Purpose: This paper takes the current COVID-19 pandemic raging around the world as a realistic background and uses the informal scientific communication mode in social media as the theoretical basis. It aims to explore the characteristics and rules of scientific communication in social media under emergency events, grasp the potential and risks of scientific communication in social media in special times and provide a perspective of academic communication for the scientific response.Design/methodology/approach: The authors select the enumeration data of the early COVID-19 theme papers spread on social media networks as the research object, apply descriptive statistical analysis to the basic statistical distribution of variables and use factor analysis and visualization methods to explore the law and characteristics of the spread of scientific papers on social media platforms.Findings: It was found that users of the COVID-19 paper are mainly in North America, Europe and South America, followed by those in East Asia, Southeast Asia and Oceania. The users are mainly public figures, doctors and other practitioners, science communicators and scientists. The process of social media communication reflects three ways of knowledge construction, social interaction and academic communication, and there are three ways of communication law and changing trend of cross transition and integration.Originality/value: This study observes the function and role of science communication in social media in a special period from a unique perspective of academic communication, so as to promote academic means to fight against the epidemic.

With the update and iteration of Internet technology, the socialized Q&A (question‐and‐answer) platform realizes the cross‐border dissemination of knowledge with the main purpose of disseminating and sharing knowledge. Zhihu, as a knowledge‐sharing platform that relies on the user‐generated content model to maintain operation and the strong willingness of users to share knowledge, plays a key role in the development of the community. Currently, social Q&A platforms are facing problems such as low user participation rate and gradual decrease in the number of active users. It is very important and urgent to explore the factors that affect users’ willingness to share knowledge. In response to this problem, this paper builds a theoretical model of the factors that are affecting users’ willingness to share knowledge and uses questionnaire research methods to conduct research design and collect sample data and uses methods such as correlation analysis and structural equation modeling to verify the model and hypothesis. The research results show that the theoretical model of planned behavior has strong explanatory power and self‐efficacy and material rewards have a positive effect on knowledge‐sharing attitudes. Finally, according to some research results, this paper shows that, with the change of time, young people have different needs for knowledge sharing than before, while realizing self‐worth through sharing experience, and we also hope to protect own interests and hope that there are more factors to encourage more users to share their knowledge and experience. Therefore, we propose that the platform can use incentive mechanisms to promote knowledge sharing while helping sharers realize their self‐worth, improve the existing functions of the platform, or carry out activities to encourage users to participate, so as to achieve the purpose of knowledge sharing and maintain the operation of the Zhihu platform.

At present, urban traffic congestion is a common problem of urban development in China. Therefore, China’s government issued the policy of opening the gated communities in 2016, hoping to alleviate traffic pressure to some extent. But, at present, the quantitative empirical research on the effect of the policy implementation is less and more idealistic. In order to complete the leap from research on local isolated traffic capacity of static gated communities to research on global coupling traffic capacity of dynamic multilayer network and, to a certain extent, reflect the innovation of the research model and method, we make a quantitative analysis of the effect of the policy more consistent with the actual situation, which provides a quantitative management basis for the implementation of the policy. Based on literature review, this paper carried out two stages of research. Stage one consists of constructing traffic capacity assessment model—Road Capacity Assessment (RCA) model based on BPR (Bureau of Public Roads) impedance function from a simple local static perspective. Considering the factors such as delay of signalized intersection and travel time, through modeling analysis and empirical test, it is found that about 12% of local traffic capacity can be improved after open community. Stage two consists of constructing a global capacity model based on multilayer complex network coupling from the perspective of complex global dynamics. Considering the various network nodes of pedestrians, nonmotor vehicles, and other factors, we construct a multilayer complex network and dynamic superposition coupling. The empirical data show that the overall traffic capacity of the open community is improved by at least 11.3%. Finally, it can be concluded that a significant increase in the global traffic capacity of complex urban road networks means that the losses caused by traffic congestion will be reduced. In some first‐tier cities, the direct monthly income of the open‐gated community is as high as 230 million yuan, not to mention its overall economic, social, and environmental benefits.

Accurate and reliable photovoltaic (PV) cell parameter identification is critical to simulation analysis, maximum output power harvest, and optimal control of PV systems. However, inherent high-nonlinear and multi-modal characteristics usually result in thorny obstacles to hinder conventional optimization methods to obtain a fast and satisfactory performance. In this study, a novel bio-inspired grouped beetle antennae search (GBAS) algorithm is devised to effectively identify unknown parameters of three different PV models, i.e., single diode model (SDM), double diode model (DDM), and triple diode model (TDM). Compared against beetle antennae search (BAS) algorithm, optimization efficiency of GBAS algorithm is markedly enhanced based on a cooperative searching group that consists of multiple individuals rather than a single beetle. Besides, a dynamic balance mechanism between local exploitation and global exploration is designed to increase the probability for a higher quality optimum. Comprehensive case studies demonstrate that GBAS algorithm can outperform other advanced meta-heuristic algorithms in both optimization precision and stability for estimating PV cell parameters, e.g., standard deviation (SD) of root mean square error (RMSE) obtained by GBAS algorithm is 64.34% smaller than the best value obtained by other algorithms in SDM, 61.86% smaller than that in DDM.

Purpose: The emergence of a coronavirus disease 2019 (COVID-19) epidemic has had a tremendous impact on the world, and the characteristics of its evolution need to be better understood.Design/methodology/approach: To explore the changes of cases and control them effectively, this paper analyzes and models the fluctuation and dynamic characteristics of the daily growth rate based on the data of newly confirmed cases around the world. Based on the data, the authors identify the inflection points and analyze the causes of the new daily confirmed cases and deaths worldwide.Findings: The study found that the growth sequence of the number of new confirmed COVID-19 cases per day has a significant cluster of fluctuations. The impact of previous fluctuations in the future is gradually attenuated and shows a relatively gentle long-term downward trend. There are four inflection points in the global time series of new confirmed cases and the number of deaths per day. And these inflection points show the state of an accelerated rise, a slowdown in the rate of decline, a slowdown in the rate of growth and an accelerated decline in turn.Originality/value: This paper has a certain guiding and innovative significance for the dynamic research of COVID-19 cases in the world.

Reforming the teaching mode by integrating CALL into college English reading teaching is a possible and efficient solution to the current situation of college reading teaching. The hypothesis about the efficiency of CALL and its feasibility in Chinese college reading courses are confirmed and computer will act as a device enabling the college students in China to have greater achievement and motivation in reading. The results of this study indicate that CALL is an effective instruction mode in teaching English reading and have practical implications for the administrators, teachers, and students.