Abstract: The present disclosure discloses an unmanned aerial vehicle and a wing thereof. The wing includes an intermediate wing assembly, configured to connect to a fuselage of an unmanned aerial vehicle; an outer wing, arranged corresponding to the intermediate wing assembly and located on a side of the intermediate wing assembly away from the fuselage when the outer wing is in an unfolded state; and a folding apparatus, including a driver and a first rotating shaft, the driver being connected to the intermediate wing assembly and the first rotating shaft including a first rotating portion and a first fixed portion that are rotatably connected, the first fixed portion being connected to the intermediate wing assembly and the first rotating portion being connected to the outer wing, the first rotating portion being further connected to an output end of the driver, to rotatably connect the outer wing and the intermediate wing assembly.

Abstract: The present invention provides a prodrug compound, a preparation method, and the use thereof. In particular, the present invention provides a compound as represented by formula (I), a preparation method, and the use thereof as a prodrug for preparing topical formulations.

Abstract: A communication method includes a terminal device sending M compressed data packets. The M compressed data packets are obtained by compressing M data packets based on a first buffer. The terminal device reestablishes a PDCP layer without resetting the first buffer. The terminal device then sends N compressed data packets. The N compressed data packets are a part or all of the M compressed data packets. When reestablishing the PDCP layer, the terminal device does not reset the first buffer. In this case, if the terminal device needs to retransmit a data packet, the terminal device may retransmit a previously obtained compressed data packet, and does not need to compress the data packet again.

Abstract: The present invention relates to the field of intelligent recognition, and discloses an intent recognition method based on a deep learning network, resolving a technical problem that accuracy of intent recognition is not high.

Abstract: The present invention relates to the field of intelligent recognition, and discloses an intent recognition method based on a deep learning network, resolving a technical problem that accuracy of intent recognition is not high.