ODD AND EVEN SYMMETRY OF ATMOSPHERIC CIRCULATION——PART Ⅱ:MEASUREMENT AND ANOMALY ANALYSIS

First of all,for the odd and even symmetry components of both hemisphere circulations are given the absolute and relative measurements with which to examine the seasonal change and interannual abnormality of the components on the 1980—1986 monthly mean 500-hPa height charts.Results show a noticeable relationship between a major El Nino event and the component anomaly,with the even part anomaly almost-synchronizing with its culmination and that of the odd part about half a year prior to the event,a relation that is still more sharply manifested at the 200-hPa height field.

Research on the relationship between geophysical structural features and earthquakes in Mid-Yunnan and the surrounding area

In this study, we analyzed the gravity and, magnetic characteristics, and the occurrence of a fault zone and discussed the relationships between the two locations. The results reveal that the subsurface structures strikes are different compared with those in the research region. In other words, the geophysical advantageous directions from the gravity and magnetic anomalies are not the same as those caused by the surface structures. The local horizontal gradient results from the gravity and magnetic anomalies show that the majority of earthquakes occur along an intense fault zone, which is a zone of abrupt gravity and negative magnetic change, where the shapes match very well. From the distribution of earthquakes in this area, we find that it has experienced more than 11 earthquake events with magnitude larger than Ms7.0. In addition, water development sites such as Jinshajiang, Lancangjiang, and the Red River and Pearl River watersheds have been hit ten times by earthquakes of this magnitude. It is observed that strong earthquakes occur frequently in the Holocene active fault zone.

User abnormal behavior analysis based on neural network clustering

It is the premise of accessing and controlling cloud environment to establish the mutual trust relationship between users and clouds. How to identify the credible degree of the user identity and behavior becomes the core problem？ This paper proposes a user abnormal behavior analysis method based on neural network clustering to resolve the problems of over-fitting and flooding the feature information, which exists in the process of traditional clustering analysis and calculating similarity. Firstly, singular value decomposition （SVD） is applied to reduce dimension and de-noise for massive data, where map-reduce parallel processing is used to accelerate the computation speed, and neural network model is used for softening points. Secondly, information entropy is added to hidden layer of neural network model to calculate the weight of each attribute. Finally, weight factor is used to calculate the similarity to make the cluster more accuracy. For the problem of analyzing the mobile cloud user behaviors, the experimental results show that the scheme has higher detection speed （DS） and clustering accuracy than traditional schemes. The proposed method is more suitable for the mobile cloud environment.

Improved model of ionosphere variability and study for long-term statistical characteristics

Ionospheric variability is influenced by many factors, such as solar radiation, neutral atmosphere composition, and geomagnetic disturbances. Mainly characterized by the total electron content(TEC) and electron density, the climatology of the ionosphere features temporal and spatial changes. Establishing a multivariant regression model helps substantially in better understanding the ionosphere characteristics and their long-term variability. In this paper, an improvement of the existing ionosphere multivariate linear fitting regression model is proposed and investigated using data from both the ionosonde and the global ionosphere map(GIM) derived from groundbased Global Navigation Satellite System(GNSS) observations. The proposed method gives more consideration to the impact of the solar activity and adds modeling of the annual periodic fluctuations and half-year periodic fluctuations for the F10.7 index. The improved model is verified to have a better correlation with the real observations and can help reduce the calculation uncertainty.Moreover, the proposed model is used to evaluate the fitting accuracy of the GIMs produced by five authorized data analysis centers from the International GNSS Service(IGS). The results show that there is a fixing hole in the North America region for the GIM model where the correlation between the GIM and the proposed model always returns lower values compared to other places.