Coronavirus disease 2019 has been considered as a global threat infectious disease, and various mathematical models are being used to conduct multiple studies to analyze and predict the evolution of this epidemic. We statistically analyze the epidemic data from February 24 to March 30, 2020 in Italy, and proposes a simple time series analysis model based on the Auto Regressive Integrated Moving Average (ARIMA). The cumulative number of newly diagnosed and newly diagnosed patients in Italy is preprocessed and can be used to predict the spread of the Italian COVID-19 epidemic. The conclusion is that an inflection point is expected to occur in Italy in early April, and some reliable points are put forward for the inflection point of the epidemic: strengthen regional isolation and protection, do a good job of personal hygiene, and quickly treat the team leaders existing medical forces. It is hoped that the "City Closure" decree issued by the Italian government will go in the right direction, because this is the only way to curb the epidemic.

Accurate prediction the remaining useful life (RUL) and estimation the state of health (SOH) are critical to the management of lithium-ion batteries. In this paper, a lithium battery capacity prediction method based on cuckoo search optimization variational mode decomposition (CS-VMD) and gated recurrent unit (GRU) is proposed. Firstly, the VMD algorithm is used to divide the capacity into some intrinsic mode functions (IMFs) to reduce the impact of capacity regeneration and other situations. The number of decomposition layers and the quadratic penalty factor of VMD are optimized by the cuckoo search (CS) algorithm. Then, the GRU network is introduced to capture small changes in the capacity degradation process and perform the capacity prediction of decomposed sequence. Finally, some prediction results are integrated effectively. Based on two publicly available lithium-ion battery datasets, the model proposed in this paper can significantly reduce the complexity of the sequence and have high prediction accuracy, which is better than other prediction models. The root mean square error (RMSE) is controlled within 2%, and the maximum mean absolute error (MAE) does not exceed 2%.

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Accurate prediction the remaining useful life (RUL) of rolling bearings under complex environmental conditions is crucial for prognostics and health management (PHM). In this paper, A new method for rolling bearing RUL prediction based on improved empirical wavelet transform (IEWT) and one-dimensional convolutional neural network (1D-CNN) is proposed to overcome the interference of noise and other disturbance signals. Firstly, in view of the problem of too many spectrum divisions in the traditional empirical wavelet transform (EWT) process, the mutual information value is used to re-determine the frequency band demarcation point in the EWT. The IEWT method is introduced to adaptively divide the original vibration signal to obtain a series of empirical mode functions (EMFs). Secondly, the effective components after IEWT decomposition are extracted by mutual information and kurtosis criteria and used to extract multi-dimensional time-frequency domain features. Finally, the 1D-CNN is constructed with the percentage of remaining life as the tracking metric to predict the RUL of the bearings. Based on two publicly available rolling bearing datasets, the model proposed in this paper have high prediction accuracy, which is better than other prediction models. Compared to other methods, its mean absolute error (MAE) and root mean square error (RMSE) are reduced.

Accurate and efficient lithium-ion battery capacity prediction plays an important role in improving performance and ensuring safe operation. In this study, a novel lithium-ion battery capacity prediction model combining successive variational mode decomposition (SVMD) and aquila optimized deep extreme learning machine (AO-DELM) is proposed. Firstly, SVMD is used to divide capacity signal and it improves short-term trend prediction, especially for capacity growth that occurs during the degradation process. Secondly, the DELM network outperforms other networks in efficiently extracting time-dependent features, and it is more accurate than other standard ELM-based methods. The AO algorithm is used to optimize the parameters of the DELM training process for the problem of sensitivity to initial weights. Finally, experiments are conducted to validate the predictive performance of the proposed model based on NASA and CALCE lithium-ion batteries discharge capacity decay sequences. The MAE (0.0066Ah, 0.0044Ah), RMSE (0.0113Ah, 0.0078Ah), MAPE (0.44%, 0.82%) are effectively reduced and the R2 (98.94%, 99.87%) are better than the prediction performance of other hybrid models.