Abstract: Disclosed are a photovoltaic energy storage offline coordination system and method based on demand management. The system includes a photovoltaic generator set, an inverter, a dispatch and control device for charge-discharge, a load monitor, a first and second energy storage device, and a remote console. The photovoltaic generator set is electrically connected to the dispatch and control device for charge-discharge via the inverter; the first energy storage device is connected to a load-side power supply bus via a first dispatch and control unit; the second energy storage device is connected to the load-side power supply bus via a third dispatch and control unit; a second dispatch and control unit is connected to the load-side power supply bus; the load monitor is provided at the load-side power supply bus, and configured to monitor a load of the load-side.

Abstract: A device for removing iron from a nickel-cobalt-manganese sulfuric acid solution and a method for continuously removing iron ions from a nickel-cobalt-manganese sulfuric acid solution. The device has an iron removal reactor (2) having a stirrer (3) and an iron removal reactor inner cylinder (5) and an aging reactor (9) having an aging reactor stirrer (7) and an automatic stone powder feeder (8), a mixing feed pipe (12) and a carbonate solution feed pipe (4) are arranged in an interlayer between the iron removal reactor (2) and the iron removal reactor inner cylinder (5), a mixer (1) for a preheating the device is arranged at a top of the mixing feed pipe (12), a compressed air inlet (11) and a feed inlet (10) of a solution to be subjected to iron removal are arranged in the mixer (1). Further disclosed is a method for removing iron of the device.

Abstract: An online concierge system allows users to order items within discrete time intervals later than a time when an order was received or for short-term fulfillment when the order was received. To account for a number of shoppers available to fulfill orders during different discrete time intervals and numbers of orders for fulfillment during different discrete time intervals, the online concierge system specifies a target rate for orders fulfilled later than a specified discrete time interval and a threshold from the target rate. A trained machine learning model periodically predicts a percentage of orders being fulfilled late, with an order associated with a predicted percentage when the order was received. The online concierge system increases a price of orders associated with predicted percentages greater than the threshold from the target rate. The increased price of an order is determined from a price elasticity curve and the predicted percentage.

Abstract: An online concierge system provides arrival prediction services for a user placing an order to be retrieved by a shopper. An order may have a predicted arrival time predicted by a model that may err under some conditions. To reduce the likelihood of providing the predicted arrival time (and related services) when the arrival time may be incorrect, the prediction model and related services are throttled (e.g., selectively provided) based on one or more predicted delivery metrics, which may include a time to accept the order by a shopper and a predicted portion of late orders that will be delivered past the respective predicted arrival times. The predicted delivery metrics are compared with thresholds and the result of the comparison used to selectively provide, or not provide, the predicted delivery services.

Abstract: The invention discloses a method and crystallization device for preparing electronic-grade nickel sulfate from nickel powder, and a control method of the crystallization device, and relates to the technical field of non-ferrous metal hydrometallurgy. The method comprises: oxidation, cooling, acid leaching, copper removal, acid adjustment, concentration, cooling crystallization, drying and screening, and secondary leaching: the oxidation comprises: controlling a temperature of nickel powder in a calcining furnace to be 400° C. to 700° C., allowing a use amount of compressed air for each kilogram of nickel powder to be 1 m3 to 5 m3, and making the reaction last for 1.0 hour to 2.5 hours; and the acid leaching comprises: placing cooled nickel oxide in a reactor, controlling a temperature to be 45° C. to 70° C., adding dilute sulfuric acid to control a pH value to be 0.5 to 1.5, and making the reaction last for 1 hour to 3 hours.

Abstract: Disclosed are a method and device for extracting and preparing battery-grade lithium carbonate from a P507 raffinate. The method comprises steps of impurity adjustment, extraction, purification, reverse extraction, alkalization, crystallization, separation, drying, the impurity adjustment comprises: adjusting a pH value of the P507 raffinate to be 8.5 to 10.5 with lithium hydroxide or alkali, and filtering to obtain a filtrate for later use; the alkalization comprises: heating a lithium solution to 85° C. to 95° C., adding lithium hydroxide or alkali to adjust a pH value to be 9.0 to 13.0, maintaining a temperature at 85° C. to 95° C. and standing for 2 hours to 8 hours, and then filtering to obtain a filtrate for later use; and the crystallization comprises: introducing compressed air into the filtrate after alkalization, evaporating and concentrating at the same time, and discharging and cooling when fine crystals exist in the concentrated solution.

Abstract: An online concierge system receives two types of orders, one of which requires fulfillment in a specific time interval, while the other can be fulfilled anytime up to a specific time interval. A machine learning model, trained on historical data about available shoppers in discrete time intervals, is used to predict how many shoppers will be available to fulfill orders in each time interval. For each time interval, the system retrieves the relevant orders of both types and creates candidate groups including orders of both types. For each group, the system determines a fulfillment cost based on items in the orders. The candidate group with the lowest cost is selected, and the orders in the selected group are sent to devices of available shoppers in that interval, prompting the shoppers to view and fulfill the orders.

Abstract: Embodiments of the present disclosure disclose a display panel and a display device. In the display panel, an array base plate is located on a light exit side of the display panel and includes a binding part. The binding part includes a plurality of binding terminals. A circuit board assembly is bound to the plurality of binding terminals and includes a plurality of electronic components. The circuit board assembly is bent to a side of a color film base plate that is away from the array base plate. Each of the electronic components is located on a side of the circuit board assembly that is away from the color film base plate.

Abstract: A routing processor implements a multi-stage prescriptive routing model engine based on harvest input data relating to the harvesting of crops at a plurality of locations by a plurality of combines and based on a harvest characteristic representing an attribute of the crops to be harvested by the combines. The multi-stage prescriptive routing model engine generates a combine routing program prescribing the movement of each combine between the locations and includes a demand stage configured to identify combine harvesting demand as a function of the harvest input data and the harvest characteristic and a scheduling stage configured to generate the combine routing program as a function of the harvesting demand.

Abstract: The present application discloses a display panel, a display module, and a display device. The display panel includes a first substrate and a second substrate disposed opposite to each other, and the first substrate includes a metal wiring located in a non-display area; wherein the first substrate includes a black shielding layer, and by providing the black shielding layer in the display panel, wherein the black shielding layer covers the metal wiring in an orthographic projection direction of the first substrate, appearance effect of the display panel is thereby improved.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes receiving metadata for the training, generating a metadata sequence that represents the metadata, at each of a plurality of iterations: generating one or more trials that each specify a respective value for each of a set of hyperparameters, comprising, for each trial: generating an input sequence for the iteration that comprises (i) the metadata sequence and (ii) for any earlier trials, a respective sequence that represents the respective values for the hyperparameters specified by the earlier trial and a measure of performance for the trial, and processing an input sequence for the trial that comprises the input sequence for the iteration using a sequence generation neural network to generate an output sequence that represents respective values for the hyperparameters.

Abstract: An online concierge system displays an interface to a user identifying an estimated time of arrival for an order. To generate the estimated time of arrival for the order, the online concierge system trains a prediction engine to predict delivery time based on a predicted selection time for a shopper to select the order for fulfillment and predicted travel time for the shopper to deliver items of the order to a location identified by the order. The online concierge system generates a policy optimization model that computes an adjustment for the predicted delivery time. The adjustment is determined by solving a stochastic optimization problem with a constraint on a probability of the order being delivered after the estimated time of arrival. The predicted delivery time combined with the adjustment determines the estimated time of delivery displayed to the user to balance between minimizing late deliveries and wait times.

Abstract: Exemplary methods for identifying progenies for use in plant breeding are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of progenies and determining a prediction score for at least a portion of the pool of progenies based on the data included in the data structure. The prediction score indicates a probability of selection of the progeny based on historical data. The method further includes selecting a group of progenies from the pool of progenies based on the prediction score, identifying a set of progenies, from the group of progenies, based on at least one of an expected performance of the group of progenies and at least one factor associated with the set of progenies, the pool of progenies and/or the group of progenies, and directing the set of progenies into a validation phase of a breeding pipeline.

Abstract: An online concierge system allows users to order items within discrete time intervals later than a time when an order was received or for short-term fulfillment when the order was received. To account for a number of shoppers available to fulfill orders during different discrete time intervals and numbers of orders for fulfillment during different discrete time intervals, the online concierge system specifies a target rate for orders fulfilled later than a specified discrete time interval and a threshold from the target rate. A trained machine learning model periodically predicts a percentage of orders being fulfilled late, with an order associated with a predicted percentage when the order was received. The online concierge system increases a price of orders associated with predicted percentages greater than the threshold from the target rate. The increased price of an order is determined from a price elasticity curve and the predicted percentage.

Abstract: An online concierge system allows users to order items within discrete time intervals later than a time when an order was received. The online concierge system allocates a specified percentage of an estimated number of shoppers during a discrete time interval to fulfilling orders received before the discrete time interval. An order may include a flag authorizing flexible fulfillment of the order along with a discrete time interval, which allows the order to be fulfilled earlier than the identified discrete time interval. The online concierge system generates groups of multiple orders authorizing flexible fulfillment and determines a cost for fulfilling different groups of orders. The online concierge system identifies a group of orders authorizing flexible fulfillment having a minimum cost for fulfillment by a shopper, allowing for more allocation of shoppers by enabling grouping of orders identifying different discrete time intervals.

Abstract: The invention provides a vehicle's brake system and a method for braking a vehicle. The brake system comprises at least two control modules and a plurality of electromechanical brake devices. Each electromechanical brake device comprises a wheel end controller, a motor actuator and a friction brake driven by the motor actuator. Each control module receives a brake demand from a brake demand generating device and sends a control signal to each wheel end controller according to the brake demand. Each wheel end controller only responds to the control signals when the control signals received from the two control modules are consistent.

Abstract: Exemplary systems and methods are disclosed for allocating resources in a breeding pipeline to multiple origins. One exemplary method includes accessing a data structure including data representative of multiple origins, in which the data includes, for each of the multiple origins, a trait performance expression or genotypic component information. The exemplary method further includes determining a resource allocation, which allocates n resources among the multiple origins based on a probability associated with the trait performance expressions and/or the genotypic components for the origins, and then allocating the n resources in the breeding pipeline for the multiple origins, based on the determined resource allocation.

Abstract: An online concierge system displays an interface to a user identifying an estimated time of arrival for an order. To generate the estimated time of arrival for the order, the online concierge system trains a prediction engine to predict delivery time based on a predicted selection time for a shopper to select the order for fulfillment and predicted travel time for the shopper to deliver items of the order to a location identified by the order. The online concierge system generates a policy optimization model that computes an adjustment for the predicted delivery time. The adjustment is determined by solving a stochastic optimization problem with a constraint on a probability of the order being delivered after the estimated time of arrival. The predicted delivery time combined with the adjustment determines the estimated time of delivery displayed to the user to balance between minimizing late deliveries and wait times.

Abstract: Exemplary methods for identifying progenies for use in plant breeding are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of progenies and determining a prediction score for at least a portion of the pool of progenies based on the data included in the data structure. The prediction score indicates a probability of selection of the progeny based on historical data. The method further includes selecting a group of progenies from the pool of progenies based on the prediction score, identifying a set of progenies, from the group of progenies, based on at least one of an expected performance of the group of progenies and at least one factor associated with the set of progenies, the pool of progenies and/or the group of progenies, and directing the set of progenies into a validation phase of a breeding pipeline.

Abstract: Exemplary systems for identifying hybrids for use in a plant breeding pipeline are disclosed. One exemplary system includes a computing device configured to access phenotypic data related to a pool of hybrids from a data structure and determine a prediction score for each of the hybrids in the pool of hybrids based on the accessed phenotypic data. The prediction score is indicative of a probability of selection and/or a probability of success of the hybrid based on historical data. The computing device is also configured to select a group of hybrids from the pool of hybrids based on the prediction score, identify a set of hybrids, from the selected group of hybrids, based on one or more factors associated with the hybrids, and then direct the set of hybrids to a validation phase of the plant breeding pipeline for planting and/or testing.