Nanocomposite catalysts containing both magnetite (Fe 3 O 4 ) and palladium (Pd) nanoparticles with magnetic separation and recyclability were successfully fabricated via polymer encapsulation and then silica coating and applied for catalytic hydrogenation of 4-nitrophenol. Fe 3 O 4 nanoparticles were used as not only the prerequisite of magnetic separation but also the supports to prevent the aggregation of Pd nanoparticles at high temperature. Moreover, the surfactants and polymer supports on the particle surface were removed by calcination, and thus the catalysis centers (Pd nanoparticles) were totally exposed to the reactants, which is preferable for a good catalysis efficacy. The results of catalytic hydrogenation of 4-nitrophenol demonstrated that the catalytic activity of these as-prepared nanocomposite catalysts were well maintained even after 10 repeated cycles. Unlike the noble metal nanoparticle decorated large magnetic nanosphere with residual magnetism, these as-prepared nanocomposites based on superparamagnetic nanoparticles possess many advantages including high catalytic activity, convenient magnetic separation, good dispersibility, high water stability, and excellent recyclability.
Fall detection has become a hot issue in the field of video surveillance recently. Different from most traditional vision-based methods relying on hand-crafted features, fall detection methods based on deep learning technology can automatically mine features to detect fall events due to the powerful ability of deep learning in data analysis, and hence have received much more attention in recent years. However, information loss has become a problem that cannot be ignored, especially for the neural networks with deep layers, because loss of information will affect representativeness of features and further influence the performance of fall detection. To solve the abovementioned problem, we propose a fall detection method based on dense block with a multi-channel convolutional fusion (MCCF) strategy. In this method, MCCF-DenseBlock, a densely connected network structure, is proposed to fully extract information with its densely connected layers, and to avoid network overloading by breaking dense connections appropriately, and especially to reduce data redundancy and numerous parameters in the network via the MCCF strategy fusing its grouped features. Besides, an improved transition layer is presented to further lessen data accumulation by using a multi-level downsampling structure. Experimental results demonstrate that, the proposed method can provide accurate fall detection results (satisfactory F-score of 0.973 on the UR Fall Dataset) and outperforms several state-of-the-art methods. INDEX TERMS Fall detection, deep learning, multi-channel convolutional fusion (MCCF), MCCF-DenseBlock.
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