The 2019 novel coronavirus (2019-nCoV) outbreak has been treated as a Public Health Emergency of International Concern by the World Health Organization. This work made an early prediction of the 2019-nCoV outbreak in China based on a simple mathematical model and limited epidemiological data. Combing characteristics of the historical epidemic, we found part of the released data is unreasonable. Through ruling out the unreasonable data, the model predictions exhibit that the number of the cumulative 2019-nCoV cases may reach 76,000 to 230,000, with a peak of the unrecovered infectives (22,000-74,000) occurring in late February to early March. After that, the infected cases will rapidly monotonically decrease until early May to late June, when the 2019-nCoV outbreak will fade out. Strong anti-epidemic measures may reduce the cumulative infected cases by 40%-49%. The improvement of medical care can also lead to about one-half transmission decrease and effectively shorten the duration of the 2019-nCoV.INDEX TERMS Epidemic transmission, infection rate, mathematical model, novel coronavirus, prediction, removal rate.

The most important motivation for streamflow forecasts is flood prediction and longtime continuous prediction in hydrological research. As for many traditional statistical models, forecasting flood peak discharge is nearly impossible. They can only get acceptable results in normal year. On the other hand, the numerical methods including physics mechanisms and rainfall-atmospherics could provide a better performance when floods coming, but the minima prediction period of them is about one month ahead, which is too short to be used in hydrological application. In this study, a deep neural network was employed to predict the streamflow of the Hankou Hydrological Station on the Yangtze River. This method combined the Empirical Mode Decomposition (EMD) algorithm and Encoder Decoder Long Short-Term Memory (En-De-LSTM) architecture. Owing to the hydrological series prediction problem usually contains several different frequency components, which will affect the precision of the longtime prediction. The EMD technique could read and decomposes the original data into several different frequency components. It will help the model to make longtime predictions more efficiently. The LSTM based En-De-LSTM neural network could make the forecasting closer to the observed in peak flow value through reading, training, remembering the valuable information and forgetting the useless data. Monthly streamflow data (from January 1952 to December 2008) from Hankou Hydrological Station on the Yangtze River was selected to train the model, and predictions were made in two years with catastrophic flood events and ten years rolling forecast. Furthermore, the Root Mean Square Error (RMSE), Coefficient of Determination (R 2), Willmott's Index of agreement (WI) and the Legates-McCabe's Index (LMI) were used to evaluate the goodness-of-fit and performance of this model. The results showed the reliability of this method in catastrophic flood years and longtime continuous rolling forecasting. INDEX TERMS Yangtze River, hydrological time series forecasting, streamflow prediction, empirical mode decomposition, deep learning.

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Accurate long-term streamflow and flood forecasting have always been an important research direction in hydrology research. Nowadays, climate change, floods, and other anomalies occurring more and more frequently and bringing great losses to society. The prediction of streamflow, especially flood prediction, is important for disaster prevention. Current hydrological models based on physical mechanisms can give accurate predictions of streamflow, but the effective prediction period is only about 1 month in advance, which is too short for decision making. The artificial neural network (ANN) has great potential for predicting runoff and is not only good at handling non-linear data but can also make long-period forecasts. However, most of ANN models are unstable in their predictions when faced with raw flow data, and have excessive errors in predicting extreme flows. Previous studies have shown a link between the El Niño–Southern Oscillation (ENSO) and the streamflow of the Yangtze River. In this paper, we use ENSO and the monthly streamflow data of the Yangtze River from 1952 to 2018 to predict the monthly streamflow of the Yangtze River in two extreme flood years and a small flood year by using deep neural networks. In this paper, three deep neural network frameworks are used: stacked long short-term memory, Conv long short-term memory encoder–decoder long short-term memory and Conv long short-term memory encoder–decoder gate recurrent unit. The results show that the use of ConvLSTM improves the stability of the model and increases the accuracy of the flood prediction. Besides, the introduction of ENSO to the experimental data resulted in a more accurate prediction of the time of the occurrence of flood peaks and flood flows. Furthermore, the best results were obtained on the convolutional long short-term memory + encoder–decoder gate recurrent unit model.

Over the past few decades, floods have severely damaged production and daily life, causing enormous economic losses. Streamflow forecasts prepare us to fight floods ahead of time and mitigate the disasters arising from them. Streamflow forecasting demands a high-capacity model that can make precise long-term predictions. Traditional physics-based hydrological models can only make short-term predictions for streamflow, while current machine learning methods can only obtain acceptable results in normal years without floods. Previous studies have demonstrated a close relation between El Niño-Southern Oscillation (ENSO) and the streamflow of the Yangtze River. However, traditional models, holding the encoder-decoder architecture, only have one encoder block that can not support bivariate time series forecasting. In this study, a transformer-based double-encoder-enabled model was proposed, called the double-encoder Transformer, with a distinctive characteristic: "cross-attention" mechanism that can capture the relation between two time series sequences. Using river flow observation collected by the Yangtze River Water Resources Commission and El Niño-Southern Oscillation (ENSO) observation collected by the National Oceanic and Atmospheric Administration, the model can achieve better performance. By using variational mode decomposition (VMD) technique for preprocessing, the model can make precise long-term predictions for the river flow of the Yangtze River. A monthly prediction of 21 years (from January 1998 to December 2018) was made, and the results indicate that the double-encoder Transformer outperforms mainstream time series models.

As an anode material, SnO2 nanoparticles have the problem of volume expansion and agglomeration, limiting their applications in energy storage. Herein, a nanocomposite material with hybridization structure using SnO2 interpenetrated MXene V2C as an anode for Li‐ion battery is fabricated by a simple method. The laminated structure of V2C can restrain the volume expansion of SnO2 nanoparticles anchored on the surface of V2C layers, whereas the intercalation of SnO2 nanoparticles into the V2CTx layer can effectively prevent the restacking of the V2CTx nanosheets in charging and discharging processes. This heterogeneous structure enables high Li‐ion storage on the surface and in the near‐surface region, which results in rapid transport of Li ions and optimizes the rate performance and cycling property. Consequently, the V2CTx@SnO2 nanocomposite has a large reversible capacity of ≈768 mAh g−1 after 200 cycles at a current density of 1000 mA g−1. Competitively, its reversible capacity can reach 260 mAh g−1 at high current density of 8000 mA g−1 after 1000 cycles, showing excellent cycling stability and superior rate capability. In addition to the high Li‐ion capacity offered by the composite structure, the anode also maintains the structural and mechanical integrity provided by MXene in charging and discharging processes.

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