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Novel durian-like TiO2@CdS core-shell particles were synthesized through a solvothermal method in ethylenediamine solution and the obtained nanocomposites were characterized by scanning electron microscopy (SEM), powder X-ray diffraction (XRD), and transmission electron microscopic (TEM) techniques. It can be seen from the characterization that the synthesized core-shell structured particles show uniform size. The possible formation mechanism of TiO2@CdS core-shell particles is also presented schematically. CdS grows on the TiO2 surface in the form of nanorods, turning the TiO2@CdS composite particles into durian-like structures. The durian-like TiO2@CdS core-shell particles prepared in the experiment can overcome the disadvantages of TiO2 and CdS, respectively. They not only produce a higher yield of H2 than pure TiO2; the durian-like TiO2@CdS nanostructures formed at 180 °C for 16 h produced 2.5 times as much H2 as did TiO2, also showing enhanced stability as compared with pure CdS. [ABSTRACT FROM AUTHOR]

Joint Approximate Diagonalization of Eigen-matrices (JADE) cannot deal with non-stationary data. Therefore, in this paper, a method called Non-stationary Kernel JADE (NKJADE) is proposed, which can extract non-stationary features and fuse multi-sensor features precisely and rapidly. In this method, the non-stationarity of the data is considered and the data from multi-sensor are used to fuse the features efficiently. The method is compared with EEMD-SVD-LTSA and EEMD-JADE using the bearing fault data of CWRU, and the validity of the method is verified. Considering that the vibration signals of high-speed trains are typically non-stationary, it is necessary to utilize a rapid feature fusion method to identify the evolutionary trends of hunting motions quickly before the phenomenon is fully manifested. In this paper, the proposed method is applied to identify the evolutionary trend of hunting motions quickly and accurately. Results verify that the accuracy of this method is much higher than that of the EEMD-JADE and EEMD-SVD-LTSA methods. This method can also be used to fuse multi-sensor features of non-stationary data rapidly. [ABSTRACT FROM AUTHOR]