Abstract: Methods that increase production of a liquid transportation fuel blend component by utilizing C5 hydrocarbon streams taken from both a refinery naphtha stream and an NGL fractionator pentanes plus stream. A high vapor pressure pentane fraction from the NGL fractionator is separated to remove isopentane and produce lower vapor pressure commodity natural gasoline. A refinery naphtha stream (that is optionally an FCC naphtha stream) is separated to produce a C5 olefins stream that is then oligomerized to produce an upgraded stream having lower vapor pressure and higher octane rating, then combined with the remainder of the naphtha stream as well as the isopentane stream to produce a gasoline blend component that meets specifications for vapor pressure and octane rating.

Abstract: Embodiments of this application disclose a method and apparatus for training an image region segmentation model, and an image region segmentation method and apparatus. The method includes acquiring a sample image set, and each image of the sample image set having first annotation information; generating graph structure data corresponding to a sample image in the sample image set, the graph structure data comprising multiple nodes, and each node comprising at least one pixel in the sample image; determining second annotation information of each node according to the graph structure data and the first annotation information corresponding to the sample image by using a graph convolutional network model, a granularity of the second annotation information being smaller than a granularity of the first annotation information, the graph convolutional network model being a part of an image segmentation model; and training the image segmentation model according to the second annotation information.

Abstract: An artificial intelligence-based image processing method includes: obtaining a first sample image of a source domain and a second sample image of a target domain, the first sample image of the source domain carrying a corresponding target processing result; converting the first sample image into a target sample image, the target sample image carrying a corresponding target processing result; training a first image processing model based on the target sample image and the target processing result corresponding to the target sample image, to obtain a second image processing model; and inputting, in response to obtaining a human tissue image of the target domain, the human tissue image into the second image processing model, positioning, by the second image processing model, a target human tissue in the human tissue image, and outputting position information of the target human tissue in the human tissue image.

Abstract: This disclosure discloses a microscope system, a smart medical device, an automatic focusing method, and a storage medium. The smart medical device includes an objective lens, a beam splitter, an image projector assembly, a camera assembly, and a focusing device. The objective lens includes a first end and a second end, and the first end faces a to-be-observed sample. The beam splitter is disposed on the second end. The image projector assembly is in communication with the beam splitter, the image projector assembly includes a first lens and an image projection device, and light generated by the image projector assembly enters the beam splitter through the first lens. The camera assembly includes a camera. The focusing device is disposed on the camera assembly, and the focusing device is configured to perform focus adjustment on the camera.

Abstract: An artificial intelligence-based image processing method includes: obtaining a first sample image of a source domain and a second sample image of a target domain, the first sample image of the source domain carrying a corresponding target processing result; converting the first sample image into a target sample image, the target sample image carrying a corresponding target processing result; training a first image processing model based on the target sample image and the target processing result corresponding to the target sample image, to obtain a second image processing model; and inputting, in response to obtaining a human tissue image of the target domain, the human tissue image into the second image processing model, positioning, by the second image processing model, a target human tissue in the human tissue image, and outputting position information of the target human tissue in the human tissue image.

Abstract: An AI-based object classification method and apparatus, a computer-readable storage medium, and a computer device. The method includes: obtaining a target image to be processed, the target image including a target detection object; separating a target detection object image of the target detection object from the target image; inputting the target detection object image into a feature object prediction model to obtain a feature object segmentation image of a feature object in the target detection object image; obtaining quantitative feature information of the target detection object according to the target detection object image and the feature object segmentation image; and classifying the target detection object image according to the quantitative feature information to obtain category information of the target detection object in the target image.

Abstract: This application discloses an artificial intelligence-based image processing method, apparatus, device, and storage medium, and relates to the field of computer technology. The method includes: obtaining a slice image; dividing the slice image to obtain a plurality of image blocks; feeding the plurality of image blocks into a labeling model, extracting, by the labeling model, a pixel feature of the slice image based on the plurality of image blocks, determining a plurality of vertex positions of a polygonal region in the slice image based on the pixel feature, concatenating the plurality of vertex positions, and outputting label information of the slice image, the polygonal region being a region in which a target pathological tissue of interest is located.

Abstract: Various embodiments of a process for converting light alkanes to diesel are disclosed. In general, the process includes reacting a feed rich in one or more light alkanes with an aromatization catalyst to convert the light alkanes to aromatic hydrocarbons, reacting the aromatic hydrocarbons with a hydroalkylation catalyst to convert the aromatic hydrocarbons into diesel range hydrocarbons, and hydrogenating the diesel range hydrocarbons to produce a diesel product.

Abstract: A method is provided and includes acquiring a digital slide comprising objects of at least two sizes, the objects including a first object of a first size and a second object of a second size different from the first size, for each of the first object and the second object, acquiring images of a corresponding object in at least two scales based on the digital slide, where the acquired images of the corresponding object in the at least two scales include an image of a larger scale and an image of a smaller scale, the image of the larger scale having a smaller image size and a higher image resolution than the image of the smaller scale, and determining, from the images of the corresponding object in the at least two scales, an image having an image size that corresponds to a size of the corresponding object.

Abstract: Embodiments of this application provide a method and apparatus for determining antigenic specificity. The method includes acquiring double-stranded biological information of a cell receptor; performing word coding processing on the double-stranded biological information to obtain an amino acid word sequence, the amino acid word sequence comprising an amino acid word representation; performing feature extraction on the cell receptor based on the amino acid word sequence with a pre-trained amino acid sequence prediction model to obtain an amino acid sequence representation of the cell receptor, the amino acid prediction model being trained with masked sample data; and determining the antigenic specificity of the cell receptor based on the amino acid sequence representations.

Abstract: An antigen prediction method including inputting genetic information, sequence information, and three-dimensional structure features of an immune cell receptor into an antigen prediction model, performing, by the antigen prediction model, feature extraction on the genetic information and the sequence information to obtain genetic features and sequence features of the immune cell receptor, integrating, by the antigen prediction model, the genetic features, the sequence features, and the three-dimensional structure features to obtain receptor features of the immune cell receptor, performing, by the antigen prediction model, full connection and normalization on the receptor features to output a probability of the immune cell receptor being associated with each candidate antigen of a plurality of candidate antigens, and determining, based on the probability of the immune cell receptor being associated with each candidate of the plurality of candidate antigens, an antigen binding to the immune cell receptor from t

Abstract: An augmented reality microscope (ARM) includes an objective lens, an eyepiece, an N-ocular observation tube, where N is a positive integer greater than 2, an image obtaining assembly physically connected to the N-ocular observation tube by a physical interface on the N-ocular observation tube, and an image projection assembly including, an image projection apparatus, a lens apparatus, and a light splitting apparatus. Light generated by an observed object during observation that enters an optical path through the objective lens and light generated by the image projection apparatus that enters the optical path through the lens apparatus converges at the light splitting apparatus in the image projection assembly, the converged light passes through the N-ocular observation tube.

Abstract: An apparatus, method and storage medium for controlling a pathologic microscope are provided. The method includes obtaining a pathological digital image from an incident optical path of the pathologic microscope; performing artificial intelligence (AI) analysis on the pathological digital image to generate AI analysis information; and controlling an augmented reality (AR) projection component to project the AI analysis information on a microscopic field of the pathologic microscope on an outgoing optical path.

Abstract: A gene regulatory relationship detection model training method performed by an electronic device, and relate to the field of biology technologies. The gene regulatory relationship detection model training method includes: obtaining material group data of a plurality of sample genes and an annotated regulatory relationship between at least one sample gene pair; determining a predicted probability that a regulatory relationship exists between each two sample genes among the plurality of sample genes by using a neural network model based on the material group data of the plurality of sample genes; and training the neural network model to obtain a gene regulatory relationship detection model, based on the annotated regulatory relationship between the at least one sample gene pair and the predicted probability between each two sample genes.

Abstract: This application provides a data processing method performed by an electronic device. The method includes: obtaining target data, and parsing the target data to obtain spatial location information and first feature information of each object in N objects; converting the target data into a first image based on the spatial location information and the first feature information of each object; extracting second feature information of each object in the target data from the first image; and performing preset processing on the second feature information of each object in the target data, to obtain a processing result of the target data. The entire data processing process is simple, occupies less computing resources, and has high data processing efficiency.

Abstract: This application provides a pathological section image processing method performed by a computer device. The method includes: obtaining stained images of a pathological section after cell membrane staining; determining cell nucleus positions of cancer cells in a stained image under an ith field of view in the n fields of view; generating a cell membrane description result of the stained image under the ith field of view, the cell membrane description result being used for indicating completeness and staining intensity of the cell membrane staining; determining quantities of cells of types in the stained image under the ith field of view according to the cell nucleus positions and the cell membrane description result; and determining an analysis result of the pathological section according to quantities of the cells of types in the stained images under the n fields of view.

Abstract: A method of making an anode material. The method begins by mixing a pre-passivated anode graphite with a supplement and a solvent to create a mixture. The solvent is then evaporated from the mixture to create a passivated anode graphite particle.

Abstract: Embodiments of this application disclose methods, systems, and devices for medical image analysis and medical video stream processing. In one aspect, a method comprises extracting video frames from a medical image video stream that includes at least two pathological-section-based video frames. The method also comprises identifying single-frame image features in the video frames, mapping the single-frame image features into single-frame diagnostic classification results, and performing a classification mapping based on a video stream feature sequence that comprises the single-frame image features. The classification mapping comprises performing a convolution operation on the video stream feature sequence through a preset convolutional layer, obtaining a convolution result in accordance with the convolution operation, and performing fully connected mapping on the convolution result through a preset fully connected layer.

Abstract: A 3D medical image recognition method and apparatus, a device, a non-transitory computer-readable storage medium, and a computer program product are provided, which relate to the field of artificial intelligence. View rearrangement processing is performed in an ith-round feature extraction process on an (i?1)th-round 3D medical image feature to obtain 2D image features. The (i?1)th-round 3D medical image feature is obtained by performing (i?1)th-round feature extraction on a 3D medical image. Different 2D image features are features of the (i?1)th-round 3D medical image feature in different views. Semantic feature extraction processing is performed on each 2D image feature in different views. Feature fusion processing is performed on the image semantic features to obtain an ith-round 3D medical image feature. Image recognition processing is performed on an Ith-round 3D medical image feature obtained through Ith-round feature extraction to obtain image recognition of the 3D medical image.

Abstract: The disclosed process relates to removal of benzene from a reformate stream and in turn providing gasoline and diesel products along with commodity chemicals (such as cyclohexylbenzene). The disclosed process further relates to the upgrading of heart-cut reformate benzene to higher value products.