No abstract

With the rapid increase in the ageing population (60+) in China since 1999, the problem of supporting the aged is facing increasingly severe challenges. Based on the 2072 valid samples from the Chinese General Social Survey (CGSS) of 2017, a non-sequential multinomial logistic regression model was established to analyse the changing trends and micro-influencing factors of Chinese people’s cognition of old-age care responsibility (COACR). The result shows that offspring responsibility still is a common COACR, but this concept has been gradually weakened and been replaced by the responsibility of the government and the aged. Individual characteristics and relationships with relatives in the models all significantly affect people’s COACR. It is obviously unrealistic for China to completely rely on government and society to provide for the aged. The traditional ethical role of inter-generational responsibility in providing for the aged should be brought into play. Reshaping the inter-generational responsibility ethics of old-age care requires the joint efforts of government, society, families, individuals and other responsible subjects to construct a diversified old-age care service system.

With the improvement of the economic level and the continuous improvement of people living standards, international tourism has become a major boom. International tourism can not only improve the local economic income but also improve the construction of local facilities. Not only does the development of a country tourism depend on the local tourism attraction, but also the economic situation of country is more critical for tourism. The arrangement of sports facilities is also key to the popularity of international tourist cities and to increase tourism, because public sports facilities can not only enhance the physical exercise of tourists but also display local characteristics in the form of sports facilities. The decisions on public sports facilities are based on local government policies and other factors such as travelers preferences, which requires reference to the layout of public sports facilities in successful international tourist cities. This research uses fuzzy multicriteria decision-making algorithm and neural network technology to conduct decision-making and prediction research on the relevant factors of public sports resource allocation in international tourist cities. The research results show that the fuzzy multicriteria algorithm has high accuracy for decision-making and prediction of public sports resource setting. The maximum error of multicriteria algorithm and neural network method in predicting the relevant factors of public sports setting is only 2.45%, and this part of the error comes from the needs of tourists. The smallest error is only 1.34%. The fuzzy multicriteria algorithm has an accuracy of more than 95% for decision-making and prediction of public sports facilities, which is beneficial to decision makers of sports settings in international tourist cities.

Based on the analysis of bacterial parasitic behavior and biological immune mechanism, this paper puts forward the basic idea and implementation method of an embedding adaptive dynamic probabilistic parasitic immune mechanism into a particle swarm optimization algorithm and constructs particle swarm optimization based on an adaptive dynamic probabilistic parasitic immune mechanism algorithm. The specific idea is to use the elite learning mechanism for the parasitic group with a strong parasitic ability to improve the ability of the algorithm to jump out of the local extreme value, and the host will generate acquired immunity against the parasitic behavior of the parasitic group to enhance the diversity of the host population’s particles. Parasitic behavior occurs when the number of times reaches a predetermined algebra. In this paper, an example simulation is carried out for the prescheduling and dynamic scheduling of immune inspection. The effectiveness of prescheduling for immune inspection is verified, and the rules constructed by the adaptive dynamic probability particle swarm algorithm and seven commonly used scheduling rules are tested on two common dynamic events of emergency task insertion and subdistributed immune inspection equipment failure. In contrast, the experimental data was analyzed. From the analysis of experimental results, under the indicator of minimum completion time, the overall performance of the adaptive dynamic probability particle swarm optimization algorithm in 20 emergency task insertion instances and 20 subdistributed immune inspection equipment failure instances is better than that of seven scheduling rules. Therefore, in the two dynamic events of emergency task insertion and subdistributed immune inspection equipment failure, the adaptive dynamic probabilistic particle swarm algorithm proposed in this paper can construct effective scheduling rules for the rescheduling of the system when dynamic events occur and the constructed scheduling. The performance of the rules is better than that of the commonly used scheduling rules. Among the commonly used scheduling rules, the performance of the FIFO scheduling rules is also better. In general, the immune inspection scheduling multiagent system in this paper can complete the prescheduling of immune inspection and process dynamic events of the inspection process and realize the prereactive scheduling of the immune inspection process.