The residues of four insecticides belonging to different families were studied on head lettuce (Lactuca sativa L. var. capitata L.), cabbage (Brassica oleracea Linn. var. capitata DC.), Chinese cabbage (Brassica pekinensis Skeels), and broccoli (Brassica oleracea var. italica) after pesticide application. To reduce application variability, a tank mix of acetamiprid 20% SP, chlorpyrifos 22.5% EC, deltamethrin 2.4% SC, and methomyl 40% SP was applied at recommended and double doses. Initial deposits of all pesticides on head lettuce were higher than those of the other three crops. The residues of chlorpyrifos and deltamethrin were higher than the maximum residue limits (MRLs) at recommended preharvest intervals (PHIs) on head lettuce and Chinese broccoli treated with higher doses. The residues of methomyl on head lettuce also showed the same phenomenon.

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The unmanned aerial vehicle (UAV) swarm is regarded as having a significant role in modern warfare. The demand for UAV swarms with the capability of attack-defense confrontation is urgent. The existing decision-making methods of UAV swarm confrontation, such as multi-agent reinforcement learning (MARL), suffer from an exponential increase in training time as the size of the swarm increases. Inspired by group hunting behavior in nature, this paper presents a new bio-inspired decision-making method for UAV swarms for attack-defense confrontation via MARL. Firstly, a UAV swarm decision-making framework for confrontation based on grouping mechanisms is established. Secondly, a bio-inspired action space is designed, and a dense reward is added to the reward function to accelerate the convergence speed of training. Finally, numerical experiments are conducted to evaluate the performance of our method. The experiment results show that the proposed method can be applied to a swarm of 12 UAVs, and when the maximum acceleration of the enemy UAV is within 2.5 times ours, the swarm can well intercept the enemy, and the success rate is above 91%.

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