Ligament Repair Method for Sports Dancers Based on Health Monitoring

In sports dance training, the injury rate of athletes' ligaments is higher than that of ordinary ones. Careful study of ligament repair methods can better prolong the sports life of sports dance athletes. The purpose of this paper is to investigate how to analyze ligament repair in sports dancers based on health monitoring. This paper proposes a deep learning algorithm based on health monitoring and a neural network algorithm, both of which have the function of prediction. The experimental results of this paper show that the common injuries in sports dance include ankle ligament injury, medial and lateral knee ligament strain, lumbar muscle strain, toe joint strain, shoulder deltoid strain and other injuries. Among them, the number of ankle ligament injuries is the largest, with 36 people, accounting for 30%. The number of people with medial and lateral ligament strains in the knee joint ranks second with 32 people, accounting for 26%. The total proportion of two kinds of ligament strains is 56%, accounting for more than half. It can be seen that ligament strains are the most common in sports dance injuries, so it is of great research significance to repair the ligaments of sports dancers based on health monitoring.

Public View of Public Health Emergencies Based on Artificial Intelligence Data

In the current environment where the network and the real society are intertwined, the network public view of public emergencies has involved in reality and altered the ecology of communal public views in China. A new online court of influence has been created, and it affected the trend of events. As the main type of public emergencies, public health emergencies are directly related to people's health and life insurance. Therefore, the public often pays special attention. At present, correct media guidance plays an irreplaceable and important role in calming people's hearts and stabilizing social order. If news and public view are left unchecked, it is likely to cause panic among the people. However, in reality, public view research has always been a research object that is difficult to intelligentize and quantify. Based on such a realistic background, the article conducts a research on public view of public health emergencies based on artificial intelligence data analysis. This study designs an expert system for network public view and optimizes the algorithm for the key problem: SFC deployment. Finally, the system was put into real news and public opinion research on new coronavirus epidemic prevention, and experimental tests were carried out. The experimental results have shown that in the new coronavirus incident, the nuclear leakage incident, and the epidemic prevention policy, the data obtained by the public through the Internet are 50%, 68.06%, and 64.35%, respectively. For the system function in this study, both ICSO and IPSO are far better than the optimization results of CSO and PSO. For most of the test functions, IPSO is better than ICSO's optimization results, which better fulfills the needs of the research content. This study will make an in-depth analysis of the evolution process of online public opinion on public emergencies from the macro-, meso-, and micro-perspectives, in order to analyze the dissemination methods and internal evolution mechanism of various public emergencies of online public opinion, which provides countermeasures and suggestions for the government to guide and manage network public opinion.

Research on Factors Affecting Chinese College Students' Marriage Intention: Applying the Theory of Planned Behaviour

With the second demographic transition, the marriage rate in China has decreased annually. This reduction will have a key impact on national economic and social development. There is a need to investigate the factors that influence college students' marriage intentions, which can predict the development trend of marriage and family formation in China's future society to some extent. Previous studies focus on the extraction of college students' beliefs about marriage and love, whereas few studies have investigated how these beliefs affect marriage intention and marriage behaviour. Based on the theory of planned behaviour, this study aims to explore the impact of attitude towards behaviour, subjective norm, and perceived behavioural control on marriage intention; analysed the key predictors of college students' marriage intentions; and provided implications for pro-marriage policy. Using convenience sampling, data were obtained from 772 college students (mean age = 20.38 ± 2.38 years; 64.5% women) from three universities in China. Two types of questionnaires were designed to elicit salient beliefs and marriage intentions, respectively. Students' marriage intentions were positively correlated with attitude towards behaviour, subjective norm, and perceived behavioural control. Most participants were inclined to get married, and the marriage intention of women was significantly lower than that of men. The important beliefs and factors influence students' marriage intention included stereotypical gender identity, low fertility intention, weakened inter-generational transmission of traditional family ideas, materialisation of marriage, and negative information about marriage disseminated by the mass media. To promote college students' marriage intention, pro-marriage policies conducive to fertility, good economic prospects, more employment opportunities, positive publicity of marriage by the mass media are suggested to make, and it is necessary for universities to offer marriage and love courses to college students.

Predicting the Success of Internet Social Welfare Crowdfunding Based on Text Information

This study explored how the success of project crowdfunding can be predicted based on the texts of Internet social welfare crowdfunding projects. Through a calculation of the quantity of information and a mining of the sentimental value of the text, how the text information of the interconnected social welfare crowdfunding project affects the success of the project was studied. To this aim, a sentimental dictionary of Chinese Internet social welfare crowdfunding texts was constructed, and information entropy was used to calculate the quantity of information in the text. It was found that, compared with the information presented in the text, the fundraiser’s social network factors are key in improving the success of fundraising. The sentimental value of the text positively affects the success of fundraising, while the influence of the quantity of information is represented as an inverted, U-shaped relationship. The non-ideal R-squared indices reflected that the multiple linear regression models do not perform well regarding this prediction. Furthermore, this paper validated and analyzed the prediction efficiency of four machine-learning models, including a multiple regression model, a decision tree regression model, a random forest regression model, and an AdaBoost regression model, and the AdaBoost regressor showed the best efficiency, with an accuracy R2 of up to 97.7%. This study provides methods for the quantified processing of information contained in social welfare crowdfunding texts, as well as effective prediction on social welfare crowdfunding, and also seeks to raise the success rate of crowdfunding and thus features commercial and social value.

Medical Crowdfunding Campaign Sharing Behaviour on Mobile Social Media

This study explores factors influencing the sharing behaviour of medical crowdfunding campaigns on mobile social media regarding retweeter and retweeted text features from the social influence perspective and verifies the moderating effect of potential supporter donations. Two scenario-based surveys of medical crowdfunding users are conducted in China. The first (524 samples) and second (510 samples) surveys emphasize scenarios without and with retweeted text, respectively. These surveys are tested by PLS-SEM and suggest that the credibility of retweeters and the strength of their relationship with supporters can positively promote potential supporters' sharing behaviour, regardless of whether retweeters provide retweeted text. Three features of retweeted text significantly influence medical crowdfunding sharing behaviour. Retweeted text can promote sharing behaviour among individuals. With retweeted text available, if potential supporters donate before sharing the campaign, then the influences of retweeter and retweeted text features can be increased.