Abstract: A pixel circuit and a driving method therefor and a display panel are provided. The pixel circuit includes a driving circuit, a data writing circuit, a storage circuit, and a first reset circuit; the driving circuit includes a control terminal, a first terminal, and a second terminal, and is configured to control a driving current flowing through the first terminal and the second terminal for driving a light-emitting element to emit light; the data write circuit is configured to write a data signal into the control terminal of the driving circuit; the storage circuit is configured to store the data signal; the first reset circuit is configured to apply a first initialization voltage to the control terminal of the driving circuit; the driving circuit and the data write circuit each include an N-type thin film transistor; and the first reset circuit includes an N-type oxide thin film transistor.

Abstract: A unit configured as constituent part of a fuel cell for use in novel electrochemical hydrogen compressor material technology system includes a combination of a hydrocarbon auto-thermal reformer, a water-gas shift reactor, and at least two countercurrent flow heat recuperators at least one of which is downstream from both the reformer and reactor. Optionally, two of the at least two recuperators are separated by the reactor to generate H2 in addition to that already contained in reformate formed at the reformer. The unit may include a proton conducting membrane that includes an inorganic polymer with pores filled with an organic polymer, each of which is configured to operate individually within a wide range of temperatures with no added solvent.

Abstract: A high anti-interference microsystem based on System In Package (SIP) for a power grid is provided. The high anti-interference microsystem comprises a ceramic cavity, a ceramic substrate, a magnetic cover plate, a digital signal processing circuit, an analog signal conditioning circuit and a shield, wherein the ceramic cavity supports the ceramic substrate, the magnetic cover plate is in sealed contact with the ceramic cavity, and the ceramic substrate is arranged in a cavity formed by the ceramic cavity and the magnetic cover plate; a sealed shell of the microsystem based on SIP is composed of the magnetic cover plate and the ceramic cavity; the digital signal processing circuit and the analog signal conditioning circuit are arranged on the ceramic substrate and respectively process received signals to be processed; the shield covers an outer side of the sealed shell and is used for shielding external magnetic field interference.

Abstract: Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network (14) configured to generate, according to the feature combination set, a judgmental result of lesions.

Abstract: Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network (14) configured to generate, according to the feature combination set, a judgmental result of lesions.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: The present disclosure provides an artificial neural network system for identifying a lesion in a retinal fundus image that comprises a pre-processing module configured to separately pre-process a target retinal fundus image and a reference retinal fundus image taken from a same person; a first neural network (12) configured to generate a first advanced feature set from the target retinal fundus image; a second neural network (22) configured to generate a second advanced feature set from the reference retinal fundus image; a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set; and a third neural network (14) configured to generate, according to the feature combination set, a diagnosis result.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: A dexrabeprazole sodium compound includes crystal forms B and C. The crystal form B has good stability and flowability. The crystal form C is an anhydrous crystal form and has good stability and low hygroscopicity. The crystal forms are suitable for preparing a dexrabeprazole sodium preparation.

Abstract: A dexrabeprazole sodium compound includes crystal forms B and C. The crystal form B has good stability and flowability. The crystal form C is an anhydrous crystal form and has good stability and low hygroscopicity. The crystal forms are suitable for preparing a dexrabeprazole sodium preparation.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: The present disclosure provides an artificial neural network system for identifying a lesion in a retinal fundus image that comprises a pre-processing module configured to separately pre-process a target retinal fundus image and a reference retinal fundus image taken from a same person; a first neural network (12) configured to generate a first advanced feature set from the target retinal fundus image; a second neural network (22) configured to generate a second advanced feature set from the reference retinal fundus image; a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set; and a third neural network (14) configured to generate, according to the feature combination set, a diagnosis result.