Abstract: In some embodiments, a method can include receiving first images of produce. The method can further include executing a first machine learning model to generate second images of produce based on the first images of produce. The first images of produce can include (1) images of non-bagged produce or (2) images of bagged produce. The second images of produce can include the other of (1) images of non-bagged produce or (2) images of bagged produce. The method can further include training a second machine learning model based on the first images of produce and the second images of produce. The method can further include executing, after the training, the second machine learning model to classify as a bagged produce or a non-bagged produce an image not included in the first images and not included in the second images.

Abstract: The invention discloses a mineralization method of a calcium chloride-type from lithium-containing salt lake brine by evaporation and brine mixing, comprising the following steps of: (1) naturally evaporating the calcium chloride-type from lithium-containing salt lake brine to precipitate sodium salt and potassium-containing mixed salt; and (2) when calcium in the brine is saturated, adding saturated solution of magnesium chloride in a certain proportion for brine mixing operation, and then naturally evaporating to precipitate carnallite, wherein a lithium-containing old brine with low potassium and sodium contents is obtained when magnesium in the brine is saturated. The method has the characteristics of simple process, simple and convenient operation, high potassium yield and easy extraction of lithium from lithium-containing brine, and has practical significance for the development and utilization of potassium and lithium resources in calcium chloride salt lakes.

Abstract: The present invention provides a train-like pharmaceutical composition (configuration, or dosage form) comprising segments linearly arranged in series like cars of a train. When the pharmaceutical composition is administrated into a patient's body, some of the segments are “visible” to an instrument, but others are not. However, “invisible” segments can be estimated, deduced, calculated, or inferred from “visible” segments. The “invisible” segments can be used for neutralizing intratumoral lactic acidosis combined with glucose deprivation to control tumor, among other applications.

Abstract: In some embodiments, a method can include receiving first images of produce. The method can further include executing a first machine learning model to generate second images of produce based on the first images of produce. The first images of produce can include (1) images of non-bagged produce or (2) images of bagged produce. The second images of produce can include the other of (1) images of non-bagged produce or (2) images of bagged produce. The method can further include training a second machine learning model based on the first images of produce and the second images of produce. The method can further include executing, after the training, the second machine learning model to classify as a bagged produce or a non-bagged produce an image not included in the first images and not included in the second images.

Abstract: The present disclosure provides a multi-substrate handling system having an alignment apparatus capable of positioning each of a set of substrates in predetermined orientations for transfer. A buffer chamber is configured to receive and condition the set of substrates which are disposed on a substrate carrier. A first transfer assembly is configured to transfer the set of substrates to and from the buffer chamber and is capable of transferring each of the set of substrates from the alignment apparatus to the carrier in the buffer chamber. The carrier includes a plurality of modules capable of securing the set of substrates. The system includes a second transfer assembly having at least two robots configured to transfer the carrier of the set of substrates between the buffer chamber and a process chamber. The process chamber is capable of processing the set of substrates using different process parameters for each substrate.

Abstract: In some embodiments, a method can include receiving first images of produce. The method can further include executing a first machine learning model to generate second images of produce based on the first images of produce. The first images of produce can include (1) images of non-bagged produce or (2) images of bagged produce. The second images of produce can include the other of (1) images of non-bagged produce or (2) images of bagged produce. The method can further include training a second machine learning model based on the first images of produce and the second images of produce. The method can further include executing, after the training, the second machine learning model to classify as a bagged produce or a non-bagged produce an image not included in the first images and not included in the second images.

Abstract: Provided are an application interface implementation method and apparatus in a host platform layer, a device, and a storage medium, which relate to the field of computer technologies. The implementation scheme includes: the host platform layer acquiring description data of a terminal capability interface; parsing the description data to acquire a communication mode of the terminal capability interface; and configuring a corresponding processor according to the communication mode, configuring a corresponding concept mapping relationship according to the communication mode, or configure a corresponding processor and a corresponding concept mapping relationship according to the communication mode to encapsulate a platform layer interface of the terminal capability interface, where the platform layer interface is configured to process data of communication interactions in a process in which a mini program calls the terminal capability interface through the host platform layer.

Abstract: In some embodiments, a method can include capturing images of produce. The method can further include generating simulated images of produce based on the images of produce. The method can further include associating each image of produce from the images of produce and each simulated image of produce from the simulated images of produce with a category indicator, an organic type indicator, and a bag type indicator, to generate a training set. The method can further include training a machine leaning model using the training set such that when the machine learning model is executed, the machine learning model receives an image and generates a predicted category indicator of the image, a predicted organic type indicator of the image, and a predicted bag type indicator of the image.

Abstract: A data processing method and apparatus for a mini app, a device and a medium are provided. An implementation of the method may include: intercepting a request message of the mini app, and sending the request message to a target server, where the request message comes from a technology stack; acquiring returned data targeting at the request message and returned from the target server, where the returned data includes cookie content, field information for indicating storage of the cookie content, and a data set corresponding to the request message; and storing the cookie content into a cookie storage database of the mini app according to the field information, and returning the returned data to the technology stack issuing the request message.

Abstract: In some embodiments, a method can include capturing images of produce. The method can further include generating simulated images of produce based on the images of produce. The method can further include associating each image of produce from the images of produce and each simulated image of produce from the simulated images of produce with a category indicator, an organic type indicator, and a bag type indicator, to generate a training set. The method can further include training a machine leaning model using the training set such that when the machine learning model is executed, the machine learning model receives an image and generates a predicted category indicator of the image, a predicted organic type indicator of the image, and a predicted bag type indicator of the image.

Abstract: A method for calling a program module in a programmable logic controller (PLC) ladder programming environment comprises: determining a first feature to be called in a PLC ladder programming environment and a second feature not to be called from features implemented by an artificial intelligence (AI) module compiled by a computer programming language; configuring the first feature as an interface conforming to an interface specification of the PLC ladder programming environment; configuring the second feature as a non-call method; packaging the non-call method and the interface into a dynamic link library (DLL); importing the DLL into the PLC ladder programming environment.

Abstract: In some embodiments, a method can include capturing images of produce. The method can further include generating simulated images of produce based on the images of produce. The method can further include associating each image of produce from the images of produce and each simulated image of produce from the simulated images of produce with a category indicator, an organic type indicator, and a bag type indicator, to generate a training set. The method can further include training a machine leaning model using the training set such that when the machine learning model is executed, the machine learning model receives an image and generates a predicted category indicator of the image, a predicted organic type indicator of the image, and a predicted bag type indicator of the image.

Abstract: One or more computing devices obtains service data of a user payment service. The one or more computing devices determines whether the service data of the user payment service falls within a service indicator range. In response to determining that the service data does not fall within the service indicator range, the one or more computing devices obtains location information of a target customer corresponding to the service data. The one or more computing devices obtains one or more local events corresponding to the location information. The one or more computing devices determines, based on the one or more local events, whether to output an alarm for the service data. In response to determining to output the alarm for the service data, the one or more computing devices outputs the alarm for the service data.

Abstract: In some embodiments, a method can include receiving first images of produce. The method can further include executing a first machine learning model to generate second images of produce based on the first images of produce. The first images of produce can include (1) images of non-bagged produce or (2) images of bagged produce. The second images of produce can include the other of (1) images of non-bagged produce or (2) images of bagged produce. The method can further include training a second machine learning model based on the first images of produce and the second images of produce. The method can further include executing, after the training, the second machine learning model to classify as a bagged produce or a non-bagged produce an image not included in the first images and not included in the second images.

Abstract: Provided are an application interface implementation method and apparatus in a host platform layer, a device, and a storage medium, which relate to the field of computer technologies. The implementation scheme includes: the host platform layer acquiring description data of a terminal capability interface; parsing the description data to acquire a communication mode of the terminal capability interface; and configuring a corresponding processor according to the communication mode, configuring a corresponding concept mapping relationship according to the communication mode, or configure a corresponding processor and a corresponding concept mapping relationship according to the communication mode to encapsulate a platform layer interface of the terminal capability interface, where the platform layer interface is configured to process data of communication interactions in a process in which a mini program calls the terminal capability interface through the host platform layer.

Abstract: The present application relates to the field of Internet of Things technologies, and in particular, to a message queuing telemetry transport (MQTT) data transmission method, apparatus, and system, so as to provide a solution for simply implementing a security function of the MQTT protocol. A data obtaining relationship between an MQTT publishing device and an MQTT subscribing device is verified before MQTT data publishing, and data sending is controlled by using an SDP gateway. Therefore, security of the MQTT data publishing can be effectively ensured. Moreover, protocol modification is relatively little, and implementation is simple.

Abstract: A method for calling a program module in a programmable logic controller (PLC) ladder programming environment comprises: determining a first feature to be called in a PLC ladder programming environment and a second feature not to be called from features implemented by an artificial intelligence (AI) module compiled by a computer programming language; configuring the first feature as an interface conforming to an interface specification of the PLC ladder programming environment; configuring the second feature as a non-call method; packaging the non-call method and the interface into a dynamic link library (DLL); importing the DLL into the PLC ladder programming environment.

Abstract: One or more computing devices obtains service data of a user payment service. The one or more computing devices determines whether the service data of the user payment service falls within a service indicator range. In response to determining that the service data does not fall within the service indicator range, the one or more computing devices obtains location information of a target customer corresponding to the service data. The one or more computing devices obtains one or more local events corresponding to the location information. The one or more computing devices determines, based on the one or more local events, whether to output an alarm for the service data. In response to determining to output the alarm for the service data, the one or more computing devices outputs the alarm for the service data.

Abstract: One or more computing devices obtains service data of a user payment service. The one or more computing devices determines whether the service data of the user payment service falls within a service indicator range. In response to determining that the service data does not fall within the service indicator range, the one or more computing devices obtains location information of a target customer corresponding to the service data. The one or more computing devices obtains one or more local events corresponding to the location information. The one or more computing devices determines, based on the one or more local events, whether to output an alarm for the service data. In response to determining to output the alarm for the service data, the one or more computing devices outputs the alarm for the service data.

Abstract: A data processing method and apparatus for a mini app, a device and a medium are provided. An implementation of the method may include: intercepting a request message of the mini app, and sending the request message to a target server, where the request message comes from a technology stack; acquiring returned data targeting at the request message and returned from the target server, where the returned data includes cookie content, field information for indicating storage of the cookie content, and a data set corresponding to the request message; and storing the cookie content into a cookie storage database of the mini app according to the field information, and returning the returned data to the technology stack issuing the request message.