Abstract: This disclosure discloses signal configuration methods and apparatuses. In an implementation, a method comprises: generating at least two reference signals of a same type corresponding to at least two antenna ports indicated to a terminal device, wherein the at least two reference signals comprise a first reference signal and a second reference signal, wherein a first sequence of the first reference signal is obtained based on a first initialization factor determined based on an index of a first code division multiplexing (CDM) group, and wherein a second sequence of the second reference signal different from the first sequence is obtained based on a second initialization factor determined based on an index of a second CDM group; and transmitting the at least two reference signals.

Abstract: A driving substrate, a light-emitting apparatus and a manufacturing method thereof, a splicing display apparatus, the driving substrate includes: a device disposing area, a bending area and a bonding area, the bending area is located between the device disposing area and the bonding area; the driving substrates located in the device disposing area, the bending area, and the bonding area include a buffer layer, a first conductive layer and a flexible dielectric layer that are stacked in sequence; the driving substrates located in the device disposing area and the bonding area further include a base plate disposed at a side of the buffer layer away from the first conductive layer, and a second conductive layer disposed at a side of the flexible dielectric layer away from the first conductive layer; and the driving substrate located in the bending area is configured to be able to bend along a bending axis.

Abstract: A flyback converter control architecture is provided in which primary-only feedback techniques are used to ensure smooth startup and detection of fault conditions. During steady-state operation, secondary-side regulation is employed. In addition, current limits are monitored during steady-state operation using primary-only feedback techniques to obviate the need for a secondary-side current sense resistor.

Abstract: An array substrate including a display area having a plurality of subpixels is provided. The plurality of subpixels includes a plurality of first subpixels in a display-bonding sub-area and a plurality of second subpixels in a regular display sub-area. The array substrate includes a plurality of thin film transistors on a first side of the base substrate and respectively in the plurality of subpixels. A respective one of the plurality of first subpixels includes a bonding pad on a second side of a base substrate; a lead line electrically connecting a respective one of a plurality of thin film transistors to the bonding pad; and a via extending through the base substrate. The lead line is unexposed in the array substrate. The lead line extends from the first side to the second side of the base substrate through the via, to connect to the bonding pad.

Abstract: The present disclosure relates to a method and system for evaluating the macro resilience of offshore oil well control equipment. The method for evaluating the macro resilience of offshore oil well control equipment comprises six steps: determining the type and strength of external disaster; calculating the failure rate of components; calculating the recovery rate of the components; modeling the BN for a degradation process; modeling the BN for a maintenance process; and calculating the resilience of the offshore oil well control equipment. A system for evaluating the macro resilience of offshore oil well control equipment comprises an external disaster evaluation module, a component failure rate calculation subsystem, a reliability degradation process simulation module, a fault identification module, a component recovery rate calculation module, a reliability recovery process simulation module, a reliability change curve derivation unit and an resilience calculation unit.

Abstract: The present application provides a displaying base plate and a displaying device, which relates to the technical field of displaying. The displaying device can ameliorate the problem of screen greening caused by electrostatic charges, thereby improving the effect of displaying. The displaying base plate includes an active area and a non-active area connected to the active area, the non-active area includes an edge region and a first-dam region, and the first-dam region is located between the active area and the edge region; the displaying base plate further includes: a substrate; an anti-static layer disposed on the substrate, wherein the anti-static layer is located at least within the edge region; and a driving unit and a touch unit that are disposed on the substrate, wherein the driving unit is located within the active area.

Abstract: A BVP10 protein shown in SEQ ID NO:2 for controlling tetranychid mites and use of the protein is provided. The BVP10 protein has a median lethal concentration of 19.07 ?g/mL against Tetranychus urticae, a median lethal concentration of 58.05 ?g/mL against Panonychus citri, a median lethal concentration of 36.08 ?g/mL against Tetranychus cinnabarinus, and a control effect of 79.53%-95.45% against strawberry red spider mites. The protein is provided for preparing a novel mite pesticide.

Abstract: A spray device includes a spray assembly. The spray assembly is configured to spray cleaning liquid towards a to-be-cleaned surface of a to-be-processed workpiece. The spray assembly includes a spray head and multiple liquid inlet pipelines. The spray assembly is configured to spray cleaning liquids with different concentrations, temperatures, and/or flow rates corresponding to different positions in a radial direction of the to-be-cleaned surface to cause a cleaning rate of the cleaning liquids at the different positions in the radial direction of the to-be-cleaned surface to be consistent.

Abstract: The technology described herein trains a reinforcement-learning model in a simulated environment. A simulated environment contrasts with a live environment. A live environment is a computing environment with which the reinforcement-learning model will interact once it is deployed. In order to be effective, the simulated environment may provide inputs to the reinforcement-learning model in the same format as the reinforcement-learning model receives from the live environment. In aspects, the training in the simulated environment may act as pre-training for training in the live environment. Once pre-trained, the reinforcement-learning model may be deployed in a live environment and continue to learn how to perform the same task in different ways, learn how to perform additional tasks, and/or improve performance of a task learned in pre-training. In aspects, the reinforcement-learning model may be used to discover unhealthy conditions in software by performing the tasks it has learned.

Abstract: The technology described herein provides a cloud reinforcement-learning architecture that allows a single reinforcement-learning model to interact with multiple live software environments. The live software environments and the single reinforcement-learning model run in a distributed computing environment (e.g., cloud environment). The single reinforcement-learning model may run on a first computing device(s) with a graphical processing unit (GPU) to aid in training the single reinforcement-learning model. At a high level, the single reinforcement-learning model may receive state telemetry data from the multiple live environments. The single reinforcement-learning model selects an available action for each set of state telemetry data received and communicates the selection to appropriate the test agent. The test agent then facilitates completion of the action within the software instance being tested in the live environment. A reward is then determined for the action.

Abstract: The technology described herein provides an automated software-testing platform that uses reinforcement learning to discover how to perform tasks used in testing. The technology described herein is able to perform quality testing even when prescribed paths to completing tasks are not provided. The reinforcement-learning agent is not directly supervised to take actions in any given situation, but rather learns which sequences of actions generate the most rewards through the observed states and rewards from the environment. In the software-testing environment, the state can be user interface features and actions are interactions with user interface elements. The testing system may recognize when a sought after state is achieved by comparing a new state to a reward criteria.

Abstract: A BVP8 protein for killing tetranychid mites and use thereof are provided. The protein is as set forth in SEQ ID NO. 2. The BVP8 protein has a median lethal concentration of 12.98 ?g/mL against Tetranychus urticae, 33.45 ?g/mL against Panonychus citri, and 26.32 ?g/mL against Tetranychus cinnabarinus, and shows an inhibitory effect against the hatching and cleavage of Tetranychus urticae eggs, with the egg cleavage rate of 75.86% after 72 h. The protein provides a new option for the preparation of a novel miticide.

Abstract: A polymer composition containing an impact modifier and a fibrous filler dispersed within a polymer matrix is provided. The polymer matrix contains a high performance thermoplastic polymer that exhibits a deflection temperature under load of about 40° C. or more as determined in accordance with ISO 75-2:2013 at a load of 1.8 MPa and a melting temperature of about 140° C. or more. The polymer composition exhibits a thermal shock resistance value of about 800 or more.

Abstract: By determining the lethality rate to Meloidogyne incognita and Caenorhabditis elegans, it was found that the methylmalonic acid has a better nematicidal effect on the Caenorhabditis elegans, with the LC50 being 13.11 and 1.20 for the Meloidogyne incognita and the Caenorhabditis elegans, respectively. After compounding the methylmalonic acid with betaine, the LC50 was 2.85 and 0.27 for the Meloidogyne incognita and the Caenorhabditis elegans, respectively. Meanwhile, the methylmalonic acid also has an inhibiting effect on Pseudomonas solanacearum and Erwinia carotovora. The preparation of the methylmalonic acid provides a new choice for preparing novel biocontrol agents against the root-knot nematodes.

Abstract: A BVP10 protein shown in SEQ ID NO:2 for controlling tetranychid mites and use of the protein is provided. The BVP10 protein has a median lethal concentration of 19.07 ?g/mL against Tetranychus urticae, a median lethal concentration of 58.05 ?g/mL against Panonychus citri, a median lethal concentration of 36.08 ?g/mL against Tetranychus cinnabarinus, and a control effect of 79.53%-95.45% against strawberry red spider mites. The protein is provided for preparing a novel mite pesticide.

Abstract: A BVP8 protein for killing tetranychid mites and use thereof are provided. The protein is as set forth in SEQ ID NO. 2. The BVP8 protein has a median lethal concentration of 12.98 ?g/mL against Tetranychus urticae, 33.45 ?g/mL against Panonychus citri, and 26.32 ?g/mL against Tetranychus cinnabarinus, and shows an inhibitory effect against the hatching and cleavage of Tetranychus urticae eggs, with the egg cleavage rate of 75.86% after 72 h. The protein provides a new option for the preparation of a novel miticide.

Abstract: A spiking convolutional neural network based on a FLASH storage and computing array, including: a sampling module, a FLASH-based storage and computing array and a corresponding neuron module, and a counter module; the sampling module is used to sample an input image to obtain an input spike; the FLASH-based storage and computing array stores a weight matrix, and is used to perform a vector matrix multiplying operation on the input spike and the weight matrix, and an operation result is output in a form of current; the neuron module is used to integrate the operation result of the FLASH-based storage and computing array so as to generate an output spike; the counter module is used to count a number of spikes generated by the neuron module of an output layer, and determine the number of spikes of the neuron module with a largest number of spikes as a recognition result.