Beauty and the market: Actress postcards and their senders in early twentieth century Australia

A hundred years ago the international craze for picture postcards distributed millions of images of popular stage actresses around the world. The cards were bought, sent, and collected by many whose contact with live theatre was sometimes minimal. Veronica Kelly's study of some of these cards sent in Australia indicates the increasing reach of theatrical images and celebrity brought about by the distribution mechanisms of industrial mass modernity. The specific social purposes and contexts of the senders are revealed by cross-reading the images themselves with the private messages on the backs, suggesting that, once outside the industrial framing of theatre or the dramatic one of specific roles, the actress operated as a multiply signifying icon within mass culture – with the desires and consumer power of women major factors in the consumption of the glamour actress card. A study of the typical visual rhetoric of these postcards indicates the authorized modes of femininity being constructed by the major postcard publishers whose products were distributed to theatre fans and non-theatregoers alike through the post. Veronica Kelly is working on a project dealing with commercial managements and stars in early twentieth-century Australian theatre. She teaches in the School of English, Media Studies, and Art History at the University of Queensland, is co-editor of Australasian Drama Studies, and author of databases and articles dealing with colonial and contemporary Australian theatre history and dramatic criticism. Her books include The Theatre of Louis Nowra (1998) and the collection Our Australian Theatre in the 1990s (1998).

Modeling of adsorption on nongraphitized carbon surface: GCMC simulation studies and comparison with experimental data

We model nongraphitized carbon black surfaces and investigate adsorption of argon on these surfaces by using the grand canonical Monte Carlo simulation. In this model, the nongraphitized surface is modeled as a stack of graphene layers with some carbon atoms of the top graphene layer being randomly removed. The percentage of the surface carbon atoms being removed and the effective size of the defect ( created by the removal) are the key parameters to characterize the nongraphitized surface. The patterns of adsorption isotherm and isosteric heat are particularly studied, as a function of these surface parameters as well as pressure and temperature. It is shown that the adsorption isotherm shows a steplike behavior on a perfect graphite surface and becomes smoother on nongraphitized surfaces. Regarding the isosteric heat versus loading, we observe for the case of graphitized thermal carbon black the increase of heat in the submonolayer coverage and then a sharp decline in the heat when the second layer is starting to form, beyond which it increases slightly. On the other hand, the isosteric heat versus loading for a highly nongraphitized surface shows a general decline with respect to loading, which is due to the energetic heterogeneity of the surface. It is only when the fluid-fluid interaction is greater than the surface energetic factor that we see a minimum-maximum in the isosteric heat versus loading. These simulation results of isosteric heat agree well with the experimental results of graphitization of Spheron 6 (Polley, M. H.; Schaeffer, W. D.; Smith, W. R. J. Phys. Chem. 1953, 57, 469; Beebe, R. A.; Young, D. M. J. Phys. Chem. 1954, 58, 93). Adsorption isotherms and isosteric heat in pores whose walls have defects are also studied from the simulation, and the pattern of isotherm and isosteric heat could be used to identify the fingerprint of the surface.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results We show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (