No abstract

Time series data is common in data sets has become one of the focuses of current research. The prediction of time series can be realized through the mining of time series data, so that we can obtain the development process and regularity of social economic phenomena reflected by time series, and extrapolate to predict its development trend. More and more attention has been paid to time series prediction in the era of big data. It is the basic application of time series prediction to accurately predict the trend. In this paper, we introduce various time series autoregressive (AR) model, moving average (MA) model, and ARIMA model that is combined by AR and MA. As the time series prediction in general scenarios, the ARIMA is applied to the risk prediction of the National SME Stock Trading (New Third Board) in combination with specific scenarios. The case studies show that the results of our analysis are basically consistent with the actual situation, which has greatly helped the prediction of financial risks. INDEX TERMS Data mining, time series, financial forecast, AR, MA, ARIMA, financial risk.

In order to keep the bottom line of systemic financial risks and prevent the mitigation of major risks, this work focuses on the investigation of multi-source heterogeneous data fusion algorithms and cleaning technologies to establish a suitable style for data analysis and big data computation frame. In this paper, according to the above method, we provide the basis for early analysis of economic security. Utilizing the big data analysis, an emerging information technology method, we can be able to explore new risk early-warning methods, build a risk monitoring and early-warning platform and achieve scientific economic decision-making, so that the sources of economic risk in national economic security can be traced. INDEX TERMS Big data, pre-warning, economic security, early-warning methods.

Self-assembled monolayer (SAM) with tunable surface chemistry and smooth surface provides an approach to adhesion improvement and suppressing deleterious chemical interactions. Here, we demonstrate the SAM comprising of designed and synthesized 6-(3-triethoxysilylpropyl)amino-1,3,5-triazine-2,4-dithiol molecule, which can enhance interfacial adhesion to inhibit copper diffusion used in device metallization. The formation of the triazinedithiolsilane SAM is confirmed by X-ray photoelectron spectroscopy. The adhesion strength between SAM-coated substrate and electroless deposition copper film was up to 13.8 MPa. The design strategy of triazinedithiolsilane molecule is expected to open up the possibilities for replacing traditional organosilane to be applied in microelectronic industry.