Internal cladding to north-east facade, Mooloomba House, Point Lookout, North Stradbroke Island

View of internal cladding to north-east facade as seen from dining studio.

Dining studio, Mooloomba House, Point Lookout, North Stradbroke Island

View through courtyard to dining studio as seen from upper living room.

Mooloomba House, Point Lookout, North Stradbroke Island

North elevation, deck below and belvedere above, as seen from path to beach.

Cladding detail, Mooloomba House, Point Lookout, North Stradbroke Island

Detail view of cladding to upper level dining studio.

The Coefficient of Variance as an index of L2 lexical processing skill

The Coefficient of Variance (mean standard deviation/mean Response time) is a measure of response time variability that corrects for differences in mean Response time (RT) (Segalowitz & Segalowitz, 1993). A positive correlation between decreasing mean RTs and CVs (rCV-RT) has been proposed as an indicator of L2 automaticity and more generally as an index of processing efficiency. The current study evaluates this claim by examining lexical decision performance by individuals from three levels of English proficiency (Intermediate ESL, Advanced ESL and L1 controls) on stimuli from four levels of item familiarity, as defined by frequency of occurrence. A three-phase model of skill development defined by changing rCV-RT.values was tested. Results showed that RTs and CVs systematically decreased as a function of increasing proficiency and frequency levels, with the rCV-RT serving as a stable indicator of individual differences in lexical decision performance. The rCV-RT and automaticity/restructuring account is discussed in light of the findings. The CV is also evaluated as a more general quantitative index of processing efficiency in the L2.

Eco-efficiency for the Dairy Processing Industry - Fact Sheet Sumary

This Toolkit was developed for the Australian dairy processing industry on behalf of Dairy Australia. At the conclusion of the project, industry participants gained exclusive access to a comprehensive Eco-Efficiency Manual, which outlined many of the opportunities available to the industry. Summary fact sheets were also prepared as publicly available resources and these are available for download below

Eco-efficiency for the Dairy Processing Industry

This manual has been developed to help the Australian dairy processing industry increase its competitiveness through increased awareness and uptake of eco-efficiency. The manual seeks to consolidate and build on existing knowledge, accumulated through projects and initiatives that the industry has previously undertaken to improve its use of raw materials and resources and reduce the generation of wastes. Where there is an existing comprehensive report or publication, the manual refers to this for further information. Eco-efficiency is about improving environmental performance to become more efficient and profitable. It is about producing more with less. It involves applying strategies that will not only ensure efficient use of resources and reduction in waste, but will also reduce costs. This chapter outlines the environmental challenges faced by Australian dairy processors. The manual explores opportunities for reducing environmental impacts in relation to water, energy, product yield, solid and liquid waste reduction and chemical use.

Data partitioning for parallel spatial join processing

The cost of spatial join processing can be very high because of the large sizes of spatial objects and the computation-intensive spatial operations. While parallel processing seems a natural solution to this problem, it is not clear how spatial data can be partitioned for this purpose. Various spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for spatial operation processing is proposed for parallel spatial join processing. Object duplication caused by multi-assignment in spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve spatial locality in task decomposition. We show in this paper that a near-optimal speedup can be achieved for parallel spatial join processing using our new algorithms.