Integrating health and sustainability in life cycle assessment

Until now health impact assessment and environmental impact assessment are two different issues, often not addressed together. Both issues have to be dealt with for sustainable building. The aim of this paper is to link healthy and sustainable housing in life cycle assessment. Two strategies are studied: clean air as a functional unity and health as a quality indicator. The strategies are illustrated with an example on the basis of Eco-Quantum, which is a Dutch whole-building assessment tool. It turns out that both strategies do not conflict with the LCA methodology. The LCA methodology has to be refined for this purpose.

Two main challenges in service description : Web service tunnel vision and semantic myopia

Through media such as newspapers, letterbox flyers, corporate brochures and television we are regularly confronted with descriptions for conventional (bricks 'n' mortar style) services. These representations vary in the terminology utilised, the depth of the description, the aspects of the service that are characterised and their applicability to candidate service requestors. Existing service catalogues (such as the Yellow Pages) provide little relief for service requestors from the burdensome task of discovering, comparing and substituting services. Add to this environment the rapidly evolving area of web services with its associated surfeit of standards, and the result is a considerably fragmented approach to the description of services. It leaves the reality of the Semantic Web somewhat clouded. --------- Let's consider service description briefly, before discussing our concerns with existing approaches to description. The act of describing is performed prior to advertising. This simple fact provides an interesting paradox as services cannot be described exactly before advertisement. This doesn't mean they can't be described comprehensively. By "exactly", we are referring to the fact that context provided by a service requestor (and their service needs) will alter the description of the service that is presented to the discoverer. For example, a service provider who operates a cinema wants to describe the price of their service. Let's say the advertised price is $15. They also want to state that a pensioner discount and a student discount is available which provides a 50% discount. A customer (i.e. service requestor) uses the cinema web site to purchase tickets online. They find the movie of their choice at a time that suits. However, its not until some context is provided by the requestor that the exact price is determined. The requestor might state that they are a pensioner. The same is applicable for a service requestor who purchases multiple tickets perhaps on behalf of other people. The disconnect between when the service is described and when a requestor provides context introduces challenges to the description process. A service provider would be ill-advised to offer independent descriptions that represent all the permutations possible for a single service. The descriptive effort would be prohibitive.

Particle and gaseous emissions from commercial aircraft at each stage of the landing and takeoff cycle

A novel technique was used to measure emission factors for commonly used commercial aircraft including a range of Boeing and Airbus airframes under real world conditions. Engine exhaust emission factors for particles in terms of particle number and mass (PM2.5), along with those for CO2, and NOx were measured for over 280 individual aircraft during the various modes of landing/takeoff (LTO) cycle. Results from this study show that particle number, and NOx emission factors are dependant on aircraft engine thrust level. Minimum and maximum emissions factors for particle number, PM2.5, and NOx emissions were found to be in the range of 4.16×1015-5.42×1016 kg-1, 0.03-0.72 g.kg-1, and 3.25-37.94 g.kg-1 respectively for all measured airframes and LTO cycle modes. Number size distributions of emitted particles for the naturally diluted aircraft plumes in each mode of LTO cycle showed that particles were predominantly in the range of 4 to 100 nm in diameter in all cases. In general, size distributions exhibit similar modality during all phases of the LTO cycle. A very distinct nucleation mode was observed in all particle size distributions, except for taxiing and landing of A320 aircraft. Accumulation modes were also observed in all particle size distributions. Analysis of aircraft engine emissions during LTO cycle showed that aircraft thrust level is considerably higher during taxiing than idling suggesting that International Civil Aviation Organization (ICAO) standards need to be modified as the thrust levels for taxi and idle are considered to be the same (7% of total thrust) [1].

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.

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.

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.