A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E) are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the OISST products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC) algorithm is designed to search the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then the reconstructed SSTs from the RBFN method are compared with the results from the optimum interpolation (OI) method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and the average RMSE is 0.48°C for the RBFN method, which is quite smaller than the value of 0.69°C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed.

The Oryzaephilus surinamensis L. (Coleoptera: Silvanidae), is widely distributed all over the world, this beetle causes serious damage to stored products. Much attention has been paid to use food attraction or food volatile as a non-pollution method to achieve pest management. However, in this study, six most attractive food to O. surinamensis were selected among thirty eight food. Furthermore, the food mixture M17 with the best attractiveness were selected among the eighteen food mixtures combined by these six foods at different ratio. Subsequently food mixture M17 were tested in actual barn and compared with commercial attractant. We collected the volatile from food mixture, the volatile compounds attractive to this beetle also. We identified six electrophysio-logically active compounds responsible for the attraction of O. surinamensis, among the six chemically identified compounds, nonanal, dodecane, tridecaneand β-caryophyllene significantly attracted O. surinamensis when tested individually in behavioral assays. Blend of six chemicals according to food volatile concentration was most attractive to the beetles. The findings of this study reveal that food mixture M17 and food volatile can be potentially used for development of effective attractants for management of O. surinamensis.