01871nas a2200229 4500008004100000022001400041245017100055210006900226260007400295300001200369490000800381520094500389653002401334653001301358653003101371653002401402100002901426700001901455700001801474700001801492856013101510 2013 eng d a1388-615000aPhotopolymerization of bis-aromatic and alicyclic based solid urethane acrylate macromonomer in the presence of large excess of reactive diluent Kinetics and modeling0 aPhotopolymerization of bisaromatic and alicyclic based solid ure aVAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDSbSPRINGERcMAY a805-8130 v1123 a

A solid urethane acrylate macromonomer with bis-aromatic as well as alicyclic moieties was synthesized and the kinetics of photopolymerization reactions were studied in the presence of varying concentration of photoinitiator and large excess of reactive diluent using photo DSC. The studies show that the rate of maximum polymerization was found to increase with increase in concentration of photoinitiator while a decrease was observed by an increase in temperature. The final conversion showed a decrease at highest isothermal condition due to vitrification. Estimation of kinetic parameters including applicability of autocatalytic and modified autocatalytic models were investigated by nonlinear regression. It was observed that the modified models gave a better fit with the experimental data and kinetic parameters showed a decrease with increase in temperature and an increase with increase in concentration of photoinitiator.

10aAutocatalytic model10akinetics10aLevenberg-Marquardt method10aPhotopolymerization1 aHarikrishna, Reghunathan1 aPonrathnam, S.1 aRajan, C., R.1 aTambe, S., S. uhttps://library.ncl.res.in/content/photopolymerization-bis-aromatic-and-alicyclic-based-solid-urethane-acrylate-macromonomer-002302nas a2200253 4500008004100000022001400041245009500055210006900150260011100219300001400330490000700344520138000351653001801731653002901749653002401778653000801802653000801810100001501818700001601833700003301849700001801882700002101900856012701921 2014 eng d a1570-180800aPharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method0 aPharmacokinetic modeling of caco2 cell permeability using geneti aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcNOV a1112-11180 v113 a

An accurate prediction of the pharmacokinetic properties of orally administered drugs is of paramount importance in pharmaceutical industry. Caco-2 cell permeability is a well established parameter for assessing the drug absorption profiles of lead molecules. Due to the restrictions on animal testing, prohibitive in situ models and ethical issues, the development of predictive models is essential. Genetic programming (GP) is an artificial intelligence (AI)-based exclusively data driven modeling paradigm. Given an example input-output data, it searches and optimizes, both the structure and parameters of a well fitting linear/non-linear input-output model. Despite this novelty, GP has not been widely exploited in drug design. Accordingly, in this study we propose a GP based approach for the in silico prediction of Caco-2 cell permeability using a diverse set of molecules. The predictions yielded a high magnitude for the training and test set correlation coefficient with low RMSE, indicating accurate Caco-2 permeability prediction and generalization performance by the GP model. The predictions were better or comparable to artificial neural networks (ANN) and support vector regression (SVR) methods. The GP based modeling approach illustrated will find diverse applications in (QSAR, QSPR and QSTR) modeling for the virtual screening of large libraries.

10aADME modeling10aCaco-2 cell permeability10agenetic programming10aMLP10aSVR1 aVyas, Renu1 aGoel, Purva1 aKarthikeyan, Muthukumarasamy1 aTambe, S., S.1 aKulkarni, B., D. uhttps://library.ncl.res.in/content/pharmacokinetic-modeling-caco-2-cell-permeability-using-genetic-programming-gp-method-002882nas a2200253 4500008004100000022001400041245010300055210006900158260005700227300001200284490000600296520197700302653002802279653003102307653001802338653002402356653002502380653002602405100002302431700001502454700001802469700001802487856012302505 2014 eng d a1939-123400aPrediction of higher heating value of solid biomass fuels using artificial intelligence formalisms0 aPrediction of higher heating value of solid biomass fuels using  a233 SPRING ST, NEW YORK, NY 10013 USAbSPRINGERcJUN a681-6920 v73 a

The higher heating value (HHV) is an important property defining the energy content of biomass fuels. A number of proximate and/or ultimate analysis based predominantly linear correlations have been proposed for predicting the HHV of biomass fuels. A scrutiny of the relationships between the constituents of the proximate and ultimate analyses and the corresponding HHVs suggests that all relationships are not linear and thus nonlinear models may be more appropriate. Accordingly, a novel artificial intelligence (AI) formalism, namely genetic programming (GP) has been employed for the first time for developing two biomass HHV prediction models, respectively using the constituents of the proximate and ultimate analyses as the model inputs. The prediction and generalization performance of these models was compared rigorously with the corresponding multilayer perceptron (MLP) neural network based as also currently available high-performing linear and nonlinear HHV models. This comparison reveals that the HHV prediction performance of the GP and MLP models is consistently better than that of their existing linear and/or nonlinear counterparts. Specifically, the GP- and MLP-based models exhibit an excellent overall prediction accuracy and generalization performance with high (> 0.95) magnitudes of the coefficient of correlation and low (< 4.5 %) magnitudes of mean absolute percentage error in respect of the experimental and model-predicted HHVs. It is also found that the proximate analysis-based GP model has outperformed all the existing high-performing linear biomass HHV prediction models. In the case of ultimate analysis-based HHV models, the MLP model has exhibited best prediction accuracy and generalization performance when compared with the existing linear and nonlinear models. The AI-based models introduced in this paper due to their excellent performance have the potential to replace the existing biomass HHV prediction models.

10aArtificial intelligence10aArtificial neural networks10aBiomass fuels10agenetic programming10aHigher heating value10aMultilayer perceptron1 aGhugare, Suhas, B.1 aTiwary, S.1 aElangovan, V.1 aTambe, S., S. uhttps://library.ncl.res.in/content/prediction-higher-heating-value-solid-biomass-fuels-using-artificial-intelligence-0