Abstract: A detection chip is disclosed. The detection chip includes a sample injection structure, a filter structure, and a reaction structure which are sequentially connected. The filter structure includes a first main body, and a first inlet portion and a first outlet portion respectively on two sides of the first main body. A width of the first inlet portion gradually decreases in a direction away from the first main body, and a width of the first outlet portion gradually decreases in a direction away from the first main body.

Abstract: An analysis device for a detection chip, a method for operating an analysis device, and an analysis system are provided. The analysis device includes a base and a control module. The control module includes a positioning sub-module, an operation sub-module, and a detection sub-module. The positioning sub-module includes an accommodating structure, and the accommodating structure is configured to accommodate the detection chip. The operation sub-module includes at least one operation part, and the at least one operation part is configured to perform a contact mechanical operation. The detection sub-module is configured to perform a detection operation.

Abstract: A detection chip, a method for using a dejection chip, and a detection device are provided. The detection chip includes a chip substrate and a first sealing film that are stacked. The chip substrate includes a first surface, and the first sealing film covers the first surface of the chip substrate The chip substrate further includes a fluid channel on the first surface, and the fluid channel includes a plurality of membrane valve portions. The membrane valve portions are configured to allow a portion of fee first sealing film covering the membrane valve portions to approach and separate, so as to close and open the fluid channel correspondingly.

Abstract: A computer-implemented method is provided. The computer-implemented method includes classifying an image using a classification model having a residual network. Classifying the image using the classification model includes inputting an input image into the residual network having N number of residual blocks sequentially connected, N?2, (N?1) number of pooling layers respectively between two adjacent residual blocks of the N number of residual blocks, and (N?1) number of convolutional layers respectively connected to first to (N?1)-th residual blocks of the N number of residual blocks; processing outputs from the first to the (N?1)-th residual blocks of the N number of residual blocks respectively through the (N?1) number of convolutional layers; vectorizing outputs respectively from the (N?1) number of convolutional layers to generate (N?1) number of vectorized outputs; vectorizing an output from a last residual block of the N number of residual blocks to generate a last vectorized output.

Abstract: A detection chip, a method for using a detection chip and a detection apparatus are provided the detection chip includes a chip substrate and the chip substrate includes a fluid channel and a plurality of liquid cells. The fluid channel is arranged on a surface if the chip substrate and includes a main path and a plurality of branch paths. The plurality of branch paths are respectively communicated to the plurality of liquid cells, the plurality of branch paths are all communicated to the main path, and communication points between the plurality of branch paths and the main path are different. The plurality of branch paths are configured to allow liquid in the plurality of branch paths to be capable of merging into the main path in a same direction.

Abstract: A detection chip and a detection system are provided. The detection chip includes a base substrate, a flow channel defining layer, and at least one driving electrode group. The at least driving electrode group is on the base substrate, and the flow channel defining layer is on a side of the at least one driving electrode group away from the base substrate, the flow channel defining laver includes a flow channel structure, and the flow channel structure is configured to accommodate liquid; and each of the at least one driving electrode group includes a plurality of driving electrodes, and the plurality of driving electrodes are configured to contact the liquid and drive the liquid to move within the flow channel structure.

Abstract: A detection apparatus and a detection method. The detection apparatus includes: a main body, a detection assembly, and a detection object receiving portion. The detection assembly includes: a signal transmitter, configured to transmit detection signals; a signal receiver, configured to receive at least part of the detection signals transmitted by the signal transmitter; and a detection connection portion, connected to the signal transmitter and the signal receiver and causing the signal transmitter and the signal receiver to be spaced from each other by the detection connection portion. The detection object receiving portion is configured to receive a detection object. The detection object receiving portion and the detection assembly are relatively movably arranged on the main body so as to allow the detection object received by the detection object receiving portion to be located in a transmission path of the detection signals between the signal transmitter and the signal receiver.

Abstract: A data processing method, a detection device administration server, a user terminal, a network server, and a data processing system are provided. The data processing method is applicable to a detection device administration server and includes: communicating with a first detection device; sending, in response to a user terminal of a user binding the first detection device through first identification information of the first detection device, a first detection request to the first detection device; and receiving, after the user completes the first detection, first detection data sent by the first detection device.

Abstract: A computer-implemented method is provided. The computer-implemented method includes classifying an image using a classification model having a residual network. Classifying the image using the classification model includes inputting an input image into the residual network having N number of residual blocks sequentially connected, N?2, (N?1) number of pooling layers respectively between two adjacent residual blocks of the N number of residual blocks, and (N?1) number of convolutional layers respectively connected to first to (N?1)-th residual blocks of the N number of residual blocks; processing outputs from the first to the (N?1)-th residual blocks of the N number of residual blocks respectively through the (N?1) number of convolutional layers; vectorizing outputs respectively from the (N?1) number of convolutional layers to generate (N?1) number of vectorized outputs; vectorizing an output from a last residual block of the N number of residual blocks to generate a last vectorized output.

Abstract: A method for processing physiological data, a device for processing physiological data, and a non-volatile storage medium are provided. The method includes: acquiring one or more physiological data of a detected object; calculating a state judgment value corresponding to each of the one or more physiological data based at least in part on a basic assignment table; and calculating health data of the detected object based at least in part on a weight assignment table and the state judgment value.