Abstract: The present invention discloses a production method of enzymatic reaction using adenosine instead of ATP. The method comprises the following steps: (1) adding ATP regeneration enzyme, AK enzyme and adenosine in proportion to carry out an enzymatic reaction in an enzymatic reaction system; (2) separating the ATP regeneration enzyme and AK enzyme by either directly separating ATP regeneration enzyme and AK enzyme immobilized in a reaction tank, or separating free ATP regeneration enzyme and AK enzyme by an ultrafiltration membrane in a filter; and (3) separating and purifying the filtrate of step (2) to obtain a product. The disclosed method provides: greatly reduced industrial production costs; faster reaction rate; stable enzyme recovery system that is energy efficient and environmentally friendly; and capability of reusing the byproducts or collecting them for the production of ATP.

Abstract: Techniques are described herein that are capable of automatic mapping of a question or compliance controls associated with a compliance standard to compliance controls associated with another compliance standard. Reference controls having respective first subsets of text-based features are identified. A question having a second subset of the text-based features or custom controls having respective second subsets of the text-based features are identified. Scores for the respective reference controls are determined for the question or each custom control using a supervised natural language processing machine learning model based at least on the first subsets of the text-based features and the second subset(s) of the text-based features. A compliance map is generated by automatically mapping the question or each custom control to a respective subset of the reference controls using the supervised natural language processing machine learning model based at least on the scores.

Abstract: The present invention discloses a production method of enzymatic reaction using adenosine instead of ATP. The method comprises the following steps: (1) adding ATP regeneration enzyme, AK enzyme and adenosine in proportion to carry out an enzymatic reaction in an enzymatic reaction system; (2) separating the ATP regeneration enzyme and AK enzyme by either directly separating ATP regeneration enzyme and AK enzyme immobilized in a reaction tank or separating free ATP regeneration enzyme and AK enzyme by an ultrafiltration membrane in a filter; and (3) separating and purifying the filtrate of step (2) to obtain a product. The disclosed method provides: greatly reduced industrial production costs; faster reaction rate; stable enzyme recovery system that is energy efficient and environmentally friendly; and capability of reusing the byproducts or collecting them for the production of ATP.

Abstract: A method for compact and flat bismuth metal preparation by electrolysis is provided. In the method, one or more of ?-naphthol, acacia, sulfonated and vulcanized alkylphenol ethoxylate and naphthol ethoxylate oxides are added to the acidic solution of bismuth methanesulfonate as additives, and the cathodic bismuth is obtained by electrolysis at 20-80° C. The method for bismuth metal preparation is simple and easy to promote, environment-friendly, and the obtained bismuth metal has a flat and compact surface and good plate formation effect.

Abstract: The present disclosure provides a method for visual question answering, which relates to fields of computer vision and natural language processing. The method includes: acquiring an input image and an input question; detecting visual information and position information of each of at least one text region in the input image; determining semantic information and attribute information of each of the at least one text region based on the visual information and the position information; determining a global feature of the input image based on the visual information, the position information, the semantic information, and the attribute information; determining a question feature based on the input question; and generating a predicted answer for the input image and the input question based on the global feature and the question feature. The present disclosure further provides a device for visual question answering, a computer device and a medium.

Abstract: A method for training an image recognition model includes: obtaining a training data set, in which the training data set includes first text images of each vertical category in a non-target scene and second text images of each vertical category in a target scene, and a type of text content involved in the first text images is the same as a type of text content involved in the second text image; training an initial recognition model by using the first text images, to obtain a basic recognition model; and modifying the basic recognition model by using the second text images, to obtain an image recognition model corresponding to the target scene.

Abstract: The present disclosure discloses a method and apparatus for recognizing a text. The method comprises: acquiring images of a text area of an input image, the acquired images including a text centerline graph, a text direction offset graph, a text boundary offset graph, and a text character classification graph; extracting coordinates of feature points of a character center from the text centerline graph; sorting the extracted coordinates of the feature points based on the text direction offset graph to obtain a coordinate sequence of the feature points; determining a polygonal bounding box of the text area based on the coordinate sequence of the feature points of the character center and the text boundary offset graph; and determining a classification result of the feature points of the character center, based on the coordinate sequence of the feature points of the character center and the text character classification graph.

Abstract: The present application discloses a method and an apparatus for recognizing text content, and an electronic device, and relates to a text recognition technique in the field of computer technology. The specific implementation is as follows: acquiring a dial picture; detecting at least one text centerline and a bounding box corresponding to each text centerline in the dial picture; and recognizing text content in each line of text in the dial picture based on the at least one text centerline and the bounding box corresponding to each text centerline.

Abstract: The present invention relates an enzymatic method for preparing glutathione, comprising the following steps: (1) producing glutathione in a reactor using GshF enzyme, ATP regeneration enzyme and AK enzyme; (2) separating immobilized GshF enzyme, ATP regeneration enzyme and AK enzyme in the reactor, or separating free GshF enzyme using a filtration device; (3) separating the filtrate obtained in step (2) to obtain a crude product GSH and a small amount of ATP, ADP and AMP; and (4) subjecting the GSH obtained in step (3) to concentration, crystallization, and drying to prepare a finished GSH. The described method provides: greatly reduced industrial production costs; faster reaction rate; stable enzyme recovery system that is energy efficient and environmentally friendly; and capability of reusing the byproducts or collecting them for the production of ATP.

Abstract: A character recognition method, a character recognition apparatus, an electronic device and a computer readable storage medium are disclosed. The character recognition method includes: determining semantic information and first position information of each individual character recognized from an image; constructing a graph network according to the semantic information and the first position information of each individual character; and determining a character recognition result of the image according to a feature of each individual character calculated by the graph network.

Abstract: The present disclosure provides technical solutions related to intelligent cloud management based on profile. Artificial intelligent is applied to cloud management and cloud management suggestion may be proposed intelligently. In daily work, behaviors in using cloud resources may show characteristics of cloud users or cloud tenants themselves. The technical solution of intelligent cloud management of the present disclosure generates profile identifying cloud using characteristics by extracting behavior data in using cloud and intelligently proposes cloud management suggestions based on the profile.

Abstract: A curved light-emitting substrate and its formation method, and a display apparatus are provided in the present disclosure. The curved light-emitting substrate includes a substrate; an array layer, disposed on a side of the substrate; a plurality of light-emitting elements, electrically connected to the array layer, where a light-emitting element of the plurality of light-emitting elements includes a light-emitting main body and a first soldering pad on a side of the light-emitting main body facing the array layer; a second soldering pad which is on a side of the array layer facing the light-emitting element and electrically connected to the first soldering pad; and a flexible padding layer, between the light-emitting main body and the array layer along a first direction, where the first direction is perpendicular to a surface of the array layer.

Abstract: The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

Abstract: The present application discloses a method and an apparatus for optical character recognition, an electronic device and a storage medium, and relates to the fields of artificial intelligence and deep learning. The method may include: determining, for a to-be-recognized image, a text bounding box of a text area therein, and extracting a text area image from the to-be-recognized image according to the text bounding box; determining a bounding box of text lines in the text area image, and extracting a text-line image from the text area image according to the bounding box; and performing text sequence recognition on the text-line image, and obtaining a recognition result. The application of the solution in the present application can improve a recognition speed and the like.

Abstract: A method for recognizing text includes: obtaining a first feature map of an image; for each target feature unit, performing a feature enhancement process on a plurality of feature values of the target feature unit respectively based on the plurality of feature values of the target feature unit, in which the target feature unit is a feature unit in the first feature map along a feature enhancement direction; and performing a text recognition process on the image based on the first feature map after the feature enhancement process.

Abstract: A method for text recognition is disclosed. The method includes obtaining a whole-image scenario for an image to be processed and a text image in the image to be processed. The method further includes determining a first text recognition model corresponding to the whole-image scenario. The method further includes performing text recognition on the text image according to the first text recognition model to obtain text information.

Abstract: A diverter valve assembly, a dishwasher, and a household appliance are disclosed. The diverter valve assembly has a housing and a diverter valve. A water diversion chamber is formed in the housing. The housing is provided with a water inlet, a water return port, a first water outlet, and a second water outlet. The diverter valve partitions the water diversion chamber into a first chamber and a second chamber. The first chamber is communicated with the water inlet. The diverter valve is rotatable in the water diversion chamber to selectively communicate the first chamber with the water inlet and the first water outlet and communicate the second chamber with the second water outlet and the water return port, or block the water inlet from the first water outlet and communicate the first chamber with the water inlet and the second water outlet.

Abstract: The present disclosure provides technical solutions related to intelligent cloud management based on profile. Artificial intelligent is applied to cloud management and cloud management suggestion may be proposed intelligently. In daily work, behaviors in using cloud resources may show characteristics of cloud users or cloud tenants themselves. The technical solution of intelligent cloud management of the present disclosure generates profile identifying cloud using characteristics by extracting behavior data in using cloud and intelligently proposes cloud management suggestions based on the profile.

Abstract: Provided are a text recognition method, an electronic device, and a non-transitory computer-readable storage medium, which are applicable in an OCR scenario. In the particular solution, a text image to be recognized is acquired. Feature extraction is performed on the text image, to obtain an image feature corresponding to the text image, where a height-wise feature and a width-wise feature of the image feature each have a dimension greater than 1. According to the image feature, sampling features corresponding to multiple sampling points in the text image are determined. According to the sampling features corresponding to the multiple sampling points, a character recognition result corresponding to the text image is determined.