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Electric-field control of magnetism in ferromagnetic/ferroelectric multiferroic heterostructures is a promising way to realize fast and nonvolatile random-access memory with high density and low-power consumption. An important issue that has not been solved is the magnetic responses to different types of ferroelectric-domain switching. Here, for the first time three types of magnetic responses are reported induced by different types of ferroelectric domain switching with in situ electric fields in the CoFeB mesoscopic discs grown on PMN-PT(001), including type I and type II attributed to 109°, 71°/180° ferroelectric domain switching, respectively, and type III attributed to a combined behavior of multiferroelectric domain switching. Rotation of the magnetic easy axis by 90° induced by 109° ferroelectric domain switching is also found. In addition, the unique variations of effective magnetic anisotropy field with electric field are explained by the different ferroelectric domain switching paths. The spatially resolved study of electric-field control of magnetism on the mesoscale not only enhances the understanding of the distinct magnetic responses to different ferroelectric domain switching and sheds light on the path of ferroelectric domain switching, but is also important for the realization of low-power consumption and high-speed magnetic random-access memory utilizing these materials.

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Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement’s causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network’s over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system’s big measurement data to improve prediction performance.