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Avermectin (AVM) as a nonsystemic pesticide possesses a low effective utilization rate. Studies of the multifunctional pesticide delivery system for improving biological activity are developing prosperously. In this study, multifunctional avermectin/polysuccinimide with glycine methyl ester nanoparticles (AVM-PGA) were prepared by the self-assembly process. The AVM loading capacity was up to 23.7%. After 24 h of UV irradiation, there was still about 70% of AVM remaining in PGA42nanocarriers, as opposed to less than 5% of the free-form AVM. The rising ambient pH promoted the release of AVM using an in vitro releasing test, revealing a favorable pH-responsively controlled-release property. The mortality rate of Plutella xylostellawith 2.5 μg/mL of AVM content of AVM-PGA42was 96.3% after 48 h, while that of free AVM was only 51.5%. In addition, the AVM could be detected in stems and all leaves treated with AVM-PGA42nanoparticles, whereas rare AVM was detected only in treated leaves for the free-form AVM, which achieved the transportation of nanocarriers carrying AVM in rice for the first time. Furthermore, the PGA nanoparticles performed a good growth promoting effect on rice. These results show that the AVM-PGA42nanopesticides have a great potential application prospect to control the pest and improve the drug utilization efficiency on agriculture.

Grouping of jobs from the same family on bottleneck resource is significant to reduce setup time of jobs. But, the grouping may result in delay of jobs. How to balance the contradiction between utilisation of bottleneck resource and delivery time is a great challenge in production scheduling. This paper considers a single machine scheduling problem in which each job to be scheduled belongs to a family and family setup times exist. This paper is aimed to identify a schedule of families of jobs to minimise total tardiness with respect to the given due dates of the jobs. A mathematical model is formulated for this problem and a heuristic solution approach is presented. Computational experiments show that the proposed heuristic algorithm performs better both in terms of solution quality and computational time.