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

Thermoelectric performance of multiphase XNiSn (X = Ti, Zr, Hf) half-Heusler alloys

Quantitative X-ray powder diffraction analysis demonstrates that mixing Ti, Zr and Hf on the ionic site in the half-Heusler structure, which is a common strategy to lower the lattice thermal conductivity in this important class of thermoelectric materials, leads to multiphase behaviour. For example, nominal Ti0.5Zr0.5NiSn has a distribution of Ti1−xZrxNiSn compositions between 0.24 ≤ x ≤ 0.70. Similar variations are observed for Zr0.50Hf0.5NiSn and Ti0.5Hf0.5NiSn. Electron microscopy and elemental mapping demonstrate that the main compositional variations occur over micrometre length scales. The thermoelectric power factors of the mixed phase samples are improved compared to the single phase end-members (e.g. S2/ρ = 1.8 mW m−1 K−2 for Ti0.5Zr0.5NiSn, compared to S2/ρ = 1.5 mW m−1 K−2 for TiNiSn), demonstrating that the multiphase behaviour is not detrimental to electronic transport. Thermal conductivity measurements for Ti0.5Zr0.5NiSn0.95 suggest that the dominant reduction comes from Ti/Zr mass and size difference phonon scattering with the multiphase behaviour a secondary effect

Multi Agent Diagnosis: an analysis

The paper analyzes the use of a Multi Agent System for Model Based Diagnosis. In a large dynamical system, it is often infeasible or even impossible to maintain a model of the whole system. Instead, several incomplete models of the system have to be used to detect possible faults. These models may also be physically be distributed. A Multi Agent System of diagnostic agents may offer solutions for establishing a global diagnosis. If we use a separate agent for each incomplete model of the system, establishing a global diagnosis becomes a problem cooperation and negotiation between the diagnostic agents. This raises the question whether `a set of diagnostic agents, each having an incomplete model of the system, can (efficiently) determine the same global diagnosis as an ideal single diagnostic agent having the combined knowledge of the diagnostic agents?''economics of technology ;

Europe, Middle East, and North Africa region population projections : 1988-89 edition

Population projections are provided in this paper for the individual countries comprising EMENA region. The projections cover the period 1985-2150. The length of the projection period was chosen to allow countries to approach stability, which for several is projected to take as long as two centuries. The paper begins with an introduction which explains what projection results are provided in the detailed tables, describes the projection methodology, and summarizes and interprets the main results.Demographics,Health Indicators,,Health Information&Communications Technologies,Health Monitoring&Evaluation