Abstract: This disclosure relates to the field of electrochemistry, and in particular, to a positive electrode material, an electrochemical energy storage apparatus and a vehicle. The positive electrode material of this disclosure includes a substrate, with a formula of the substrate being LixNiyCOzMkMepOrAm, where 0.95?x?1.05, 0.50?y?0.95, 0?z?0.2, 0?k?0.4, 0?p?0.05, 1?r?2, 0?m?2, m+r?2; a coating layer is disposed on the substrate, where the coating layer includes a coating element; and absorbance of nickel leachate per unit mass of the positive electrode material w?0.7.

Abstract: The present invention belongs to the bioengineering field, and relates to a method for fermentation production of L-theanine by using an Escherichia coli genetically engineered bacterium. The engineered bacterium is obtained by serving a strain as an original strain, wherein the strain is obtained after performing a single copy of T7RNAP, a dual copy of gmas, xylR knockout, and sucCD knockout on an Escherichia coli W3110 genome, and by integrating genes xfp, pta, acs, gltA, and ppc, and knocking out ackA on the genome. The present invention has a high yield, and stable production performance; after 20-25 h, L-theanine has a titer of 75-80 g/L, and the yield is up to 52-55%. The fermentation broth is purified by membrane separation in combination with a cation-anion resin series technique. Moreover, the one-step crystallization yield is 72.3% and the L-theanine final product has a purity of 99%.

Abstract: A video data obtaining method comprises: deploying at least two simulators, each of the at least two simulators being used to simulate operation of one mobile terminal; sending video access requests to a server respectively by means of the at least two simulators; and respectively receiving, by means of the at least two simulators, video data returned by the server according to the corresponding video access requests. During the respectively receiving, by means of the at least two simulators, video data returned by the server according to the corresponding video access requests, the obtaining method further comprises: intercepting the video data returned by the server according to the corresponding video access requests, and storing the intercepted video data in a set database.

Abstract: This disclosure relates to the field of electrochemistry, and in particular, to a positive electrode material. The positive electrode material of this disclosure includes a substrate, with a formula of the substrate being LixNiyCozMkMepOrAm, where 0.95?x?1.05, 0.50?y?0.95, 0?z?0.2, 0?k?0.4, 0?p?0.05, 1?r?2, 0?m?2, m+r?2; a coating layer is disposed on the subs hate, where the coating layer includes a coating element; and absorbance of nickel leachate per unit mass of the positive electrode material w?0.7.

Abstract: A gaze estimation method includes receiving, by a processor, input data including a current image and a previous image each including a face of a user, determining, by the processor, a gaze mode indicating a relative movement between the user and a camera that captured the current image and the previous image based on the input data, and estimating, by the processor, a gaze of the user based on the determined gaze mode, wherein the determined gaze mode is one of a plurality of gaze modes comprising a stationary mode and a motion mode.

Abstract: The present specification discloses a trusted execution environment (TEE)-based model training method and apparatus. In one or more embodiments, the method includes: obtaining encrypted target samples from an encrypted training sample set in a first execution environment, inputting the encrypted target samples into a second execution environment that is a trusted execution environment (TEE) different from the first execution environment, decrypting the encrypted target samples in the TEE to obtain decrypted target samples, inputting the decrypted target samples into a feature extraction model in the TEE to determine sample features, determining the sample features output from the TEE as target sample features for a current iteration of a training process for a target model, and performing, based on the target sample features, the current iteration on the target model in the first execution environment.

Abstract: The present invention belongs to the bioengineering field, and relates to a method for fermentation production of L-theanine by using an Escherichia coli genetically engineered bacterium. The engineered bacterium is obtained by serving a strain as an original strain, wherein the strain is obtained after performing a single copy of T7RNAP, a dual copy of gmas, xylR knockout, and sucCD knockout on an Escherichia coli W3110 genome, and by integrating genes xfp, pta, acs, gltA, and ppc, and knocking out ackA on the genome. The present invention has a high yield, and stable production performance; after 20-25 h, L-theanine has a titer of 75-80 g/L, and the yield is up to 52-55%. The fermentation broth is purified by membrane separation in combination with a cation-anion resin series technique. Moreover, the one-step crystallization yield is 72.3% and the L-theanine final product has a purity of 99%.

Abstract: Embodiments of the present specification provide chips and chip-based data processing methods. In an embodiment, a method comprises: obtaining data associated with one or more neural networks transmitted from a server; for each layer of a neural network of the one or more neural networks, configuring, based on the data, a plurality of operator units based on a type of computation each operator unit performs; and invoking the plurality of operator units to perform computations, based on neurons of a layer of the neural network immediately above, of the data for each neuron to produce a value of the neuron.

Abstract: A gaze tracking method and apparatus, and a gaze tracking neural network training method and apparatus are provided. The gaze tracking apparatus includes one or more processors and a memory, and the one or more processors obtain output position information from an input face image of a user using a neural network model, determines a position adjustment parameter for the user, and predicts gaze position information of the user by adjusting the output position information based on the position adjustment parameter.

Abstract: The present specification discloses a trusted execution environment (TEE)-based model training method and apparatus. In one or more embodiments, the method includes: obtaining encrypted target samples from an encrypted training sample set in a first execution environment, inputting the encrypted target samples into a second execution environment that is a trusted execution environment (TEE) different from the first execution environment, decrypting the encrypted target samples in the TEE to obtain decrypted target samples, inputting the decrypted target samples into a feature extraction model in the TEE to determine sample features, determining the sample features output from the TEE as target sample features for a current iteration of a training process for a target model, and performing, based on the target sample features, the current iteration on the target model in the first execution environment.

Abstract: The present disclosure discloses an intelligent management method and system based on edge computing, and belongs to the field of edge computing technology. By adopting the present disclosure, time required for data analysis and processing can be shortened and thus time delay is lower. Moreover, in case of network interruption and cloud downtime, it can ensure that edge computing not only can run local computing software offline in an internal network to implement local autonomy, but also can implement, through the offline running local edge computing group, interconnection and intercommunication between machine devices to construct a complete intelligent management network and system. Meanwhile, it can save bandwidth resources for data transmission when networking, and effectively alleviate the problems of data calculation and storage pressure, high traffic, difficulty in application development, and insufficient intelligence of an underlying machine device when using the cloud computing.

Abstract: This disclosure relates to the field of electrochemistry, and in particular, to a positive electrode material. The positive electrode material of this disclosure includes a substrate, with a formula of the substrate being LixNiyCozMkMepOrAm, where 0.95?x?1.05, 0.50?y?0.95, 0?z?0.2, 0?k?0.4, 0?p?0.05, 1?r?2, 0?m?2, m+r?2; a coating layer is disposed on the subs hate, where the coating layer includes a coating element; and absorbance of nickel leachate per unit mass of the positive electrode material w?0.7.

Abstract: This application provides a positive active material, a positive electrode plate, an electrochemical energy storage apparatus, and an apparatus. The positive active material is LixNiyCozMkMepOrAm, or LixNiyCozMkMepOrAm with a coating layer on its surface; and the positive active material is single crystal or quasi-single crystal particles, and a particle size Dn10 of the positive active material satisfies: 0.3 ?m?Dn10?2 ?m. In this application, particle morphology of the positive active material and an amount of micro powder in the positive active material are properly controlled, to effectively reduce side reactions between the positive active material and an electrolyte solution, decrease gas production of the electrochemical energy storage apparatus, and improve storage performance of the electrochemical energy storage apparatus without deteriorating an energy density, cycle performance, and rate performance of the electrochemical energy storage apparatus.

Abstract: This disclosure relates to the electrochemical field, and in particular, to a positive electrode material and a preparation method and usage thereof. The positive electrode material of this disclosure includes a substrate, where a general formula of the substrate is LixNiyCozMkMepOrAm, where 0.95?x?1.05, 0.5?y?1, 0?z?1, 0?k?1, 0?p?0.1, 1?r?5.2, 0?m?2, m+r?2, M is selected from one or more of Mn and Al, Me is selected from one or more of Zr, Zn, Cu, Cr, Mg, Fe, V, Ti, Sr, Sb, Y, W, and Nb, and A is selected from one or more of N, F, S, and Cl; and an oxygen defect level of the positive electrode material satisfies at least one of condition (1) or condition (2): (1) 1.77?OD1?1.90; or (2) 0.69?OD2?0.74.

Abstract: This application relates to the field of battery technologies, and in particular, to a pressure-resistant positive active material and an electrochemical energy storage apparatus. The positive active material includes secondary particles composed of primary particles, and a quantity ? of primary particles per unit sphere area in a SEM graph of the secondary particles is 5/?m2 to 30/?m2. A single-particle pressure-resistant strength of the secondary particles is 60 MPa to 300 MPa. A molecular formula of the positive active material is LixNiyCozMkMepOrAm, where 0.95?x?1.05, 0?y?1, ?z?1, 0?k?1, 0?p?0.1, 1?r?2, 0?m?2, and m+r?2. The positive active material in this application has a compact particle structure and a high single-particle pressure-resistant strength.

Abstract: This application relates to the field of battery technologies, and in particular, to a high-compacted-density positive electrode material and an electrochemical energy storage apparatus. The positive electrode material includes a lithium-nickel transition metal oxide A and a lithium-nickel transition metal oxide B. The lithium-nickel transition metal oxide A is secondary particles, whose chemical formula is shown in formula I: Lia1(Nib1Coc1Mnd1)x1M1-x1O2-e1Xe1. The lithium-nickel transition metal oxide B is a monocrystalline structure or a monocrystalline-like structure, whose chemical formula is shown in formula II: Lia2(Nib2Coc2Mnd2)x2M?1-x2O2-e2X?e2 (II). The positive electrode material of this application includes the large-particle lithium-nickel transition metal oxide A and the small-particle lithium-nickel transition metal oxide B to improve an energy density of the battery.

Abstract: Embodiments of the present specification provide chips and chip-based data processing methods. In an embodiment, a method comprises: obtaining data associated with one or more neural networks transmitted from a server; for each layer of a neural network of the one or more neural networks, configuring, based on the data, a plurality of operator units based on a type of computation each operator unit performs; and invoking the plurality of operator units to perform computations, based on neurons of a layer of the neural network immediately above, of the data for each neuron to produce a value of the neuron.

Abstract: Provided is a method and system for uniform execution of feature extraction. The method comprises: acquiring a feature extraction script for defining a processing logic related to feature extraction; analyzing the feature extraction script to generate an execution plan for feature extraction; and executing the generated execution plan by a local machine or a cluster based on a feature extraction scene. Based on the method and system, feature extraction can be uniformly executed at various feature extraction scenes.

Abstract: The present application provides a positive electrode sheet for a secondary battery, the positive electrode sheet includes a positive electrode current collector and a positive electrode active material layer on a surface of the positive electrode current collector, the positive electrode active material layer includes a positive electrode active material, the positive electrode active material includes a first lithium nickel transition metal oxide and a second lithium nickel transition metal oxide, the first lithium nickel transition metal oxide includes a first substrate and a first coating layer on a surface of the first substrate, the first substrate is secondary particles, and the second lithium nickel transition metal oxide is a single crystal or single-crystal-like morphological particles.