对马克斯·韦伯著名演讲《以学术为志业》中的"der alte Mill",流行的英译本、日译本和汉译本将之译为"the old Mill"、"老三ミル"和"老密尔",并均认为韦伯所指是詹姆斯·密尔。通过查...对马克斯·韦伯著名演讲《以学术为志业》中的"der alte Mill",流行的英译本、日译本和汉译本将之译为"the old Mill"、"老三ミル"和"老密尔",并均认为韦伯所指是詹姆斯·密尔。通过查找W.G.Runciman专著《马克斯·韦伯社会科学哲学批判》,并引证约翰·斯图亚特密尔《有神论》原文,说明应将"der alte Mill"译为"年老的密尔",其中的Mill实际上是约翰斯图亚特·密尔,即俗称的"小密尔"。展开更多
The melting points of organic compounds were estimated using a combined method that includes a backpropagation neural network and quantitative structure property relationship （QSPR） parameters in quantum chemistry. ...The melting points of organic compounds were estimated using a combined method that includes a backpropagation neural network and quantitative structure property relationship （QSPR） parameters in quantum chemistry. Eleven descriptors that reflect the intermolecular forces and molecular symmetry were used as input variables. QSPR parameters were calculated using molecular modeling and PM3 semi-empirical molecular orbital theories. A total of 260 compounds were used to train the network, which was developed using MatLab. Then, the melting points of 73 other compounds were predicted and results were compared to experimental data from the literature. The study shows that the chosen artificial neural network and the quantitative structure property relationships method present an excellent alternative for the estimation of the melting point of an organic compound, with average absolute deviation of 5%.展开更多
文摘对马克斯·韦伯著名演讲《以学术为志业》中的"der alte Mill",流行的英译本、日译本和汉译本将之译为"the old Mill"、"老三ミル"和"老密尔",并均认为韦伯所指是詹姆斯·密尔。通过查找W.G.Runciman专著《马克斯·韦伯社会科学哲学批判》,并引证约翰·斯图亚特密尔《有神论》原文,说明应将"der alte Mill"译为"年老的密尔",其中的Mill实际上是约翰斯图亚特·密尔,即俗称的"小密尔"。
文摘The melting points of organic compounds were estimated using a combined method that includes a backpropagation neural network and quantitative structure property relationship （QSPR） parameters in quantum chemistry. Eleven descriptors that reflect the intermolecular forces and molecular symmetry were used as input variables. QSPR parameters were calculated using molecular modeling and PM3 semi-empirical molecular orbital theories. A total of 260 compounds were used to train the network, which was developed using MatLab. Then, the melting points of 73 other compounds were predicted and results were compared to experimental data from the literature. The study shows that the chosen artificial neural network and the quantitative structure property relationships method present an excellent alternative for the estimation of the melting point of an organic compound, with average absolute deviation of 5%.
