http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Kiasat, Ali Reza,Badri, Rashid,Sayyahi, Soheil Korean Chemical Society 2009 Bulletin of the Korean Chemical Society Vol.30 No.5
Aromatic aldehydes are efficiently self-condensed into $\alpha$-hydroxy carbonyl compounds by polystyrene-supported ammonium cyanide as an excellent organocatalyst in C-C bond formation. The reaction proceeds in water under mild reaction conditions. The polymeric catalyst can be easily separated by filtration and reused several times without appreciable loss of activity.
Ali Reza Kiasat,Rashid Badri,Soheil Sayyahi 대한화학회 2009 Bulletin of the Korean Chemical Society Vol.30 No.5
Aromatic aldehydes are efficiently self-condensed into α-hydroxy carbonyl compounds by polystyrene-supported ammonium cyanide as an excellent organocatalyst in C-C bond formation. The reaction proceeds in water under mild reaction conditions. The polymeric catalyst can be easily separated by filtration and reused several times without appreciable loss of activity.
Armin Azad,Hojat Karami,Saeed Farzin,Amir Saeedian,Hamed Kashi,Fatemeh Sayyahi 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.7
Water quality management and control has high importance in planning and developing of water resources. This study investigatedapplication of Genetic Algorithm (GA), Ant Colony Optimization for Continuous Domains (ACOR) and Differential Evolution (DE)in improving the performance of adaptive neuro-fuzzy inference system (ANFIS), for evaluating the quality parameters ofGorganroud River water, such as Electrical Conductivity (EC), Sodium Absorption Ratio (SAR) and Total Hardness (TH). Accordingly, initially most suitable inputs were estimated for every model using sensitivity analysis and then all of the qualityparameters were predicted using mentioned models. Investigations showed that for predicting EC and TH in test stage, ANFIS-DEwith R2 values of 0.98 and 0.97, respectively and RMSE values of 73.03 and 49.55 and also MAPE values of 5.16 and 9.55,respectively were the most appropriate models. Also, ANFIS-DE and ANFIS-GA models had the best performance in prediction ofSAR (R2 = 0.95, 0.91; RMSE = 0.43, 0.37 and MAPE = 13.43, 13.72) in test stage. It is noteworthy that ANFIS showed the bestperformance in prediction of all mentioned water quality parameters in training stage. The results indicated the ability of mentionedalgorithms in improving the accuracy of ANFIS for predicting the quality parameters of river water.