China Petroleum & Chemical Corporation

Beijing Research Institute of Chemical Industry, China Petroleum & Chemical Corporation

Canon Kabushiki Kaisha

Beijing University of Chemical Tehcnology

Beijing Minhai Biotechnology Co., Ltd.

Beijing Research Institute of Chemical Industry Sinopec

Feyman (beijing) Technology Co. Ltd

Feyman Technology (usa) Inc

Junjie Liu

Guoqing Wang

Lijun Zhang

Yonggang Zhang

Cong Zhou

Zhiguo Du

Zhaobin Zhang

Xianfeng Zhou

Tsewei Chen

Dongchao Wen

Wei Tao

Hongxing Gao

Ying Shi

Xiaofeng Li

Shifang Yang

Jinghang Guo

Dongfa Shen

Shasha Yang

Wei Li

Qiang Li

Feng Li

Lei Chen

Xue Fei Duan

Lin Guo

Guoshun Li

Meirong Gu

Jiankai Liu

Gaimei Zhang

Yingzhi Xu

Haifeng Xiao

Wei Jian

Lili Zhao

Xuechao Xie

The present disclosure provides an engineeredfungus that efficiently expresses CA10 (Coxsackievirus A10) virus-like particles and uses thereof. The engineered fungus includes a recombinant vector carrying the P1 and 3CD genes of the CA10 virus optimized according to preferred codons of. The present disclosure also provides a preparation method for CA10 virus-like particles and vaccines prepared therefrom.

Engineered Hansenula fungi efficiently expressing CA10 virus-like particles and uses thereof

The present disclosure provides a training and application method of a multi-layer neural network model, apparatus and a storage medium. In a forward propagation of the multi-layer neural network model, the number of input feature maps is expanded and a data computation is performed by using the expanded input feature maps.

Training and application method of a multi-layer neural network model, apparatus and storage medium

The present invention relates to the field of fluid heat transfer, and discloses a heat transfer enhancement pipe as well as a cracking furnace and an atmospheric and vacuum heating furnace including the same. The heat transfer enhancement pipe () includes a pipe body () of tubular shape having an inlet () for entering of a fluid and an outlet () for said fluid to flow out; internal wall of the pipe body () is provided with a fin () protruding towards interior of the pipe body (), wherein the fin () has one or more fin sections extending spirally in the axial direction of the pipe body (), and each fin section has a first end surface facing the inlet () and a second end surface facing the outlet (), at least one of the first end surface and the second end surface of at least one of the rib sections is formed as a transition surface along spirally extending direction. The heat transfer enhancement pipe can reduce thermal stress of itself, thereby increasing service life of the heat transfer enhancement pipe.

Heat transfer enhancement pipe as well as cracking furnace and atmospheric and vacuum heating furnace including the same

The present invention relates to the field of fluid heat transfer, and discloses a heat transfer enhancement pipe as well as a cracking furnace and an atmospheric and vacuum heating furnace including the same. The heat transfer enhancement pipe () includes a pipe body () of tubular shape having an inlet () for entering of a fluid and an outlet () for said fluid to flow out; internal wall of the pipe body () is provided with a fin () protruding towards interior of the pipe body (), the fin () spirally extends in an axial direction of the pipe body (), wherein a height of the fin () gradually increases from one end in at least a part extension of the fin. The heat transfer enhancement pipe can reduce thermal stress of itself, thereby increasing service life of the heat transfer enhancement pipe.

The present disclosure provides a training and application method of a multi-layer neural network model, apparatus and storage medium. A number of channels of a filter in at least one convolutional layer in the multi-layer neural network model is expanded, and a convolution computation is performed by using the filter after expanding the number of channels, so that the performance of the network model does not degrade while simplifying the network model.

A method for tilt measurement and compensation of a GNSS RTK surveying instrument based on a GNSS receiver and an IMU sensor is proposed. Based on the algorithm principle of the Kalman filter, the prediction and the correction of the designed multi-dimensional unknown state variable are cycled for multiple times to make the multi-dimensional unknown state variable approach the true value as much as possible. Then, based on the multi-dimensional unknown state variable, the latest three-dimensional attitude angle from the XYZ coordinate system to the NED coordinate system and the latest rotation matrix can be obtained efficiently and accurately. Finally, the coordinate of the antenna phase center of the GNSS receiver is reduced to the coordinate of the bottom end of the pole by a geometric transformation. In addition, no factory calibration is needed as the self-calibration can always be done during the operation process.

Method for tilt measurement and compensation of surveying instrument based on GNSS receiver and IMU sensor

A heat transfer tube includes a twisted baffle arranged in an inner wall of the tube. The twisted baffle extends spirally along an axial direction of the heat transfer tube. The twisted baffle is provided with a non-through gap extending along an axial direction of the heat transfer tube from an end to the other end of the twisted baffle. A cracking furnace uses the heat transfer tube. The heat transfer tube and cracking furnace have good heat transfer effects and small pressure loss.

Heat transfer tube and cracking furnace using the same

A training and application method for a neural network model is provided. The training method determines the first network model to be trained and sets a downscaling layer for at least one layer in the first network model, wherein the number of filters and filter kernel of the downscaling layer are identical to those of layers to be trained in the second network model. Filter parameters of the downscaling layer are transmitted to the second network model as training information. By this training method, training can also be performed even when the scale of the layer for training in the first network model is different from that of the layers to be trained in the second network model, and the amount of lost data is small.

Training and application method of neural network model, apparatus, system and storage medium

The present disclosure provides a steam cracking process, comprising heating a liquid feed stock in a convection section of a cracking furnace and subsequently conveying the material to a radiant section of the cracking furnace for cracking reaction therein, wherein a monoolefin-containing stream is conveyed to the cracking furnace for cracking reaction through at least one of modes A to C.

Steam cracking processes

The present disclosure relates to a heat transfer tube and a cracking furnace using the heat transfer tube. The heat transfer tube comprises a twisted baffle arranged in an inner wall of the tube, said twisted baffle extending spirally along an axial direction of the heat transfer tube. The twisted baffle defines a closed circle viewed from an end of the heat transfer tube. Along the trajectory of the circle a casing is arranged, which is fixedly connected to a radial inner end of the twisted baffle. The twisted baffle is provided with a plurality of holes. The heat transfer tube according to the present disclosure has a good heat transfer effect and small pressure loss.

Heat transfer tube and cracking furnace using the heat transfer tube

The invention describes a method of preparing magnetic ferrites from layered precursors in which Feis first introduced into the layers of layered double hydroxides (LDHs) in order to prepare Me-Fe&#x2014;FeLDHs, and then by utilizing the easily oxidized nature of Fe, binary or multi-component ferrite materials containing Fein a single crystalline phase can be prepared. Values of the saturation magnetization of ferrites prepared by the method are significantly increased compared with ferrites prepared by traditional methods. Because the metal elements in the layered precursor have the characteristics of a high degree of dispersion, high activity and small particle size (average particle size 40-200 nm), no milling is required before calcination, thus simplifying the production process, shortening the production period, reducing capital investment in equipment and economizing on energy costs. In addition, the method does not corrode production equipment and does not pollute the environment.

Method for preparation of magnetic spinel ferrites from layered double hydroxide precursors

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Beijing, China

Junjie Liu