952 resultados para Analytic number theory
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University of Queensland Working Papers in Linguistics is an opportunity to share and showcase ongoing research by staff, students, and associates of UQ’s Linguistics program, housed in the School of English, Media Studies, and Art History. This, the first volume, covers a number of topics ranging from formal syntactic theory to second language acquisition, and is representative of the broad spectrum of research that is carried out at The University of Queensland. While the papers herein represent works in progress, they have all been reviewed by two peer assessors, and revised in accordance with the assessors’ reports.
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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).
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General background is provided on the potential of tourism growth to contribute to economic development, paying particular attention to the experience of Sri Lanka. An overview of trends in inbound tourism to Sri Lanka is presented with particular consideration being given to its number of inbound tourist arrivals. Sri Lanka’s comparative position in international tourism markets, the composition of its tourist arrivals by area of origin, the extent of foreign earnings by the Sri Lankan tourism industry and variations in the amount of these earnings, and the extent of employment generation by this industry are examined. Regional aspects of the tourism industry in Sri Lanka are given special consideration, and this is followed by a report on the regional economic impact of tourism of Pinnawala Elephant Orphanage. This major tourist attraction near the edge of the Western Highlands of Sri Lanka is shown to make a significant contribution to economic decentralisation. A general discussion follows of tourism development in the Sri Lankan context. The main factors that have hindered tourism growth in Sri Lanka and its decentralisation are considered. Indications are that major impediment posed by civil disturbance and terrorism is at an end in Sri Lanka.
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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)
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Polytomous Item Response Theory Models provides a unified, comprehensive introduction to the range of polytomous models available within item response theory (IRT). It begins by outlining the primary structural distinction between the two major types of polytomous IRT models. This focuses on the two types of response probability that are unique to polytomous models and their associated response functions, which are modeled differently by the different types of IRT model. It describes, both conceptually and mathematically, the major specific polytomous models, including the Nominal Response Model, the Partial Credit Model, the Rating Scale model, and the Graded Response Model. Important variations, such as the Generalized Partial Credit Model are also described as are less common variations, such as the Rating Scale version of the Graded Response Model. Relationships among the models are also investigated and the operation of measurement information is described for each major model. Practical examples of major models using real data are provided, as is a chapter on choosing an appropriate model. Figures are used throughout to illustrate important elements as they are described.
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The Systems Theory Framework was developed to produce a metatheoretical framework through which the contribution of all theories to our understanding of career behaviour could be recognised. In addition it emphasises the individual as the site for the integration of theory and practice. Its utility has become more broadly acknowledged through its application to a range of cultural groups and settings, qualitative assessment processes, career counselling, and multicultural career counselling. For these reasons, the STF is a very valuable addition to the field of career theory. In viewing the field of career theory as a system, open to changes and developments from within itself and through constantly interrelating with other systems, the STF and this book is adding to the pattern of knowledge and relationships within the career field. The contents of this book will be integrated within the field as representative of a shift in understanding existing relationships within and between theories. In the same way, each reader will integrate the contents of the book within their existing views about the current state of career theory and within their current theory-practice relationship. This book should be required reading for anyone involved in career theory. It is also highly suitable as a text for an advanced career counselling or theory course.
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OctVCE is a cartesian cell CFD code produced especially for numerical simulations of shock and blast wave interactions with complex geometries, in particular, from explosions. Virtual Cell Embedding (VCE) was chosen as its cartesian cell kernel for its simplicity and sufficiency for practical engineering design problems. The code uses a finite-volume formulation of the unsteady Euler equations with a second order explicit Runge-Kutta Godonov (MUSCL) scheme. Gradients are calculated using a least-squares method with a minmod limiter. Flux solvers used are AUSM, AUSMDV and EFM. No fluid-structure coupling or chemical reactions are allowed, but gas models can be perfect gas and JWL or JWLB for the explosive products. This report also describes the code’s ‘octree’ mesh adaptive capability and point-inclusion query procedures for the VCE geometry engine. Finally, some space will also be devoted to describing code parallelization using the shared-memory OpenMP paradigm. The user manual to the code is to be found in the companion report 2007/13.
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Pulsed field gel electrophoresis of intact chromosomes of Babesia bovis revealed four chromosomes in the haploid genome. A telomere probe, derived from Plasmodium berghei, hybridised to eight SfiI restriction fragments of genomic B. bovis DNA digests indicating the presence of four chromosomes. A small subunit (18S) ribosomal RNA gene probe hybridised to the third chromosome only. The genome size of B. bovis is estimated to be 9.4 million base pairs. The sizes of chromosomes 1, 2, 3 and 4 are estimated to be 1.4, 2.0, 2.8 and 3.2 million base pairs, respectively. (C) 1997 Australian Society for Parasitology. Published by Elsevier Science Ltd.
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Potential errors in the application of mixture theory to the analysis of multiple-frequency bioelectrical impedance data for the determination of body fluid volumes are assessed. Potential sources of error include: conductive length; tissue fluid resistivity; body density; weight and technical errors of measurement. Inclusion of inaccurate estimates of body density and weight introduce errors of typically < +/-3% but incorrect assumptions regarding conductive length or fluid resistivities may each incur errors of up to 20%.