Abstract: The present disclosure provides a device and a method for measuring levelness of a top cover of a reactor pressure vessel. The device includes a mounting and fixing mechanism, an inclination sensor mounting base, and an inclination sensor. The mounting and fixing mechanism includes a mounting bottom plate, and a fixing mechanism disposed on the mounting bottom plate. The inclination sensor mounting base includes a bearing plate located on the mounting bottom plate, and a portal frame disposed on the bearing plate and having a top surface. The inclination sensor is mounted on a sensor mounting surface of the bearing plate and located in the portal frame, and configured to obtain levelness of the sealing surface of the top cover by measuring levelness of the sensor mounting surface of the bearing plate.

Abstract: The present disclosure provides a device and a method for measuring levelness of a top cover of a reactor pressure vessel. The device includes a mounting and fixing mechanism, an inclination sensor mounting base, and an inclination sensor. The mounting and fixing mechanism includes a mounting bottom plate, and a fixing mechanism disposed on the mounting bottom plate. The inclination sensor mounting base includes a bearing plate located on the mounting bottom plate, and a portal frame disposed on the bearing plate and having a top surface. The inclination sensor is mounted on a sensor mounting surface of the bearing plate and located in the portal frame, and configured to obtain levelness of the sealing surface of the top cover by measuring levelness of the sensor mounting surface of the bearing plate.

Abstract: A display panel and an electronic terminal are provided, including a plurality of opening areas, an array substrate, a color filter substrate positioned opposite to the array substrate, a backlight module arranged on one side of the array substrate away from the color filter substrate; and an optical layer disposed in at least one of the color filter substrate and the array substrate. The color filter substrate includes color resist portions in the respective opening areas. The optical layer is configured to converge light and is arranged corresponding to the color resist portions.

Abstract: A computer-implemented method is provided. The computer-implemented method includes obtaining one or more attributes of one or more entities; performing a vectorized representation on a respective attribute of the one or more attributes, the vectorized representation being encoded as a vector of d dimensions; and performing computation on vectorized representation of the one or more attributes based on attention mechanism to construct a graph neural network model. Vectors of m number of dimensions are used to represent an individual entity in the graph neural network model, (m?1) dimensions out of m dimensions represent (m?1) number of attributes, and one out of the m dimensions represents a flag bit. Constructing the graph neural network model includes performing self-attention on the individual entity, during which the flag bit is converted into a fused flag bit by fusing the individual entity's own attributes.

Abstract: Disclosed are (2,2-dimethyl-1,3-dioxolan-4-yl)methyl 2-bromo-2,2-difluoroacetate, a waterborne polyurethane, and preparation methods thereof. The (2,2-dimethyl-1,3-dioxolan-4-yl)methyl 2-bromo-2,2-difluoroacetate could be used as a modified monomer for preparing a waterborne polyurethane, and substituents at a C2 position of the (2,2-dimethyl-1,3-dioxolan-4-yl)methyl 2-bromo-2,2-difluoroacetate are two fluorine atoms and one bromine atom. When it is used for preparing the waterborne polyurethane, fluorine and bromine groups are introduced into the structure of the waterborne polyurethane, and the resultant waterborne polyurethane exhibits good moisture resistance and flame retardance.

Abstract: The present disclosure relates to an electric control leveling valve for a vehicle, comprising: a vehicle height variation detection device, which is used for detecting a height variation of a vehicle body of the vehicle and generating a height variation electrical signal characterizing the height variation of the vehicle body; a control device, which generates a corresponding valve control command based on the height variation electrical signal; and a valve device, which is in gaseous coupling to a secondary suspension system of the vehicle and adjusts air mass of the secondary suspension system based on the valve control command. The present disclosure further relates to a corresponding vehicle, comprising such an electric control leveling valve. In some embodiments, the control can be made more flexible and active to facilitate diagnosis of faults of the electric control leveling valve, so that the valve can be replaced in time.

Abstract: The present disclosure provides a training method and apparatus for an image-text matching model, a device and a storage medium. The method includes: acquiring a positive sample and a negative sample; where the positive sample includes text and an image, the text in the positive sample is used to describe content of the image in the positive sample; the negative sample includes text and an image, the text in the negative sample describes content that is inconsistent with content of the image in the negative sample; training the image-text matching model by using the acquired positive sample and the acquired negative sample based on a manner of contrastive learning; where the image-text matching model is used to predict, for an input image and input text, whether the input text is used to describe content of the input image.

Abstract: A model training method includes: acquiring a sample set including a plurality of sample groups; the sample group includes an original image sample and original text samples; performing mask processing on the original image sample and the original text samples to generate a mask image sample and mask text samples; using the mask image sample and the mask text samples to perform adversarial training on a generator and a discriminator to obtain a target model; the generator includes a feature extraction network and an output network, the feature extraction network is used to perform feature extraction after information fusion of an input image and an input text of the generator.

Abstract: The embodiments of the present disclosure relate to the technical field of communications. Provided are a method for processing a message, an electronic device and a storage medium. The method includes: after receiving a short message, firstly acquiring a signaling simplification flow corresponding to the short message, wherein the signaling simplification flow is generated after simplifying a signaling standard flow; then a signaling operation is performed on the short message according to the signaling simplification flow.

Abstract: A method for controlling a vehicle, including: simulating and deducing, based on current state information between a target vehicle and a traffic participant, an interaction process between the target vehicle and the traffic participant to obtain an initial part of a cooperative game tree; updating, in response to there being a target leaf node that is capable of being expanded to obtain a target child node in the initial part, respective nodes on a deduction path from the root node to the target child node based on a path parameter of an updated predicted trajectory, to obtain a complete cooperative game tree composed of the initial part and the target child node; and determining a first control instruction of the target vehicle using the complete cooperative game tree, to control the target vehicle using the first control instruction.

Abstract: Provided is an object operating method, includes: acquiring an object to be operated; inputting the object to be operated into a target model, wherein the target model is a trained neural network model and at least one set of parameters in the target model is acquired in a predetermined manner, and the target model is configured to carry out a recognition operation or a processing operation on the object to be operated; and acquiring an operation result output by the target model; wherein the predetermined manner includes: acquiring a collection of sample parameters corresponding to a first set of parameters of the target model, performing a plurality of iteration processing on the collection of sample parameters; acquiring a target set of parameters based on the collection of sample parameters subjected to the plurality of iteration processing; and determining the target set of parameters as the first set of parameters.

Abstract: A multi-layer and multi-cavity laminating machine including a multi-layer feeding device, at least two multi-layer laminating devices, and a multi-layer finished product conveying device connected in sequence is provided. The number of multi-layer laminating devices are increased, and the laminating steps are carried out step by step. After completing the first laminating step, the solar cell module can be transported to the next multi-layer laminating device, so that the next solar cell module can be transported to the previous multi-layer laminating device, thereby improving production efficiency. In the same production time, it is possible to increase the production quantity or reduce the production time by producing the same quantity of products.

Abstract: Provided herein are methods of detecting single molecules that include binding single molecules in a sample solution to a first surface of an optically transparent substrate include the optically transparent substrate is free of a metallic coating. In some embodiments, the methods include irradiating the first surface of the substrate with an incident light having an incident angle selected to achieve total internal reflection of the incident light, thereby scattering light from the first surface and from the single molecules bound to the surface in which a wavelength of the incident light is between 10 nm and 350 ?m and the optically transparent substrate has a refractive index at the wavelength of the incident light exceeding that of the sample solution, and collecting an image that captures interference between evanescent light scattered from the single molecules and the first surface. Systems and additional methods are also provided.

Abstract: A compound comprises a donor and an acceptor, wherein at least one donor (“D”) and at least one acceptor (“A”) may be arranged in an order of D-A; D-A-D; A-D-A; D-D-A-D-D; A-A-D-A-A; D-A-D-A-D; and A-D-A-D-A. The compound may be selected from the group consisting of: MTPE-TP, MTPE-TT, TPE-TP A-TT, PTZ-BT-TP A, NPB-TQ, TPE-TQ-A, MTPE-BTSe, DCDPP-2TP A, DCDPP-2TPA4M, DCDP-2TPA, DCDP-2TPA4M, TTS, ROpen-DTE-TPECM, and RClosed-DTE-TPECM. The compound may be used as a probe and may be functionalized with special targeted groups to image biological species. As non-limiting examples, the compound may be used in cellular cytoplasms or tissue imaging, blood vessel imaging, in vivo fluorescence imaging, brain vascular imaging, sentinel lymph node mapping, and tumor imaging, and the compound may be used as a photoacoustic agent.

Abstract: The present invention belongs to the field of nanomedicine, and provides a DC biomimetic membrane nanoparticle loaded with an NIR-II AIE dye, a preparation method therefor and use thereof. The DC biomimetic membrane nanoparticle loaded with the NIR-II AIE dye comprises a DC membrane and an NIR-II AIE dye loaded therein, wherein the NIR-II AIE dye has photothermal characteristics. The DC biomimetic membrane nanoparticle loaded with the NIR-II AIE dye provided by the present invention not only solves the problem that the NIR-II AIE dye is poor in water solubility and limited in fluorescence imaging, but also increases the enrichment efficiency of the NIR-II AIE dye coated by the DC membrane at a tumor site and successfully activates the activity of T cells in vivo.

Abstract: The present disclosure relates to a method for training a natural language processing model, including: obtaining a sample text of natural language; determining one or more triples in the sample text, wherein each of the triples comprises two entities in the sample text and a relation between the two entities; processing the sample text based on the triples to obtain one or more knowledge fusion vectors; and training a natural language processing model by inputting the knowledge fusion vectors into the natural language processing model to obtain a target model.

Abstract: A load control method and a load control apparatus for a wind turbine generator system are provided, and the load control method includes: obtaining feature parameters of the wind turbine generator system for load prediction; obtaining a load estimation value of the wind turbine generator system by inputting the obtained feature parameters into a virtual load sensor; adjusting a control strategy of the wind turbine generator system based on the obtained load estimation value. A controller and a computer readable storage medium storing a computer program are further included. With the load control method and apparatus for the wind turbine generator system, a trained virtual load sensor can be used to realize real-time monitoring of the load of the on-site wind turbine generator system, and a reference for adjusting the control strategy can be provided according to the load.

Abstract: A T-Cell receptor (TCR) specific embedding model uses a bidirectional long short-term memory (LSTM) to generate representations for TCR sequences and predict a “next token” in a TCR sequence. The embedding model can be trained in an unsupervised manner using a large collection of TCR sequences, and can be combined with downstream models to perform tasks, such as a TCR-epitope binding prediction model and a clustering algorithm. The embedding model demonstrates significant of prediction improvement when compared to existing models.

Abstract: Provided in the present disclosure are a text recognition method, and a model and an electronic device, which are applied to a mode in which primary classification is first performed from different dimensions, and secondary classification is then performed, such that the meaning of text is analyzed from different dimensions, thereby improving the accuracy of text recognition. The method includes: acquiring text to be recognized, and performing primary classification on the text to obtain a plurality of text features, wherein the primary classification is used for performing feature extraction on the text from different dimensions, and there are differences between features extracted from the different dimensions (100); splicing the plurality of text features, so as to obtain spliced features (101); and performing secondary classification on the spliced features to obtain a text category corresponding to the text, wherein the secondary classification is used for classifying the spliced features (102).