Abstract: The present invention provides a fast-curing molding process for epoxy resin. The method includes the following steps: S1, mixing epoxy resin A glue and a protein-grafted manganese-zinc-iron oxide nanomaterial well into a colloidal state, and grounding the mixture for 10 to 30 min; S2, adding epoxy resin B glue to the mixed colloid in S1, and performing ultrasonic dispersion at 20 to 30° C. for 10 to 30 min; and S3, placing the mixture obtained after ultrasonic dispersion in S2 in a vacuum environment for 20 to 40 min, then taking the mixture out and injecting the mixture into a mold, placing the mold and the mixed colloid into an electromagnetic induction heater, placing the electromagnetic induction heater in a magnetic field environment with a magnetic field intensity of 1 to 1.5 mT for 2 to 3 h, cooling and then taking them out to obtain the cured epoxy resin.

Abstract: The present invention provides a fast-curing molding process for epoxy resin. The method includes the following steps: S1, mixing epoxy resin A glue and a protein-grafted manganese-zinc-iron oxide nanomaterial well into a colloidal state, and grounding the mixture for 10 to 30 min; S2, adding epoxy resin B glue to the mixed colloid in S1, and performing ultrasonic dispersion at 20 to 30° C. for 10 to 30 min; and S3, placing the mixture obtained after ultrasonic dispersion in S2 in a vacuum environment for 20 to 40 min, then taking the mixture out and injecting the mixture into a mold, placing the mold and the mixed colloid into an electromagnetic induction heater, placing the electromagnetic induction heater in a magnetic field environment with a magnetic field intensity of 1 to 1.5 mT for 2 to 3 h, cooling and then taking them out to obtain the cured epoxy resin.

Abstract: The present disclosure provides a drop impact prediction method and system for heavy equipment airdrop based on a neural network. The drop impact prediction method includes the following steps: S1: acquiring a plurality of sets of sample data by using a finite element model for drop simulation of heavy equipment airdrop; S2: determining structural parameters of a BP neural network, and pre-processing the structural parameters; S3: constructing a BP neural network model; and S4: predicting a drop impact situation of heavy equipment airdrop in an actual application process by using the trained BP neural network model.

Abstract: A method for image processing, an electronic device, and a storage medium are provided. The method includes: acquiring a current frame image captured by a camera and motion parameters of a gyroscope when capturing the current frame image, determining a region of interest of the current frame image, determining and displaying, based on the region of interest and the motion parameters, a display area of a next frame image. A region of interest of a previous frame image and a motion state of a gyroscope when capturing the previous frame image are determined, a display area of a subsequent frame image is determined based on the region of interest and the motion state; the display area is no longer fixed at a center of a field of view, but rather adjusted based on the region of interest and the motion state, thus facilitating tracking of an object.

Abstract: The invention relates to a method and means for the non-invasive diagnosis of type II diabetes mellitus. The glycation state is determined in at least one glycation position of selected plasma proteins.

Abstract: Embodiments of the present invention provide a method and a system for detecting a malicious code. The method includes obtaining first system information and second system information, and detecting the malicious code by identifying difference between the first system information and the second system information, which thus can detect an unknown malicious code, improve the system security, and can be easily implemented.