The integration of magnetic fields with magnetoelectric (ME) coupling materials has been recently reported for electrocatalysis applications. Highly efficient energy conversion and storage can be potentially provided by this emerging approach. The ME properties, that is, the coexistence of ferromagnetic (FM) and ferroelectric (FE) ordering in some multiferroic materials, can be manipulated by magnetic or electric fields. The ME coupling can result in unique spin‐related physical properties in catalysts, further leading to interesting effects on electrocatalytic reactions. Herein, a discussion on the ME coupling multiferroic materials, as well as their potential opportunities and challenges as electrocatalysts in selected electrochemical reactions, is provided.

Detecting community structures and analyzing temporal evolution in dynamic networks are challenging tasks to explore the inherent characteristics of the complex networks. In this paper, we propose a semi-supervised evolutionary clustering model based on symmetric nonnegative matrix factorization to detect communities in dynamic networks, named sEC-SNMF. We use the results of community partition at the previous time step as the priori information to modify the current network topology, then smooth-out the evolution of the communities and reduce the impact of noise. Furthermore, we introduce a community transition probability matrix to track and analyze the temporal evolutions. Different from previous algorithms, our approach does not need to know the number of communities in advance and can deal with the situation in which the number of communities and nodes varies over time. Extensive experiments on synthetic datasets demonstrate that the proposed method is competitive and has a superior performance.

According to the research status of SDN(Software Defined Network) control layer traffic scheduling, we find the current common problems, including single path, easy congestion, QoS(Quality of Service) requirements and high delay. To solve these four problems, we design and implement a QoS-oriented global multi-path traffic scheduling algorithm for SDN, referred to as QOGMP. First, we propose a link weight calculation algorithm based on the idea of traction links and deep reinforcement learning, and conduct experimental verifications related to traction links. The algorithm considers QoS requirements and alleviates the problems of easy congestion and high delay. Then, we propose a traffic scheduling algorithm based on link weight and multi-path scheme, which also considers QoS requirements and solves the problem of single path. Finally, we combined the link weight calculation algorithm and the traffic scheduling algorithm to implement QOGMP, and carried out comparative experiments in the built simulation environment. The experimental results show that QOGMP is better than the two comparison algorithms in terms of delay and rescheduling rate.

Multi-path transmission can well solve the data transmission reliability problems and life cycle problems caused by single-path transmission. However, the accuracy of the routing scheme generated by the existing multi-path routing algorithms is difficult to guarantee. In order to improve the accuracy of the multi-path routing scheme, this paper innovatively proposes a multi-path routing algorithm for a wireless sensor network (WSN) based on the evaluation. First, we design and implement the real-time evaluation algorithm based on semi-supervised learning (RESL). We prove that RESL is better in evaluation time and evaluation accuracy through comparative experiments. Then, we combine RESL to design and implement the multi-path routing algorithm for wireless sensor networks based on semi-supervised learning (MRSSL). Then, we prove that MRSSL has advantages in improving the accuracy of the multi-path routing scheme through comparative experiments.