A multi-disciplinary approach for the design and management of airport terminals

Multi-disciplinary approaches to complex problems are becoming more common – they enable criteria manifested in distinct (and potentially conflicting) domains to be jointly balanced and satisfied. In this paper we present airport terminals as a case study which requires multi-disciplinary knowledge in order to balance conflicting security, economic and passenger-driven needs and correspondingly enhance the design, management and operation of airport terminals. The need for a truly multi-disciplinary scientific approach which integrates information, process, people, technology and space domains is highlighted through a brief discussion of two challenges currently faced by airport operators. The paper outlines the approach taken by this project, detailing the aims and objectives of each of seven diverse research programs.

Innovation in construction : a case study of the Australian context

The dominant economic paradigm currently guiding industry policy making in Australia and much of the rest of the world is the neoclassical approach. Although neoclassical theories acknowledge that growth is driven by innovation, such innovation is exogenous to their standard models and hence often not explored. Instead the focus is on the allocation of scarce resources, where innovation is perceived as an external shock to the system. Indeed, analysis of innovation is largely undertaken by other disciplines, such as evolutionary economics and institutional economics. As more has become known about innovation processes, linear models, based on research and development or market demand, have been replaced by more complex interactive models which emphasise the existence of feedback loops between the actors and activities involved in the commercialisation of ideas (Manley 2003). Currently dominant among these approaches is the national or sectoral innovation system model (Breschi and Malerba 2000; Nelson 1993), which is based on the notion of increasingly open innovation systems (Chesbrough, Vanhaverbeke, and West 2008). This chapter reports on the ‘BRITE Survey’ funded by the Cooperative Research Centre for Construction Innovation which investigated the open sectoral innovation system operating in the Australian construction industry. The BRITE Survey was undertaken in 2004 and it is the largest construction innovation survey ever conducted in Australia. The results reported here give an indication of how construction innovation processes operate, as an example that should be of interest to international audiences interested in construction economics. The questionnaire was based on a broad range of indicators recommended in the OECD’s Community Innovation Survey guidelines (OECD/Eurostat 2005). Although the ABS has recently begun to undertake regular innovation surveys that include the construction industry (2006), they employ a very narrow definition of the industry and only collect very basic data compared to that provided by the BRITE Survey, which is presented in this chapter. The term ‘innovation’ is defined here as a new or significantly improved technology or organisational practice, based broadly on OECD definitions (OECD/Eurostat 2005). Innovation may be technological or organisational in nature and it may be new to the world, or just new to the industry or the business concerned. The definition thus includes the simple adoption of existing technological and organisational advancements. The survey collected information about respondents’ perceptions of innovation determinants in the industry, comprising various aspects of business strategy and business environment. It builds on a pilot innovation survey undertaken by PricewaterhouseCoopers (PWC) for the Australian Construction Industry Forum on behalf of the Australian Commonwealth Department of Industry Tourism and Resources, in 2001 (PWC 2002). The survey responds to an identified need within the Australian construction industry to have accurate and timely innovation data upon which to base effective management strategies and public policies (Focus Group 2004).

Concept building

In this world of continuous change, there’s probably one certainty: more change lies ahead. Our students will encounter challenges and opportunities that we can’t even imagine. How do we prepare our students as future citizens for the challenges of the 21st century? One of the most influential public intellectuals of our time, Howard Gardner, suggests that in the future individuals will depend to a great extent on the capacity to synthesise large amounts of information. ‘They will need to be able to gather together information from disparate sources and put it together in ways that work for themselves and can be communicated to other persons’(Gardner 2008, p. xiii). One of the first steps in ‘putting things together’ so they ‘work’ in the mind is ‘to group objects and events together on the basis of some similarity between them’ (Lee & das Gupta 1995, p. 116). When we do this and give them a collective name, we are conceptualising. Apart from helping to save our sanity by simplifying the vast amounts of data we encounter every day, concepts help us to understand and gain meaning from what we experience. Concepts are essential for synthesising information and they also help us to communicate with others. Put simply, concepts serve as building blocks for knowledge, understanding and communication. This chapter addresses the importance of teaching and learning about concepts and conceptual development in studies of society and environment. It proceeds as follows: first, it considers how individuals use concepts, and, second, it explores the characteristics of concepts; the third section presents a discussion of approaches that might be adopted by teachers intending to help their students build concepts in the classroom.

Semantic oscillations : encoding context and structure in complex valued holographic vectors

In computational linguistics, information retrieval and applied cognition, words and concepts are often represented as vectors in high dimensional spaces computed from a corpus of text. These high dimensional spaces are often referred to as Semantic Spaces. We describe a novel and efficient approach to computing these semantic spaces via the use of complex valued vector representations. We report on the practical implementation of the proposed method and some associated experiments. We also briefly discuss how the proposed system relates to previous theoretical work in Information Retrieval and Quantum Mechanics and how the notions of probability, logic and geometry are integrated within a single Hilbert space representation. In this sense the proposed system has more general application and gives rise to a variety of opportunities for future research.

Fast and accurate phonetic spoken term detection

For the first time in human history, large volumes of spoken audio are being broadcast, made available on the internet, archived, and monitored for surveillance every day. New technologies are urgently required to unlock these vast and powerful stores of information. Spoken Term Detection (STD) systems provide access to speech collections by detecting individual occurrences of specified search terms. The aim of this work is to develop improved STD solutions based on phonetic indexing. In particular, this work aims to develop phonetic STD systems for applications that require open-vocabulary search, fast indexing and search speeds, and accurate term detection. Within this scope, novel contributions are made within two research themes, that is, accommodating phone recognition errors and, secondly, modelling uncertainty with probabilistic scores. A state-of-the-art Dynamic Match Lattice Spotting (DMLS) system is used to address the problem of accommodating phone recognition errors with approximate phone sequence matching. Extensive experimentation on the use of DMLS is carried out and a number of novel enhancements are developed that provide for faster indexing, faster search, and improved accuracy. Firstly, a novel comparison of methods for deriving a phone error cost model is presented to improve STD accuracy, resulting in up to a 33% improvement in the Figure of Merit. A method is also presented for drastically increasing the speed of DMLS search by at least an order of magnitude with no loss in search accuracy. An investigation is then presented of the effects of increasing indexing speed for DMLS, by using simpler modelling during phone decoding, with results highlighting the trade-off between indexing speed, search speed and search accuracy. The Figure of Merit is further improved by up to 25% using a novel proposal to utilise word-level language modelling during DMLS indexing. Analysis shows that this use of language modelling can, however, be unhelpful or even disadvantageous for terms with a very low language model probability. The DMLS approach to STD involves generating an index of phone sequences using phone recognition. An alternative approach to phonetic STD is also investigated that instead indexes probabilistic acoustic scores in the form of a posterior-feature matrix. A state-of-the-art system is described and its use for STD is explored through several experiments on spontaneous conversational telephone speech. A novel technique and framework is proposed for discriminatively training such a system to directly maximise the Figure of Merit. This results in a 13% improvement in the Figure of Merit on held-out data. The framework is also found to be particularly useful for index compression in conjunction with the proposed optimisation technique, providing for a substantial index compression factor in addition to an overall gain in the Figure of Merit. These contributions significantly advance the state-of-the-art in phonetic STD, by improving the utility of such systems in a wide range of applications.

Informational city

Many cities worldwide face the prospect of major transformation as the world moves towards a global information order. In this new era, urban economies are being radically altered by dynamic processes of economic and spatial restructuring. The result is the creation of ‘informational cities’ or its new and more popular name, ‘knowledge cities’. For the last two centuries, social production had been primarily understood and shaped by neo-classical economic thought that recognized only three factors of production: land, labor and capital. Knowledge, education, and intellectual capacity were secondary, if not incidental, factors. Human capital was assumed to be either embedded in labor or just one of numerous categories of capital. In the last decades, it has become apparent that knowledge is sufficiently important to deserve recognition as a fourth factor of production. Knowledge and information and the social and technological settings for their production and communication are now seen as keys to development and economic prosperity. The rise of knowledge-based opportunity has, in many cases, been accompanied by a concomitant decline in traditional industrial activity. The replacement of physical commodity production by more abstract forms of production (e.g. information, ideas, and knowledge) has, however paradoxically, reinforced the importance of central places and led to the formation of knowledge cities. Knowledge is produced, marketed and exchanged mainly in cities. Therefore, knowledge cities aim to assist decision-makers in making their cities compatible with the knowledge economy and thus able to compete with other cities. Knowledge cities enable their citizens to foster knowledge creation, knowledge exchange and innovation. They also encourage the continuous creation, sharing, evaluation, renewal and update of knowledge. To compete nationally and internationally, cities need knowledge infrastructures (e.g. universities, research and development institutes); a concentration of well-educated people; technological, mainly electronic, infrastructure; and connections to the global economy (e.g. international companies and finance institutions for trade and investment). Moreover, they must possess the people and things necessary for the production of knowledge and, as importantly, function as breeding grounds for talent and innovation. The economy of a knowledge city creates high value-added products using research, technology, and brainpower. Private and the public sectors value knowledge, spend money on its discovery and dissemination and, ultimately, harness it to create goods and services. Although many cities call themselves knowledge cities, currently, only a few cities around the world (e.g., Barcelona, Delft, Dublin, Montreal, Munich, and Stockholm) have earned that label. Many other cities aspire to the status of knowledge city through urban development programs that target knowledge-based urban development. Examples include Copenhagen, Dubai, Manchester, Melbourne, Monterrey, Singapore, and Shanghai. Knowledge-Based Urban Development To date, the development of most knowledge cities has proceeded organically as a dependent and derivative effect of global market forces. Urban and regional planning has responded slowly, and sometimes not at all, to the challenges and the opportunities of the knowledge city. That is changing, however. Knowledge-based urban development potentially brings both economic prosperity and a sustainable socio-spatial order. Its goal is to produce and circulate abstract work. The globalization of the world in the last decades of the twentieth century was a dialectical process. On one hand, as the tyranny of distance was eroded, economic networks of production and consumption were constituted at a global scale. At the same time, spatial proximity remained as important as ever, if not more so, for knowledge-based urban development. Mediated by information and communication technology, personal contact, and the medium of tacit knowledge, organizational and institutional interactions are still closely associated with spatial proximity. The clustering of knowledge production is essential for fostering innovation and wealth creation. The social benefits of knowledge-based urban development extend beyond aggregate economic growth. On the one hand is the possibility of a particularly resilient form of urban development secured in a network of connections anchored at local, national, and global coordinates. On the other hand, quality of place and life, defined by the level of public service (e.g. health and education) and by the conservation and development of the cultural, aesthetic and ecological values give cities their character and attract or repel the creative class of knowledge workers, is a prerequisite for successful knowledge-based urban development. The goal is a secure economy in a human setting: in short, smart growth or sustainable urban development.

Low intensity CBT by mail

Introduction: There are many low intensity (LI) cognitive behavoural therapy (CBT) solutions to the problem of limited service access. In this chapter, we aim to discuss a relatively low-technology approach to access using standard postal services-CBT by mail, or M-CBT. Bibliotherapies including M-CBT teach key concepts and self-management techniques, together with screening tools and forms to structure home practice. M-CBT differs from other bibliotherapies by segmenting interventions and mailing them at regular intervals. Most involve participants returning copies of monitoring forms or completed handouts. Therapist feedback is provided, often in personal letters that accompany the printed materials. Participants may also be given access to telephone or email support. ----- ----- M-CBT clearly fulfills criteria for an LI CBT (see Bennett-Levy et al., Chapter 1, for a definition of LI interventions). Once written, they involve little therapist time and rely heavily on self-management. However, content and overall treatment duration need not be compromised. Long-term interventions with multiple components can be delivered via this method, provided their content can be communicated in letters and engagement is maintained.

Implementing low intensity CBT in case management of clients with severe mental illness

In a previous chapter (Dean and Kavanagh, Chapter 37), the authors made a case for applying low intensity (LI) cognitive behaviour therapy (CBT) to people with serious mental illness (SMI). As in other populations, LI CBT interventions typically deal with circumscribed problems or behaviours. LI CBT retains an emphasis on self-management, has restricted content and segment length, and does not necessarily require extensive CBT training. In applying these interventions to SMI, adjustments may be needed to address cognitive and symptomatic difficulties often faced by these groups. What may take a single session in a less affected population may require several sessions or a thematic application of the strategy within case management. In some cases, the LI CBT may begin to appear more like a high-intensity (HI) intervention, albeit simple and with many LI CBT characteristics still retained. So, if goal setting were introduced in one or two sessions, it could clearly be seen as an LI intervention. When applied to several different situations and across many sessions, it may be indistinguishable from a simple HI treatment, even if it retains the same format and is effectively applied by a practitioner with limited CBT training. ----- ----- In some ways, LI CBT should be well suited to case management of patients with SMI. treating staff typically have heavy workloads, and find it difficult to apply time-consuming treatments (Singh et al. 2003). LI CBT may allow provision of support to greater numbers of service users, and allow staff to spend more time on those who need intensive and sustained support. However, the introduction of any change in practice has to address significant challenges, and LI CBT is no exception. ----- ----- Many of the issues that we face in applying LI CBT to routine case management in a mnetal health service and their potential solutions are essentially the same as in a range of other problem domains (Turner and Sanders 2006)- and, indeed, are similar to those in any adoption of innovation (Rogers 2003). Over the last 20 years, several commentators have described barriers to implementing evidence-based innovations in mental health services (Corrigan et al. 1992; Deane et al. 2006; Kavanagh et al. 1993). The aim of the current chapter is to present a cognitive behavioural conceptualisation of problems and potential solutions for dissemination of LI CBT.

Adapting low intensity CBT interventions for clients with severe mental illness

Many people with severe mental illness (SMI) such as schizophrenia, whose psychotic symptoms are effectively managed, continue to experience significant functional problems. This chapter argues that low intensity (LI) cognitive behaviour therapy (CBT; e.g. for depression, anxiety, or other issues) is applicable to these clients, and that LI CBT can be consistent with long-term case management. However, adjustments to LI CBT strategies are often necessary and boundaries between LI CBT and high intensity (HI) CBT (with more extensive practitioner contact and complexity) may become blurred. Our focus is on LI CBT's self-management emphasis, its restricted content and segment length, and potential use after limited training. In addition to exploring these issues, it draws on the authors' Collaborative Recovery (CR; Oades et al. 2005) and 'Start Over and Survive' programs (Kavanagh et al. 2004) as examples. ----- ----- Evidence for the effectiveness of LI CBT with severe mental illness is often embedded within multicomponent programs. For example, goal setting and therapeutic homework are common components of such programs, but they can also be used as discrete LI CBT interventions. A review of 40 randomised controlled trials involving recipients with schizophrenia or other sever mental illnesses has identified key components of illness management programs (Mueser et al. 2002). However, it is relatively rare for specific components of these complex interventions to be assessed in isolation. Given these constraints, the evidence for specific LI CBT interventions with severe mental ilnness is relatively limited.

Brief motivational interviewing for depression and anxiety

Motivational interviewing (MI)can be applied as a brief, low intensity (LI) intervention of 1-4 individualised sessions (typically 45-60 minutes in duration), including screening, assessment feedback, and psycho-education. MI is a client-centred, directive therapeutic style that enhances readiness for change by helping clients explore and resolve ambivalence (Miller and Rollnick 2002). A summary of the key components of brief MI interventions is provided in Table 16.1. There is a well-established evidence base for MI in the treatment of substance misuse (particularly alcohol misuse; Moyer et al. 2002), as well as a growing evidence for the use of MI in the treatment of other mental disorders (e.g. depression, PTSD, OCD), as well as suicidality and physical health problems (Hettema et al. 2005). Brief MI intervention can be delivered as a standalone treatment or as a motivational prelude to pharmacological and/or other psychological treatments (Hettema et al. 2005). MI has been used as an accompaniment to cognitive behavioural therapy (CBT) in the treatment of both depression and anxiety for resolving ambivalence about change and developing strategies for responding to resistance (e.g. treatment attendance, homework/medication compliance; Arkowitz et al. 2008a, 2008b). This chapter will describe how to apply brief MI interventions to the treatment of depression and anxiety as applied to the case of Megan (see Box 16.1) along with some of the challenges and potential solutions to applying MI in practice.

Will insulation always bring benefits in energy saving and thermal comfort?

Building insulation is often used to reduce the conduction heat transfer through building envelope. With a higher level of insulation (or a greater R-value), the less the conduction heat would transfer through building envelope. In this paper, using building computer simulation techniques, the effects of building insulation levels on the thermal and energy performance of a sample air-conditioned office building in Australia are studied. It is found that depending on the types of buildings and the climates of buildings located, increasing the level of building insulation will not always bring benefits in energy saving and thermal comfort, particularly for internal-load dominated office buildings located in temperate/tropical climates. The possible implication of building insulation in face of global warming has also been examined. Compared with the influence of insulation on building thermal performance, the influence on building energy use is relatively small.

A pattern mining approach for information filtering systems

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well.

A Personalized Ontology Model for Web Information Gathering

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.