The purpose of this study was to examine the prevalence of Internet addiction in a nationally representative sample of Chinese elementary and middle school students and to investigate Internet addiction among Internet users with different usages. The data were from the National Children's Study of China (NCSC) in which 24,013 fourth- to ninth-grade students were recruited from 100 counties in 31 provinces in China. Only 54.2% of the students had accessed the Internet. According to the criteria of Young's Diagnostic Questionnaire (YDQ), an eight-item instrument, the prevalence of Internet addiction in the total sample was 6.3%, and among Internet users was 11.7%. Among the Internet users, males (14.8%) and rural students (12.1%) reported Internet addiction more than females (7.0%) and urban students (10.6%). The percentage of Internet addicts in elementary school students (11.5%) was not significantly lower than the percentage of middle school students (11.9%). There was no statistically significant difference between the four geographical regions (9.6%, 11.5%, 12.3%, 11.1%) characterized by different levels of economy, health, education, and social environment. As the frequency of Internet use and time spent online per week increased, the percentage of Internet addicts increased. When considering the location and purpose of Internet use, the percentage of Internet addicts was highest in adolescents typically surfing in Internet cafes (18.1%) and playing Internet games (22.5%).

Abstract:The satellite clocks used in the BeiDou-2 satellite navigation System (BDS) are Chinese self-developed Rb atomic clocks, and their performances and stabilities are worse than GPS and Galileo satellite clocks. Due to special periodic noises and nonlinear system errors existing in the BDS clock offset series, the GPS ultra-rapid clock model, which uses a simple quadratic polynomial plus one periodic is not suitable for BDS. Therefore, an improved prediction model for BDS satellite clocks is proposed in order to enhance the precision of ultra-rapid predicted clock offsets. First, a basic quadratic polynomial model which is fit for the rubidium (Rb) clock is constructed for BDS. Second, the main cyclic terms are detected and identified by the Fast Fourier Transform (FFT) method according to every satellite clock offset series. The detected results show that most BDS clocks have special cyclic terms which are different from the orbit periods. Therefore, two main cyclic terms are added to absorb the periodic effects. Third, after the quadratic polynomial plus two periodic fitting, some evident nonlinear system errors also exist in the model residual, and the Back Propagation (BP) neural network model is chosen to compensate for these nonlinear system errors. The simulation results show that the performance and precision using the improved model are better than that of China iGMAS ultra-rapid prediction (ISU-P) products and the Deutsches GeoForschungsZentrum GFZ BDS ultra-rapid prediction (GBU-P) products. Comparing to ISU-P products, the average improvements using the proposed model in 3 h, 6 h, 12 h and 24 h are 23.1%, 21.3%, 20.2%, and 19.8%, respectively. Meanwhile the accuracy improvements of the proposed model are 9.9%, 13.9%, 17.3%, and 21.2% compared to GBU-P products. In addition, the kinematic Precise Point Positioning (PPP) example using 8 Multi-GNSS Experiment MGEX stations shows that the precision based on the proposed clock model has improved about 16%, 14%, and 38% in the North (N), East (E) and Height (H) components.

A fundamental problem in data management is to draw a sample of a large data set, for approximate query answering, selectivity estimation, and query planning. With large, streaming data sets, this problem becomes particularly difficult when the data is shared across multiple distributed sites. The challenge is to ensure that a sample is drawn uniformly across the union of the data while minimizing the communication needed to run the protocol and track parameters of the evolving data. At the same time, it is also necessary to make the protocol lightweight, by keeping the space and time costs low for each participant. In this paper, we present communication-efficient protocols for sampling (both with and without replacement) from k distributed streams. These apply to the case when we want a sample from the full streams, and to the sliding window cases of only the W most recent items, or arrivals within the last w time units. We show that our protocols are optimal, not just in terms of the communication used, but also that they use minimal or near minimal (up to logarithmic factors) time to process each new item, and space to operate.

This paper gives a first attempt to answer the following general question: Given a set of machines connected by a point-to-point communication network, each having a noisy dataset, how can we perform communication-efficient statistical estimations on the union of these datasets? Here 'noisy' means that a real-world entity may appear in different forms in different datasets, but those variants should be considered as the same universe element when performing statistical estimations. We give a first set of communicationefficient solutions for statistical estimations on distributed noisy datasets, including algorithms for distinct elements, L0-sampling, heavy hitters, frequency moments and empirical entropy.