Abstract: The present disclosure provides a tea plant CsFAAH6 gene and use thereof. The tea plant CsFAAH6 gene has a nucleotide sequence shown in SEQ ID NO: 1 in sequence listing. A protein coded by the tea plant CsFAAH6 gene has an amino acid sequence shown in SEQ ID NO: 2 in the sequence listing. The CsFAAH6 is highly expressed in mature leaves of tea plants; there is significant negative correlation between theanine content in shoots (one bud and two leaves) of different tea cultivars in different months and expression level of the CsFAAH6; instantaneous silencing of CsFAAH6 expression can significantly increase the theanine content, indicating that CsFAAH6 has a physiological function of theanine degradation and a molecular mechanism thereof.

Abstract: A sintering furnace can have a housing, one or more heating elements, and a conveying assembly. Each heating element can be disposed within the housing and can subject a heating zone to a thermal shock temperature profile. A substrate with one or more precursors thereon can be moved by the conveying assembly through an inlet of the housing to the heating zone, where it is subjected to a first temperature of at least 500° C. for a first time period. The conveying assembly can then move the substrate with one or more sintered materials thereon from the heating zone and through an outlet of the housing.

Abstract: In an image processing method, style conversion is performed on a sample image by using a generation network, to obtain a reference image. Style recognition is performed on the reference image by using an adversarial network, to determine a style loss between the reference image and the sample image. Image content recognition is performed on the reference image and the sample image, to determine a content loss between the reference image and the sample image. The generation network is trained based on the style loss and the content loss, to obtain a trained generation network.

Abstract: An image processing method includes obtaining a sample image and a generative adversarial network (GAN), including a generation network and an adversarial network, and performing style conversion on the sample image, to obtain a reference image. The method further includes performing global style recognition on the reference image, to determine a global style loss between the reference image and the sample image, and performing image content recognition on the reference image and the sample image, to determine a content loss between the reference image and the sample image. The method also includes performing local style recognition on the reference image and the sample image, to determine a local style loss of the reference image and a local style loss of the sample image, training the generation network to obtain a trained generation network, and performing style conversion on a to-be-processed image by using the trained generation network.

Abstract: An image processing method includes obtaining a sample image and a generative adversarial network (GAN), including a generation network and an adversarial network, and performing style conversion on the sample image, to obtain a reference image. The method further includes performing global style recognition on the reference image, to determine a global style loss between the reference image and the sample image, and performing image content recognition on the reference image and the sample image, to determine a content loss between the reference image and the sample image. The method also includes performing local style recognition on the reference image and the sample image, to determine a local style loss of the reference image and a local style loss of the sample image, training the generation network to obtain a trained generation network, and performing style conversion on a to-be-processed image by using the trained generation network.