Abstract: A sheath for loading and retrieving a prosthetic implant is disclosed, which includes a tube. The tube has opposing distal and proximal ends in its axial direction, and the distal end has a plurality of support rods arranged at intervals in a circumferential direction of the tube and having relative converged and flared configurations. A plurality of connecting strips are provided in the axial direction between two adjacent support rods, and each connecting strip is folded back and forth in the converged configuration and tends to be straightened in the flared configuration relative to the converged configuration. The straightened lengths of the connecting strips increase sequentially from the proximal end to the distal end, and two ends of each connecting strip are connected to the support rods on the corresponding sides and the connecting portions have the same axial level.

Abstract: A method for establishing an application prediction model, a storage medium, and a terminal are provided. The method includes the following. In response to an operation switching an application previously running in the foreground to a first application currently running, a first preorder usage sequence of the first application currently running is collected as sample usage information. Applications launched within a time-window are monitored, and a preset number of applications launched first in the applications launched within the time-window are set as sample labels for the sample usage information. A predetermined machine learning model is trained based on the sample usage information and the sample labels for the sample usage information, to obtain the application prediction model.

Abstract: A method for preloading an application, a storage medium, and a terminal are provided. The method includes the following. Current usage information of a terminal is acquired in response to detecting that an event of prediction of preloading an application is triggered. The current usage information is input to a model used for application prediction. The model used for application prediction includes a hybrid network model generated according to a usage rule of applications of the terminal corresponding to historical status feature information. At least one target application to be launched is predicted according to an output result of the model used for application prediction. The at least one target application is preloaded.

Abstract: A method for establishing an application prediction model, a storage medium, and a terminal are provided. The method includes the following. In response to an operation switching an application previously running in the foreground to a first application currently running, a first preorder usage sequence of the first application currently running is collected as sample usage information. Applications launched within a time-window are monitored, and a preset number of applications launched first in the applications launched within the time-window are set as sample labels for the sample usage information. A predetermined machine learning model is trained based on the sample usage information and the sample labels for the sample usage information, to obtain the application prediction model.

Abstract: A method for preloading an application, a storage medium, and a terminal are provided. The method includes the following. Current usage information of a terminal is acquired in response to detecting that an event of prediction of preloading an application is triggered. The current usage information is input to a model used for application prediction. The model used for application prediction includes a hybrid network model generated according to a usage rule of applications of the terminal corresponding to historical status feature information. At least one target application to be launched is predicted according to an output result of the model used for application prediction. The at least one target application is preloaded.