993 resultados para Pattern Making


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This conference is a landmark gathering of those from around the world concerned with the future of Built environment education and Research. It takes place at a time of great change and opportunity. Around the world the long-standing principles of what, how and who we teach for graduate entry into Built environment professions, is increasingly under review. The need for research and the way in which it is funded, conducted and knowledge shared is also under increasing pressure. Both changes are being triggered by a fast changing and increasingly challenging competitive environment for education and research. Competition for the highest quality of graduate entrants in the right numbers is becoming more intense. Competition between Universities, as funding for education and research comes under ever close scrutiny, is intensifying and we are all being forced to look for more effective and exciting ways of recruting, retaining, enhancing and maximising the achievement of our students and of our staff in their research activities. Competition amongst employees in industry is becoming more intense as professional employers increasingly recognise that people and knowledge are their key strategic resources. Universities are increasingly looking to partnerships with industry, the professions and other Universities to further improve their eduacation, research and innovation activities. These challenges are unfolding at a time of accelerating development in information technologies and systems and in our understanding of principles of knowledge management and pedagogical advancement. This environment presents both opportunities and threats to the world of education.

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Knowledge cities are seen as fundamental to the economic growth and development of the 21st century cities. The purpose of this paper is to explore the knowledge city concept in depth. This paper discusses the principles of a knowledge city, and portrays its distinguishing characteristics and processes. The paper relates and analyses Melbourne’s experience as a knowledge city and scrutinises Melbourne’s initiatives on science, technology and innovation and policies for economic and social development. It also illustrates how the city administration played a key role in developing Melbourne as a globally recognised, entrepreneurial and competitive knowledge city. Then this paper identifies key success factors and provides some insights to policy makers of the MENA region cities in designing knowledge cities.

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The use of computing to support environmental planning and the development of land use models dates back to the late 1950s. The main thrust of computing applications, which by the early 1980s increasingly included the use of geospatial technologies, is their contribution to better planning and decision making. The computing tools and technologies are designed to enhance the planners’ capability to deal with complex environments and to plan for prosperous and livable communities. This paper examines the role of Information Technologies (IT) and particularly Internet Based Geographic Information Systems (Internet GIS) as spatial decision support systems to aid community based local decision making. The paper also covers the advantages and challenges of these internet based mapping applications and tools for collaborative decision making on the environment.

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Since the industrial revolution, our world has experienced rapid and unplanned industrialization and urbanization. As a result, we have had to cope with serious environmental challenges. In this context, explanation of how smart urban ecosystems can emerge, gains a crucial importance. Capacity building and community involvement have always been the key issues in achieving sustainable development and enhancing urban ecosystems. By considering these, this paper looks at new approaches to increase public awareness of environmental decision making. This paper will discuss the role of Information and Communication Technologies (ICT), particularly Web-based Geographic Information Systems (Web-based GIS) as spatial decision support systems to aid public participatory environmental decision making. The paper also explores the potential and constraints of these web-based tools for collaborative decision making.

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Our brief is to investigate the role of community and lifestyle in the making of a globally successful knowledge city region. Our approach is essentially pragmatic. We start by broadly examining knowledge-based urban development from a number of different perspectives. The first view is historical. In this context knowledge work and knowledge workers are seen as vital parts of a new emergent mode of production reliant on the continual production of abstract knowledge. We briefly develop this perspective to encompass the work of Richard Florida who has, notedly, claimed: “Wherever talent goes, innovation, creativity, and economic growth are sure to follow.” Our next perspective examines concepts of knowledge and modes of its production to discover knowledge is not an unchanging object but a human activity that changes in form and content through history. The suggestion emerges that not only is the production of contemporary ‘knowledge’ organised in a specific (and new) manner but also the output of this networked production is a particular type of knowledge (i.e. techné). The third perspective locates knowledge production and its workers in the contemporary urban context. As such, it co-ordinates the knowledge city in the increasingly global structure of cities and develops a typology of different groups of knowledge workers in their preferred urban environment(s). We see emerging here a distinctive geography of knowledge production. It is an urban phenomenon. There is, in short, something about the nature of cities that knowledge workers find particularly attractive. In the next, essentially anthropological, perspective we start to explore the needs and desires of the individual knowledge worker. Beyond the needs basic to any modern human household an attempt is made to deduce, from a base understanding of knowledge work as mental labour, the compensatory cultural needs of the knowledge worker when not at work - and the expression of these needs in the urban fabric. Our final perspective consists of two case studies. In a review of the experiences of Austin, Texas and Singapore’s one-north precinct we collect empirical data on, respectively, a knowledge city that has sustained itself for over 50 years and an urban precinct newly launched into the global market for knowledge work and knowledge workers. Interwoven The Role of Community and Lifestyle in the Making of a Knowledge City Urban Research Program 8 through all perspectives, in the form of apposite citation, is that of ‘expert opinion’ gathered in a rudimentary poll of academic and industry sources. This opinion appears in text boxes while details of the survey can be found in Appendix A. In the conclusion of the report we interpret the wide range of evidence gathered above in a policy frame. It is our hope this report will leave the reader with a clearer picture of the decisive organisational, infrastructural, aesthetic and social dimensions of a knowledge precinct.

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Cat’s claw creeper, Macfadyena unguis-cati (L.) Gentry (Bignoniaceae) is a major environmental weed of riparian areas, rainforest communities and remnant natural vegetation in coastal Queensland and New South Wales, Australia. In densely infested areas, it smothers standing vegetation, including large trees, and causes canopy collapse. Quantitative data on the ecology of this invasive vine are generally lacking. The present study examines the underground tuber traits of M. unguis-cati and explores their links with aboveground parameters at five infested sites spanning both riparian and inland vegetation. Tubers were abundant in terms of density (~1000 per m2), although small in size and low in level of interconnectivity. M. unguis-cati also exhibits multiple stems per plant. Of all traits screened, the link between stand (stem density) and tuber density was the most significant and yielded a promising bivariate relationship for the purposes of estimation, prediction and management of what lies beneath the soil surface of a given M. unguis-cati infestation site. The study also suggests that new recruitment is primarily from seeds, not from vegetative propagation as previously thought. The results highlight the need for future biological-control efforts to focus on introducing specialist seed- and pod-feeding insects to reduce seed-output.

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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.

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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.

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Qualitative research methods require transparency to ensure the ‘trustworthiness’ of the data analysis. The intricate processes of organizing, coding and analyzing the data are often rendered invisible in the presentation of the research findings, which requires a ‘leap of faith’ for the reader. Computer assisted data analysis software can be used to make the research process more transparent, without sacrificing rich, interpretive analysis by the researcher. This article describes in detail how one software package was used in a poststructural study to link and code multiple forms of data to four research questions for fine-grained analysis. This description will be useful for researchers seeking to use qualitative data analysis software as an analytic tool.

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Objectives The procurement research of Sydney Opera House FM Exemplar Project aims to develop innovative methods and guidelines for the procurement of FM services, applicable to iconic and / or performing arts centre facilities, or facilities with similar FM functions. The initial procurement report in June 2005 analysed the strategic objectives and operational requirements that provide ‘demand statements’ as evaluation criteria in the service procurement process. The subsequent interim procurement report in September 2005 discussed the elements contributing to the criteria for decision-making in the service procurement process. This procurement report concentrates on the research on procurement strategies and innovative methods using a case study approach. The objectives of this report are: • to investigate service procurement methods and process in iconic and/or performing arts centre facilities; • to showcase FM innovation in Sydney Opera House through a case study; • to establish a preliminary decision-making framework and guidelines for selection of appropriate FM procurement routes to provide a useful model for FM community. Findings Findings from this procurement research are presented as follows. • FM innovation and experience of Sydney Opera House • Innovative procurement methods and processes, drawn from a case study of Sydney Opera House as exemplar • An integrated performance framework to link maintenance service functions to high level organisational objective and strategies • Procurement methods and contract outcomes, focusing on building maintenance and cleaning services of Sydney Opera House • Multi-dimensional assessment of Service Providers • General decision-making strategies and guidelines for selection of appropriate FM procurement routes Further Research Whilst the Sydney Opera House case study emphasises the experience of Sydney Opera House, a study of procurement strategies and methods from published research and FM good practice will supply facilities managers with alternative procurement routes. Further research on the procurement theme will develop a final decision-making model for the procurement of FM services, drawn from the evaluation of the case study outcomes, as well as FM good practice and findings from current published research.

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A complete change of career forces a seismic shift in every aspect of your life. From day one, you have to face the loss of long held beliefs, behaviours, the known world of self, and security. We came from professions that themselves are poles apart, and many of the challenges we faced entering the profession were the same: juggling full-time work, part time study, and family commitmemts, taking a pay cut, and loss of social life. But over a short period of time we both transitioned to our new profession successfully. so what make our successful transition possible?

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The aim of this project is to develop a systematic investment decision-making framework for infrastructure asset management by incorporation economic justification, social and environmental consideration in the decision-making process. This project assesses the factors that are expected to provide significant impacts on the variability of expenditures. A procedure for assessing risk and reliability for project investment appraisals will be developed. The project investigates public perception, social and environmental impacts on road infrastructure investment. This research will contribute to the debate about how important social and environmental issues should be incorporated into the investment decision-making process for infrastructure asset management.

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This document provides the findings of an international review of investment decision-making practices in road asset management. Efforts were concentrated on identifying the strategic objectives of agencies in road asset management, establishing and understanding criteria different organisations adopted and ascertaining the exact methodologies used by different countries and international organisations. Road assets are powerful drivers of economic development and social equity. They also have significant impacts on the natural and man-made environment. The traditional definition of asset management is “A systematic process of maintaining, upgrading and operating physical assets cost effectively. It combines engineering principles with sound business practices and economic theory and it provides tools to facilitate a more organised, logical approach to decision-making” (US Dept. of Transportation, 1999). In recent years, the concept has been broadened to cover the complexity of decision making, based on a wider variety of policy considerations as well as social and environmental issues rather than is covered by Benefit-Cost analysis and pure technical considerations. Current international practices are summarised in table 2. It was evident that Engineering-economic analysis methods are well advanced to support decision-making. A range of tools available supports performance predicting of road assets and associated cost/benefit in technical context. The need for considering triple plus one bottom line of social, environmental and economic as well as political factors in decision-making is well understood by road agencies around the world. The techniques used to incorporate these however, are limited. Most countries adopt a scoring method, a goal achievement matrix or information collected from surveys. The greater uncertainty associated with these non-quantitative factors has generally not been taken into consideration. There is a gap between the capacities of the decision-making support systems and the requirements from decision-makers to make more rational and transparent decisions. The challenges faced in developing an integrated decision making framework are both procedural and conceptual. In operational terms, the framework should be easy to be understood and employed. In philosophical terms, the framework should be able to deal with challenging issues, such as uncertainty, time frame, network effects, model changes, while integrating cost and non-cost values into the evaluation. The choice of evaluation techniques depends on the feature of the problem at hand, on the aims of the analysis, and on the underlying information base At different management levels, the complexity in considering social, environmental, economic and political factor in decision-making is different. At higher the strategic planning level, more non-cost factors are involved. The complexity also varies based on the scope of the investment proposals. Road agencies traditionally place less emphasis on evaluation of maintenance works. In some cases, social equity, safety, environmental issues have been used in maintenance project selection. However, there is not a common base for the applications.

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This document provides the findings of a national review of investment decision-making practices in road asset management. Efforts were concentrated on identifying the strategic objectives of agencies in road asset management, establishing and understanding criteria different organisations adopted and ascertaining the exact methodologies used by different sate road authorities. The investment objectives of Australian road authorities are based on triple-bottom line considerations (social, environmental, economic and political). In some cases, comparing with some social considerations, such as regional economic development, equity, and access to pubic service etc., Benefit-Cost Ratio has limited influence on the decision-making. Australian road authorities have developed various decision support tools. Although Multi-Criteria Analysis has been preliminarily used in case by case study, pavement management systems, which are primarily based on Benefit Cost Analysis, are still the main decision support tool. This situation is not compatible with the triple-bottom line objectives. There is need to fill the gap between decision support tools and decision-making itself. Different decision criteria should be adopted based on the contents of the work. Additional decision criteria, which are able to address social, environmental and political impacts, are needed to develop or identify. Environmental issue plays a more and more important role in decision-making. However, the criteria and respective weights in decision-making process are yet to be clearly identified. Social and political impacts resulted from road infrastructure investment can be identified through Community Perceptions Survey. With accumulative data, prediction models, which are similar as pavement performance models, can be established. Using these models, the decision-makers are able to foresee the social and political consequences of investment alternatives.