The effects of Australian tall poppy attitudes on American value based leadership theory

A survey study of twenty-two Australian CEOs and their subordinates assessed relationships between Australian leader motives, Australian value based leader behaviour, subordinate tall poppy attitudes and subordinate commitment, effectiveness, motivation and satisfaction (CEMS). On the whole, the results showed general support for value based leadership processes. Subsequent regression analyses of the second main component of Value Based Leadership Theory, value based leader behaviour, revealed that the collectivistic, inspirational, integrity and visionary behaviour sub-scales of the construct were positively related with subordinate CEMS. Although the hypothesis that subordinate tall poppy attitudes would moderate value based leadership processes was not clearly supported, subsequent regression analyses found that subordinate tall poppy attitudes were negatively related with perceptions of value based leader behaviour and CEMS. These findings suggest complex relationships between the three constructs, and the proposed model for the Australian context is accordingly amended. Overall, the research supports the need to consider cultural-specific attitudes in management development.

Trace Semantics for the Owicki-Gries Theory Integrated with the Progress Logic from UNITY

The theory of Owicki and Gries has been used as a platform for safety-based verifcation and derivation of concurrent programs. It has also been integrated with the progress logic of UNITY which has allowed newer techniques of progress-based verifcation and derivation to be developed. However, a theoretical basis for the integrated theory has thus far been missing. In this paper, we provide a theoretical background for the logic of Owicki and Gries integrated with the logic of progress from UNITY. An operational semantics for the new framework is provided which is used to prove soundness of the progress logic.

APSR Market Day

Notes for presentation

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

A hybrid planning method for transmission networks in a deregulated environment

The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open access schemes. The method starts with a candidate pool of feasible expansion plans. Consequent selection of the best candidates is carried out through a MOOP approach, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in context of deregulated system. Human knowledge has been applied in both stages to ensure the selection with practical engineering and management concerns. The expansion plan from MOOP is assessed by reliability criteria before it is finalized. The proposed method has been tested with the IEEE 14-bus system and relevant analyses and discussions have been presented.

Comparison of BR and QR Eigenvalue Algorithms for Power System Small Signal Stability Analysis

The BR algorithm is a novel and efficient method to find all eigenvalues of upper Hessenberg matrices and has never been applied to eigenanalysis for power system small signal stability. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement. The BR algorithm utilizes accelerating strategies to improve its performance when computing eigenvalues of narrowly banded, nearly tridiagonal upper Hessenberg matrices. These strategies significantly reduce the computation time at a reasonable level of precision. Compared with the QR algorithm, the BR algorithm requires fewer iteration steps and less storage space without depriving of appropriate precision in solving eigenvalue problems of large-scale power systems. Numerical examples demonstrate the efficiency of the BR algorithm in pursuing eigenanalysis tasks of 39-, 68-, 115-, 300-, and 600-bus systems. Experiment results suggest that the BR algorithm is a more efficient algorithm for large-scale power system small signal stability eigenanalysis.

Glow worms as a tourist attraction in Springbrook National Park: Visitor attitudes and economic issues

nsect-based tourism mainly caters to a niche market, but its popularity has been growing in recent years. Despite its popularity this form of tourism has remained under-researched and in a sense its contribution to the tourism industry has gone mostly unnoticed. This paper reports the results of a study undertaken on one form of popular insect-based tourism, namely glow worms. The study was undertaken in Springbrook National Park (Natural Bridge section) southeast Queensland, which has one of the largest glow worm colonies in Australia that attracts thousands of visitors each year. A study of this form of tourism is important and useful for several reasons. It is important to understand this hitherto under-studied tourism activity to determine the type of visitors, their socio-economic attributes, economic benefits to the local economy, visitors’ knowledge of glow worms, education imparted, visitor satisfaction of glow worm viewing and visitor attitudes for the introduction of a user fee system to view glow worms. An understanding of these issues could not only help to better manage this valuable biological resource, but can be used to develop the industry to cater to a growing number of visitors. Tourism in glow worms can potentially be used not only to educate the public on the threats affecting glow worms and their colonies, but could also be used to conserve them. Lessons learnt from glow worms as an attraction to Springbrook National Park can be used to better manage and further develop other existing and new glow worm sites in Australia and elsewhere for tourism. Furthermore, it could provide some guidance for the management and development of other forms of current insect-based tourism activities (eg. butterflies) and develop new tourism ventures based on species such as stick insects and jewel beetles for which Australia is well known (Reader’s Digest, 1997)