Abstract: A medical image processing method and apparatus, and an image processing method and apparatus, terminal and storage medium that obtains a to-be-processed medical image; generates a difference image according to the first image data, the second image data, and the third image data included in the to-be-processed medical image; and performs binarization processing on the difference image to obtain a binarized image, a foreground region of the binarized image corresponding to a pathological tissue region of the to-be-processed medical image. A difference image is generated based on color information of different channels before binarization processing is performed on an image, thereby effectively using the color information in the image. The pathological tissue region extracted based on the difference image is more accurate and facilitates subsequent image analysis.

Abstract: An image generation method, apparatus, device, and storage medium. The method includes: obtaining contour information and target region information; determining at least one target contour according to the contour information and the target region information, the at least one target contour wholly or partly located in a target region; decreasing first coordinates of a plurality of contour points in the at least one target contour to obtain second location information, second location information of the at least one target contour includes second coordinates of a plurality of contour points in the at least one target contour, and the first coordinates of the plurality of contour points in the at least one target contour having a same decreasing extent; and generating a target image corresponding to the target region according to the second location information of the at least one target contour and the target region information.

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: A process for activating a hydrogenation catalyst comprising nickel to produce a selective hydrogenation catalyst, comprising contacting the hydrogenation catalyst with a mixed gas comprising and hydrogen sulfide and periodically increasing the temperature of the mixed gas in increments until the mixed gas reaches a temperature that facilities the efficient catalytic hydrogenation of both acetylene and butadiene by the modified catalyst, while the modified catalyst is simultaneously characterized by low selectivity for the hydrogenation of ethylene. The disclosure further claims a process that utilizes the modified catalyst to selectively hydrogenate acetylene and butadiene contaminants in a raw light olefin stream produced by thermal cracking, thereby extending the useful catalytic lifespan of a downstream oligomerization catalyst that converts the light olefins stream to a liquid transportation fuel, or a blend stock thereof.

Abstract: Methods for modifying a ZSM-5 zeolite by contacting the zeolite with an alkaline solution prior to combining with a binder material to produce a modified ZSM-5 catalyst extrudate that has substantially longer catalyst life, relative to an untreated ZSM-5 catalyst, for converting light olefins to products that may be used as a liquid transportation fuel blend stock. The alkaline solution is optionally sodium hydroxide. The binder is optionally alumina, bentonite or silica.

Abstract: A process comprising a heterogeneous reaction between a solid oxyanion-modified metal organic framework and a hydrocarbon feed to form a modified hydrocarbon stream. The modified hydrocarbon stream comprises essentially of C6+ hydrocarbons.

Abstract: A process comprising a heterogeneous reaction between a solid oxyanion-modified metal organic framework and a hydrocarbon feed to form a modified hydrocarbon stream. The modified hydrocarbon stream comprises essentially of C6+ hydrocarbons.

Abstract: A process comprising a heterogeneous reaction between a solid metal organic framework supported catalyst and a hydrocarbon feed to form a modified hydrocarbon stream. The modified hydrocarbon stream comprises essentially of C6+ hydrocarbons.

Abstract: A solid metal organic framework composition comprising a solid metal organic framework supported sulfonic acid wherein the sulfur content is greater than 0.5 mmol/gram.

Abstract: A process comprising a heterogeneous reaction between a solid metal organic framework supported sulfonic acid and a hydrocarbon feed to form a modified hydrocarbon stream. The modified hydrocarbon stream comprises essentially of C6+ hydrocarbons.

Abstract: A solid metal organic framework composition comprising a solid oxyanion-modified metal organic framework wherein the oxyanion loading is at least 2 per node.

Abstract: A process comprising a heterogenous reaction between a solid metal organic framework supported heteropolyacid catalyst and a hydrocarbon feed to form a modified hydrocarbon stream. The modified hydrocarbon stream comprises essentially of C6+ hydrocarbons.

Abstract: A solid metal organic framework composition comprising a solid metal organic framework supported heteropolyacid wherein the heteropolyacid loading is greater than 25% by weight; and the pore volume is less than 2.0 mL/g.

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: 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: 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: A flexible self-adaptive composite carbon brush-type electromagnetic composite field synchronous laser cladding device, comprising an electromagnetic field synchronous coupling module that processes a part to be processed, a mechanical arm that drives the electromagnetic field synchronous coupling module to move, and a laser that generates laser; the electromagnetic field synchronous coupling module comprises a laser head, an electric field portion and a magnetic field portion; the magnetic field portion comprises two magnetic field generating modules, and the electric field portion comprises two electric field generating modules, the magnetic field portion and the electric field portion both being fixedly erected at the periphery of the laser head by means of a supporting structure.

Abstract: Systems for the catalytic activation and/or dehydrogenation of a paraffin feed stream that is enriched in C5 alkanes to produce olefins that are then hydrated in the presence of water to produce C5 alcohols. Optionally, paraffin isomers are separated and the n-paraffins isomerized prior to catalytic activation and/or dehydrogenation.

Abstract: Processes for the catalytic activation and/or dehydrogenation of a paraffin feed stream that is enriched in C5 alkanes to produce olefins that are then hydrated in the presence of water to produce C5 alcohols. Optionally, paraffin isomers are separated and the n-paraffins isomerized prior to catalytic activation and/or dehydrogenation.

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.