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This paper proposes the application of neuro-wavelet technique for modeling daily suspended sediment–discharge relationship. The neuro-wavelet models are obtained by combining two methods, artificial neural networks (ANN) and discrete wavelet transform. The accuracy of the neuro-wavelet and the ANN models is compared with each other in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The comparison results reveal that the suggested model could increase the estimation accuracy. [ABSTRACT FROM AUTHOR]

A universal method using Raman spectroscopy for the quantitative determination of oxytetracycline in three water-based medicines was developed. Five calibration models based on different mathematical data processing, i.e., one-dimensional calibration (Beer–Lambert law, classical least squares, stepwise multiple linear regression) and multi-dimensional calibration (partial least squares and principal component regression), were compared. The most accurate quantitative determinations were achieved when a calibration model based on partial least squares using two principal components was used. [ABSTRACT FROM AUTHOR]