900 resultados para Universal tree
Resumo:
This article discusses the design of a memorial space of the Tree of Knowledge, located in Oak Street, Barcaldine, Queensland designed by Brian Hooper Architect and M3 Architecture. Features of the design include the dead tree trunk underneath pieces of timber hanging down to show the original size of the tree in the 1890s and the root ball visible under a glass floor.
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The Australian Government has committed $970 million over 5 years to fund the expansion of preschool education and has established a National Early Childhood Education Partnership Agreement with States and Territories to achieve universal preschool access by 2013. The Partnership Agreement acknowledges the role of State and Territory Government in preschool education, and different approaches to preschool provision. It also recognises differences in current preschool participation rates across states and territories. This paper offers snapshots of a number of different models of preschool provision, spanning traditional sessional approaches to some integrated and innovative approaches within the long day care context. The paper explores the newer long day care model and offers recommendations for the delivery of preschool education within this different context.
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This paper describes the approach taken to the clustering task at INEX 2009 by a group at the Queensland University of Technology. The Random Indexing (RI) K-tree has been used with a representation that is based on the semantic markup available in the INEX 2009 Wikipedia collection. The RI K-tree is a scalable approach to clustering large document collections. This approach has produced quality clustering when evaluated using two different methodologies.
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Habitat models are widely used in ecology, however there are relatively few studies of rare species, primarily because of a paucity of survey records and lack of robust means of assessing accuracy of modelled spatial predictions. We investigated the potential of compiled ecological data in developing habitat models for Macadamia integrifolia, a vulnerable mid-stratum tree endemic to lowland subtropical rainforests of southeast Queensland, Australia. We compared performance of two binomial models—Classification and Regression Trees (CART) and Generalised Additive Models (GAM)—with Maximum Entropy (MAXENT) models developed from (i) presence records and available absence data and (ii) developed using presence records and background data. The GAM model was the best performer across the range of evaluation measures employed, however all models were assessed as potentially useful for informing in situ conservation of M. integrifolia, A significant loss in the amount of M. integrifolia habitat has occurred (p < 0.05), with only 37% of former habitat (pre-clearing) remaining in 2003. Remnant patches are significantly smaller, have larger edge-to-area ratios and are more isolated from each other compared to pre-clearing configurations (p < 0.05). Whilst the network of suitable habitat patches is still largely intact, there are numerous smaller patches that are more isolated in the contemporary landscape compared with their connectedness before clearing. These results suggest that in situ conservation of M. integrifolia may be best achieved through a landscape approach that considers the relative contribution of small remnant habitat fragments to the species as a whole, as facilitating connectivity among the entire network of habitat patches.
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Digital collections are growing exponentially in size as the information age takes a firm grip on all aspects of society. As a result Information Retrieval (IR) has become an increasingly important area of research. It promises to provide new and more effective ways for users to find information relevant to their search intentions. Document clustering is one of the many tools in the IR toolbox and is far from being perfected. It groups documents that share common features. This grouping allows a user to quickly identify relevant information. If these groups are misleading then valuable information can accidentally be ignored. There- fore, the study and analysis of the quality of document clustering is important. With more and more digital information available, the performance of these algorithms is also of interest. An algorithm with a time complexity of O(n2) can quickly become impractical when clustering a corpus containing millions of documents. Therefore, the investigation of algorithms and data structures to perform clustering in an efficient manner is vital to its success as an IR tool. Document classification is another tool frequently used in the IR field. It predicts categories of new documents based on an existing database of (doc- ument, category) pairs. Support Vector Machines (SVM) have been found to be effective when classifying text documents. As the algorithms for classifica- tion are both efficient and of high quality, the largest gains can be made from improvements to representation. Document representations are vital for both clustering and classification. Representations exploit the content and structure of documents. Dimensionality reduction can improve the effectiveness of existing representations in terms of quality and run-time performance. Research into these areas is another way to improve the efficiency and quality of clustering and classification results. Evaluating document clustering is a difficult task. Intrinsic measures of quality such as distortion only indicate how well an algorithm minimised a sim- ilarity function in a particular vector space. Intrinsic comparisons are inherently limited by the given representation and are not comparable between different representations. Extrinsic measures of quality compare a clustering solution to a “ground truth” solution. This allows comparison between different approaches. As the “ground truth” is created by humans it can suffer from the fact that not every human interprets a topic in the same manner. Whether a document belongs to a particular topic or not can be subjective.
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Focuses on a study which introduced an iterative modeling method that combines properties of ordinary least squares (OLS) with hierarchical tree-based regression (HTBR) in transportation engineering. Information on OLS and HTBR; Comparison and contrasts of OLS and HTBR; Conclusions.
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High reliability of railway power systems is one of the essential criteria to ensure quality and cost-effectiveness of railway services. Evaluation of reliability at system level is essential for not only scheduling maintenance activities, but also identifying reliability-critical components. Various methods to compute reliability on individual components or regularly structured systems have been developed and proven to be effective. However, they are not adequate for evaluating complicated systems with numerous interconnected components, such as railway power systems, and locating the reliability critical components. Fault tree analysis (FTA) integrates the reliability of individual components into the overall system reliability through quantitative evaluation and identifies the critical components by minimum cut sets and sensitivity analysis. The paper presents the reliability evaluation of railway power systems by FTA and investigates the impact of maintenance activities on overall reliability. The applicability of the proposed methods is illustrated by case studies in AC railways.
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University students are a high risk population for mental health problems, yet few seek professional help when experiencing problems. This study explored the potential role of an online intervention for promoting wellbeing in university students, by investigating students' help-seeking behaviour, intention to use online interventions and student content preference for such interventions; 254 university students responded to an online survey designed for this study. As predicted, students were less likely to seek help as levels of psychological distress increased. Conversely, intention to use an online intervention increased at higher levels of distress, with 39.1%, 49.4% and 57.7% of low, moderate and severely distressed students respectively indicating they would use an online program supporting student well-being. Results suggest that online interventions may be a useful way to provide help to students in need who otherwise may not seek formal help.
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Fault tree analysis (FTA) is presented to model the reliability of a railway traction power system in this paper. First, the construction of fault tree is introduced to integrate components in traction power systems into a fault tree; then the binary decision diagram (BDD) method is used to evaluate fault trees qualitatively and quantitatively. The components contributing to the reliability of overall system are identified with their relative importance through sensitivity analysis. Finally, an AC traction power system is evaluated by the proposed methods.
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This paper reports on a unique approach to inclusive practice that responds to several critical issues highlighted in the first Universal Design (UD) Conference in Yokohama as well as in more recent literature on universal design. The approach, as explained in the paper, involves a not-for-profit community organization, university researchers, and a design action group comprising practitioners from across the design disciplines, together with other specialist consultants, developing a voluntary capacity an independent housing model for people with disabilities and their families or caretakers. With a focus on relationships and "a system that places human beings at the centre in all respects", this paper presents the approach and the ermerging theoretical framework which addresses three issues that afacan and Erbug (2009) propose hinder the integration of universal design with design practice, namely: theory-practice inconsistency involving the lack of flow-on of universal design awareness into design practice; a way of thinking that exhibits very little empathy with and understanding of the requirements of diverse users; and poorly implemented and coordinated collaboration and communication involving designers and other professionals (pp. 731 - 732).
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In this paper we present pyktree, an implementation of the K-tree algorithm in the Python programming language. The K-tree algorithm provides highly balanced search trees for vector quantization that scales up to very large data sets. Pyktree is highly modular and well suited for rapid-prototyping of novel distance measures and centroid representations. It is easy to install and provides a python package for library use as well as command line tools.