Abstract: Disclosed are a chimeric antigen receptor (CAR) targeting CLD18A2, and preparation method and use thereof. The extracellular binding region of the CAR comprises a protein specifically recognizing CLD18A2. The immune effector cell modified by the CAR can be used to treat tumors such as pancreatic cancer and stomach cancer.

Abstract: This application discloses an antenna structure and an electronic device. The antenna structure includes an antenna radiation body, a first coupled radiation branch, a second coupled radiation branch, a first feed, a first matching circuit, and a second matching circuit. The antenna radiation body is located between the first coupled radiation branch and the second coupled radiation branch, there is a gap between the antenna radiation body and the first coupled radiation branch, and there is a gap between the antenna radiation body and the second coupled radiation branch. The first feed is disposed between a first feed point on the antenna radiation body and a ground plane. The first matching circuit is disposed between a first position of the antenna radiation body and a ground plane. The second matching circuit is disposed between a second position of the antenna radiation body and a ground plane.

Abstract: A computer-implemented method for providing guidance on the use of machine learning tools is provided. Aspects include receiving, from a user, an input data set and a type of problem to solve based on the input data set and identifying a set of machine learning pipelines from a database comprising a plurality of machine learning pipelines. Aspects also include recommending, to the user, a first machine learning pipeline of the set of machine learning pipelines from the set of machine learning to the user, wherein each of the plurality of machine learning pipelines includes a pipeline score and wherein the first machine learning pipeline has a highest pipeline score of the set of machine learning pipelines and providing, to the user, a rule set associated with the first machine learning pipeline, wherein the rule set includes one or more suggested settings associated with the first machine learning pipeline.

Abstract: A charging apparatus for use with an electric vehicle includes a power transmission path, a switch, a first controller, a communication unit, and a second controller. The switch is disposed on the power transmission path. The communication unit is coupled to a second connection port. The first controller is coupled to the power transmission path, the switch, the second controller, and the communication unit. When the second controller receives a first request from a power management system and correspondingly notifies the first controller, the first controller switches from a first signal to a second signal to communicate with the electric vehicle and turns off the switch, and when the first controller receives a first EV notification provided from the electric vehicle, the controller turns on the switch.

Abstract: A computer-implemented method for model selection is provided. The computer-implemented method includes building models for predicting characteristics of pipeline models, receiving user specifications for pipeline model performance, defining a metric for weighing the characteristics of the pipeline models, using the metric to iteratively reduce a number of the pipeline models capable of meeting the user specifications to a reduced number of the pipeline models, determining which one pipeline model of the reduced number of the pipeline models exhibits a best capability of meeting the user specifications and deploying the one pipeline model.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to determining time series model stability and robustness in refreshment. The computer-implemented system can comprise a memory that can store computer executable components. The computer-implemented system can further comprise a processor that can execute the computer executable components stored in the memory, wherein the computer executable components can comprise a computation component that can employ weighted model evaluation to compute stability of time series pipelines over respective holdout datasets and a determination component that can select, based on the computed pipeline stabilities, a most stable time series pipeline.

Abstract: Disclosed in the present invention is an effect of flavonoid glycoside in the resistance of rice to weeds. Provided in the present invention is the use of isoorientin in any one of the following: inhibiting the growth of weeds; regulating the allelopathy of plants; and regulating the resistance of plants to weeds. The results of the present invention show that isoorientin can regulate the allelopathy of plants and can be used for inhibiting the growth of weeds. The present invention is of great significance for developing an environment-friendly green pesticide, and provides theoretical guidance for controlling weeds in rice fields.

Abstract: A method, computer system, and a computer program product are provided for backup and restoration of data. Data is obtained from a plurality of resources during a plurality of tasks. The resources are located in one or more computing networks. A plurality of parameters are extracted from obtained data. A table is generated and stored that includes the parameters extracted and an associated related task. A predict time is calculated based on the table for recovery of data when any of the resources become unavailable. A task priority is established based on calculated predict time and the table to optimize and improve the predict recovery time. A restoration model is generated based on updated restoration recommendation. The restoration model is to be used during resource unavailability or failure of one or more of the resources.

Abstract: Systems, computer program products and/or computer-implemented methods described herein relate to a process to optimize performance of an operating neural network. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components, which can comprise an identification component that, employing an operating, multi-layered virtual computation module of looped neurons, identifies a first neuron of a first cluster of a first layer of the looped neurons as being an outlier neuron, an adjustment component that reassigns the outlier neuron from the first cluster to a second cluster of the first layer, and a scheduling component that, based on a dependency among layers of the multi-layered virtual computation module, including the first layer, adjusts a cross-layer functionality of the looped neurons for a workload currently being performed by the multi-layered virtual computation module.

Abstract: A memory device includes a first memory array, a first isolation cell abutting a first side of the first memory array, a first edge cell array abutting a second side, opposite to the first side, of the first memory array, a second memory array arranged at a first side, opposite to the first memory array, of the first isolation cell, a second edge cell array, and multiple first word lines passing through the first edge cell array, the first memory array and being terminated at the first isolation cell. A first width of the first isolation cell is different from a second width of the first edge cell array. The second memory array is sandwiched between the second edge cell array and the first isolation cell.

Abstract: In example implementations, a fluidic die is provided. The fluidic die includes a silicon sliver with a plurality of nozzles to eject a printing fluid. A conductive path is deposited along a length of the silicon sliver on opposite sides of the silicon sliver. The conductive path and a portion of the silicon sliver are encapsulated by an epoxy molding compound.

Abstract: A memory device, comprising: a first driving circuit configured to provide a first current signal to a first node according to a decoder signal; a second driving circuit configured to provide a second current signal to a second node according to the decoder signal; and a modulating circuit coupled to the first node and the second node, configured to transmit each of the first current signal and the second current signal to a reference voltage terminal. A method is also disclosed herein. A method is also disclosed herein.

Abstract: A method for forecasting server demand includes collecting a historical number of scoring requests from a network using a serverless architecture. A scoring request capacity per server is determined using the historical number of scoring requests. A prediction model predicts a first future value of scoring requests for a first future time span. A current number of servers in a pool of servers handling the scoring requests. Using the prediction model, a determination of whether the current number of servers is capable of handling the first future value of scoring requests for the first future time span is made. Upon determining that the current number of servers is incapable of handling the first future value of scoring requests, one or more additional servers are warmed up. The warmed-up additional servers are added to the pool of servers prior to an arrival of the first future time span.

Abstract: A drive apparatus, a camera module, and an electric device are provided. A fixed assembly, a carrier, and an electrically drive assembly are included. A first end of the electrically drive assembly is connected to the fixed assembly, and a second end of the electrically drive assembly is connected to the carrier; and the carrier is used for carrying a function device; where in a case of applying a voltage to the electrically drive assembly, the electrically drive assembly deforms to drive the carrier to move.

Abstract: A method for forming a multi-material mechanical functional member in additive manufacturing. The method includes the following steps: S1: dividing an object to be formed into a plurality of portions, analyzing and measuring mechanical properties of each portion, and constructing a unit cell library; S2: forming a lattice structure by using a unit cell structure in the unit cell library to obtain the lattice structure corresponding to each portion; S3: selecting a raw material of the lattice structure, measuring and comparing mechanical properties of each lattice structure with the mechanical properties of each portion of the object to be formed, where when the mechanical properties of each portion are satisfied, the lattice structure is the required lattice structure, otherwise, step S2 is repeated; and S4: forming a three-dimensional model by a method of additive manufacturing to accordingly obtain the required object to be formed.

Abstract: A memory device includes a first memory array, a first isolation cell abutting a first side of the first memory array, a first edge cell array abutting a second side, opposite to the first side, of the first memory array, a second memory array arranged at a first side, opposite to the first memory array, of the first isolation cell, a second edge cell array, and multiple first word lines passing through the first edge cell array, the first memory array and being terminated at the first isolation cell. A first width of the first isolation cell is different from a second width of the first edge cell array. The second memory array is sandwiched between the second edge cell array and the first isolation cell.

Abstract: A memory device includes a first transistor, a second transistor and a third transistor. The first transistor is coupled to a first word line at a first node. The second transistor is coupled to a second word line different from the first word line at a second node. A control terminal of the first transistor is coupled to a control terminal of the second transistor. The third transistor is coupled between a ground and a third node which is coupled to each of the first node and the second node. In a layout view, each of the first transistor and the second transistor has a first length along a direction. The first transistor, the third transistor and second transistor are arranged in order along the direction. A method is also disclosed herein.

Abstract: The present invention provides an antibody against glypican-3 (GPC3) and application thereof, and the antibody comprises a single chain antibody and humanized antibody.

Abstract: An epoxy resin compound is provided. The epoxy resin compound includes a hardener, an inorganic based filler, a catalyst, and a conductive additive. The amount of inorganic based filler is greater than 80 weight percent (wt %) of the epoxy resin compound. The catalyst is to accelerate curing of the epoxy resin compound. The amount of conductive additive is 0.1 to 5 wt % of the epoxy resin compound.

Abstract: The present invention relates to a process for production of a powder of metal in an electrolytic cell comprising an anode made of the metal, a cathode and an electrolyte solution, which comprises a) anodic dissolution to form ions of the metal in the electrolyte solution and cathodic deposition of metal particles from the electrolyte solution, b) removal of the metal particles from the cathode into the electrolyte solution, and c) isolation of the metal particles from the electrolyte solution, wherein the metal is copper or silver, and wherein the electrolyte solution comprises (i) an alkane sulfonic acid or alkanol sulfonic acid and (ii) a soluble metal salt of an alkane sulfonic acid or alkanol sulfonic acid. The present invention also relates to the copper or silver powder obtained or obtainable by the process and use an alkane sulfonic acid or alkanol sulfonic acid in an electrolyte solution for production of a silver or copper powder by electrolytic deposition.