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Background Although the existing cases of COVID-19 in China have been reducing since late February 2020, the confirmed cases are surging abroad. Improving public knowledge regarding COVID-19 is critical to control the epidemic. The study aimed to determine the China’s public knowledge of COVID-19 and attitude towards the control measures.Methods A cross-sectional study was conducted in 48 hours, from 29 February 2020, 22:30 to 2 March 2020, 22:30, based on a self-administered web-based questionnaire. The survey was conducted on the WeChat network. Exponential non-discriminative snowball sampling were applied. The questionnaire was voluntarily submitted by WeChat users. The questionnaire covered the basic demographic information, public knowledge about epidemiological and clinical characteristics of COVID-19, psychological state, and attitude towards overall control measures. The primary outcome was the Chinese public knowledge regarding COVID-19 and the attitude towards the control measures and secondary outcome was psychological state of the public during this epidemic.Results The study included 10,905 participants and 10,399 valid questionnaires were included for analysis. Participants with tertiary education, younger age and healthcare workers had better overall knowledge compared with other participants (all P<0.05). About 91.9% of the participants believed in person-to-person transmission and 39.1% believed in animal-to-person transmission. No significant correlation between anxiety and regional number of existing cases was found, while participants in Hubei were more anxious than those in other regions. In general, 74.1% of participants acknowledged the effectiveness of overall control measures and it was negatively correlated with regional number of existing cases (r=-0.492, P=0.007).Conclusions In conclusion, the survey revealed that Chinese public had overall good knowledge regarding COVID-19 except for those indeterminate knowledge. With the dynamic change of global epidemic situation and more researches, further study would be conducted to explore the change of public knowledge and attitude about COVID-19 in the future.

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In a pallet pool, pallets would be delivered through a supply chain. The operation procedure that consists of at least five operation processes as distribution, reposition, recycling, purchase (or rent), and maintenance is quite complex. These pallets are likely to be damaged, lost, destroyed, and so on. So, it is necessary to monitor the pallets using radio-frequency identification technology. However, there is no literature on the management of a pallet pool with both radio-frequency identification–tagged pallets and non-tagged pallets being put into consideration. In our research, an optimization model is presented to manage such a pallet pool. The objective of the optimization model is to minimize the total operation cost of a pallet pool including distribution cost, reposition cost, recycling cost, purchase or rent cost, loss cost, maintenance cost, loading and unloading cost, storage cost, and punishment cost. A particle swarm optimization algorithm is developed in Microsoft Visual Basic. Our numerical example shows that the optimization model and particle swarm optimization algorithm are effective. It is proved that the model and algorithm also can be used to measure whether the investment of a radio-frequency identification system is valuable or not. We proposed some suggestions for the pallet pools management.

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