find resources available in those areas.

The literature of mechatronics is extensive and multidisciplinary. Based on the results from Compendex, the following data were found: most productive authors, list of leading journals and conference proceedings, publishers and grant organizations, authors’ affiliations and other minor details. Based on the analysis of subject guides, the following types of resources were found: research databases, reference books and ebook collections.

Part of the analysis is based on a search performed in one technical database, Compendex; it was the database that generated the largest number of citations as compared to Inspec and the Web of Science. The results have a strong English language focus. It is possible that by using the results from multiple data bases, some additional sources could be obtained.

Mechatronics is a relatively new technological field comprising a number of scientific and engineering areas. The results obtained summarized a significant amount of bibliographic information.

The work is original; to the best of the authors’ knowledge, no other study has analyzed the literature on this subject.