The building and construction product system : the emergence of new innovative businesses

This report examines the conditions surrounding the emergence and growth of innovative new firms in the building and construction product system. The focus on innovative new firms was prompted by diverse literatures which highlight the pivotal roles of innovation and new business activity in driving economic growth. 9 The literature concludes that the founders of new firms are likely to be innovative and that new firms are likely to promote industry innovation and efficiency. New businesses have also been found to have positive employment impacts and to grow faster than established firms.

Mapping the building and construction product system : preliminary report

This Preliminary Report has been prepared by researchers at The Australian Expert Group in Industry Studies (AEGIS) for the Commonwealth Department of Industry, Science and Resources. It is intended to provide a preliminary 'product system map' of the building and construction industries which defines the system, identifies the major segments, describes key industry players and institutions and provides the basis for exploring relationships, innovation and information flows within the industries. This Preliminary Report is the first of a series of five which will explore the building and construction product system in some depth. This first report does not present original research, although it does include some new interview data and analysis of a variety of written sources. This report is rather a reformulation of existing statistical and analytical material from a product system-based perspective. It is intended to provide the basis for subsequent studies by putting what is already known into an alternative framework and allowing us to see it through a new lens.

The dynamics and performance of the building and construction product system

During 1999 the Department of Industry, Science and Resources (ISR) published 4 research reports it had commissioned from the Australian Expert Group in Industry Studies (AEGIS), a research centre of the University of Western Sydney, Macarthur. ISR will shortly publish the fifth and final report in this series. The five reports were commissioned by the Department, as part of the Building and Construction Action Agenda process, to investigate the dynamics and performance of the sector, particularly in relation its innovative capacity. Professor Jane Marceau, PVCR at the University of Western Sydney and Director of AEGIS, led the research team. Dr Karen Manley was the researcher and joint author on three of the five reports. This paper outlines the approach and key findings of each of the five reports. The reports examined 5 key elements of the ‘building and construction product system’. The term ‘product system’ reflects the very broad range of industries and players we consider to contribute to the performance of the building and construction industries. The term ‘product system’ also highlights our focus on the systemic qualities of the building and construction industries. We were most interested in the inter-relationships between key segments and players and how these impacted on the innovation potential of the product system. The ‘building and construction product system’ is hereafter referred to as ‘the industry’ for ease of presentation. All the reports are based, at least in part, on an interviewing or survey research phase which involved gathering data from public and private sector players nationally. The first report ‘maps’ the industry to identify and describe its key elements and the inter-relationships between them. The second report focuses specifically on the linkages between public-sector research organisations and firms in the industry. The third report examines the conditions surrounding the emergence of new businesses in the industry. The fourth report examines how manufacturing businesses are responding to customer demands for ‘total solutions’ to their building and construction needs, by providing various services to clients. The fifth report investigates the capacity of the industry to encourage and undertake energy efficient building design and construction.

Leveraging R&D to advance digital modelling practice in Australian construction

This paper describes a lead project currently underway through Australia’s Sustainable Built Environment National Research Centre, evaluating impacts, diffusion mechanisms and uptake of R&D in the Australian building and construction industry. Building on a retrospective analysis of R&D trends and industry outcomes, a future-focused industry roadmap will be developed to inform R&D policies more attuned to future industry needs to improve investment effectiveness. In particular, this research will evaluate national R&D efforts to develop, test and implement advanced digital modelling technologies into the design/construction/asset management cycle. This research will build new understandings and knowledge relevant to R&D funding strategies, research team formation and management (with involvement from public and private sectors, and research and knowledge institutions), dissemination of outcomes and uptake. This is critical due to the disaggregated nature of the industry, intense competition, limited R&D investment; and new challenges (e.g. digital modelling, integrated project delivery, and the demand for packaged services). The evaluation of leading Australian and international efforts to integrate advanced digital modelling technologies into the design/construction/asset management cycle will be undertaken as one of three case studies. Employing the recently released Australian Guidelines for Digital Modelling developed with buildingSMART (International Alliance for Interoperability) and the Australian Institute of Architects, technical and business benefits across the supply chain will be highlighted as drivers for more integrated R&D efforts.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

Data and process requirements for product recall coordination

When an organisation becomes aware that one of its products may pose a safety risk to customers, it must take appropriate action as soon as possible or it can be held liable. The ability to automatically trace potentially dangerous goods through the supply chain would thus help organisations fulfill their legal obligations in a timely and effective manner. Furthermore, product recall legislation requires manufacturers to separately notify various government agencies, the health department and the public about recall incidents. This duplication of effort and paperwork can introduce errors and data inconsistencies. In this paper, we examine traceability and notification requirements in the product recall domain from two perspectives: the activities carried out during the manufacturing and recall processes and the data collected during the enactment of these processes. We then propose a workflow-based coordination framework to support these data and process requirements.

Deterministic diffraction loss modelling for novel broadband communication in rural environments

This paper presents a deterministic modelling approach to predict diffraction loss for an innovative Multi-User-Single-Antenna (MUSA) MIMO technology, proposed for rural Australian environments. In order to calculate diffraction loss, six receivers have been considered around an access point in a selected rural environment. Generated terrain profiles for six receivers are presented in this paper. Simulation results using classical diffraction models and diffraction theory are also presented by accounting the rural Australian terrain data. Results show that in an area of 900 m by 900 m surrounding the receivers, path loss due to diffraction can range between 5 dB and 35 dB. Diffraction loss maps can contribute to determine the optimal location for receivers of MUSA-MIMO systems in rural areas.

Prognostic modelling options for remaining useful life estimation by industry

Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.

A prototype augmented reality collaborative process modelling tool

Identifying, modelling and documenting business processes usually requires the collaboration of many stakeholders that may be spread across companies in inter-organizational business settings. While there are many process modelling tools available, the support they provide for remote collaboration is still limited. This demonstration showcases a novel prototype application that implements collaborative virtual environment and augmented reality technologies to improve remote collaborative process modelling, with an aim to assisting common collaboration tasks by providing an increased sense of immersion in an intuitive shared work and task space. Our tool is easily deployed using open source software, and commodity hardware, and is expected to assist with saving money on travel costs for large scale process modelling projects covering national and international centres within an enterprise.