Abstract: A whey protein composition includes a whey protein and ?-casein. The mass ratio of the ?-casein to the whey protein is not less than 4.5:95.5. The whey protein composition provided by the present invention is mainly composed of a particular whey protein ingredient that has a slower digestion characteristic than other whey proteins currently on the market. The whey protein composition is a ?-casein-riched whey protein concentrate to which micronized and complex natural polysaccharides can be added, and the function of delaying digestion to promote muscle synthesis and provide adequate nutrition can be achieved.

Abstract: This application discloses a method, an apparatus, and a device for scheduling virtual objects in a virtual environment, which relate to artificial intelligence (AI), and belong to the field of computer technologies. The method includes: obtaining frame data generated by an application program associated with the virtual environment; performing feature extraction on the frame data to obtain a state feature of a target virtual object; performing deduction on the state feature of the target virtual object according to N scheduling policies; invoking a value network prediction model to process the N subsequent state features, to obtain expected returns of executing N scheduling policies; and controlling the target virtual object to execute a scheduling policy having a highest expected return.

Abstract: An adhesive promoter, an organic silicon encapsulant composition, and an organic silicon encapsulant are provided. The adhesive promoter used for the organic silicon encapsulant is formed from a borosiloxane polymer represented by a general formula of: (R1R22SiO1/2)x(R2R3SiO2/2)y(R3SiO3/2)z(SiO4/2)i(BO(3-k)/2)j(OR4)k. R1 is a hydrogen atom or a C2-C6 alkenyl group. R2 and R4 are respectively a C1-C6 alkyl group. R3 is a C6-C12 aromatic group. In the general formula, x, y, z, i, j, and k represent a molar ratio. In the general formula, x, y, z, i, and j are a non-negative number smaller than or equal to 1, and k is a positive number ?3. A sum of x, y, z, and i is 1, and x is larger than 0.

Abstract: This application discloses a method, an apparatus, and a device for scheduling virtual objects in a virtual environment, which relate to artificial intelligence (AI), and belong to the field of computer technologies. The method includes: obtaining frame data generated by an application program associated with the virtual environment; performing feature extraction on the frame data to obtain a state feature of a target virtual object; performing deduction on the state feature of the target virtual object according to N scheduling policies; invoking a value network prediction model to process the N subsequent state features, to obtain expected returns of executing N scheduling policies; and controlling the target virtual object to execute a scheduling policy having a highest expected return.

Abstract: This application discloses a method, an apparatus, and a device for scheduling virtual objects in a virtual environment, which relate to artificial intelligence (AI), and belong to the field of computer technologies. The method includes: obtaining frame data generated by an application program associated with the virtual environment; performing feature extraction on the frame data to obtain a state feature of a target virtual object; performing deduction on the state feature of the target virtual object according to N scheduling policies; invoking a value network prediction model to process the N subsequent state features, to obtain expected returns of executing N scheduling policies; and controlling the target virtual object to execute a scheduling policy having a highest expected return.

Abstract: This application discloses a method, an apparatus, and a device for scheduling virtual objects in a virtual environment, which relate to artificial intelligence (AI), and belong to the field of computer technologies. The method includes: obtaining frame data generated by an application program associated with the virtual environment; performing feature extraction on the frame data to obtain a state feature of a target virtual object; performing deduction on the state feature of the target virtual object according to N scheduling policies; invoking a value network prediction model to process the N subsequent state features, to obtain expected returns of executing N scheduling policies; and controlling the target virtual object to execute a scheduling policy having a highest expected return.

Abstract: An adhesive promoter, an organic silicon encapsulant composition, and an organic silicon encapsulant are provided. The adhesive promoter used for the organic silicon encapsulant is formed from a borosiloxane polymer represented by a general formula of: (R1R22S1/2)x(R2R3SiO2/2)y(R3SiO3/2)z(SiO4/2)i(BO(3-k)/2)j(OR4)k. R1 is a hydrogen atom or a C2-C6 alkenyl group. R2 and R4 are respectively a C1-C6 alkyl group. R3 is a C6-C12 aromatic group. In the general formula, x, y, z, i, j, and k represent a molar ratio. In the general formula, x, y, z, i, and j are a non-negative number smaller than or equal to 1, and k is a positive number ?3. A sum of x, y, z, and i is 1, and x is larger than 0.

Abstract: A direct current voltage conversion device includes a direct current to alternating current converter, a transformer, a first converter switch, a second converter switch and a clamping circuit. The clamping circuit clamps a voltage across the second converter switch to a preset value, and stores energy of a voltage peak across the second converter switch.

Abstract: A power conversion device includes a full-bridge switch circuit, a converter circuit, and a control circuit. The full-bridge switch circuit is operable to convert a direct current input voltage to a converted voltage. The converter circuit converts the converted voltage into a direct current output voltage. The converter circuit includes a resonant inductor, a transformer, a first converter switch, a second converter switch, an output inductor, and an output capacitor. The direct current output voltage is provided across the output capacitor. The control circuit controls the full-bridge switch circuit, the first converter switch and the second converter switch based on the direct current output voltage and a reference voltage.

Abstract: A direct current voltage conversion device includes a direct current to alternating current converter, a transformer, a first converter switch, a second converter switch and a clamping circuit. The clamping circuit clamps a voltage across the second converter switch to a preset value, and stores energy of a voltage peak across the second converter switch.

Abstract: A power conversion device includes a full-bridge switch circuit, a converter circuit, and a control circuit. The full-bridge switch circuit is operable to convert a direct current input voltage to a converted voltage. The converter circuit converts the converted voltage into a direct current output voltage. The converter circuit includes a resonant inductor, a transformer, a first converter switch, a second converter switch, an output inductor, and an output capacitor. The direct current output voltage is provided across the output capacitor. The control circuit controls the full-bridge switch circuit, the first converter switch and the second converter switch based on the direct current output voltage and a reference voltage.

Abstract: A vertical turning-milling complex machining center comprises a horizontally-arranged bed body (6) and a vertically-arranged column (7). The bed body (6) is provided with an X-axis lateral supporting linear track (2) and an X-axis guide screw (5). The bed body (6) is also provided with a uniaxial rotating table (1) which can reciprocate and is driven directly by a first external rotor torque motor. The column (7) is vertically provided with a Z-axis lateral supporting linear track (10), a Z-axis guide screw (9) and a crossbeam (11) that reciprocates up and down. The crossbeam (11) is provided with a transverse Y-axis linear track (13), a Y-axis guide screw (12) and a single-pendulum milling head seat frame that can reciprocate along the Y-axis guide screw (12). The single-pendulum milling head is driven directly by a second external rotor torque motor.

Abstract: A vertical turning-milling complex machining center comprises a horizontally-arranged bed body (6) and a vertically-arranged column (7). The bed body (6) is provided with an X-axis lateral supporting linear track (2) and an X-axis guide screw (5). The bed body (6) is also provided with a uniaxial rotating table (1) which can reciprocate and is driven directly by a first external rotor torque motor. The column (7) is vertically provided with a Z-axis lateral supporting linear track (10), a Z-axis guide screw (9) and a crossbeam (11) that reciprocates up and down. The crossbeam (11) is provided with a transverse Y-axis linear track (13), a Y-axis guide screw (12) and a single-pendulum milling head seat frame that can reciprocate along the Y-axis guide screw (12). The single-pendulum milling head is driven directly by a second external rotor torque motor.

Abstract: A DC power source conversion module is provided, including a DC power source module and a DC to DC conversion module. The DC to DC conversion module includes a DC to DC converter and a control module. The DC to DC converter is powered by the DC power source module to generate an output signal. The control module senses a responding signal of the DC to DC conversion module and controls the DC to DC converter according to the sensed responding signal, such that the DC power source conversion module is operated at a predetermined output power, in which the responding signal responds to the output signal of the DC to DC converter.

Abstract: A method for production planning includes receiving a first order quantity of a first device. A first yield estimate of the first device from a production line is determined. The first yield estimate is adjusted based on a first confidence factor associated with the first order quantity. A dispatch quantity for processing in the production line is determined based on the first order quantity and the adjusted first yield estimate.

Abstract: A method includes determining production targets for devices of different types in a production line. A queue level of devices of a first type that have completed performance of a first operation configured in accordance with a first setup state in the production line and await performance of a second operation in the production line is determined. Based on the determined queue level, a second type of device is selected for subsequent processing in the first operation based on the production targets and a setup time associated with configuring the first operation from the first setup state to a second setup state associated with the second type of device. The first operation is configured in accordance with the second setup state for processing devices of the second type.

Abstract: A method for determining a loading plan for a testing system includes determining demand quotas for a plurality of devices to be processed through the testing system. Each demand quota specifies a number of required devices and an associated performance specification. A bin classification distribution for devices processed through the testing system is estimated. Each device has an associated product type. Each bin classification indicates a performance grade of the associated devices. A number of devices matching each performance specification exiting the testing system is estimated based on the bin classification distribution. A device loading plan specifying a count of devices for each of a plurality of the product types to be dispatched to the testing system to provide the estimated number of devices matching each performance specification exiting the testing system to substantially meet the demand quota associated with each performance specification is determined.

Abstract: A method for determining priority of a selected workpiece in a process flow including a plurality of operations includes providing an objective function relating manufacturing losses to workpiece priority for the operations in the process flow. The objective function is solved to generate priority metrics for at least a subset of the operations remaining for the selected workpiece to allow completion of the selected workpiece in the process flow by a target completion due time.

Abstract: A method includes determining production targets for devices of different types in a production line. A queue level of devices of a first type that have completed performance of a first operation configured in accordance with a first setup state in the production line and await performance of a second operation in the production line is determined. Based on the determined queue level, a second type of device is selected for subsequent processing in the first operation based on the production targets and a setup time associated with configuring the first operation from the first setup state to a second setup state associated with the second type of device. The first operation is configured in accordance with the second setup state for processing devices of the second type.

Abstract: A method for production planning includes receiving a first order quantity of a first device. A first yield estimate of the first device from a production line is determined. The first yield estimate is adjusted based on a first confidence factor associated with the first order quantity. A dispatch quantity for processing in the production line is determined based on the first order quantity and the adjusted first yield estimate.