Abstract: Provided is an engineered bacterium for producing nervonic acid and/or grease. The genome of the engineering bacterium is integrated with an expression cassette expressing a protein encoded by 3-ketoacyl-CoA synthase (KCS) gene and/or an exterase gene.

Abstract: A system may instantiate a virtual whiteboard during a video conference controlled by a host device. The whiteboard may include a first page configured to output first visual content and a second page configured to output second visual content to displays of participant devices connected to the video conference. The system may move the first page to a first breakout room associated with the video conference and the second page to a second breakout room associated with the video conference. Access to the first page within the first breakout room may be limited to a first group of one or more of the participant devices connected to the first breakout room and the host device. Access to the second page within the second breakout room may be limited to a second group of one or more of the participant devices connected to the second breakout room and the host device.

Abstract: A computing system including a plurality of processing devices configured to execute a Mixture-of-Experts (MoE) layer. The processing devices are configured to execute the MoE layer at least in part by receiving an input tensor including input tokens. Executing the MoE layer further includes computing a gating function output vector based on the input tensor and computing a sparse encoding of the input tensor and the gating function output vector. The sparse encoding indicates one or more destination expert sub-models. Executing the MoE layer further includes dispatching the input tensor for processing at the one or more destination expert sub-models, and further includes computing an expert output tensor. Executing the MoE layer further includes computing an MoE layer output at least in part by computing a sparse decoding of the expert output tensor. Executing the MoE layer further includes conveying the MoE layer output to an additional computing process.

Abstract: Disclosed are a silicon nitride ceramic sintered body and preparation method thereof. The silicon nitride ceramic sintered body includes a sintered bulk and a hard surface layer having a thickness of 10-1000 ?m, formed on a surface of the sintered bulk, wherein the sintered bulk comprises a first silicon nitride crystalline phase and a first grain boundary phase; the hard surface layer comprises a second silicon nitride crystalline phase and a second grain boundary phase; the first grain boundary phase comprises a metal tungsten phase being tungsten elementary substance and/or a tungsten alloy; the second grain boundary phase comprises tungsten carbide particles; tungsten element in the metal tungsten phase accounts for 80-100 wt % of total tungsten element in the first grain boundary phase; and tungsten element in the tungsten carbide particles accounts for 60-100 wt % of total tungsten element in the second grain boundary phase.

Abstract: Disclosed are a silicon nitride ceramic sintered body and a-preparation method thereof. The silicon nitride ceramic sintered body includes a sintered bulk and a hard surface layer having a thickness of 10-1000 ?m, formed on a surface of the sintered bulk, wherein the sintered bulk comprises a first silicon nitride crystalline phase and a first grain boundary phase; the hard surface layer comprises a second silicon nitride crystalline phase and a second grain boundary phase; the first grain boundary phase comprises a metal tungsten phase being tungsten elementary substance and/or a tungsten alloy; the second grain boundary phase comprises tungsten carbide particles; tungsten element in the metal tungsten phase accounts for 80-100 wt % of total tungsten element in the first grain boundary phase; and tungsten element in the tungsten carbide particles accounts for 60-100 wt % of total tungsten element in the second grain boundary phase.

Abstract: Disclosed are a silicon nitride ceramic sintered body and a preparation method thereof. The silicon nitride ceramic sintered body has a content of a silicon nitride crystalline phase of not less than 98 wt %, a relative density of not less than 99%, a porosity of not larger than 1%, a grain boundary phase including Li, O, N, and Si elements, and a total content of C, F, Al, Mg, K, Ca, Na and rare-earth metals elements of less than 0.1 wt %.

Abstract: Disclosed are a method and system for temporary coating supporting and permanent bolt supporting. Tunnel excavation construction operation is composed of a plurality of operation cycle units, and each of the operation cycle units includes: excavating by at least one row pitch to form a new tunnel; spraying a coating material onto a surface of the new tunnel to form a sealing coat on a surface of surrounding rock, wherein the sealing coat has an adhesive property and a sealing property both meeting preset conditions, and has a tensile strength and a toughness needed for supporting; and inserting bolts into the new tunnel to perform permanent supporting.

Abstract: The present invention relates to a method for predicting and monitoring the severity of COVID-19 disease following infection of a subject with the SARS-CoV-2 virus. It also relates to a method for the treatment of a subject with COVID-19 disease. It also relates to kits for use in the methods of the invention.

Abstract: A GIS mechanical fault diagnosis method and the device are disclosed. The method includes: collecting vibration signals to be measured of various excitation sources of GIS in mechanical operation; performing wavelet packet-feature entropy vector extraction on the vibration signals to be measured, when it is determined that the vibration signals to be measured are abnormal according to standard vibration signals in the normal state; inputting the extracted wavelet packet-feature entropy vectors into the pre-trained BP neural network for GIS mechanical fault identification, and outputting the corresponding fault. The disclosure integrates the vibration signals under the action of various excitation sources, extracts the feature entropy vectors according to the entropy theory, and constructs and trains a BP neural network that can classify and recognize various GIS mechanical faults, so as to perform comprehensive and effective GIS mechanical faults diagnose.