Promoting sustainable urban development in fast growing city-regions : practices from Kuala Lumpur and Hong Kong

The Asia‐Pacific region is characterised by rapid population growth and urbanisation. These trends often result in an increasing consumption of land, which in turn lead to spatially expansive and discontinuous urban development. As a consequence, local communities and the environment face strong pressures. Many cities in the region have developed policies to tackle the issue of rapid growth and its associated consequences, for example climate change. The broad aim of this paper is to identify the nature, trends and strategies of growth management in major Asia‐Pacific city‐regions, and their implications for natural resource management and infrastructure provision. More specifically, this research seeks to provide insights on sustainable urban development practice, particularly on the promotion of compact urbanisation within the Asia‐Pacific’s fastest growing regions. The methodology of the paper includes a detailed literature review and a comparative analysis of existing strategies and policies. The literature review focuses on the key concepts related to sustainable urban growth management. It also includes existing applications of urban growth management approaches and planning information system in managing growth. Following the literature review, the paper undertakes a comparative analysis of the strategies of major Asia‐Pacific city‐regions of Kuala Lumpur and Hong Kong in terms of their approaches to sustainable urban development. The findings of the paper provide a clear understanding of the necessity of sustainable urban development practices. It contributes to the development of a substantial base for further research. Ultimately, this research aims to shed light on sustainable urban development by providing insights on the management of growth, natural resources and urban infrastructures.

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.

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.

The quality of interface design for educational courseware development in Malaysian educational context

Interactive educational courseware has been adopted in diverse education sectors such as primary, secondary, tertiary education, vocational and professional training. In Malaysian educational context, the ministry of education has implemented Smart School Project that aims to increase high level of academic achievement in primary and secondary schools by using interactive educational courseware. However, many researchers have reported that many coursewares fail to accommodate the learner and teacher needs. In particular, the interface design is not appropriately designed in terms of quality of learning. This paper reviews educational courseware development process in terms of defining quality of interface design and suggests a conceptual model of interface design through the integration of design components and interactive learning experience into the development process. As a result, it defines the concept of interactive learning experience in a more practical approach in order to implement each stage of the development process in a seamless and integrated way.

The Quality of Interface Design for Educational Courseware Development in Malaysian Educational Context

Interactive educational courseware has been adopted in diverse education sectors such as primary, secondary, tertiary education, vocational and professional training. In Malaysian educational context, the ministry of education has implemented Smart School Project that aims to increase high level of academic achievement in primary and secondary schools by using interactive educational courseware. However, many researchers have reported that many coursewares fail to accommodate the learner and teacher needs. In particular, the interface design is not appropriately designed in terms of quality of learning. This paper reviews educational courseware development process in terms of defining quality of interface design and suggests a conceptual model of interface design through the integration of design components and interactive learning experience into the development process. As a result, it defines the concept of interactive learning experience in a more practical approach in order to implement each stage of the development process in a seamless and integrated way.

Executive and organisational leadership development in universities : exploring key elements and perceptions

Organisational and leadership development is said to be one of the most challenging and important activities facing universities, particularly in the current environment of fast-paced change and accelerated age-related attrition. Succession leadership development being timely, the purpose of this study was to explore the nature of leadership development most suited to meeting the leadership and organisational development challenges for contemporary universities. A blend of literature-based and empirical research was undertaken. This resulted in seven papers submitted to internationally refereed journals; five papers published, one in press, and one under review. Six of these are sole authored papers and one is a co-authored paper. The papers identify some of the issues and challenges facing the tertiary sector. They shed light on factors influencing executive and organisational leadership development deriving from the literature review and from empirical research reporting the views of current university leaders. The papers and submission document herein include recommendations and suggested models informing executive and organisational leadership development in universities. The "Lantern" model - an Illuminated Model for Organisational Leadership Development - is a key original conceptual model framing the study.

Educational Development in Australasia : 2010 Benchmarking Survey

In 2010, the third bi‐annual ADAPE Australasian benchmarking study was conducted to track educational development in Australia and New Zealand. Invitations to participate were sent to ADAPE’s membership of 820. Non‐members were also welcome to participate. In total, 92% of the 250 survey respondents were members of ADAPE. The 2010 Benchmarking Survey supports and extends results from 2005 and 2008. The 2010 survey was developed by taking into account participant feedback from 2008. With a view to provide the key information that participants want to know, the 2010 survey included more questions about salaries and other employment conditions; marketing and communications, especially new electronic technologies; and major gifts.