Abstract: A composite solution for enhancing induced disease resistance of lentinan (LNT) to a plant, a preparation method of the composite solution, and a method for enhancing induced disease resistance of LNT to a plant are provided. The composite solution for enhancing induced disease resistance of LNT to a plant includes: an LNT-containing solution and an SPc-containing solution, where SPc is a dendritic macromolecule functionalized by an amino functional group, and has a structural formula shown in formula I, where n=1 to 100. An LNT/SPc complex is produced in the composite solution. SPc spontaneously combines with LNT through hydrogen bonding, such that an agglomerate structure formed by LNT in an aqueous solution is broken and reduced to a nano-scale particle size, and a spherical particle is produced, which can significantly reduce a contact angle of the LNT aqueous solution, and promote the distribution and diffusion of LNT.

Abstract: Provided are a trajectory prediction method, an electronic device, and a storage medium. The method including that: a motion intention of an object is determined according to time-series location information and time-series posture information of the object, where the time-series location information is position information of the object at different time points within a preset time period, and the time-series posture information is posture information of the object at different time points within the preset time period, where the posture information at different time points includes orientation information of multiple parts of the object at the different time points; and a future trajectory of the object is determined according to the time-series location information, the time-series posture information and the motion intention.

Abstract: Embodiments of the disclosure provide a method and apparatus for trajectory prediction, a device and a storage medium. The method includes that: location information of a reference end point of a moving object is determined according to location information of the moving object; a candidate trajectory set including multiple candidate trajectories is determined according to the location information of the moving object and the location information of the reference end point, wherein location information of an end point of each candidate trajectory is different from the location information of the reference end point; and a target trajectory of the moving object is determined from the candidate trajectory set.

Abstract: A depth recovery method includes: performing feature extraction on a monocular image to obtain a feature image of the monocular image; decoupling the feature image to obtain a scene structure graph of the feature image; performing gradient sensing on the feature image and the scene structure graph to obtain a region-enhanced feature image; and performing depth estimation according to the region-enhanced feature image to obtain a depth image of the monocular image.

Abstract: A method for identifying a travelling state of an intelligent driving device includes: a body orientation of the intelligent driving device is determined according to to-be-processed images including the intelligent driving device; a state of one or more first travelling state indicating lights included in the intelligent driving device is determined device according to the to-be-processed images; and the travelling state of the intelligent driving device is determined according to the body orientation and the state of the first travelling state indicating lights.

Abstract: A depth recovery method includes: performing feature extraction on a monocular image to obtain a feature image of the monocular image; decoupling the feature image to obtain a scene structure graph of the feature image; performing gradient sensing on the feature image and the scene structure graph to obtain a region-enhanced feature image; and performing depth estimation according to the region-enhanced feature image to obtain a depth image of the monocular image.