The rhetoric of acculturation: When integration means assimilation

Viewing traditional acculturation literature through a social constructionist lens, the present paper identifies a number of limitations with this research. A discourse analytic approach to acculturation is offered as a means of addressing some of these issues. Drawing on examples taken from British print media debate surrounding the issue of faith schooling in the UK, an analysis is presented which illustrates the manner in which, though optimally positioned within acculturative moral hierarchies directed towards the legitimization of both pro- and anti-faith schooling debates, integration rhetoric often conceals the (re-)production of a more implicit assimilationism. Findings are discussed in terms of their implications for hegemonically structured acculturative power relations. This exploratory analysis provides the basis for reflection on the benefits of a discursive approach to acculturation. Moreover, the dependence of integrationist discourse on a series of socio-spatial resources is considered and, following on from Dixon and Durrheim's (2000) discursive re-conceptualization of place-identity, is taken to signify the need for a more environmentally 'grounded' approach to acculturation.

Lagrangian description of nonlinear dust-ion acoustic waves in dusty plasmas

An analytical model is presented for the description of nonlinear dust-ion-acoustic waves propagating in an unmagnetized, collisionless, three component plasma composed of electrons, ions and inertial dust grains. The formulation relies on a Lagrangian approach of the plasma fluid model. The modulational stability of the wave amplitude is investigated. Different types of localized envelope electrostatic excitations are shown to exist.

A Description of the Stationary Wave Pattern of a Marine Craft in Shallow and Intermediate Water Depths

A stationary phase model is used to study supercritical waves generated by high speed ferries. Some general relationships in terms of wave angle, propagation direction, dispersion relationship and depth wavelength relationship are explored and discussed. In particular, it is shown that the wave pattern generated by high speed craft at supercritical speeds depends mainly on the relationship of water depth and ship speed and that the wave patterns are similar in terms of location of crests and troughs for a given depth Froude number. In addition it is found that the far field wave pattern can be described adequately using a single moving point source. The theoretical model compares well with towing tank measurements and full scale data over a range of parameters and hull shapes. The paper also demonstrates that the far field wave pattern at supercritical speeds should be non-dimensionalised by water depth and not hull length unlike it is usually done for subcritical speeds.

Cohort description: The Northern Ireland Longitudinal Study (NILS)

no abstract available

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.