Abstract: This application provides a packaging structure, including an antenna in package and a radio frequency chip that are packaged together, where the antenna in package is fastened on the radio frequency chip. The antenna in package includes a radiator and at least two feeding parts, the at least two feeding parts are electrically connected to the radio frequency chip, the radio frequency chip is configured to receive or transmit a radio frequency signal, and at least one of the at least two feeding parts provides differential feeding for the radiator. This application further provides a network device and a terminal device, to reduce an antenna size and make the antenna easier to mount.

Abstract: The invention provides a glutamate decarboxylase mutant with improved pH tolerance and use thereof in synthesis of gamma-aminobutyric acid. The mutant is obtained by mutating glutamate decarboxylase having an amino acid sequence as shown in SEQ ID NO. 3. The enzyme activity of the mutant at pH 6.5 is improved to 178% of the original enzyme (SEQ ID NO. 3). The final yield of 1000 g of substrate fed in batches in a 5L tank for 12 h is up to 688.13 g/L, which is about 52% higher than the productivity of the original glutamate decarboxylase. The final molar conversion rate can reach 98.2%. The invention not only broadens the enzyme activity of GAD under the optimum pH, but also broadens the enzyme activity of GAD under the neutral pH, and enhances the capability of the GAD to synthesize gamma-aminobutyric acid, and therefore is more suitable for industrial production.

Abstract: Provided are hybrid tRNA/pre-miRNA molecules, e.g., comprising a single tRNA and one, two or more pre-miRNA molecules, useful for the production and therapeutic delivery of an inserted RNA sequence, e.g., one or more miRNAs. Also provided are liposomes and nanoparticles that include the hybrid tRNA/pre-miRNA molecules. Methods of treating cancer by administration of the hybrid tRNA/pre-miRNA molecules are also provided.

Abstract: Provided is a terminal device, which includes: a light-emitting layer and a cover plate, camera unit; the light-emitting layer includes a first light-emitting layer and a second light-emitting layer; when the first light-emitting layer is located at a first preset position, a first orthographic projection area of the first light-emitting layer in a first direction coincides with a second orthographic projection area of the camera unit, wherein the first direction a direction pointing from the light-emitting layer to the cover plate; and when the first light-emitting layer is located at a second preset position, a third orthographic projection area of the light-emitting layer does not coincide with at least part of the second orthographic projection area.

Abstract: A distributed computing system for product defect analysis is disclosed. The distributed computing system for product defect analysis includes a computing cluster for processing product manufacturing messages, a computing cluster for identifying product defect, a product image database, and a client device.

Abstract: A method and device for detecting a defect and method for training a model are provided. The method for detecting the defect includes: acquiring a sample data set and identifying feature information of the sample data set; acquiring an initial model; configuring a training parameter based on the feature information; obtaining a target model by training, according to the training parameter, the initial model with the sample data set; and obtaining defect information of a product by inputting real data of the product into the target model. The training parameter includes at least one of a learning rate descent strategy, a total number of training rounds and a test strategy, the learning rate descent strategy includes a number of learning rate descents and a round number when a learning rate descends, and the test strategy includes a number of tests and a round number when testing.

Abstract: A distributed computing system for product defect analysis is disclosed. The distributed computing system for product defect analysis includes a computing cluster for processing product manufacturing messages, a computing cluster for identifying product defect, a product image database, and a client device.

Abstract: A method and a device for processing product manufacturing messages, and an electronic device are disclosed. The method for processing product manufacturing messages includes: monitoring a plurality of product manufacturing messages; establishing a product defect analysis task queue based on the plurality of product manufacturing messages; distributing product defect analysis tasks to product manufacturing assisting devices based on the product defect analysis task queue, wherein the product defect analysis tasks include a task of identifying product defect content based on a defect identification model; wherein the product defect content includes any one or more of: product defect type, product defect location, and product defect size.

Abstract: The present disclosure provides a method and device for detecting an image category. The method includes: acquiring a sample data set including a plurality of sample images labeled with a category, the sample data set including a training data set and a verification data set; training a deep learning model using the training data set to obtain, according to different numbers of training rounds, at least two trained models; testing the at least two trained models using the verification data set to generate a verification test result; generating, based on the verification test result, a verification test index; determining, according to the verification test index, a target model from the at least two trained models; and predict a to-be-tested image of the target object using the target model to obtain the category of the to-be-tested image.

Abstract: The present disclosure relates to a task processing method and device based on defect detection, a computer readable storage medium, and a task processing apparatus . The method includes receiving a detection task; determining a task type of the detection task; storing the detection task in a task queue if the task type is a target task type; and executing the detection task in a preset order and generating a feedback signal when a processor is idle. The detection task of the target task type includes an inference task and a training task. Executing the training task includes modifying configuration information according to a preset rule based on product information in the detection task; acquiring training data and an initial model according to the product information; and using the training data to train the initial model according to the configuration information to obtain a target model and store it in memory.

Abstract: An image marking method, apparatus and system, which relates to the technical field of image processing. The present disclosure includes, when the working mode of the first client is a first mode, receiving a first marking task assigned by a second client, on the condition that the image marking approach is the first marking approach, according to a neural network model, determining a first marking result corresponding to the first original image; on the condition that the image marking approach is the second marking approach, according to an unsupervised algorithm model, determining a second marking result corresponding to the first original image; on the condition that the image marking approach is the third marking approach, receiving a third marking result inputted by a user into the first original image; and sending a target marking result to the second client.

Abstract: Provided are an anti-CLDN18.2 antibody or an antigen-binding fragment thereof, a derivative comprising said antibody or antigen-binding fragment thereof, a pharmaceutical composition, and related use of said antibody or antigen-binding fragment thereof for treating, diagnosing and detecting cancers.