[Optimization of polysaccharide extraction from Hippocampus by deep neural network and Box-Behnken design-response surface methodology].

In this paper, the extraction rate of crude polysaccharides and the yield of polysaccharides from Hippocampus served as test indicators. The comprehensive evaluation indicators were assigned by the R language combined with the entropy weight method. The Box-Behnken design-response surface methodology(BBD-RSM) and the deep neural network(DNN) were employed to screen the optimal parameters for the polysaccharide extraction from Hippocampus. These two modeling methods were compared and verified experimentally for the process optimization. This study provides a reference for the industrialization of effective component extraction from Chinese medicinals and achieves the effective combination of modern technology and traditional Chinese medicine.

Random composites of nickel networks supported by porous alumina toward double negative materials.

Random composites with nickel networks hosted randomly in porous alumina are proposed to realize double negative materials. The random composite for DNMs (RC-DNMs) can be prepared by typical processing of material, which makes it possible to explore new DNMs and potential applications, and to feasibly tune their electromagnetic parameters by controlling their composition and microstructure. Hopefully, various new RC-DNMs with improved performance will be proposed in the future.