Non-invasive quantitative estimation of bone density in rats throughout the life cycle and in arthritic osteopenia: preliminary results

A longitudinal bone survey was conducted in 86 female Wistar rats in order to assess mineral density kinetics from young age (5 weeks: 115 g) till late adulthood (64 weeks: 586 g). In vivo quantitative radiographic scanning was performed on the caudal vertebrae, taking trabecular mass as the parameter. Measurements were expressed as Relative Optical Density (ROD) units by means of a high resolution densitometric device. Results showed a progressive increase in mineral density throughout the life cycle, with a tendency to level in the higher weight range, indicating that progressive mineral aposition occurs in rats in dependency of age. This phenomenon, however, should be always considered within the context of continuous skeletal growth and related changes typical of this species. Twelve different animals were also examined following induction of articular inflammation with Freund's adjuvant in six of them. Bone survey conducted 12 to 18 days after inoculation revealed a significant (P less than 0.01) reduction in trabecular bone mass of scanned vertebrae in comparison with the weight-matched untreated controls. It is concluded that the in vivo quantitative assessment of bone density illustrated in this report represents a sensitive and useful tool for the long-term survey of naturally occurring or experimentally induced bone changes. Scanning of the same part of the skeleton can be repeated, thereby avoiding sacrifice of the animal and time-consuming preparation of post-mortem material.

Life cycle costing procurement guide (interim).

"July 1970"--Cover.

A life cycle framework for governance of Intermodal terminals; planning, operations and strategic management.

This chapter establishes a framework for the governance of intermodal terminals throughout their life cycle, based on the product life cycle. The framework covers the initial planning by the public sector, the public/private split in funding and ownership, the selection of an operator, ensuring fair access to all users, and finally reconcessioning the terminal to a new operator, managing the handover and maintaining the terminal throughout its life cycle. This last point is especially important as industry conditions change and the terminal's role in the transport network comes under threat, either by a lack of demand or by increased demand requiring expansion, redesign and reinvestment. Each stage of the life cycle framework is operationalised based on empirical examples drawn from research by the authors on intermodal terminal planning and funding, the tender process and concession and operation contracts. In future the framework can be applied in additional international contexts to form a basis for transport cost analysis, logistics planning and government policy.

Life-cycle portfolio choice with liquid and illiquid financial assets

Traditionally, quantitative models that have studied households׳ portfolio choices have focused exclusively on the different risk properties of alternative financial assets. We introduce differences in liquidity across assets in the standard life-cycle model of portfolio choice. More precisely, in our model, stocks are subject to transaction costs, as considered in recent macroliterature. We show that when these costs are calibrated to match the observed infrequency of households׳ trading, the model is able to generate patterns of portfolio stock allocation over age and wealth that are constant or moderately increasing, thus more in line with the existing empirical evidence.

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.

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.

Challenges in residual service life assessment for refurbishment

A need for an efficient life care management of building portfolio is becoming increasingly due to increase in aging building infrastructure globally. Appropriate structural engineering practices along with facility management can assist in optimising the remaining life cycle costs for existing public building portfolio. A more precise decision to either demolish, refurbish, do nothing or rebuilt option for any typical building under investigation is needed. In order to achieve this, the status of health of the building needs to be assessed considering several aspects including economic and supply-demand considerations. An investment decision for a refurbishment project competing with other capital works and/or refurbishment projects can be supported by emerging methodology residual service life assessment. This paper discusses challenges in refurbishment projects of public buildings and with a view towards development of residual service life assessment methodology

Sustainability in road transport : an integrated life cycle analysis for estimating emissions

Despite of a significant contribution of transport sector in the global economy and society, it is one of the largest sources of global energy consumption, green house gas emissions and environmental pollutions. A complete look onto the whole life cycle environmental inventory of this sector will be helpful to generate a holistic understanding of contributory factors causing emissions. Previous studies were mainly based on segmental views which mostly compare environmental impacts of different modes of transport, but very few consider impacts other than the operational phase. Ignoring the impacts of non-operational phases, e.g., manufacture, construction, maintenance, may not accurately reflect total contributions on emissions. Moreover an integrated study for all motorized modes of road transport is also needed to achieve a holistic estimation. The objective of this study is to develop a component based life cycle inventory model which considers impacts of both operational and non-operational phases of the whole life as well as different transport modes. In particular, the whole life cycle of road transport has been segmented into vehicle, infrastructure, fuel and operational components and inventories have been conducted on each component. The inventory model has been demonstrated using the road transport of Singapore. Results show that total life cycle green house gas emissions from the road transport sector of Singapore is 7.8 million tons per year, among which operational phase and non-operational phases contribute about 55% and about 45%, respectively. Total amount of criteria air pollutants are 46, 8.5, 33.6, 13.6 and 2.6 thousand tons per year for CO, SO2, NOx, VOC and PM10, respectively. From the findings, it can be deduced that stringent government policies on emission control measures have a significant impact on reducing environmental pollutions. In combating global warming and environmental pollutions the promotion of public transport over private modes is an effective sustainable policy.

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Whole life cycle costing for Malaysia high-rise residential building : a needs?

The continuous growth of high-rise residential properties indicates that there is a need for an effective property management system to provide a sustainable high-rise residential property development. As intensive as these studies are, they do not attempt to investigate the correlation between property management systems with the trends of Malaysia high-rise residential property development. By examining the trends and scenario of Malaysia high-rise residential property development, this paper aims to gain an understanding of impacts from the effectiveness of property management in this scope area. Findings from this scoping paper will assist in providing a greater understanding and possible solutions for the current Malaysian property management systems for the expanding high-rise residential unit market.

An exploratory review of whole life cycle costing for Malaysia property development

The demand of implementing Whole Life Cycle Costing (WLCC) towards property development has increased since the launch of Value Management Circular 3/2009 by the Economic Planning Unit Malaysia. The circular made compulsory for all public construction projects and programme including property development estimated more than RM 50 Million to conduct WLCC. However, a knowledge gap on WLCC approach still exists and become a barrier among the practitioner in Malaysia particularly in property development industry. The main focus of this paper is to discuss the WLCC approach and introduce the indicator that need to be considered in Malaysia property development.