Phobic anxiety in 11 nations: Part I: Dimensional constancy of the five-factor model

The Fear Survey Schedule-III (FSS-III) was administered to a total of 5491 students in Australia, East Germany, Great Britain, Greece, Guatemala, Hungary, Italy, Japan, Spain, Sweden, and Venezuela, and submitted to the multiple group method of confirmatory analysis (MGM) in order to determine the cross-national dimensional constancy of the five-factor model of self-assessed fears originally established in Dutch, British, and Canadian samples. The model comprises fears of bodily injury-illness-death, agoraphobic fears, social fears, fears of sexual and aggressive scenes, and harmless animals fears. Close correspondence between the factors was demonstrated across national samples. In each country, the corresponding scales were internally consistent, were intercorrelated at magnitudes comparable to those yielded in the original samples, and yielded (in 93% of the total number of 55 comparisons) sex differences in line with the usual finding (higher scores for females). In each country, the relatively largest sex differences were obtained on harmless animals fears. The organization of self-assessed fears is sufficiently similar across nations to warrant the use of the same weight matrix (scoring key) for the FSS-III in the different countries and to make cross-national comparisons feasible. This opens the way to further studies that attempt to predict (on an a priori basis) cross-national variations in fear levels with dimensions of national cultures. (C) 2002 Elsevier Science Ltd. All rights reserved.

Quantum transport and integrability of the Anderson model for a quantum dot with multiple leads

We show that an Anderson Hamiltonian describing a quantum dot connected to multiple leads is integrable. A general expression for the nonlinear conductance is obtained by combining the Bethe ansatz exact solution with Landauer-Buttiker theory. In the Kondo regime, a closed form expression is given for the matrix conductance at zero temperature and when all the leads are close to the symmetric point. A bias-induced splitting of the Kondo resonance is possible for three or more leads. Specifically, for N leads, with each at a different chemical potential, there can be N-1 Kondo peaks in the conductance.

A population balance model for high shear granulation

In a previous paper, Hoornaert et al. (Powder Technol. 96 (1998); 116-128) presented data from granulation experiments performed in a 50 L Lodige high shear mixer. In this study that same data was simulated with a population balance model. Based on an analysis of the experimental data, the granulation process was divided into three separate stages: nucleation, induction, and coalescence growth. These three stages were then simulated separately, with promising results. it is possible to derive a kernel that fit both the induction and the coalescence growth stage. Modeling the nucleation stage proved to be more challenging due to the complex mechanism of nucleus formation. From this work some recommendations are made for the improvement of this type of model.

A cold model study of raceway hysteresis

The effect of a gas flow field on the size of raceway has been studied experimentally using a two-dimensional (2-D) cold model. It is observed that as the blast velocity from the tuyere increases, raceway size increases, and when the blast velocity is decreased from its highest value, raceway size does not change much until the velocity reaches a critical velocity. Below the critical velocity, raceway size decreases with decreasing velocity but is always larger than that for the same velocity when the velocity increased. This phenomenon is called raceway hysteresis. Raceway hysteresis has been studied in the presence of different gas flow rates and different particle densities. Raceway hysteresis has been observed in all the experiments. The effect of liquid flow, with various superficial velocities, on raceway hysteresis has also been studied. A study of raceway size hysteresis shows that interparticle and particle-wall friction have a very large effect on raceway size. A hypothesis has been proposed to describe the hysteresis phenomenon in the packed beds. The relevance of hysteresis to blast furnace raceways has been discussed. Existing literature correlations for raceway size ignore the frictional effects. Therefore, their applicability to the ironmaking blast furnace is questionable.

Ground water model calibration using pilot points and regularization

Use of nonlinear parameter estimation techniques is now commonplace in ground water model calibration. However, there is still ample room for further development of these techniques in order to enable them to extract more information from calibration datasets, to more thoroughly explore the uncertainty associated with model predictions, and to make them easier to implement in various modeling contexts. This paper describes the use of pilot points as a methodology for spatial hydraulic property characterization. When used in conjunction with nonlinear parameter estimation software that incorporates advanced regularization functionality (such as PEST), use of pilot points can add a great deal of flexibility to the calibration process at the same time as it makes this process easier to implement. Pilot points can be used either as a substitute for zones of piecewise parameter uniformity, or in conjunction with such zones. In either case, they allow the disposition of areas of high and low hydraulic property value to be inferred through the calibration process, without the need for the modeler to guess the geometry of such areas prior to estimating the parameters that pertain to them. Pilot points and regularization can also be used as an adjunct to geostatistically based stochastic parameterization methods. Using the techniques described herein, a series of hydraulic property fields can be generated, all of which recognize the stochastic characterization of an area at the same time that they satisfy the constraints imposed on hydraulic property values by the need to ensure that model outputs match field measurements. Model predictions can then be made using all of these fields as a mechanism for exploring predictive uncertainty.