Fields of influence of technological change in EC intercountry input-output tables, 1970-80

The paper considers a (static) portfolio system that satisfies adding-up contraints and the gross substitution theorem. The paper shows the relationship of the two conditions to the weak dominant diagonal property of the matrix of interest rate elasticities. This enables to investigate the impact of simultaneous changes in interest rates on the asset demands.

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

Normal mere exposure effect with impaired recognition in Alzheimer’s disease.

We investigated the mere exposure effect and the explicit memory in Alzheimer’s disease (AD) patients and elderly control subjects, using unfamiliar faces. During the exposure phase, the subjects estimated the age of briefly flashed faces. The mere exposure effect was examined by presenting pairs of faces (old and new) and asking participants to select the face they liked. The participants were then presented with a forced-choice explicit recognition task. Controls subjects exhibited above-chance preference and recognition scores for old faces. The AD patients also showed the mere exposure effect but no explicit recognition. These results suggest that the processes involved in the mere exposure effect are preserved in AD patients despite their impaired explicit recognition. The results are discussed in terms of Seamon et al.’s proposal (1995) that processes involved in the mere exposure effect are equivalent to those subserving perceptual priming. These processes would depend on extrastriate areas which are relatively preserved in AD patients

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%

Some procedures for computerized ability testing

For computerized test systems to be operational, the use of item response theory is a prerequisite. As opposed to classical test theory, in item response models the abilities of the examinees and the properties of the items are parameterized separately. Hence, when measuring the abilities of examinees, the model implicitly corrects for the item properties, and measurement on an item-independent scale is possible. In addition, item response theory offers the use of test and item information as local reliability indices defined on the ability scale. In this chapter, it is shown how the main features of item response theory have given rise to the development of promising procedures for computerized testing. Among the topics discussed are procedures for item bank calibration, automated test construction, adaptive test administration, generating norm distributions, and diagnosing test scores

Peri-abelian categories and the universal central extension condition

We study the relation between Bourn's notion of peri-abelian category and conditions involving the coincidence of the Smith, Huq and Higgins commutators. In particular we show that a semi-abelian category is peri-abelian if and only if for each normal subobject $K\leq X$, the Higgins commutator of $K$ with itself coincides with the normalisation of the Smith commutator of the denormalisation of $K$ with itself. We show that if a category is peri-abelian, then the condition (UCE), which was introduced and studied by Casas and the second author, holds for that category. In addition we show, using amongst other things a result by Cigoli, that all categories of interest in the sense of Orzech are peri-abelian and therefore satisfy the condition (UCE).Comment: 14 pages, final version accepted for publicatio

The determinants of structural change in the European Union : a new application of RAS

The paper considers a (static) portfolio system that satisfies adding-up contraints and the gross substitution theorem. The paper shows the relationship of the two conditions to the weak dominant diagonal property of the matrix of interest rate elasticities. This enables to investigate the impact of simultaneous changes in interest rates on the asset demands.

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

Unstable coronal loops : numerical simulations with predicted observational signatures

We present numerical studies of the nonlinear, resistive magnetohydrodynamic (MHD) evolution of coronal loops. For these simulations we assume that the loops carry no net current, as might be expected if the loop had evolved due to vortex flows. Furthermore the initial equilibrium is taken to be a cylindrical flux tube with line-tied ends. For a given amount of twist in the magnetic field it is well known that once such a loop exceeds a critical length it becomes unstableto ideal MHD instabilities. The early evolution of these instabilities generates large current concentrations. Firstly we show that these current concentrations are consistent with the formation of a current sheet. Magnetic reconnection can only occur in the vicinity of these current concentrations and we therefore couple the resistivity to the local current density. This has the advantage of avoiding resistive diffusion in regions where it should be negligible. We demonstrate the importance of this procedure by comparison with simulations based on a uniform resistivity. From our numerical experiments we are able to estimate some observational signatures for unstable coronal loops. These signatures include: the timescale of the loop brightening; the temperature increase; the energy released and the predicted observable flow speeds. Finally we discuss to what extent these observational signatures are consistent with the properties of transient brightening loops.Comment: 13 pages, 9 figure