The kinetics and isotherm studies of the immobilized lipase and the mechanism of immobilization on chitosan beads and activated chitosan beads with glutaraldehyde were investigated. The effect of glutaraldehyde on porosity of chitosan was evaluated by FESEM analysis. It was observed that the porosity of the carrier which has activated by glutaraldehyde was substantially increased. The validity ...

a segmented regression model for the description of microbial growth has been suggested. the model is able to predict the exponential growth, logistic growth, logistic growth with a phase of decline, diauxic growth, microbial growth in synchronous cultures and the oscillatory growth

MOTIVATION DNA methylation is a molecular modification of DNA that plays crucial roles in regulation of gene expression. Particularly, CpG rich regions are frequently hypermethylated in cancer tissues, but not methylated in normal tissues. However, there are not many methodological literatures of case-control association studies for high-dimensional DNA methylation data, compared with those of ...

To obtain higher accuracy of PV prediction to enhance power generation technology. This paper proposes a spatio-temporal method based on deep learning neural network model. Firstly, correlation analysis is performed for 17 sites. Secondly, we compare CNN-LSTM with single CNN or LSTM model trained the same dataset. From evaluation indexes such as loss map, regression RMSE, and MAE, that consider...

OBJECTIVE To develop an improved model for the genetic basis of reduced susceptibility to tenofovir in vitro. METHODS A dataset of 532 HIV-1 subtype B reverse transcriptase genotypes for which matched phenotypic susceptibility data were available was assembled, both as a continuous (transformed) dataset and a categorical dataset generated by imposing a cut-off on the basis of earlier studies ...

fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. several methods for evaluating fuzzy coefficients in linear regression models have been proposed. the first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. in this the...

a weighted linear regression model with impercise response and p-real explanatory variables is analyzed. the lr fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. a least square solution for estimating the parameters of the model is derived. the result are illustrated by the means of some case studies.