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In the long-term network operation, the power distribution network will be subjected to the effects of ultra-high voltage, strong electromagnetic interference and harsh natural environment on the power system, which will lead to the occurrence of different faults in the distribution network and directly affect the normal operation of the power grid.

The purpose of this study is to solve the problems of labor intensity, high risk and low efficiency of distribution network manual maintenance operation, this paper proposed a new configuration of the live working robot for distribution network maintenance, the robot is equipped with dual working arms through the mobile platform, which can realize the coordination movement, the autonomous reorganization and replacement of the end tools, respectively, so as the robot power distribution maintenance function such as stripping, trimming, wiring and the operation control problem of the distribution network-robot with small arms and in small operation space can be realized.

To effective elimination or reduce the adverse effects of the internal forces in the closed chain between the working object and manipulator under the typical task of the 10 kV distribution network, this paper has established the robot coordinated control dynamics model in the closed-chain between the dual-working object and proposed the dynamic distribution method of closed-chain internal force and the effectiveness has been proved by simulation experiments and 10 kV field operation.

The force-position hybrid control can realize the mutual compensation of force and position so as to effectively reduce the internal force in the closed chain. Finally, the engineering practicality of the method is verified by field operation experiment, the effective implementation of this control method greatly improves the robot working efficiency and the operation reliability, the promotion and application of the control method have great theoretical and practical value and maintenance management system, so as to achieve automation of electric.

This study aims to investigate the role of multidimensional social capital and consumer subjective well-being in online brand communities (OBCs). The aim was to provide practical guidance to global brand marketers for cultivating and strengthening OBC operations, optimizing consumer-brand-community relationships and creating value in the digital age.

A total of 576 valid questionnaires were collected through an online survey, and the model was tested using partial least squares structural equation modeling.

In OBCs, the cognitive dimension of social capital (i.e. shared language and shared vision) strongly affects the relational dimension of social capital (i.e. social trust and reciprocity). Both these dimensions also positively influence consumer community subjective well-being, which, in turn, enhances consumer brand subjective well-being. Thus, community subjective well-being has a mediating role in the aforementioned relationship, and brand community is an antecedent to brand subjective well-being.

Future studies should investigate other dimensions of social capital and well-being, as well as moderator variables, social environments and types of culture.

This study constructed a conceptual framework that focused on the effect of multidimensional social capital in OBCs to elucidate antecedents of brand subjective well-being from the perspectives of social networks and relationships. Moreover, it examined how brands strategically expand their clientele base with regard to target customers.

The return behavior of customers has a great impact on the e-retail industry and has resulted in the emergence of return-freight insurance (RI). Additionally, customer loss aversion arising from returns affects e-retailers' decisions and manufacturers' profits. Therefore, the main purpose of the authors' study is to determine how e-retailers and manufacturers choose their RI strategy and pricing according to customers' loss aversion.

The authors propose three scenarios: no RI, customer purchase RI and free e-retail RI (FRI). Meanwhile, the authors also model a Stackelberg game between e-retailers and manufacturers for analysis. Then, according to customer return behavior and loss aversion, the authors study the optimal pricing decision and RI premium allocation scheme for e-retailers and manufacturers under different scenarios.

It was found that the loss sensitivity reduces customers' willingness to buy RI, which is not conducive to the development of e-retailers and manufacturers. Additionally, with higher loss sensitivity, e-retailers and manufacturers offer FRI to gain higher profits, which supports the implementation of the FRI strategy.

The authors introduce customers' loss aversion into RI to analyze the optimal pricing decisions and profits of e-retailers and manufacturers, enriching the application of loss aversion theory. In addition, this study analyzes the two-way cost-sharing mechanism between manufacturers and e-retailers to provide FRI, which provides a theoretical basis for RI premium sharing.

Consumers share negative brand experience in many occasions to vent their emotion and seek support. The purpose of this paper is to investigate the impact of negative sharing on members’ happiness in online brand communities by drawing from two opposing constructs: social support and social exclusion.

Both survey and experiment methods were employed to test the conceptual model. Online survey data were collected from 1,015 mobile internet users.

The findings reveal that negative sharing may enhance a sharer’s happiness through online social support particularly for novice community members. The findings also indicate greater online social exclusion for experienced members than for novice members. These findings cast doubt on the widely held assumption that increased engagement in a community will always produces positive outcomes. The moderating effect of membership duration is confirmed with an experiment of MI’s brand community members.

The study of happiness in online brand community sheds new light on consumer–brand and user–community relationships.

While most previous studies on negative sharing only explored the negative side of consequences, the authors contribute to this line of research by introducing both positive (social support) and negative (social exclusion) outcomes of negative reviews. The model also explains the conditions under which negative reviews enhance social support and social exclusion.

The purpose of this paper is to present an approach to reduce energy consumption in data centers. Subsequently, it reduces electricity bills and carbon dioxide footprints resulting from their use.

The authors present a mathematical model of the energy dissipation optimization problem. The authors formulate analytically the server selection problem and the supply air temperature as a non-linear programming, and propose an algorithm to solve it dynamically.

A simulation study on SimWare, using real workload traces, shows considerable savings for different data center sizes and utilization rates as compared to three other classic algorithms. The results prove that the proposed algorithm is efficient in handling the energy-performance trade-off, and that the proposed algorithm provides significant energy savings and maintains a relatively homogenous and stable thermal state at the different rack units in the data center.

The proposed algorithm ensures energy provisioning, performance optimization over existing state-of-the-art heuristics, and on-demand workload allocation.

The purpose of this study is to investigate the influence of green finance on human well-being in China in the context of urbanization and aging population. It aims to explore the contributions of green finance in such demographic scenarios.

This study innovates and optimizes the calculation of the carbon intensity of human well-being (CIWB) index and strengthens the integrity of the assessment model for green finance development. It uses the serial multiple mediator model and moderation effect analysis to address the impact of green finance on human well-being in China on the provincial level from 2009 to 2020.

Green finance has a significant, positive and direct impact on human well-being. Simultaneously, it influences human well-being indirectly through three transmission channels. Urbanization and an ageing population are significant individual mediators through which green finance contributes to human well-being improvement. Notably, these two mediators also work together to transfer the promotional impact of green finance to human well-being.

The government can perfect the regulations to strengthen the market ecosystem to accelerate the development of green finance. Reforms on the administrative division to expand the size of cities with the implementation of ageing friendly development strategy is also necessary. Attracting incoming foreign direct investment in sustainable projects and adjusting public projects and trade activities to fulfil the sustainable principles are also regarded as essential.

The findings challenge traditional views on the impact of aging populations, highlighting the beneficial role of green finance in improving well-being amidst demographic changes. This offers a new perspective on economic and environmental sustainability in aging societies.

A multi-dimensional well-being indicator, CIWB and the serial multiple mediator model are used and direct and indirect impacts of green finance on human well-being is exhibited. It offers novel insights on the transmission channels behind, identifies the mediating role of urbanization and ageing population and offers empirical evidences with strong academic and policy implications.

Following the hierarchy-of-effects model, this study aims to propose a two-step process framework to investigate social media post efficacy via attraction and likes.

The study analyzes 113,785 social media posts from 126 WeChat official accounts to explore how external (headline features and account type) and internal (content features and media type) features impact social media post attractions and likes, respectively.

The antecedents of post attraction differ from those of post likes. First, headline features (punctuation, length, sentiment and lexical density) and account type significantly influence social media post attraction. Second, content features (depth, tone, domain specificity, lexical density and readability) and media type affect social media post likes.

First, this study considers online user engagement as a two-step process regarding social media posts and explores different influencing factors. Second, the study constructs new variables (account type and domain specificity) in each stage of the two-step process model.

This study aims to investigate whether and to what extent the characteristics of headlines impact the attraction of online financial articles by using data collected from WeChat, a popular social app in China.

By integrating the methods of econometric and text mining, this study analyzed the content of 113,917 headlines published by 126 official accounts from the day account being created to May 12, 2016. Hierarchical regression was used to investigate the effects of headline features, account ownership type and stock market volatility on the attraction of online financial articles.

The empirical results show that sentiment, length, domain specificity and language intensity in a headline are significantly associated with the attraction of an online financial article. In addition, the relative and moderating roles of stock market volatility and account ownership type were also explored, showing significant moderating effects on the relationship between sentiment and online article attraction.

This study had several limitations. First, the sample data for this research were collected from one social media platform. While WeChat is the most popular social media application in China, it is just one of the many social media applications that can be used to publish online financial articles, and it differs from other social media applications greatly. This makes it hard to generalize the conclusions of the study. Future studies could compare the different features of headlines and their effects on the attraction of financial articles on different platforms. Second, in mining the characteristics of headline, this study only analyzed the influence of the sentiment, domain specificity, length and language intensity of the headline on article attraction. In future studies, in-depth analysis of the headline content could be conducted, such as the similarity between the body text and the headline, the theme and the sense of humor. However, the authors believe that these limitations do not have major negative implications for the results and contributions of this study.

From a practical perspective, this work could help official WeChat accounts to write better headlines for the articles they publish to attract more readers and fans and thus improve the value of their accounts, which would enable them to maximize the tangible benefits through differential pricing on advertisement placement.

The contributions of this study are as follows. First, the paper explored how headline sentiment influences article attraction and found that positive sentiment is negatively related to article attraction, while negative sentiment is positively related to article attraction. In addition, there is an inverted U-shaped relationship between the extent of negative sentiment and article attraction. Second, the paper investigates how headline domain specificity affects article attraction and there is an inverted U-shaped relationship between headline domain specificity and article attraction. Third, to the best of the authors’ knowledge, this is the first large-scale case study that explores the association between stock market volatility and the attraction of an online financial article.