The temperate coniferous and broadleaf mixed forest (TCBMF) in Northeastern China is the contact zone between southern warm-temperate forests and northern cool-temperate forests, and is a key region for studying the influence of Quaternary climate changes on genetic patterns. Vegetation reconstructions have shown the TCBMF has retreated southward to 25°-30° N during the last glacial maximum (LGM). However, phylogeographic studies indicated plants of TCBMF could have survived in one refugium or multiple refugia at 35° N during the LGM. The Mt. Changbai and Korean Peninsula are the two most important refugia for the TCBMF, and other refugia may also exist, of which the northernmost ones would reach the Xiaoxing'an Range and Russian Far East. The interglacial or postglacial expansion from refugia resulted in complicated distribution patterns with respect to genetic diversity. The northward population expansion from one southern refugium does not necessarily cause a significant decrease in inter-population genetic diversity as latitude increases. Inter-population genetic diversity always shows a uniform distribution in plants which have multiple refugia. Although previous studies have shown the influence of the quaternary, especially the LGM, on the evolutionary history of plants in the TCBMF, these studies have concentrated on tree species and mainly focused on one species. Therefore, adaptive evolution of closely-related species or sibling species between Northeast and South China, and the mechanisms driving community assembly are two directions for future studies.
To improve the quality of service and network performance for the Flash P2P video-on-demand, the prediction Flash P2P network traffic flow is beneficial for the control of the network video traffic. In this paper, a novel prediction algorithm to forecast the traffic rate of Flash P2P video is proposed. This algorithm is based on the combination of the ensemble local mean decomposition (ELMD) and the generalized autoregressive conditional heteroscedasticity (GARCH). The ELMD is used to decompose the original long-related flow into the summation of the short-related flow. Then, the GRACH is utilized to predict the short-related flow. The developed algorithm is tested in a university’s campus network. The predicted results show that our proposed method can further achieve higher accuracy than those obtained by existing algorithms, such as GARCH and Local Mean Decomposition and Generalized AutoRegressive Conditional Heteroskedasticity (LMD-GARCH) while keeping lower computational complexity.
To improve the quality of service and network performance of the FlashP2P video-on-demand, the prediction FlashP2P network traffic flow is very useful to t control the network video traffic. In this paper, a novel prediction algorithm to forecast the traffic rate of the Flash P2P video is proposed. This method is based on the combination of the local mean decomposition (LMD) and the generalized autoregressive conditional heteroscedasticity (GARCH). LMD is used to decompose the original long-related flow into the summation of the short-related flow. Then, GRACH is utilized to predict the short-related flow. The developed algorithm is tested on a university's campus network. The predicted results show that our proposed method can achieve higher accuracy than those obtained by existing algorithms, such as EMD-ARMA(Empirical Mode Decomposition and Auto-Regressive and Moving Average Model) and WNN(Wavelet Neural Network), while keeping lower computational complexity.
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