母配分系数与旋量玻色-爱因斯坦凝聚(英文)

研究了由自旋为 1的玻色子组成的N_体系统的总自旋态。导出了具有 {N}和 {N- 1,1} 对称性的总自旋态的母配分系数。这些系数使有关矩阵元的计算大为方便。因而它们将有助于建立一个关于旋量玻色爱因斯坦凝聚的改进了的理论 ,其中总自旋S及它的Z_分量SZ

Partition coefficient prediction of Baker's yeast invertase in aqueous two phase systems using hybrid group method data handling neural network

A hybrid GMDH neural network model has been developed in order to predict the partition coefficients of invertase from Baker's yeast. ATPS experiments were carried out changing the molar average mass of PEG(1500–6000 Da), p H(4.0–7.0), percentage of PEG(10.0–20.0 w/w), percentage of MgSO_4(8.0–16.0 w/w), percentage of the cell homogenate(10.0–20.0 w/w) and the percentage of MnSO_4(0–5.0 w/w) added as cosolute. The network evaluation was carried out comparing the partition coefficients obtained from the hybrid GMDH neural network with the experimental data using different statistical metrics. The hybrid GMDH neural network model showed better fitting(AARD = 32.752%) as well as good generalization capacity of the partition coefficients of the ATPS than the original GMDH network approach and a BPANN model. Therefore hybrid GMDH neural network model appears as a powerful tool for predicting partition coefficients during downstream processing of biomolecules.