Heat capacities of aqueous mixtures of monoethanolamine with N-methyldiethanolamine were measured from 30 to 80 °C with a differential scanning calorimeter (DSC). The binary systems studied were N-methyldiethanolamine + water and monoethanolamine + N-methyldiethanolamine. For mole fractions of water ranging from 0.2 to 0.8, 16 concentrations of the monoethanolamine + N-methyldiethanolamine + water systems were studied. The estimated uncertainty of the measured heat capacities is (2% including the effect of up to 1% impurities in a substance. An excess molar heat capacity expression using the Redlich-Kister equation for the composition dependence is used to represent the measured C p of aqueous alkanolamine solutions. The overall average absolute percentage deviation of the calculation of the molar heat capacity for a total of 176 data points for the monoethanolamine + N-methyldiethanolamine + water system is 0.5%. The heat capacities of aqueous mixtures of monoethanolamine with N-methyldiethanolamine presented in this study are, in general, of sufficient accuracy for most engineering-design calculations.

Heat capacities of aqueous mixtures of monoethanolamine with 2-amino-2-methyl-l-propanol were measured from 30 °C to 80 °C with a differential scanning calorimeter (DSC). The heat capacities of 2-amino-2-methyl-l-propanol, 2-amino-2-methyl-l-propanol + water, and monoethanolamine + 2-amino-2-methyl-l-propanol were also studied. Eight binary systems and sixteen ternary systems were studied. The estimated uncertainty of the measured heat capacities is ±2%. An excess molar heat capacity expression using the Redlich−Kister equation for the composition dependence is used to represent the measured C p of aqueous alkanolamine solutions. The results (AAD %, the average absolute percentage deviation) for the calculation of the excess molar heat capacities are 3.6% and 13.2% for the binary and ternary systems, respectively. The heat capacities of aqueous mixtures of monoethanolamine with 2-amino-2-methyl-l-propanol presented in this study are, in general, of sufficient accuracy for most engineering-design calculations.

Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.

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A subset of nonfunctioning pituitary macroadenomas (NFMAs) show early progression/recurrence (P/R) after surgery. In clinical practice, one of the main challenges in the treatment of NFMAs is to determine factors that associated with P/R. This study investigated the role of deep learning for the prediction of P/R in NFMAs. 78 patients diagnosed with NFMAs were included. The hybrid CNN-MLP model using both clinical and MRI features showed the best performance for prediction of P/R in NFMAs, with accuracy of 84%, precision of 88%, and AUC of 0.87.