Processing of signals from an ion-elective electrode array by a neural network

Neural network software is described for processing the signals of arrays of ion-selective electrodes. The performance of the software was tested in the simultaneous determination of calcium and copper(II) ions in binary mixtures of copper(II) nitrate and calcium chloride and the simultaneous determination of potassium, calcium, nitrate and chloride in mixtures of potassium and calcium chlorides and ammonium nitrate. The measurements for the Ca2+/Cu2+ determinations were done with a pH-glass electrode and calcium and copper ion-selective electrodes; results were accurate to ±8%. For the K+/Ca2+NO−3/Cl− determinations, the measurements were made with the relevant ion-selective electrodes and a glass electrode; the mean relative error was ±6%, and for the worst cases the error did not exceed 20%

Artificial neural networks as a multivariate calibration tool: modelling the Fe-Cr-Ni system in X-ray fluorescence spectroscopy

The performance of artificial neural networks (ANNs) for modeling the Cr---Ni---Fe system in quantitative x-ray fluorescence spectroscopy was compared with the classical Rasberry-Heinrich model and a previously published method applying the linear learning machine in combination with singular value decomposition. Apart from determining if ANNs were capable of modeling the desired non-linear relationships, also the effects of using non-ideal and noisy data were studied. For this goal, more than a hundred steel samples with large variations in composition were measured at their primary and secondary K¿ and Kß lines. The optimal calibration parameters for the Rasberry-Heinrich model were found from this dataset by use of a genetic algorithm. ANNs were found to be robust and to perform generally better than the other two methods in calibrating over large ranges

Modelling the permeability of polymers: a neural network approach

In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a polymer to its permeability. The underlying assumption is that the chemical information hidden in the IR spectrum is sufficient for the prediction. The best neural network investigated so far does indeed show predictive capabilities

On the unsteady behavior of turbulence models

Periodically forced turbulence is used as a test case to evaluate the predictions of two-equation and multiple-scale turbulence models in unsteady flows. The limitations of the two-equation model are shown to originate in the basic assumption of spectral equilibrium. A multiple-scale model based on a picture of stepwise energy cascade overcomes some of these limitations, but the absence of nonlocal interactions proves to lead to poor predictions of the time variation of the dissipation rate. A new multiple-scale model that includes nonlocal interactions is proposed and shown to reproduce the main features of the frequency response correctly

Trends in Competition and Profitability in the Banking Industry: A Basic Framework

This paper brings to the forefront the assumptions that we make when focusing on a particular type of explanation for bank profitability. We evaluate a broad field of research by introducing a general framework for a profit maximizing bank and demonstrate how different types of models can be fitted into this framework. Next, we present an overview of the current major trends in European banking and relate them to each model’s assumptions, thereby shedding light on the relevance, timeliness and shelf life of the different models. This way, we arrive at a set of recommendations for a future research agenda. We advocate a more prominent role for output prices, and suggest a modification of the intermediation approach. We also suggest ways to more clearly distinguish between market power and efficiency, and explain why we need time-dependent models. Finally, we propose the application of existing models to different size classes and sub-markets. Throughout we emphasize the benefits from applying several, complementary models to overcome the identification problems that we observe in individual models.