440 resultados para Sanskrit language, Buddhist Hybrid


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Most of the research into ELT has focused on its linguistic and methodological aspects, which are based on Western scientific traditions. The contributions and experiences of English language teachers themselves, especially their work in overseas contexts, have frequently been overlooked. This volume aims to document the complexity of ELT as ‘work’ in new global economic and cultural conditions, and to explore how this complexity is realised in the everyday experiences of ELT teachers. The development of ELT from the colonial experience to its current status as a global commodity is explored; ELT is then situated in the discourses of globalisation, specifically within Appadurai’s theorisation of global flows of people, images, ideas, technology and money, or scapes. Within this framework, narratives are constructed from the experiences of Native-speaking English teachers. These reveal much about the personal, pedagogical and cultural dimensions of ELT work in non-Centre countries, and will contribute to a greater understanding of the intercultural dimensions of ELT for all those who work in it, and in related educational fields.

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This chapter describes the nature of human language and introduces theories of language acquisition.

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This paper presents a Critical Discourse Analysis (CDA) of four policy documents currently offering ‘sets of possibilities’ for the teaching of English as an additional or second language (hereafter EAL/ESL) in senior classrooms in Queensland, Australia. The aim is to identify the ways in which each document re-presents the notion of critical literacy. Leximancer software, and Fairclough’s textually-oriented discourse analysis method (2001, 2003) are used to interrogate the relevant sections of the documents for the ways in which they re-present (sic) and construct the discourses around critical language study. This paper presents the description, interpretation and explanation of the discourses in these documents which constitute part of a larger project in which teacher interviews and classroom teaching are also investigated for the ways in which ‘the critical’ is constructed and contested in knowledge and practice.

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This thesis addresses computational challenges arising from Bayesian analysis of complex real-world problems. Many of the models and algorithms designed for such analysis are ‘hybrid’ in nature, in that they are a composition of components for which their individual properties may be easily described but the performance of the model or algorithm as a whole is less well understood. The aim of this research project is to after a better understanding of the performance of hybrid models and algorithms. The goal of this thesis is to analyse the computational aspects of hybrid models and hybrid algorithms in the Bayesian context. The first objective of the research focuses on computational aspects of hybrid models, notably a continuous finite mixture of t-distributions. In the mixture model, an inference of interest is the number of components, as this may relate to both the quality of model fit to data and the computational workload. The analysis of t-mixtures using Markov chain Monte Carlo (MCMC) is described and the model is compared to the Normal case based on the goodness of fit. Through simulation studies, it is demonstrated that the t-mixture model can be more flexible and more parsimonious in terms of number of components, particularly for skewed and heavytailed data. The study also reveals important computational issues associated with the use of t-mixtures, which have not been adequately considered in the literature. The second objective of the research focuses on computational aspects of hybrid algorithms for Bayesian analysis. Two approaches will be considered: a formal comparison of the performance of a range of hybrid algorithms and a theoretical investigation of the performance of one of these algorithms in high dimensions. For the first approach, the delayed rejection algorithm, the pinball sampler, the Metropolis adjusted Langevin algorithm, and the hybrid version of the population Monte Carlo (PMC) algorithm are selected as a set of examples of hybrid algorithms. Statistical literature shows how statistical efficiency is often the only criteria for an efficient algorithm. In this thesis the algorithms are also considered and compared from a more practical perspective. This extends to the study of how individual algorithms contribute to the overall efficiency of hybrid algorithms, and highlights weaknesses that may be introduced by the combination process of these components in a single algorithm. The second approach to considering computational aspects of hybrid algorithms involves an investigation of the performance of the PMC in high dimensions. It is well known that as a model becomes more complex, computation may become increasingly difficult in real time. In particular the importance sampling based algorithms, including the PMC, are known to be unstable in high dimensions. This thesis examines the PMC algorithm in a simplified setting, a single step of the general sampling, and explores a fundamental problem that occurs in applying importance sampling to a high-dimensional problem. The precision of the computed estimate from the simplified setting is measured by the asymptotic variance of the estimate under conditions on the importance function. Additionally, the exponential growth of the asymptotic variance with the dimension is demonstrated and we illustrates that the optimal covariance matrix for the importance function can be estimated in a special case.

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Competency in language and literacy are central to contemporary debates about education in Anglophone nations around the world. This paper suggests that such debates are informing not just educational policy but children’s literature itself as can be seen in Almond and McKean’s The Savage. This hybrid text combines prose and graphic narrative and narration in order to tell the story of Blue, a young British boy negotiating his identity in the aftermath of his father's death. While foregrounding a narrative of ideal masculinity, The Savage enacts and privileges a formal and thematic ideal of literacy as index of individual agency and development. Almond and McKean produce a politicised understanding of language and literacy that simultaneously positions The Savage in a textual tradition of socio-culturally disenfranchised youth, and intervenes in that tradition to (perhaps ironically) affirm the very conditions previously critiqued by that very tradition. Where earlier authors such as Barry Hines sought to challenge normative accounts of language and literacy in order to indict educational policy and praxes, Almond and McKean work to naturalise the very logics of education and agency by which their protagonist has been disenfranchised. In doing so, The Savage exemplifies current approaches to education which claim to value social and cultural diversity while imposing national standardised testing predicated on assumptions about the legitimacy of uniform standards and definitions of literacy.

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Web service composition is an important problem in web service based systems. It is about how to build a new value-added web service using existing web services. A web service may have many implementations, all of which have the same functionality, but may have different QoS values. Thus, a significant research problem in web service composition is how to select a web service implementation for each of the web services such that the composite web service gives the best overall performance. This is so-called optimal web service selection problem. There may be mutual constraints between some web service implementations. Sometimes when an implementation is selected for one web service, a particular implementation for another web service must be selected. This is so called dependency constraint. Sometimes when an implementation for one web service is selected, a set of implementations for another web service must be excluded in the web service composition. This is so called conflict constraint. Thus, the optimal web service selection is a typical constrained ombinatorial optimization problem from the computational point of view. This paper proposes a new hybrid genetic algorithm for the optimal web service selection problem. The hybrid genetic algorithm has been implemented and evaluated. The evaluation results have shown that the hybrid genetic algorithm outperforms other two existing genetic algorithms when the number of web services and the number of constraints are large.

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The highly unstructured nature of coral reef environments makes them difficult for current robotic vehicles to efficiently navigate. Typical research and commercial platforms have limited autonomy within these environments and generally require tethers and significant external infrastructure. This paper outlines the development of a new robotic vehicle for underwater monitoring and surveying in highly unstructured environments and presents experimental results illustrating the vehicle’s performance. The hybrid AUV design developed by the CSIRO robotic reef monitoring team realises a compromise between endurance, manoeuvrability and functionality. The vehicle represents a new era in AUV design specifically focused at providing a truly low-cost research capability that will progress environmental monitoring through unaided navigation, cooperative robotics, sensor network distribution and data harvesting.

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This paper presents the application of advanced optimization techniques to unmanned aerial system mission path planning system (MPPS) using multi-objective evolutionary algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA nondominated sorting genetic algorithm II and a hybrid-game strategy are implemented to produce a set of optimal collision-free trajectories in a three-dimensional environment. The resulting trajectories on a three-dimensional terrain are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different positions with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a hybrid-game strategy to a MOEA and for a MPPS.

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In this paper, the optimal design of an active flow control device; Shock Control Bump (SCB) on suction and pressure sides of transonic aerofoil to reduce transonic total drag is investigated. Two optimisation test cases are conducted using different advanced Evolutionary Algorithms (EAs); the first optimiser is the Hierarchical Asynchronous Parallel Evolutionary Algorithm (HAPMOEA) based on canonical Evolutionary Strategies (ES). The second optimiser is the HAPMOEA is hybridised with one of well-known Game Strategies; Nash-Game. Numerical results show that SCB significantly reduces the drag by 30% when compared to the baseline design. In addition, the use of a Nash-Game strategy as a pre-conditioner of global control saves computational cost up to 90% when compared to the first optimiser HAPMOEA.

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The paper compares three different methods of inclusion of current phasor measurements by phasor measurement units (PMUs) in the conventional power system state estimator. For each of the three methods, comprehensive formulation of the hybrid state estimator in the presence of conventional and PMU measurements is presented. The performance of the state estimator in the presence of conventional measurements and optimally placed PMUs is evaluated in terms of convergence characteristics and estimator accuracy. Test results on the IEEE 14-bus and IEEE 300-bus systems are analyzed to determine the best possible method of inclusion of PMU current phasor measurements.

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In cloud computing resource allocation and scheduling of multiple composite web services is an important challenge. This is especially so in a hybrid cloud where there may be some free resources available from private clouds but some fee-paying resources from public clouds. Meeting this challenge involves two classical computational problems. One is assigning resources to each of the tasks in the composite web service. The other is scheduling the allocated resources when each resource may be used by more than one task and may be needed at different points of time. In addition, we must consider Quality-of-Service issues, such as execution time and running costs. Existing approaches to resource allocation and scheduling in public clouds and grid computing are not applicable to this new problem. This paper presents a random-key genetic algorithm that solves new resource allocation and scheduling problem. Experimental results demonstrate the effectiveness and scalability of the algorithm.