Spaces of hybridity : creating a sense of belonging through spatial awareness

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.

The use of Creative Commons licensing to enable open access to public sector information and publicly funded research results : an overview of recent Australian developments

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.

Hierarchical models for 2D presence/absence data having ambiguous zeroes: With a biogeographical case study on dingo behaviour

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.

Central and peripheral oxygen transmissibility thresholds to avoid corneal swelling during open eye soft contact lens wear

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

Optimal track access rights allocation for agent negotiation in an open railway market

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.

Service bid comparisons by fuzzy ranking in open railway market timetabling

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.

Intelligent negotiation behaviour model for an open railway access market

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.

Conflict resolution through negotiation in a railway open access market : a multi-agent system approach

Open access reforms to railway regulations allow multiple train operators to provide rail services on a common infrastructure. As railway operations are now independently managed by different stakeholders, conflicts in operations may arise, and there have been attempts to derive an effective access charge regime so that these conflicts may be resolved. One approach is by direct negotiation between the infrastructure manager and the train service providers. Despite the substantial literature on the topic, few consider the benefits of employing computer simulation as an evaluation tool for railway operational activities such as access pricing. This article proposes a multi-agent system (MAS) framework for the railway open market and demonstrates its feasibility by modelling the negotiation between an infrastructure provider and a train service operator. Empirical results show that the model is capable of resolving operational conflicts according to market demand.

Feasibility of open markets for mainline railways in China

For decades, the development, construction, track ownership and operation of mainline railways in China have been overseen by the state-owned authorities. From mid-90’s, the mainline railway management has undergone revamps to revitalize the intra-modal competitiveness of railway transportation and to steer it toward the direction of modern business management. With the rapid economic growth; the large-scale expansion of the mainline network; and the increasing expectation on service, the mainline railways in China require further restructuring. Inevitably, a sustainable approach to ensure business viability and service quality in the next few decades is an imminent challenge. This paper reviews the operations and management of mainline railway in China and discusses the possibility of introducing open access market. Drawing the experiences on railway open markets outside China, the discussions include the need and feasibility of railway open market in China; and the suitability and limitations of different models. Particular considerations will be given to the unique characteristics of the mainline railways in China, where the developments across neighbouring regions are unbalanced; freight and passenger services are of similar demands; and the high-speed train operations are operated with low-speed ones in mixed traffic.

Application of reinforcement learning in an open railway access market price negotiation

In an open railway access market price negotiation, it is feasible to achieve higher cost recovery by applying the principles of price discrimination. The price negotiation can be modeled as an optimization problem of revenue intake. In this paper, we present the pricing negotiation based on reinforcement learning model. A negotiated-price setting technique based on agent learning is introduced, and the feasible applications of the proposed method for open railway access market simulation are discussed.