838 resultados para Investment knowledge


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There is clearly contention over the shape and formation of science curriculum and over, ultimately, what will count as scientific knowledge, skill, capacity and world view. The Cold War set the policy context for an ongoing focus on science education across Western nations. Sputnik-era US and UK educational policy offered a broad premise for the purpose of school science: in a risky geopolitical environment, high levels of advanced scientific expertise were central to the national interest and necessary for the maintenance of military/industrial and technological power. Half a century on, in the context of global economic and environmental crisis, as a justification for digital, industrial and biomedical innovation, the rationale for the production of scientific capital is central to curriculum settlements and educational policy in Europe, Asia and the Americas.

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The topic of designers’ knowledge and how they conduct design process has been widely investigated in design research. Understanding theoretical and experiential knowledge in design has involved recognition of the importance of designers’ experience of experiencing, seeing, and absorbing ideas from the world as points of reference (or precedents) that are consulted whenever a design problem arises (Lawson, 2004). Hence, various types of design knowledge have been categorized (Lawson, 2004), and the nature of design knowledge continues to be studied (Cross, 2006); nevertheless, the study of the experiential aspects embedded in design knowledge is a topic not fully addressed. In particular there has been little emphasis on the investigation of the ways in which designers’ individual experience influences different types of design tasks. This research focuses on the investigation of the ways in which designers inform a usability design process. It aims to understand how designers design product usability, what informs their process, and the role their individual experience (and episodic knowledge) plays within the design process. This paper introduces initial outcomes from an empirical study involving observation of a design task that emphasized usability issues. It discusses the experiential knowledge observed in the visual representations (sketches) produced by designers as part of the design tasks. Through the use of visuals as means to represent experiential knowledge, this paper presents initial research outcomes to demonstrate how designers’ individual experience is integrated into design tasks and communicated within the design process. Initial outcomes demonstrate the influence of designers’ experience in the design of product usability. It is expected that outcomes will help identify the causal relationships between experience, context of use, and product usability, which will contribute to enhance our understanding about the design of user-product interactions.

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Contemporary debates on the role of journalism in society are continuing the tradition of downplaying the role of proactive journalism - generally situated under the catchphrase of the Fourth Estate - in public policy making. This paper puts the case for the retention of a notion of a proactive form of journalism which can be broadly described as "investigative ", because it is important to the public policy process in modern democracies. It argues that critiques that downplay the potential of this form of journalism are flawed and overly deterministic. Finally. it seeks to illustrate how journalists can proactively inquire in ways that are relevant to the lives ofpeople in a range of settings, and that question elite sources in the interests ofthose people.

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Information graphics have become increasingly important in representing, organising and analysing information in a technological age. In classroom contexts, information graphics are typically associated with graphs, maps and number lines. However, all students need to become competent with the broad range of graphics that they will encounter in mathematical situations. This paper provides a rationale for creating a test to measure students’ knowledge of graphics. This instrument can be used in mass testing and individual (in-depth) situations. Our analysis of the utility of this instrument informs policy and practice. The results provide an appreciation of the relative difficulty of different information graphics; and provide the capacity to benchmark information about students’ knowledge of graphics. The implications for practice include the need to support the development of students’ knowledge of graphics, the existence of gender differences, the role of cross-curriculum applications in learning about graphics, and the need to explicate the links among graphics.

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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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Review of Haunting the Knowledge Economy by Jane Kenway, Elizabeth Bullen, Johannah Fahey with Simon Robb. London: Routledge, 2006; for the journal of the Higher Education Research & Development Society of Australia.

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This thesis examines the changing relationships between television, politics, audiences and the public sphere. Premised on the notion that mediated politics is now understood “in new ways by new voices” (Jones, 2005: 4), and appropriating what McNair (2003) calls a “chaos theory” of journalism sociology, this thesis explores how two different contemporary Australian political television programs (Sunrise and The Chaser’s War on Everything) are viewed, understood, and used by audiences. In analysing these programs from textual, industry and audience perspectives, this thesis argues that journalism has been largely thought about in overly simplistic binary terms which have failed to reflect the reality of audiences’ news consumption patterns. The findings of this thesis suggest that both ‘soft’ infotainment (Sunrise) and ‘frivolous’ satire (The Chaser’s War on Everything) are used by audiences in intricate ways as sources of political information, and thus these TV programs (and those like them) should be seen as legitimate and valuable forms of public knowledge production. It therefore might be more worthwhile for scholars to think about, research and teach journalism in the plural: as a series of complementary or antagonistic journalisms, rather than as a single coherent entity.

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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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The K-Adv has been developed around the concept that it comprises an ICT enabling infrastructure that encompasses ICT hardware and software infrastructure facilities together with an enabling ICT support system; a leadership infrastructure support system that provides the vision for its implementation and the realisation capacity for the vision to be realised; and the necessary people infrastructure that includes the people capabilities and capacities supported by organisational processes that facilitates this resource to be mobilised.

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Australia’s civil infrastructure assets of roads, bridges, railways, buildings and other structures are worth billions of dollars. Road assets alone are valued at around A$ 140 billion. As the condition of assets deteriorate over time, close to A$10 billion is spent annually in asset maintenance on Australia's roads, or the equivalent of A$27 million per day. To effectively manage road infrastructures, firstly, road agencies need to optimise the expenditure for asset data collection, but at the same time, not jeopardise the reliability in using the optimised data to predict maintenance and rehabilitation costs. Secondly, road agencies need to accurately predict the deterioration rates of infrastructures to reflect local conditions so that the budget estimates could be accurately estimated. And finally, the prediction of budgets for maintenance and rehabilitation must provide a certain degree of reliability. A procedure for assessing investment decision for road asset management has been developed. The procedure includes: • A methodology for optimising asset data collection; • A methodology for calibrating deterioration prediction models; • A methodology for assessing risk-adjusted estimates for life-cycle cost estimates. • A decision framework in the form of risk map

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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.