Supplementary data are available at Bioinformatics online.

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Because of the complex nonstationary and nonlinear characteristics of annual runoff time series, it is difficult to achieve good prediction accuracy. In this paper, ensemble empirical mode decomposition (EEMD) coupled with Elman neural network (ENN)-namely the EEMD-ENN model-is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The annual runoff time series from four hydrological stations in the lower reaches of the four main rivers in the Dongting Lake basin, and one at the outlet of the lake, are used as a case study to test this new hybrid model. First, the nonstationary and nonlinear original annual runoff time series are decomposed to several relatively stable intrinsic mode functions (IMFs) by using EEMD. Then, each IMF is predicted by using ENN. Next, the predicted results of each IMF are aggregated as the final prediction results for the original annual runoff time series. Finally, five statistical indices are adopted to measure the performance of the proposed hybrid model compared with a back propagation (BP) neural network, EEMD-BP, and ENN models-mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), Pearson correlation coefficient (R) and Nash-Sutcliffe coefficient of efficiency (NSCE). The performance comparison results show that the proposed hybrid model performs better than the BP, EEMD-BP or ENN models. In short, the developed hybrid model can provide a significant improvement in annual runoff time series forecasting.

The Versatile Video Coding (H.266/VVC) standard has developed by Joint Video Exploration Team (JVET). Compared with the previous generation video coding standard, the H.266/VVC is more outstanding. Since the H.266/VVC introduces multi-type tree (MTT) structure including binary tree (BT) and ternary tree (TT), which brings the significant coding efficiency but increases coding complexity. Moreover, the intra prediction modes have increased from 35 to 67, which can provide more accurate prediction than H.265/High Efficiency Video Coding (HEVC). Therefore, these can improve the encoding quality, but increase computational complexity. To reduce the computational complexity, this paper designs a fast coding unit (CU) partition and intra mode decision algorithm, which includes fast CU partition based on random forest classifier (RFC) model and fast intra prediction modes optimization based on texture region features. Simulation results indicate that the proposed scheme can save 54.91% encoding time with only 0.93% increase in BDBR. INDEX TERMS H.266/VVC, fast CU partition, intra mode decision, random forest, texture feature