No abstract

With the acceleration of social modernization, the level of public management has also been significantly improved compared with previous times. People are more and more aware that a harmonious public life is the basis for realizing a good personal life. As an important guarantee for a harmonious public life, public management occupies an extremely important position. However, the great changes in the economy and society have brought forth new problems in modern public management, and the tasks and burdens of public management have become heavier and heavier, which has had a huge impact on the effectiveness of social governance. On the basis of studying the factors affecting the development of public management and the problems of management, this paper integrates the new situation of reliability mathematics application, and explores and solves the problems of public management. In the experiment, the effectiveness of the method proposed in this paper is tested from two aspects of management analysis and management effect, and the feasibility of this method is verified by comparing with the traditional management method. The final experimental data show that the innovation degree and coordination degree of management work under the traditional management method are 16.6% and 58.4%, respectively. The innovation degree and coordination degree of management work under the method of this paper reach 36.1% and 78.8%, respectively. It shows that the analysis and realization of the new situation of reliability mathematics application in public management have certain operability.

Micro-retail enterprises (MREs) have had to adapt to changing conditions in regards to mobile payments (m-payments) and access to loans. The authors built on the published results of two previous surveys of MREs. One thousand nine hundred and ninety-eight MREs in four cities in China were surveyed in May and June of 2018 to determine the use and importance of m-payments to their business, their access to loans, and patterns of entrepreneurship. While some trends remain unchanged, at least two significant findings emerge. M-payments have not only gained almost universal acceptance by MREs in China; they are the overwhelmingly preferred payment method. In our last paper, we argued that "m-payments may represent a form of leapfrogging technology which will spur growth in this sector" and this paper provides additional evidence this is occurring. M-payments show every sign of being an example of a leapfrogging technology that is quickly changing the rules for retail in the MRE sector and opening new opportunities for businesses while lowering transaction costs, reducing fraud, and improving customer satisfaction. There are intriguing implications, including changes to the pace of m-payments replacing credit cards. Second, while it remains true that overall, access to loans for MREs is limited; there is a great deal of variation between tier-one cities and tier-two and tier-three cities.

The size of funds managed by all hedge funds in the world has exceeded 2.7 trillion US dollars. The funds of various funds and asset management products managed by quantitative investment account for about 30% of the total global trading volume, and in various large stock exchanges around the world, various quantitative investment methods contribute nearly 50% volume of transactions. The construction of a quantitative trading strategy requires first statistical analysis of the information in the securities and futures market and then backtesting the quantitative model with historical data. In view of the practical application of quantitative trading, this study designs a quantitative trading system based on the data mining method. The main development tool used is the numerical computing software MATLAB, and four cores are designed: quantitative stock selection, strategy backtesting, time-series analysis, and portfolio management. The system supports modules for simple trading decisions. It abandons the traditional method of predicting the absolute value of the future price of stock index futures and adopts a new method of predicting the future price trend of stock index futures. This method avoids the huge impact of the accuracy of the absolute value of the prediction on the final investment in the traditional method and also reduces the high dependence of investors on the accuracy of the absolute value. This study also introduces the support-vector machine algorithm in data mining and the quantitative trading system model in data mining. The accuracy of investment transactions in the experiment is also simulated by using the support-vector machine.