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With the development of electronic information technology, electronic medical records (EMRs) have been a common way to store the patients' data in hospitals. They are stored in different hospitals' databases, even for the same patient. Therefore, it is difficult to construct a summarized EMR for one patient from multiple hospital databases due to the security and privacy concerns. Meanwhile, current EMRs systems lack a standard data management and sharing policy, making it difficult for pharmaceutical scientists to develop precise medicines based on data obtained under different policies. To solve the above problems, we proposed a blockchain-based information management system, MedBlock, to handle patients' information. In this scheme, the distributed ledger of MedBlock allows the efficient EMRs access and EMRs retrieval. The improved consensus mechanism achieves consensus of EMRs without large energy consumption and network congestion. In addition, MedBlock also exhibits high information security combining the customized access control protocols and symmetric cryptography. MedBlock can play an important role in the sensitive medical information sharing.

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Wind speed forecasting is challenging due to its intermittent nature. The wind speed time series (TS) has nonlinear and nonstationary characteristics and not normally distributed, which make it difficult to be predicted by statistical or computational intelligent methods. Empirical mode decomposition (EMD) and its improved versions are powerful tools to decompose a complex TS into a collection of simpler ones. The improved versions discussed in this paper include ensemble EMD (EEMD), complementary EEMD (CEEMD), and complete EEMD with adaptive noise (CEEMDAN). The EMD and its improved versions are hybridized with two computational intelligence-based predictors: support vector regression (SVR) and artificial neural network (ANN). The EMD-based hybrid forecasting methods are evaluated with 12 wind speed TS. The performances of the hybrid methods are compared and discussed. It shows that EMD and its improved versions enhance the performance of SVR significantly but marginally on ANN, and among the EMD-based hybrid methods, the proposed CEEMDAN-SVR is the best method. Possible future works are also recommended for wind speed forecasting.Index Terms-Artificial neural networks (ANNs), empirical mode decomposition (EMD), support vector regression (SVR), wind speed forecasting.

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