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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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With the dramatic developments of renewable and environmental‐friendly electrochemical energy conversion systems, there is an urgent need to fabricate durable and efficient electrocatalysts to address the limitation of high overpotentials exceeding thermodynamic requirements to facilitate practical applications. Recently, tellurium‐based nanomaterials (Te NMs) with unique chemical, electronic, and topological properties, including Te‐derived nanostructures and transition metal tellurides (TMTs), have emerged as one of the most promising electrocatalytic materials. In the absence of comprehensive and guiding reviews, this review comprehensively summarizes the main advances in designing emerging Te NMs for electrocatalysis. First, the engineering strategies and principles of Te NMs to enhance their electrocatalytic activity and stability from the nanostructures to the catalytic atoms are discussed in detail, especially on the chemical/physical/multiplex templating strategies, heteroatom doping, vacancy/defect engineering, phase engineering, and the corresponding mechanisms and structure‐performance correlations. Then, typical applications of Te NMs in electrocatalysis are also discussed in detail. Finally, the existing key issues and main challenges of Te NMs for electrocatalysis are highlighted, and the development trend of Te NMs as electrocatalysts is expounded. This review provides new concepts to guide future directions for developing Te NMs‐based electrocatalysts, thereby promoting their future wide applications in electrochemical energy systems.

With the increasing requirements for computing in modern society, Multi-access Edge Computing (MEC) has received widespread attention for meeting low-latency. In MEC network, mobile devices can offload computing-intensive tasks to edge servers for computing. Wireless Power Transmission (WPT) provides initial energy for mobile devices, and the tasks of mobile devices consume energy when they are locally calculated or completely offloaded. The combination of the two technologies forms the Wireless Powered Mobile Edge Computing (WP-MEC) network. In this article, considering the impact of WPT transmission time τ0, we study the offloading and scheduling of tasks for multiple mobile devices in the WP-MEC network, which is an NP-hard problem. We formulate this scheduling problem to minimize the time delay under the constraint of WPT transmission energy. We regard our problem studied in this paper as a multidimensional knapsack problem (MKP). The difference is that the knapsack capacity in MKP is limited, while in our problem, the knapsack that one item can choose is limited. Therefore, we improve the Artificial Fish Swarm Algorithm (AFSA) and propose Computation Scheduling Based on the Artificial Fish Swarm Algorithm (CS-AFSA) to find the optimal scheduling. We encode a scheduling scheme as an artificial fish and regard the delay corresponding to the scheduling as the optimization object. The optimal artificial fish can be gradually approached and determined through the swarm, follow and prey behavior of artificial fish. The optimal artificial fish is the optimal scheduling scheme. More importantly, based on the original behavior of AFSA, we also improve the scheme that does not meet the WPT energy constraint, including the modification of infeasible artificial fish and insufficient artificial fish. Besides, we also consider how to find the best WPT transmission time τ0. Finally, we perform data simulation on the proposed algorithm.INDEX TERMS Artificial Fish Swarm Algorithm, WP-MEC network, WPT transmission time.

NoSQL database (NoSQL DB) covers the shortage of traditional database and has been widely used in recent years. Currently, researches on NoSQL DB mainly focus on performance issues; few of them are about energy consumption (EC) evaluation and optimization. Waiting Energy Consumption (WEC) is another critical reason causing energy waste besides computer idleness. Study the WEC regularities of NoSQL DB facilitates achieving real "green computing". This paper first analyzes the model and measurement approaches of EC; then designs test cases to study the WEC regularities; finally proposes approaches of "reducing WEC" for EC optimization. Plenty of experiments show that, despite that NoSQL DB is an application of "green cloud computing", the ECs of selected NoSQL DBs are widely divergent, and some of them remain to be further optimized.