Intelligent Early Warning of Internet Financial Risks Based on Mobile Computing

In order to establish the early warning model of internet finance, K-means algorithm improved by quantum evolutionary is used in this paper to divide risk early-warning interval by combining with the given initial value and the value-at-risk measured by China's well-known internet finance company. With the characteristics of parallelism and randomness, quantization algorithm is introduced into K-means algorithm to improve the search efficiency of original algorithm on the basis of maintaining the diversity of population. The sample is conducted with optimal segmentation by using improved algorithm to obtain the accurate early-warning interval and then the risk prediction model for internet financial institutions will be established by using the advantages of GMDH predictive mining and combining with the value-at-risk measured by “Renren Loan” Company. The effectiveness of early-warning model will be illustrated by comparing the actual situation of internet financial companies with more than 40,000 data of “Renren Loan” Company from January 2017 to October 2018.

Analysis of Early Warning Spatial and Temporal Differences of Tourism Carrying Capacity in China’s Island Cities

Tourism is the leading industry of island cities and the tourism carrying capacity is of great significance to the sustainable development of cities. This paper adopts the state-space model to construct an early warning indicator system for tourism carrying capacity from three aspects: nature, economy, and society, explores the early warning status, and spatial and temporal differences of tourism carrying capacity in Chinese island cities, and makes use of the BP(Back Propagation) neural network model to predict the development trend of early warnings. The results show that (1) from 2012 to 2018, the early warning status of China’s island cities’ tourism carrying capacity is generally on the rise, the natural carrying capacity system’s early warning situation has deteriorated, which is in a state of severe warning interval. The economic carrying capacity and social carrying capacity are on the rise, and the warning degree is from the super warning interval to the severe warning interval and then to the moderate warning degree. The forecast of the overall tourism carrying capacity early warning index from 2019 to 2021 presents an upward trend and is in the moderate warning interval. (2) The tourism carrying capacity early warning in China’s island cities shows a large spatial and temporal difference and the early warning values of each island city are different. The early warning value of Putuo tourism carrying capacity always ranks first, and Changdao has the worst performance. (3) In accordance with the contribution status of the subsystem to the total system, the Chinese island cities show regional differences in the northern, central, and southern area, showing two forms of pressure cities and pressure-carrying cities. The government can adopt different policies and measures in accordance with different characteristics of human environmental activities.

Anticipatory Adjustments during a Warning Interval: Cortical Negativity and Perceptual Sensitivity

Cortical negative afterwaves were recorded while subjects performed a warned signal detection task. Warning intervals of 500, 1200 and 1900 msec, and immediate and delayed responses were employed as experimental conditions. Detection sensitivity was best at the 1200 msec warning interval, which coincided with maximum cortical negativity. The response requirement manipulation had no effect on detection performance or brain wave amplitude. The results are interpreted as indicating an arousal based allocation of processing resources, indexed by cortical negativity.