Entrance, Agriculture and Entomology Building, Brisbane

View to entrance.

Stair, Agriculture and Entomology Building, Brisbane

View to circulation stair from exterior.

Entrance, Agriculture and Entomology Building, Brisbane

View to entrance.

Brick detail, Agriculture and Entomology Building, Brisbane

Detailed view of venting in brick work. Bricks used were commons, with prevalent dark blue iron staining.

Door and window detail, Agriculture and Entomology Building, Brisbane

View to side entrance opening and window detail with sliding timber shutters above, from exterior.

Agriculture and Entomology Building, Brisbane

View to north and east facing wings, along circulation path to the roof feature connection between wings.

Windows and venting to elevation, Agriculture and Entomology Building, Brisbane

View to elevation with timber sliding shutters, aluminium double hung sash windows and brick venting.

Agriculture and Entomology Building, Brisbane

View across car park to north-west elevation.

Stair exterior, Agriculture and Entomology Building, Brisbane

View to circulation stair, with venting detail, from exterior.

Assessing the competitiveness of loss allocation methods in a deregulated electricity market

This paper critically assesses several loss allocation methods based on the type of competition each method promotes. This understanding assists in determining which method will promote more efficient network operations when implemented in deregulated electricity industries. The methods addressed in this paper include the pro rata [1], proportional sharing [2], loss formula [3], incremental [4], and a new method proposed by the authors of this paper, which is loop-based [5]. These methods are tested on a modified Nordic 32-bus network, where different case studies of different operating points are investigated. The varying results obtained for each allocation method at different operating points make it possible to distinguish methods that promote unhealthy competition from those that encourage better system operation.

(FAB01_091) Book reviews

Leon Battista Alberti, 'On the Art of Building in Ten Books' Translated by Joseph Rykwert, Neil Leach and Robert Tavemor L. A. Zhadova (ed.), 'Tatlin' (Budapest 1984). English translation Helen Ross, 'Just For Living, Aboriginal Perceptions of Housing in North West Australia' Tony Fry, 'Design History Australia: A Source Text in Methods and Resources' Phillip Cox and David Moore, 'The Australian Functional Tradition' Lenore Coltheart and Don Fraser (eds.), 'Lamdmarks in Public Works, Engineers and Their Works in New South Wales 1884-1914' Peter Bridges and Don MacDonald, 'James Barnet, Colonial Architect' Don Watson and Judith McKay, 'A Directory of Queensland Architects to 1940' Russell Walden, 'Voices of Silence: New Zealand's Chapel of Futuna' Jeremy Salmond, 'Old New Zealand Houses 1800-1940' Victoria Middleton, 'The Legend of Green Valley' Dyranda Prevost and Ann Rado, 'Living Places' Mark Jackson and Mark Stiles (directors), 'Universal Provider' Lars Lerup, 'Planned Assaults'

Calculation of line integrals in boundary integral methods

We propose quadrature rules for the approximation of line integrals possessing logarithmic singularities and show their convergence. In some instances a superconvergence rate is demonstrated.

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.