Amblyopia treatment : an evidence-based approach to maximising treatment outcome

The basis of treatment for amblyopia (poor vision due to abnormal visual experience early in life) for 250 years has been patching of the unaffected eye for extended times to ensure a period of use of the affected eye. Over the last decade randomised controlled treatment trials have provided some evidence on how to tailor amblyopia therapy more precisely to achieve the best visual outcome with the least negative impact on the patient and the family. This review highlights the expansion of knowledge regarding treatment for amblyopia and aims to provide optometrists with a summary of research evidence to enable them to better treat amblyopia. Treatment for amblyopia is effective, as it reduces overall prevalence and severity of visual loss in this population. Correction of refractive error alone significantly improves visual acuity, sometimes to the point where further amblyopia treatment is not required. Atropine penalisation and patch occlusion are effective in treating amblyopia. Lesser amounts of occlusion or penalisation have been found to be just as effective as greater amounts. Recent evidence has highlighted that occlusion or penalisation in amblyopia treatment can create negative changes in behaviour in children and impact on family life. These complications should be considere when prescribing treatment because they can negatively affect compliance. Studies investigating the maximum age at which treatment of amblyopia can still be effective and the importance of near activities during occlusion are ongoing.

Fallen Awake

Photo from process: David Megarrity, albury 2007 - example of convergence of writing/design/perfomance/video RESEARCH COMPONENT Fallen Awake was a practice-led research process that opened the development process to the influence of collaborative authorship across artforms. The project focused on of how multiple artforms and artists converge their vision into a singular text, in the context of collaborative authorship. The work also uncovered new questions relating to the dream-life of children. The stimulus for the work was a selection of verbal statements by three-year-olds, raising complex ethical questions as the project progressed about the child’s voice, mediated by the adult artist, for the eventual presentation to a child audience. With the text emergent and open to influence, this project raised other questions related to the lived experience of children, dreaming, creative play and the development of consciousness. It pushed the creative process to experiment with associative, rather then causal narratives, and to negotiate the challenges this raises for traditional story structures and the development processes that usually shape them. It led to the consideration of each artforms and artist as equal contributors in the development of story: traditionally the province of the sole author. The outcomes appeared in various artforms, none of which was live-performance based. An ‘artist’s book’ by the designer, a ‘video treatment’ - a DVD capturing the approach to the performance and a script for an innovative large-scale performance. Fallen Awake was developed with the assistance of Strut & Fret Production House, Arts Queensland, and HotHouse Theatre, Albury Wodonga, through their ‘Month in the Country’ initiative.

A framework for fully decentralised cycle stealing

Ordinary desktop computers continue to obtain ever more resources – in-creased processing power, memory, network speed and bandwidth – yet these resources spend much of their time underutilised. Cycle stealing frameworks harness these resources so they can be used for high-performance computing. Traditionally cycle stealing systems have used client-server based architectures which place significant limits on their ability to scale and the range of applica-tions they can support. By applying a fully decentralised network model to cycle stealing the limits of centralised models can be overcome. Using decentralised networks in this manner presents some difficulties which have not been encountered in their previous uses. Generally decentralised ap-plications do not require any significant fault tolerance guarantees. High-performance computing on the other hand requires very stringent guarantees to ensure correct results are obtained. Unfortunately mechanisms developed for traditional high-performance computing cannot be simply translated because of their reliance on a reliable storage mechanism. In the highly dynamic world of P2P computing this reliable storage is not available. As part of this research a fault tolerance system has been created which provides considerable reliability without the need for a persistent storage. As well as increased scalability, fully decentralised networks offer the ability for volunteers to communicate directly. This ability provides the possibility of supporting applications whose tasks require direct, message passing style communication. Previous cycle stealing systems have only supported embarrassingly parallel applications and applications with limited forms of communication so a new programming model has been developed which can support this style of communication within a cycle stealing context. In this thesis I present a fully decentralised cycle stealing framework. The framework addresses the problems of providing a reliable fault tolerance sys-tem and supporting direct communication between parallel tasks. The thesis includes a programming model for developing cycle stealing applications with direct inter-process communication and methods for optimising object locality on decentralised networks.

Springboards into design : exploring multiple representations of interaction in a dental surgery

This paper demonstrates that in order to understand and design for interactions in complex work environments, a variety of representational artefacts must be developed and employed. A study was undertaken to explore the design of better interaction technologies to support patient record keeping in a dental surgery. The domain chosen is a challenging real context that exhibits problems that could potentially be solved by ubiquitous computing and multi-modal interaction technologies. Both transient and durable representations were used to develop design understandings. We describe the representations, the kinds of insights developed from the representations and the way that the multiple representations interact and carry forward in the design process.

Marketplace forces and the history of retailing : The cycle of control

This paper traces the history of store (retailer-controlled) and national (manufacture controlled)brands; identifies the key historical characteristics of the past 200 years of marketing history;describes the four main time periods of U.S. retail marketing (1800 - 2000); and comments on the most likely developments within the current phases of brand marketing. Will the future focus on technology and new forms of communications? The Internet exemplifies an unconventional retailing environment, with etailer numbers growing rapidly. The central proposition of this paper is that a "cycle of control" - a pattern of marketing developments within the history of retailing and national marketing communications - Can indicate the success of marketing strategies in the future.

Too young to drink but old enough to drive under the influence : a study of underage offenders as seen in substance abuse treatment in Texas

Driving under the influence (DUI) is a major road safety problem. Historically, alcohol has been assumed to play a larger role in crashes and DUI education programs have reflected this assumption, although recent evidence suggests that younger drivers are becoming more likely to drive drugged than to drive drunk. This is a study of 7096 Texas clients under age 21 who were admitted to state-funded treatment programs between 1997 and 2007 with a past-year DUI arrest, DUI probation, or DUI referral. Data were obtained from the State’s administrative dataset. Multivariate logistic regressions models were used to understand the differences between those minors entering treatment as a DUI as compared to a non-DUI as well as the risks for completing treatment and for being abstinent in the month prior to follow-up. A major finding was that over time, the primary problem for underage DUI drivers changed from alcohol to marijuana. Being abstinent in the month prior to discharge, having a primary problem with alcohol rather than another drug, and having more family involved were the strongest predictors of treatment completion. Living in a household where the client was exposed to alcohol abuse or drug use, having been in residential treatment, and having more drug and alcohol and family problems were the strongest predictors of not being abstinent at follow-up. As a result, there is a need to direct more attention towards meeting the needs of the young DUI population through programs that address drug as well as alcohol consumption problems.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Improving the management of building life cycle : a data mining approach

The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.

Damage assessment in multiple-girder composite bridge using vibration characteristics

This paper uses dynamic computer simulation techniques to apply a procedure using vibration-based methods for damage assessment in multiple-girder composite bridge. In addition to changes in natural frequencies, this multi-criteria procedure incorporates two methods, namely the modal flexibility and the modal strain energy method. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on modal flexibility and modal strain energy change before and after damage are obtained and used as the indices for the assessment of structural health state. The feasibility and capability of the approach is demonstrated through numerical studies of proposed structure with six damage scenarios. It is concluded that the modal strain energy method is competent for application on multiple-girder composite bridge, as evidenced through the example treated in this paper.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

Negotiating multiple identities between school and the outside world : A critical discourse analysis

This article examines interview talk of three students in an Australian high school to show how they negotiate their young adult identities between school and the outside world. It draws on Bakhtin’s concepts of dialogism and heteroglossia to argue that identities are linguistically and corporeally constituted. A critical discourse analysis of segments of transcribed interviews and student-related public documents finds a mismatch between a social justice curriculum at school and its transfer into students’ accounts of outside school lived realities. The article concludes that a productive social justice pedagogy must use its key principles of (con)textual interrogation to engage students in reflexive practice about their positioning within and against discourses of social justice in their student and civic lives. An impending national curriculum must decide whether or not it negotiates the discursive divide any better.

Nation & internation practices in decision support tools in road asset management

This document provides a review of international and national practices in investment decision support tools in road asset management. Efforts were concentrated on identifying analytic frameworks, evaluation methodologies and criteria adopted by current tools. Emphasis was also given to how current approaches support Triple Bottom Line decision-making. Benefit Cost Analysis and Multiple Criteria Analysis are principle methodologies in supporting decision-making in Road Asset Management. The complexity of the applications shows significant differences in international practices. There is continuing discussion amongst practitioners and researchers regarding to which one is more appropriate in supporting decision-making. It is suggested that the two approaches should be regarded as complementary instead of competitive means. Multiple Criteria Analysis may be particularly helpful in early stages of project development, say strategic planning. Benefit Cost Analysis is used most widely for project prioritisation and selecting the final project from amongst a set of alternatives. Benefit Cost Analysis approach is useful tool for investment decision-making from an economic perspective. An extension of the approach, which includes social and environmental externalities, is currently used in supporting Triple Bottom Line decision-making in the road sector. However, efforts should be given to several issues in the applications. First of all, there is a need to reach a degree of commonality on considering social and environmental externalities, which may be achieved by aggregating the best practices. At different decision-making level, the detail of consideration of the externalities should be different. It is intended to develop a generic framework to coordinate the range of existing practices. The standard framework will also be helpful in reducing double counting, which appears in some current practices. Cautions should also be given to the methods of determining the value of social and environmental externalities. A number of methods, such as market price, resource costs and Willingness to Pay, are found in the review. The use of unreasonable monetisation methods in some cases has discredited Benefit Cost Analysis in the eyes of decision makers and the public. Some social externalities, such as employment and regional economic impacts, are generally omitted in current practices. This is due to the lack of information and credible models. It may be appropriate to consider these externalities in qualitative forms in a Multiple Criteria Analysis. Consensus has been reached in considering noise and air pollution in international practices. However, Australia practices generally omitted these externalities. Equity is an important consideration in Road Asset Management. The considerations are either between regions, or social groups, such as income, age, gender, disable, etc. In current practice, there is not a well developed quantitative measure for equity issues. More research is needed to target this issue. Although Multiple Criteria Analysis has been used for decades, there is not a generally accepted framework in the choice of modelling methods and various externalities. The result is that different analysts are unlikely to reach consistent conclusions about a policy measure. In current practices, some favour using methods which are able to prioritise alternatives, such as Goal Programming, Goal Achievement Matrix, Analytic Hierarchy Process. The others just present various impacts to decision-makers to characterise the projects. Weighting and scoring system are critical in most Multiple Criteria Analysis. However, the processes of assessing weights and scores were criticised as highly arbitrary and subjective. It is essential that the process should be as transparent as possible. Obtaining weights and scores by consulting local communities is a common practice, but is likely to result in bias towards local interests. Interactive approach has the advantage in helping decision-makers elaborating their preferences. However, computation burden may result in lose of interests of decision-makers during the solution process of a large-scale problem, say a large state road network. Current practices tend to use cardinal or ordinal scales in measure in non-monetised externalities. Distorted valuations can occur where variables measured in physical units, are converted to scales. For example, decibels of noise converts to a scale of -4 to +4 with a linear transformation, the difference between 3 and 4 represents a far greater increase in discomfort to people than the increase from 0 to 1. It is suggested to assign different weights to individual score. Due to overlapped goals, the problem of double counting also appears in some of Multiple Criteria Analysis. The situation can be improved by carefully selecting and defining investment goals and criteria. Other issues, such as the treatment of time effect, incorporating risk and uncertainty, have been given scant attention in current practices. This report suggested establishing a common analytic framework to deal with these issues.

Decision support tools for concrete infrastructure rehabilitation (using FRP composites)

This report presents a summary of the research conducted by the research team of the CRC project 2002-005-C, “Decision support tools for concrete infrastructure rehabilitation”. The project scope, objectives, significance and innovation and the research methodology is outlined in the introduction, which is followed by five chapters covering different aspects of the research completed. Major findings of a review of literature conducted covering both use of fibre reinforced polymer composites in rehabilitation of concrete bridge structures and decision support frameworks in civil infrastructure asset management is presented in chapter two. Case study of development of a strengthening scheme for the “Tenthill Creek bridge” is covered in the third chapter, which summarises the capacity assessment, traditional strengthening solution and the innovative solution using FRP composites. The fourth chapter presents the methodology for development of a user guide covering selection of materials, design and application of FRP in strengthening of concrete structures, which were demonstrated using design examples. Fifth chapter presents the methodology developed for evaluating whole of life cycle costing of treatment options for concrete bridge structures. The decision support software tool developed to compare different treatment options based on reliability based whole of life cycle costing will be briefly described in this chapter as well. The report concludes with a summary of findings and recommendations for future research.