278 resultados para Baire-Open Maps
Resumo:
This paper argues a model of complex system design for sustainable architecture within a framework of entropy evolution. The spectrum of sustainable architecture consists of the efficient use of energy and material resource in life-cycle of buildings, the active involvement of the occupants in micro-climate control within buildings, and the natural environmental context. The interactions of the parameters compose a complex system of sustainable architectural design, of which the conventional linear and fragmented design technologies are insufficient to indicate holistic and ongoing environmental performance. The complexity theory of dissipative structure states a microscopic formulation of open system evolution, which provides a system design framework for the evolution of building environmental performance towards an optimization of sustainability in architecture.
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The standard Blanchard-Quah (BQ) decomposition forces aggregate demand and supply shocks to be orthogonal. However, this assumption is problematic for a nation with an inflation target. The very notion of inflation targeting means that monetary policy reacts to changes in aggregate supply. This paper employs a modification of the BQ procedure that allows for correlated shifts in aggregate supply and demand. It is found that shocks to Australian aggregate demand and supply are highly correlated. The estimated shifts in the aggregate demand and supply curves are then used to measure the effects of inflation targeting on the Australian inflation rate and level of GDP.
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The activities of governments, by their very nature, involve interactions with a broad array of public and private sector entities, from other governments, to business, academia and individual citizens. In the current era, there is a growing expectation that government programs and services will be delivered in a ‘simple, seamless and connected’ manner,1 leading to increased efficiency in government operations and improved service delivery.2 Achieving ‘collaborative, effective and efficient government and the delivery of seamless government services’ requires the implementation of interoperable technologies and procedures.3 Standards, which aim to enable organisations, platforms and systems to work with each other, are fundamental to interoperability.
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When the colonisers first came to Australia there was an urgent desire to map, name and settle. This desire, in part, stemmed from a fear of the unknown. Once these tasks were completed it was thought that a sense of identity and belonging would automatically come. In Anglo-Australian geography the map of Australia was always perceived in relationship to the larger map of Europe and Britain. The quicker Australia could be mapped the quicker its connection with the ‘civilised’ world could be established. Official maps could be taken up in official history books and a detailed monumental history could begin. Australians would feel secure in where they were placed in the world. However, this was not the case and anxieties about identity and belonging remained. One of the biggest hurdles was the fear of the open spaces and not knowing how to move across the land. Attempts to transpose colonisers’ use of space onto the Australian landscape did not work and led to confusion. Using authors who are often perceived as writers of national fictions (Henry Lawson, Barbara Baynton, Patrick White, David Malouf and Peter Carey) I will reveal how writing about space becomes a way to create a sense of belonging. It is through spatial knowledge and its application that we begin to gain a sense of closeness and identity. I will also look at how one of the greatest fears for the colonisers was the Aboriginal spatial command of the country. Aborigines already had a strongly developed awareness of spatial belonging and their stories reveal this authority (seen in the work of Lorna Little, Mick McLean) Colonisers attempted to discredit this knowledge but the stories and the land continue to recognise its legitimacy. From its beginning Australian spaces have been spaces of hybridity and the more the colonisers attempted to force predetermined structures onto these spaces the more hybrid they became.
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This chapter provides an account of the use of Creative Commons (CC) licensing as a legally and operationally effective means by which governments can implement systems to enable open access to and reuse of their public sector information (PSI). It describes the experience of governments in Australia in applying CC licences to PSI in a context where a vast range of material and information produced, collected, commissioned of funded by government is subject to copyright. By applying CC licences, governments can give effect to their open access policies and create a public domain of PSI which is available for resue by other governmental agencies and the community at large.
Resumo:
This dissertation is primarily an applied statistical modelling investigation, motivated by a case study comprising real data and real questions. Theoretical questions on modelling and computation of normalization constants arose from pursuit of these data analytic questions. The essence of the thesis can be described as follows. Consider binary data observed on a two-dimensional lattice. A common problem with such data is the ambiguity of zeroes recorded. These may represent zero response given some threshold (presence) or that the threshold has not been triggered (absence). Suppose that the researcher wishes to estimate the effects of covariates on the binary responses, whilst taking into account underlying spatial variation, which is itself of some interest. This situation arises in many contexts and the dingo, cypress and toad case studies described in the motivation chapter are examples of this. Two main approaches to modelling and inference are investigated in this thesis. The first is frequentist and based on generalized linear models, with spatial variation modelled by using a block structure or by smoothing the residuals spatially. The EM algorithm can be used to obtain point estimates, coupled with bootstrapping or asymptotic MLE estimates for standard errors. The second approach is Bayesian and based on a three- or four-tier hierarchical model, comprising a logistic regression with covariates for the data layer, a binary Markov Random field (MRF) for the underlying spatial process, and suitable priors for parameters in these main models. The three-parameter autologistic model is a particular MRF of interest. Markov chain Monte Carlo (MCMC) methods comprising hybrid Metropolis/Gibbs samplers is suitable for computation in this situation. Model performance can be gauged by MCMC diagnostics. Model choice can be assessed by incorporating another tier in the modelling hierarchy. This requires evaluation of a normalization constant, a notoriously difficult problem. Difficulty with estimating the normalization constant for the MRF can be overcome by using a path integral approach, although this is a highly computationally intensive method. Different methods of estimating ratios of normalization constants (N Cs) are investigated, including importance sampling Monte Carlo (ISMC), dependent Monte Carlo based on MCMC simulations (MCMC), and reverse logistic regression (RLR). I develop an idea present though not fully developed in the literature, and propose the Integrated mean canonical statistic (IMCS) method for estimating log NC ratios for binary MRFs. The IMCS method falls within the framework of the newly identified path sampling methods of Gelman & Meng (1998) and outperforms ISMC, MCMC and RLR. It also does not rely on simplifying assumptions, such as ignoring spatio-temporal dependence in the process. A thorough investigation is made of the application of IMCS to the three-parameter Autologistic model. This work introduces background computations required for the full implementation of the four-tier model in Chapter 7. Two different extensions of the three-tier model to a four-tier version are investigated. The first extension incorporates temporal dependence in the underlying spatio-temporal process. The second extensions allows the successes and failures in the data layer to depend on time. The MCMC computational method is extended to incorporate the extra layer. A major contribution of the thesis is the development of a fully Bayesian approach to inference for these hierarchical models for the first time. Note: The author of this thesis has agreed to make it open access but invites people downloading the thesis to send her an email via the 'Contact Author' function.
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This study was designed to derive central and peripheral oxygen transmissibility (Dk/t) thresholds for soft contact lenses to avoid hypoxia-induced corneal swelling (increased corneal thickness) during open eye wear. Central and peripheral corneal thicknesses were measured in a masked and randomized fashion for the left eye of each of seven subjects before and after 3 h of afternoon wear of five conventional hydrogel and silicone hydrogel contact lens types offering a range of Dk/t from 2.4 units to 115.3 units. Curve fitting for plots of change in corneal thickness versus central and peripheral Dk/t found threshold values of 19.8 and 32.6 units to avoid corneal swelling during open eye contact lens wear for a typical wearer. Although some conventional hydrogel soft lenses are able to achieve this criterion for either central or peripheral lens areas (depending on lens power), in general, no conventional hydrogel soft lenses meet both the central and peripheral thresholds. Silicone hydrogel contact lenses typically meet both the central and peripheral thresholds and use of these lenses therefore avoids swelling in all regions of the cornea. ' 2009 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 92B: 361–365, 2010
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In open railway access markets, a train service provider (TSP) negotiates with an infrastructure provider (IP) for track access rights. This negotiation has been modeled by a multi-agent system (MAS) in which the IP and TSP are represented by separate software agents. One task of the IP agent is to generate feasible (and preferably optimal) track access rights, subject to the constraints submitted by the TSP agent. This paper formulates an IP-TSP transaction and proposes a branch-and-bound algorithm for the IP agent to identify the optimal track access rights. Empirical simulation results show that the model is able to emulate rational agent behaviors. The simulation results also show good consistency between timetables attained from the proposed methods and those derived by the scheduling principles adopted in practice.
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In an open railway access market, the Infrastructure Provider (IP), upon the receipts of service bids from the Train Service Providers (TSPs), assigns track access rights according to its own business objectives and the merits of the bids; and produces the train service timetable through negotiations. In practice, IP chooses to negotiate with the TSPs one by one in such a sequence that IP optimizes its objectives. The TSP bids are usually very complicated, containing a large number of parameters in different natures. It is a difficult task even for an expert to give a priority sequence for negotiations from the contents of the bids. This study proposes the application of fuzzy ranking method to compare and prioritize the TSP bids in order to produce a negotiation sequence. The results of this study allow investigations on the behaviors of the stakeholders in bid preparation and negotiation, as well as evaluation of service quality in the open railway market.
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In an open railway access market, the provisions of railway infrastructures and train services are separated and independent. Negotiations between the track owner and train service providers are thus required for the allocation of the track capacity and the formulation of the services timetables, in which each party, i.e. a stakeholder, exhibits intelligence from the previous negotiation experience to obtain the favourable terms and conditions for the track access. In order to analyse the realistic interacting behaviour among the stakeholders in the open railway access market schedule negotiations, intelligent learning capability should be included in the behaviour modelling. This paper presents a reinforcement learning approach on modelling the intelligent negotiation behaviour. The effectiveness of incorporating learning capability in the stakeholder negotiation behaviour is then demonstrated through simulation.