Abstract: A process and apparatus for recovering at least argon from a feed gas that can provide an improved recovery of argon as well as an improvement in operational efficiency. Some embodiments can be adapted so that the improved argon recovery can also be obtained with improved condenser operation for an argon column without requiring an increase in power for the recovery of the argon. Some embodiments can utilize a reboiler positioned near or at the bottom of argon recovery column to increase boil-up therein and/or provide added heat duty to drive a condenser of the argon recovery column to provide improved argon recovery.

Abstract: A process for recovering at least one fluid (e.g. argon gas and/or nitrogen gas, etc.) from a feed gas (e.g. air) can provide an improved recovery of argon and/or nitrogen as well as an improvement in operational efficiency. Some embodiments can be adapted so that at least a portion of a mixed nitrogen-oxygen fluid is at least partially vaporized and fed to a low pressure column.

Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.

Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.

Abstract: The present disclosure provides a method for training a feature extraction model, a method for classifying an image and related apparatuses, and relates to the field of artificial intelligence technology such as deep learning and image recognition. The scheme comprises: extracting an image feature of each sample image in a sample image set using a basic feature extraction module of an initial feature extraction model, to obtain an initial feature vector set; performing normalization processing on each initial feature vector in the initial feature vector set using a normalization processing module of the initial feature extraction model, to obtain each normalized feature vector; and guiding training for the initial feature extraction model through a preset high discriminative loss function, to obtain a target feature extraction model as a training result.

Abstract: Provided are an image processing method and apparatus, a device, a medium and a program product. The image processing method includes: performing image augmentation on an original image to obtain at least one augmented image; performing subject detection on the original image and the at least one augmented image to obtain an original detection frame in the original image and an augmented detection frame in the at least one augmented image; determining whether the original detection frame and the augmented detection frame belong to the same subject; and in response to the original detection frame and the augmented detection frame belonging to the same subject, determining a target subject frame in the original image according to the augmented detection frame.

Abstract: The present disclosure provides an image recognition method and apparatus, an electronic device and a readable storage medium, and relates to the field of artificial intelligence technologies, such as image processing and deep learning technologies. The image recognition method includes: acquiring a to-be-recognized image, and determining a to-be-recognized subject in the to-be-recognized image; extracting a subject feature of the to-be-recognized subject, and obtaining a target feature according to the subject feature; determining a target candidate feature in a plurality of candidate features using the target feature; and taking a class corresponding to the target candidate feature as a recognition result of the to-be-recognized subject. With the present disclosure, different image recognition requirements may be met, and a speed and accuracy of image recognition may be improved.

Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.

Abstract: The present disclosure provides a method and apparatus for training an image recognition model, and a method and apparatus for recognizing an image, and relates to the field of artificial intelligence, and particularly to the fields of deep learning and computer vision. A specific implementation comprises: acquiring a tagged sample set, an untagged sample set and a knowledge distillation network; and performing following training steps: selecting an input sample from the tagged sample set and the untagged sample set, and accumulating a number of iterations; inputting respectively the input sample into a student network and a teacher network of the knowledge distillation network to train the student network and the teacher network; and selecting an image recognition model from the student network and the teacher network, if a training completion condition is satisfied.

Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.

Abstract: A chiller can be configured as a chiller for a gasification system or other type of system or plant. In some embodiments, the chiller can be configured to utilize a single heat source, such as low grade waste heat in the form of hot water, and/or low pressure steam to drive one or more absorption-based chillers to cool inlet air to one or more adsorbers of a pre-purification unit (PPU). In the event of the detection of an undesired impurity spike (e.g. carbon dioxide spike, etc.) an additional amount of heat source can be withdrawn from the gasification system to increase the level of cooling the absorption chiller can provide to improve the removal of impurities. An automated control loop can be utilized in some embodiments. The control loop can be configured to check for an impurity concentration and adjust operations accordingly.

Abstract: An air valve assembly for use with inflatable articles is provided. The air valve assembly includes a valve base (10), a valve body (20), an inflation adapter (30), a plug (40) and a fixing ring (50). The air valve assembly provides for inflation of the inflatable article through two different sized air passages, allowing inflation by multiple sizes of air inflation hoses or nozzles. The air valve assembly also provides for quick deflation of the inflatable article through a third and larger sized air passage. The configuration of the air valve assembly may be easily changed by hand without the need for tools, allowing for easy and efficient inflation or deflation of the inflatable article.

Abstract: There is provided a method and apparatus of training a model, a device, and a medium, which relate to artificial intelligence, and in particular to a deep learning and image processing technology. The method may include: determining a plurality of augmented sample sets associated with a plurality of original samples; determining a first constraint according to a first model based on the plurality of augmented sample sets; determining a second constraint according to the first model and a second model based on the plurality of augmented sample sets, wherein the second constraint is associated with a difference between outputs of the first model and the second model for one augmented sample, and the first model has a complexity lower than that of the second model; training the first model based on at least the first constraint and the second constraint, so as to obtain a trained first model.

Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.

Abstract: The disclosure provides a method and an apparatus for processing visual data of a deep model, and an electronic device. The method includes: determining a user identifier in a request of displaying a workspace in response to acquiring the request; acquiring a set of historical visual data associated with the user identifier; and displaying the set of historical visual data in a page of a workspace of a user corresponding to the user identifier.

Abstract: The present disclosure provides a method and apparatus of processing an image, a device and a medium, which relates to a field of artificial intelligence, and in particular to a field of deep learning and image processing. The method includes: determining a background image of the image, wherein the background image describes a background relative to characters in the image; determining a property of characters corresponding to a selected character section of the image; replacing the selected character section with a corresponding section in the background image, so as to obtain an adjusted image; and combining acquired target characters with the adjusted image based on the property.

Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.

Abstract: Process and apparatus for the separation of a compressed feed air stream to produce an oxygen product using a distillation column having a lower-pressure column and a higher-pressure column, a higher-pressure heat exchanger and a lower-pressure heat exchanger where the gaseous nitrogen expander receives a nitrogen-enriched fraction from a position intermediate the warmer end and the colder end of the higher-pressure heat exchanger.

Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.

Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.