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The sudden outbreak of coronavirus disease 2019 (COVID-19) has caused a huge impact on the Chinese residents' health and economic level. In the pandemic background, the country and its institutions have introduced pandemic-related insurance to stabilize the national situation. At this stage, insurance has played an increasingly important role in social life. With the popularization of insurance, the idea of buying insurance to avoid risk has gradually become popular among people. Among them, the New Rural Cooperative Medical System (NRCMS) has been farmers' common choice. The NRCMS, a mutual aid system created by farmers spontaneously in the country, plays a great role in guaranteeing farmers access to basic health services, alleviating poverty caused by disease and returning to poverty due to disease, and promoting poverty alleviation and rural revitalization. Given this backdrop, we study the efficiency of the NRCMS that can effectively promote poverty alleviation and rural revitalization and ensure the people's happy life. Implementing the Data Envelopment Analysis (DEA), we find that technological progress is one of the main factors influencing the efficiency of the NRCMS. Therefore, it is important to improve the technology for providing the efficiency of the NRCMS and promoting the happiness of the society. [ABSTRACT FROM AUTHOR]

The fast development in China's economy has caused the rapid expansion of the domestic market. Since many economists do not have optimistic views regarding the bubble economy of China, it is necessary for Taiwanese businessmen to understand in-depth the business operational performance and financial situation of enterprises in China, so as to reduce the risk of a potential investment. In this article, data from the China Economic Research Database (CCER), the financial database of financial corporations are collected for analysis to investigate the business operation and management performance and financial characteristic of enterprises in China. In this article, grey relational analysis is applied first in order to investigate the business operational performance of 600 enterprises in China. Afterwards, a more recent clustering technique is used to divide, based on financial characteristic, enterprises in China into two groups. Finally, three models, namely genetic programming, Back-Propagation Neural Network and Logistic Regression are adopted to construct an Enterprise Operational Performance model and an Enterprise Finance Characteristic model, respectively. Based on the results found, it can be concluded that genetic programming yielded the best classification and forecast performance, compared to the other three techniques. [ABSTRACT FROM AUTHOR]

Classification of operating performance of the enterprises is not only a hot issue emphasized by the management, but it is an important reference for investors too in their decision-making. Generally speaking, when predicting or analyzing business performance classification, most researchers adopt corporate financial early warning or credit-rating models, which pretty much use previous data and facts. Therefore, this paper brings about an alternative method to discriminate between excellent and poor business management, so as to take preventive measures prior to business crisis or bankruptcy. We collected the financial reports and financial ratios from the listed firms in mainland China and Taiwan as our samples to build up four kinds of forecasting models for business performance. The empirical results show that the hybrid model provides better classification forecasting capability than the other models, while the ANFIS model adjusted by genetic algorithm could effectively enhance the classification forecasting capability. [ABSTRACT FROM AUTHOR]

• Total factor productivity changes and annual growth rates of sustainable development 31 regions in Mainland China from 2015 to 2018. • Technology efficiency and scale efficiency of e-agricultural infrastructure of 31 regions in Mainland China from 2015 to 2018. • Suggestions to enhance the management of electronic agriculture and protect the environment. With the rapid development of the Internet and telecommunication systems, the electronic agricultural infrastructure has been continuously improved. This paper analyzes the regional differences and dynamic evolution of agricultural sustainable development and efficiency in 31 regions of Mainland China. The research is based on a data envelopment analysis-Malmquist productivity index model. The paper puts forward suggestions to help enhance the management of electronic agriculture and keep a balance between societal development and environmental protection. [ABSTRACT FROM AUTHOR]

As an important economic sector, logistics is becoming more important, if not crucial, in economic growth. In our nation, the logistics industry is booming, and it's just getting better. However, in addition to focusing on the positive aspects of our country's logistics industry's development, we should also analyze and address the negative aspects of our country's logistics industry's development. The overall logistics pattern has not yet been formed, and there is an urgent need for systematic construction. The regional development is extremely unbalanced. By comparing the logistics performance indices of various Belt and Road countries, this research aims to examine the major elements influencing overall logistics performance. Second, we introduce the Moran index to explore the geographical association of the subdivision indicators of the logistics performance index using the spatial econometric model. The bootstrap DEA analysis method examines and ranks the countries' logistics performance indexes, determines our country's advantages and disadvantages in comparison to other Belt and Road countries, and executes specific improvement strategies that will enhance logistics and boost the overall growth of our country's logistics sector., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Wen-Tsao Pan et al.)

This paper examines the determinants of tourism stock returns in China from October 25, 2018, to October 21, 2020, including the COVID-19 era. We propose four deep learning prediction models based on the Back Propagation Neural Network (BPNN): Quantum Swarm Intelligence Algorithms (QSIA), Quantum Step Fruit-Fly Optimization Algorithm (QSFOA), Quantum Particle Swarm Optimization Algorithm (QPSO) and Quantum Genetic Algorithm (QGA). Firstly, the rough dataset is used to reduce the dimension of the indices. Secondly, the number of neurons in the multilayer of BPNN is optimized by QSIA, QSFOA, QPSO, and QGA, respectively. Finally, the deep learning models are then used to establish prediction models with the best number of neurons under these three algorithms for the non-linear real stock returns. The results indicate that the QSFOA-BPNN model has the highest prediction accuracy among all models, and it is defined as the most effective feasible method. This evidence is robust to different sub-periods., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Pan, Huang, Yang, Zhu, Pang and Zhuang.)