COVID-19 not only poses a huge threat to public health, but also affects people’s mental health. Take scientific and effective psychological crisis intervention to prevent large-scale negative emotional contagion is an important task for epidemic prevention and control. This paper established a sentiment classification model to make sentiment annotation (positive and negative) about the 105,536 epidemic comments in 86 days on the official Weibo of People’s Daily, the test results showed that the accuracy of the model reached 88%, and the AUC value was greater than 0.9. Based on the marked data set, we explored the potential law between the changes in Internet public opinion and epidemic situation in China. First of all, we found that most of the Weibo users showed positive emotions, and the negative emotions were mainly caused by the fear and concern about the epidemic itself and the doubts about the work of the government. Secondly, there is a strong correlation between the changes of epidemic situation and people’s emotion. Also, we divided the epidemic into three period. The proportion of people’s negative emotions showed a similar trend with the number of newly confirmed cases in the growth and decay period, and the extinction period. In addition, we also found that women have more positive emotional performance than men, and the high-impact groups is also more positive than the low-impact groups. We hope that these conclusions can help China and other countries experiencing severe epidemics to guide publics respond.

Recently, most countries have entered the outbreak period of the novel coronavirus epidemic. This sudden outbreak has caused a huge impact on the global economy, which has intensified the division of globalization and the recession of the global economy. Although the epidemic situation in China has gradually stabilized, the severe situation in the world still inevitably impacts China's economy. Based on the uncertainty of future epidemic, this paper sets up three scenarios to analyze the impact of the epidemic on China's economy. The first is that in June, the epidemic both at home and abroad is under control without rebound; the second is that the domestic epidemic is basically controlled but the foreign situation is not effectively controlled; the third is that the epidemic situation in China has a serious rebound due to the influence of the imported cases from abroad, which destroy the economy again. At the same time, some corresponding guidelines are put forward for the recovery of economy, and to minimize the economic losses as well as accelerate the pace of national economic recovery. In addition, it is believed that these suggestions may have certain reference value to other countries.

The application of artificial intelligence (AI) methods in medical field is increasing year by year; however, few studies have applied AI methods in the reproductive field. In view of the complexity of infertility diagnosis and treatment, a machine learning-based

IMPORTANCEMany indicators need to be considered when judging the condition of patients with infertility, which makes diagnosis and treatment complicated. OBJECTIVE To construct a dynamic scoring system for infertility to assist clinicians in efficiently and accurately assessing the condition of patients with infertility.

With the development of the economy, environmental pollution caused by energy consumption has become increasingly prominent. Improving the efficiency of energy utilization is an important way to solve this problem. Firstly, we used a data envelopment analysis (DEA) model to calculate the energy utilization efficiency of China’s provinces and regions from the perspective of environmental constraints, including four inputs—labor force, capital stock, energy consumption and carbon emission—and one output, GDP. Secondly, an entity fixed effect model of panel data was built to investigate the influence of openness, urbanization, marketization and industrial structure on energy utilization efficiency in the process of economic structure change. The results indicate that China’s energy efficiency shows a trend of first stabilizing and then declining from 2007 to 2017. Meanwhile, the comprehensive energy efficiency of all provinces and regions is not very ideal. Only Beijing, Shanghai and Guangdong constitute the forefront of China’s energy efficiency. The lack of pure technical efficiency in most provinces is the main reason for the low comprehensive efficiency, but there are also obvious differences among provinces and regions. In addition, urbanization, openness and industrial structure have a negative impact on energy efficiency, while marketization has a significant positive impact on energy efficiency. Finally, based on the regional differences, some suggestions were put forward to improve China’s energy utilization efficiency.