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Room-temperature gas sensors have emerged as effective platforms for sensing explosive or toxic gases in ambient environment. However, room-temperature gas sensor usually suffers from extremely poor sensitivity and sluggish response/recovery characteristics due to the low reacting activity at low temperature. Herein, we present a room-temperature NO2 sensor with greatly enhanced sensitivity and rapid response/recovery speed under ultraviolet (UV) illumination. The sensor based on In2O3/ZnO yolk-shell nanofibers exhibits remarkable sensitiv

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ghout the seed for sowing system.The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of rotating machinery, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, light-weight convolution blocks