Korrosionstests an magnetischen Schichten und deren Beurteilung mit Hilfe ausgew?hlter oberfl?chenanalytischer Verfahren

Summary Besides functional behaviour of coatings their resistivity against corroding attack is a crucial criterion for quality. The application of special corroding and abrading tests yields after short times predictions about stability of the product in practical use. A number of tests have been applied to thin magnetic storage media. Besides tests of function and visual observations with light- and scanning electron microscopes, AES- and XPS-investigations were applied to discuss corrosion resistivity.     

Limit on right handed weak coupling parameters from inelastic neutrino interactions

Right handed weak quark currents coupled to the usual left handed weak lepton current would be seen in inclusive antineutrino scattering on nuclei as a contribution at largey with the quark (not antiquark) structure function. We do not see such a term, and can therefore put an upper limit on the relative strengths of such right handed currents: \(\varrho ^2 = \frac{{\sigma _R }}{{\sigma _L }}< 0.009\) , 90% confidence. This measurement puts limits on the mixing angle of left-right symmetric models. In distinction to similar limits derived from muon decay or β decay, our limits are also valid if the right handed neutrino is heavy.     

Artificial intelligence–based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method

Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile , Annona squamosal , Camellia sinensis (tea), and coriander ( Coriandrum sativum L.). It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to a classical linear model [multilinear regression analysis (MLR)], were used for the prediction of the retention time (tR) and peak area (PA) for isoquercitrin using HPLC. The simulation uses concentration of the standard, composition of the mobile phases (MP-A and MP-B), and pH as the corresponding input variables. The performance efficiency of the models was evaluated using relative mean square error (RMSE), mean square error (MSE), determination coefficient (DC), and correlation coefficient (CC). The obtained results demonstrated that all four models are capable of predicting the qualitative and quantitative properties of the bioactive compound. A predictive comparison of the models showed that M3 had the highest prediction accuracy among the three models. Further evaluation of the results showed that ANFIS–M3 outperformed the other models and serves as the best model for the prediction of PA. On the other hand, ANN–M3proved its merit and emerged as the best model for tR simulation. The overall predictive accuracy of the best models showed them to be reliable tools for both qualitative and quantitative determination.