Abstract: Provided herein are a control system and a control method for pest killing by microwave, and a computer device and a storage medium thereof. The control system for pest killing by microwave includes: an inactivation module; an electromagnetic shielding module; a conveyor belt; and a microwave pest killing device, which is a waveguide structure with two open ends and four closed sides. The pest killing method maintains the temperature of the tobacco packages within a specific range, ensuring the quality of the tobacco while effectively inactivating the pests, thereby enhancing the economic efficiency of tobacco factories. The pest killing method employs a reactance counter-current type electromagnetic shielding method, which ensures the smooth operation of the conveyor belt without affecting the health of the operators or the safety of the environment.

Abstract: The invention discloses an intelligent detection method for multiple types of faults for near-water bridges and an unmanned surface vehicle. The method includes an infrastructure fault target detection network CenWholeNet and a bionics-based parallel attention module PAM. CenWholeNet is a deep learning-based Anchor-free target detection network, which mainly comprises a primary network and a detector, used to automatically detect faults in acquired images with high precision. Wherein, the PAM introduces an attention mechanism into the neural network, including spatial attention and channel attention, which is used to enhance the expressive power of the neural network. The unmanned surface vehicle includes hull module, video acquisition module, lidar navigation module and ground station module, which supports lidar navigation without GPS information, long-range real-time video transmission and highly robust real-time control, used for automated acquisition of information from bridge underside.

Abstract: The invention discloses an intelligent detection method for multiple types of faults for near-water bridges and an unmanned surface vehicle. The method includes an infrastructure fault target detection network CenWholeNet and a bionics-based parallel attention module PAM. CenWholeNet is a deep learning-based Anchor-free target detection network, which mainly comprises a primary network and a detector, used to automatically detect faults in acquired images with high precision. Wherein, the PAM introduces an attention mechanism into the neural network, including spatial attention and channel attention, which is used to enhance the expressive power of the neural network. The unmanned surface vehicle includes hull module, video acquisition module, lidar navigation module and ground station module, which supports lidar navigation without GPS information, long-range real-time video transmission and highly robust real-time control, used for automated acquisition of information from bridge underside.

Abstract: The presently disclosed inventive concepts are directed in certain embodiments to a method of producing protoporphyrin IX by (1) cultivating a strain of Shewanella bacteria in a culture medium under conditions suitable for growth thereof, and (2) recovering the protoporphyrin IX from the culture medium. The strain of Shewanella bacteria comprises at least one mutant hemH gene which is incapable of normal expression, thereby causing an accumulation of protoporphyrin IX. In certain embodiments of the method, the strain of Shewanella bacteria is a strain of S. loihica, and more specifically may be S. loihica PV-4. In certain embodiments, the mutant hemH gene of the strain of Shewanella bacteria may be a mutant of shew_2229 and/or of shew_1140.

Abstract: The presently disclosed inventive concepts are directed in certain embodiments to a method of producing protoporphyrin IX by (1) cultivating a strain of Shewanella bacteria in a culture medium under conditions suitable for growth thereof, and (2) recovering the protoporphyrin IX from the culture medium. The strain of Shewanella bacteria comprises at least one mutant hemH gene which is incapable of normal expression, thereby causing an accumulation of protoporphyrin IX. In certain embodiments of the method, the strain of Shewanella bacteria is a strain of S. loihica, and more specifically may be S. loihica PV-4. In certain embodiments, the mutant hemH gene of the strain of Shewanella bacteria may be a mutant of shew_2229 and/or of shew_1140.