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The microenvironments with high reactive-oxygen-species (ROS) levels, inflammatory responses, and oxidative-stress effects in diabetic ulcer wounds, leading to poor proliferation and differentiation of stem cells, severely inhibit their efficient healing. Here, to overcome the unbalanced multielectron reactions in ROS catalysis, we develop a cobalt selenide-based biocatalyst with an amorphous Ru@CoSe nanolayer for ultrafast and broadspectrum catalytic ROS-elimination. Owing to the enriched electrons and more unoccupied orbitals of Ru atoms, the amorphous Ru@CoSe nanolayer-equipped biocatalyst displays excellent catalase-like kinetics (maximal reaction velocity, 23.05 μM s −1 ; turnover number, 2.00 s −1 ), which exceeds most of the currently reported metal compounds. The theoretical studies show that Ru atoms act as "regulators" to tune the electronic state of the Co sites and modulate the interaction of oxygen intermediates, thus improving the reversible redox properties of active sites. Consequently, the Ru@CoSe can efficiently rescue the proliferation of mesenchymal stem cells and maintain their angiogenic potential in the oxidative stress environment. In vivo experiments reveal the superior ROS-elimination ability of Ru@CoSe on the inflammatory diabetic wound. This study offers an effective nanomedicine for catalytic ROS-scavenging and ultrafast healing of inflammatory wounds and also provides a strategy to design biocatalytic metal compounds via bringing amorphous catalytic structures.

In recent years, automatic emotion recognition renders human–computer interaction systems intelligent and friendly. Emotion recognition based on electroencephalogram (EEG) has received widespread attention and many research results have emerged, but how to establish an integrated temporal and spatial feature fusion and classification method with improved convolutional neural networks (CNNs) and how to utilize the spatial information of different electrode channels to improve the accuracy of emotion recognition in the deep learning are two important challenges. This paper proposes an emotion recognition method based on three‐dimensional (3D) feature maps and CNNs. First, EEG data are calibrated with 3 s baseline data and divided into segments with 6 s time window, and then the wavelet energy ratio, wavelet entropy of five rhythms, and approximate entropy are extracted from each segment. Second, the extracted features are arranged according to EEG channel mapping positions, and then each segment is converted into a 3D feature map, which is used to simulate the relative position of electrode channels on the scalp and provides spatial information for emotion recognition. Finally, a CNN framework is designed to learn local connections among electrode channels from 3D feature maps and to improve the accuracy of emotion recognition. The experiments on data set for emotion analysis using physiological signals data set were conducted and the average classification accuracy of 93.61% and 94.04% for valence and arousal was attained in subject‐dependent experiments while 83.83% and 84.53% in subject‐independent experiments. The experimental results demonstrate that the proposed method has better classification accuracy than the state‐of‐the‐art methods.

Short-term load forecasting is one of the crucial sections in smart grid. Precise forecasting enables system operators to make reliable unit commitment and power dispatching decisions. With the advent of big data, a number of artificial intelligence techniques such as back propagation, support vector machine have been used to predict the load of the next day. Nevertheless, due to the noise of raw data and the randomness of power load, forecasting errors of existing approaches are relatively large. In this study, a short-term load forecasting method is proposed on the basis of empirical mode decomposition and long short-term memory networks, the parameters of which are optimized by a particle swarm optimization algorithm. Essentially, empirical mode decomposition can decompose the original time series of historical data into relatively stationary components and long short-term memory network is able to emphasize as well as model the timing of data, the joint use of which is expected to effectively apply the characteristics of data itself, so as to improve the predictive accuracy. The effectiveness of this research is exemplified on a realistic data set, the experimental results of which show that the proposed method has higher forecasting accuracy and applicability, as compared with existing methods.

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