Exploring the construction of social class in educational discourse: the rational order of the nation state versus global uncertainties

This article aims to create intellectual space in which issues of social inequality and education can be analyzed and discussed in relation to the multifaceted and multi-levelled complexities of the modern world. It is divided into three sections. Section One locates the concept of social class in the context of the modern nation state during the period after the Second World War. Focusing particularly on the impact of 'Fordism' on social organization and cultural relations, it revisits the articulation of social justice issues in the United Kingdom, and the structures put into place at the time to alleviate educational and social inequalities. Section Two problematizes the traditional concept of social class in relation to economic, technological and sociocultural changes that have taken place around the world since the mid-1980s. In particular, it charts some of the changes to the international labour market and global patterns of consumption, and their collective impact on the re-constitution of class boundaries in 'developed countries'. This is juxtaposed with some of the major social effects of neo-classical economic policies in recent years on the sociocultural base in developing countries. It discusses some of the ways these inequalities are reflected in education. Section Three explores tensions between the educational ideals of the 'knowledge economy' and the discursive range of social inequalities that are emerging within and beyond the nation state. Drawing on key motifs identified throughout, the article concludes with a reassessment of the concept of social class within the global cultural economy. This is discussed in relation to some of the major equity and human rights issues in education today.

Theoretical and experimental approaches towards the determination of solute effective diffusivities in foods

The aim of this review paper is to present experimental methodologies and the mathematical approaches used to determine effective diffusivities of solutes in food materials. The paper commences by describing the diffusion phenomena related to solute mass transfer in foods and effective diffusivities. It then focuses on the mathematical formulation for the calculation of effective diffusivities considering different diffusion models based on Fick's second law of diffusion. Finally, experimental considerations for effective diffusivity determination are elucidated primarily based on the acquirement of a series of solute content versus time curves appropriate to the equation model chosen. Different factors contributing to the determination of the effective diffusivities such as the structure of food material, temperature, diffusion solvent, agitation, sampling, concentration and different techniques used are considered. (c) 2005 Elsevier Inc. All rights reserved.

Post-genomics approaches towards monitoring changes within the microbial ecology of the gut

The human gut microbiota, comprising many hundreds of different microbial species, has closely co-evolved with its human host over the millennia. Diet has been a major driver of this co-evolution, in particular dietary non-digestible carbohydrates. This dietary fraction reaches the colon and becomes available for microbial fermentation, and it is in the colon that the great diversity of gut microorganisms resides. For the vast majority of our evolutionary history humans followed hunter-gatherer life-styles and consumed diets with many times more non-digestible carbohydrates, fiber and whole plant polyphenol rich foods than typical Western style diets today.

Microbiology of the human intestinal tract and approaches for its dietary modulation

Gut bacteria can be categorised as being either beneficial or potentially pathogenic due to their metabolic activities and fermentation end-products. Health-promoting effects of the microflora may include immunostimulation, improved digestion and absorption, vitamin synthesis, inhibition of the growth of potential pathogens and lowering of gas distension. Detrimental effects are carcinogen production, intestinal putrefaction, toxin production, diarrhoea/constipation and intestinal infections. Certain indigenous bacteria such as bifidobacteria and lactobacilli are considered to be examples of health-promoting constituents of the microflora. They may aid digestion of lactose in lactose-intolerant individuals, reduce diarrhoea, help resist infections and assist in inflammatory conditions. Probiotics, prebiotics and synbiotics are functional foods that fortify the lactate producing microflora of the human or animal gut.

Intelligent cores and intelligent agents: two approaches to achieve autonomy in swarm-array computing

How can a bridge be built between autonomic computing approaches and parallel computing systems? How can autonomic computing approaches be extended towards building reliable systems? How can existing technologies be merged to provide a solution for self-managing systems? The work reported in this paper aims to answer these questions by proposing Swarm-Array Computing, a novel technique inspired from swarm robotics and built on the foundations of autonomic and parallel computing paradigms. Two approaches based on intelligent cores and intelligent agents are proposed to achieve autonomy in parallel computing systems. The feasibility of the proposed approaches is validated on a multi-agent simulator.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.