Abstract: An enterovirus multi-antigen epitope fusion protein, a gene, a vaccine and a preparation method thereof are provided, relating to the field of vaccine preparation technologies. The amino acid sequence of the enterovirus multi-antigen epitope fusion protein is shown in SEQ ID NO: 4. The vaccine is prepared by adopting the preparation method of a genetically engineered protein vaccine, and the prepared vaccine has stronger specificity. After protein purification, there are fewer impure proteins and more specific antigen proteins, thus improving the effectiveness and safety of the combined vaccine.

Abstract: The present disclosure relates to an isocyanate electrolyte solution additive based on an imidazole structural group, belonging to the technical field of non-aqueous electrolyte solution additives of lithium batteries. The structural formula of the electrolyte solution additive is represented by formula I: R1, R2 and R3 are identical or different, the R1, R2 and R3 are each independently selected from one of hydrogen, methyl, ethyl, propyl, tert-butyl, trifluoromethyl, trifluoroethyl, perfluoroethyl, perfluoropropyl, cyanoethyl, phenyl, fluorophenyl, cyano-containing fluorophenyl, alkoxy-containing phenyl and alkyl-containing phenyl. The electrolyte solution additive is used for lithium ion batteries, which effectively inhibits the reduction in battery capacity, restricts the production of a gas caused by decomposition of an electrolyte solution and significant improvement in the service life of the battery during the high temperature cycle and high temperature storage.

Abstract: Embodiments of the present disclosure provide a method for manufacturing a transistor device, and a transistor device. The method for manufacturing a transistor device includes: injecting, into a channel of at least one first transistor located in an electrical break (EB) region, doping ions of a different type from the first transistor, and/or injecting, into a channel of at least one second transistor located outside the EB region, doping ions of a same type as the second transistor; and forming the transistor device based on the first transistor and the second transistor into which doping ions have been injected.

Abstract: A predicting system and method for the uniaxial compressive strength of rock include a point loading strength test module, a longitudinal wave velocity test module, a rock rebound value test module and a strength prediction module, wherein the longitudinal wave velocity test module performs longitudinal wave velocity tests on the rock, and transfers the longitudinal wave velocity of the rock to the strength prediction module; the rock rebound test module performs rebound test on the rock, and transfers the rebound value of the rock to the strength prediction module; the point loading strength test module performs image acquisition on a fracture surface of the rock after being loaded and fractured by the point loading test, and calculates the area of the fracture surface; and the strength prediction module outputs a uniaxial compressive strength prediction result of the rock according to the received information and a preset prediction model.

Abstract: Embodiments of the present disclosure provide a memory test method and a device thereof, an electronic device, and a computer-readable storage medium, which relate to the field of semiconductor device testing technologies. The method is executed by a built-in self-test circuit and includes: acquiring defect information of a first memory by testing the first memory; acquiring repair information of the first memory based on the defect information of the first memory; and storing the repair information of the first memory in a second memory.

Abstract: The present disclosure discloses a method for dual-motor control on an electric vehicle based on adaptive dynamic programming. First, total torque required is calculated based on obtained data information of the electric vehicle under various driving conditions, and offline training is conducted on an execution network and an evaluation network. Then total torque is dynamically distributed for two motors of the electric vehicle under various driving conditions to obtain an efficiency MAP database. Afterwards, iteration and online learning are conducted on the execution network and the evaluation network based on data information of the electric vehicle under different driving conditions that is obtained in real time, so as to find an optimal control law for the electric vehicle under a real-time driving condition. In this way, the dual-motor control on the electric vehicle is optimized.

Abstract: A recombinant AAV vector including a sequence for introducing the expression of G protein-coupled receptor 173 (GPR173) and specifically targeting GPR173 expressing neurons in a brain is provided. A method of restoring the excitatory/inhibitory (E/I) balance in brain or for prophylaxis and/or therapy of a neurological condition in a subject in need thereof by administrating the recombinant AAV vector or a GPR173 agonist to the subject is also provided.

Abstract: A TBM-mounted surrounding rock wear resistance testing system and method, the system including: a fixing module, arranged on a TBM, the module has a movable end for extending to the surrounding rock of a tunnel; a surrounding rock polishing module on the movable end, the module includes a polishing mechanism for test area; a surrounding rock wear resistance testing module on the movable end, follows the movable end moving the test area, and including a mechanism for testing the wear resistance of the polished test area and a drive mechanism thereof; and a central control module, configured to control the motion states and operations of the fixing module, the surrounding rock polishing module and surrounding rock wear resistance testing module, and determine the wear resistance of the surrounding rock according to the wear resistance test result. The system can polish tunnel surrounding rock during TBM excavation and test wear resistance.

Abstract: A system and method for identifying lithology based on images and XRF mineral inversion solving the problem that conventional lithology identification relies on manual work, which is time-consuming, subjective and can cause misjudgment. The identification system includes an autonomous vehicle; an X ray fluorescence spectrometer probe, and tests surrounding rock element information; image collection device; and vehicle-mounted processor. The processor inverts the received surrounding rock element information into mineral information based on a Barthes-Niggli standard mineral calculation method; and receive surrounding rock images and a corresponding inclination angle thereof, convert the surrounding rock images into image information in a one-dimensional vector format, splice the image and mineral information which is in a one-dimensional format, and distinguish the spliced information based on a preset neural network to identify rock lithology.

Abstract: An advanced geological prediction method and system based on perception while drilling, and relates to advanced geological prediction. The solution includes: acquiring drilling parameters during drilling; obtaining physical and mechanical parameters of tunnel surrounding rocks by inversion based on drilling parameters; acquiring rock slag or powder based on flushing fluid collected during drilling; acquiring geochemical characteristic parameters of rock slag or powder; and obtaining at least one adverse geology recognition result and surrounding rock classification result using a pre-trained deep learning model, and realizing advanced geological prediction. Combined with advanced geological drilling, the solution reflects geological characteristics from changes of physical and mechanical properties of tunnel surrounding rocks and changes of geochemical characteristic parameters.

Abstract: A system and method for identifying lithology based on images and XRF mineral inversion solving the problem that conventional lithology identification relies on manual work, which is time-consuming, subjective and can cause misjudgment. The identification system includes an autonomous vehicle; an X ray fluorescence spectrometer probe, and tests surrounding rock element information; image collection device; and vehicle-mounted processor. The processor inverts the received surrounding rock element information into mineral information based on a Barthes-Niggli standard mineral calculation method; and receive surrounding rock images and a corresponding inclination angle thereof, convert the surrounding rock images into image information in a one-dimensional vector format, splice the image and mineral information which is in a one-dimensional format, and distinguish the spliced information based on a preset neural network to identify rock lithology.

Abstract: An intelligent lithology identification system and method based on images and spectrum technology. The intelligent lithology identification system includes a rock shape analysis system, an image identification system, a sample processing system, a spectrum analysis system, and a central analysis and control system; wherein the central analysis and control system determines the final lithology of a sample according to the rock identification results from the image identification system and the analysis results from the spectrum analysis system. The technical solution further identifies the content and type of minerals by using spectrum technology, integrates and analyzes the results of spectrum analysis and image identification, and finally gives the lithology of the rock, which greatly improves the accuracy of lithology identification.

Abstract: A predicting system and method for the uniaxial compressive strength of rock include a point loading strength test module, a longitudinal wave velocity test module, a rock rebound value test module and a strength prediction module, wherein the longitudinal wave velocity test module performs longitudinal wave velocity tests on the rock, and transfers the longitudinal wave velocity of the rock to the strength prediction module; the rock rebound test module performs rebound test on the rock, and transfers the rebound value of the rock to the strength prediction module; the point loading strength test module performs image acquisition on a fracture surface of the rock after being loaded and fractured by the point loading test, and calculates the area of the fracture surface; and the strength prediction module outputs a uniaxial compressive strength prediction result of the rock according to the received information and a preset prediction model.

Abstract: A TBM-mounted surrounding rock wear resistance testing system and method, the system including: a fixing module, arranged on a TBM, the module has a movable end for extending to the surrounding rock of a tunnel; a surrounding rock polishing module on the movable end, the module includes a polishing mechanism for test area; a surrounding rock wear resistance testing module on the movable end, follows the movable end moving the test area, and including a mechanism for testing the wear resistance of the polished test area and a drive mechanism thereof; and a central control module, configured to control the motion states and operations of the fixing module, the surrounding rock polishing module and surrounding rock wear resistance testing module, and determine the wear resistance of the surrounding rock according to the wear resistance test result. The system can polish tunnel surrounding rock during TBM excavation and test wear resistance.

Abstract: A full-automatic rock specimen image acquisition device and method, the device includes a central controller and a lighting system, a rock mass attitude control system, a dust system and an image acquisition system connected to the central controller respectively; the lighting system includes a lighting chamber and light sources with adjustable light intensities, and the light sources with adjustable light intensities are uniformly arranged in the lighting chamber; the rock mass attitude control system includes a rotating stage disposed in the lighting chamber for carrying the rock, a rock holder disposed on the stage, and a rotating gripper disposed above the stage for turning over the rock; and the dust system is connected to the lighting chamber, and can diffuse the dust into the lighting chamber through an air compressor and control the dust concentration in the lighting chamber through an electrostatic precipitator.

Abstract: A TBM-mounted virtual reconstruction system and method for a tunnel surrounding rock structure, the method including: obtaining image information of surrounding rock; receiving the image information of the surrounding rock; transforming and splicing pictures taken at different angles, to form a complete image reflecting the surrounding rock; recognizing and describing a surrounding rock feature of the complete image, and transmitting the annotated image to a virtual reconstruction module; displaying the complete image with a description of the surrounding rock feature by using a virtual reality device, to reflect surrounding rock situations at different angles/different positions. This has the advantages of high equipment automation, not prolonging the construction time, not requiring the sketcher to enter the tunnel, precise surrounding rock feature parameters, and supporting remote control.

Abstract: A TBM-mounted virtual reconstruction system and method for a tunnel surrounding rock structure, the method including: obtaining image information of surrounding rock; receiving the image information of the surrounding rock; transforming and splicing pictures taken at different angles, to form a complete image reflecting the surrounding rock; recognizing and describing a surrounding rock feature of the complete image, and transmitting the annotated image to a virtual reconstruction module; displaying the complete image with a description of the surrounding rock feature by using a virtual reality device, to reflect surrounding rock situations at different angles/different positions. This has the advantages of high equipment automation, not prolonging the construction time, not requiring the sketcher to enter the tunnel, precise surrounding rock feature parameters, and supporting remote control.

Abstract: A full-automatic rock specimen image acquisition device and method, the device includes a central controller and a lighting system, a rock mass attitude control system, a dust system and an image acquisition system connected to the central controller respectively; the lighting system includes a lighting chamber and light sources with adjustable light intensities, and the light sources with adjustable light intensities are uniformly arranged in the lighting chamber; the rock mass attitude control system includes a rotating stage disposed in the lighting chamber for carrying the rock, a rock holder disposed on the stage, and a rotating gripper disposed above the stage for turning over the rock; and the dust system is connected to the lighting chamber, and can diffuse the dust into the lighting chamber through an air compressor and control the dust concentration in the lighting chamber through an electrostatic precipitator.

Abstract: An intelligent lithology identification system and method based on images and spectrum technology. The intelligent lithology identification system includes a rock shape analysis system, an image identification system, a sample processing system, a spectrum analysis system, and a central analysis and control system; wherein the central analysis and control system determines the final lithology of a sample according to the rock identification results from the image identification system and the analysis results from the spectrum analysis system. The technical solution further identifies the content and type of minerals by using spectrum technology, integrates and analyzes the results of spectrum analysis and image identification, and finally gives the lithology of the rock, which greatly improves the accuracy of lithology identification.

Abstract: The present disclosure discloses a method for dual-motor control on an electric vehicle based on adaptive dynamic programming. First, total torque required is calculated based on obtained data information of the electric vehicle under various driving conditions, and offline training is conducted on an execution network and an evaluation network. Then total torque is dynamically distributed for two motors of the electric vehicle under various driving conditions to obtain an efficiency MAP database. Afterwards, iteration and online learning are conducted on the execution network and the evaluation network based on data information of the electric vehicle under different driving conditions that is obtained in real time, so as to find an optimal control law for the electric vehicle under a real-time driving condition. In this way, the dual-motor control on the electric vehicle is optimized.