Bubble geometry and chaotic pricing behaviour

This paper is a deductive theoretical enquiry into the flow of effects from the geometry of price bubbles/busts, to price indices, to pricing behaviours of sellers and buyers, and back to price bubbles/busts. The intent of the analysis is to suggest analytical approaches to identify the presence, maturity, and/or sustainability of a price bubble. We present a pricing model to emulate market behaviour, including numeric examples and charts of the interaction of supply and demand. The model extends into dynamic market solutions myopic (single- and multi-period) backward looking rational expectations to demonstrate how buyers and sellers interact to affect supply and demand and to show how capital gain expectations can be a destabilising influence – i.e. the lagged effects of past price gains can drive the market price away from long-run market-worth. Investing based on the outputs of past price-based valuation models appear to be more of a game-of-chance than a sound investment strategy.

Cost-effectiveness of an intervention to reduce emergency re-admissions to hospital among older patients

Background. The objective is to estimate the cost-effectiveness of an intervention that reduces hospital readmission among older people at high risk. A cost-effectiveness model to estimate the costs and health benefits of the intervention was implemented. Methodology/Principal Findings. The model used data from a randomised controlled trial conducted in an Australian tertiary metropolitan hospital. Participants were acute medical admissions aged >65 years with at least one risk factor for readmission: multiple comorbidities, impaired functionality, aged >75 years, 30 recent multiple admissions, poor social support, history of depression. The intervention was a comprehensive nursing and physiotherapy assessment and an individually tailored program of exercise strategies and nurse home visits with telephone follow-up; commencing in hospital and continuing following discharge for 24 weeks. The change to cost outcomes, including the costs of implementing the intervention and all subsequent use of health care services, and, the change to health benefits, represented by quality adjusted life years, were estimated for the intervention as compared to existing practice. The mean change to total costs and quality 38 adjusted life years for an average individual over 24 weeks participating in the intervention were: cost savings of $333 (95% Bayesian credible interval $-1,932:1,282) and 0.118 extra quality adjusted life years (95% Bayesian credible interval 0.1:0.136). The mean net41 monetary-benefit per individual for the intervention group compared to the usual care condition was $7,907 (95% Bayesian credible interval $5,959:$9,995) for the 24 week period. Conclusions/Significance. The estimation model that describes this intervention predicts cost savings and improved health outcomes. A decision to remain with existing practices causes unnecessary costs and reduced health. Decision makers should consider adopting this 46 program for elderly hospitalised patients.

The graduate attributes we've overlooked : enhancing graduate employability through career management skills

Recent shifts in education and labour market policy have resulted in universities being placed under increasing pressure to produce employable graduates. However, contention exists regarding exactly what constitutes employability and which graduate attributes are required to foster employability in tertiary students. This paper argues that in the context of a rapidly changing information- and knowledge-intensive economy, employability involves far more than possession of the generic skills listed by graduate employers as attractive. Rather, for optimal economic and social outcomes, graduates must be able to proactively navigate the world of work and self-manage the career building process. A model of desirable graduate attributes that acknowledges the importance of self-management and career building skills to lifelong career management and enhanced employability is presented. Some important considerations for the implementation of effective university career management programs are then outlined.

Development and implementation of the areawide Dynamic ROad traffic NoisE (DRONE) simulator

This paper discusses the areawide Dynamic ROad traffic NoisE (DRONE) simulator, and its implementation as a tool for noise abatement policy evaluation. DRONE involves integrating a road traffic noise estimation model with a traffic simulator to estimate road traffic noise in urban networks. An integrated traffic simulation-noise estimation model provides an interface for direct input of traffic flow properties from simulation model to noise estimation model that in turn estimates the noise on a spatial and temporal scale. The output from DRONE is linked with a geographical information system for visual representation of noise levels in the form of noise contour maps.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

Nonlinear H_infinity control of UAVs for collision avoidance in gusty environments

This paper proposes a nonlinear H_infinity controller for stabilization of velocities, attitudes and angular rates of a fixed-wing unmanned aerial vehicle (UAV) in a windy environment. The suggested controller aims to achieve a steady-state flight condition in the presence of wind gusts such that the host UAV can be maneuvered to avoid collision with other UAVs during cruise flight with safety guarantees. This paper begins with building a proper model capturing flight aerodynamics of UAVs. Then a nonlinear controller is developed with gust attenuation and rapid response properties. Simulations are conducted for the Shadow UAV to verify performance of the proposed con- troller. Comparative studies with the proportional-integral-derivative (PID) controllers demonstrate that the proposed controller exhibits great performance improvement in a gusty environment, making it suitable for integration into the design of flight control systems for cruise flight of UAVs.

The stress-buffering effects of control on task satisfaction and perceived goal attainment : an experimental study of the moderating influence of desire for control

The purpose of the present study was to examine the extent to which Desire for Control (DFC) interacts with experimental manipulations of demand and control, and the consequences of these interactions on task satisfaction and perceived goal attainment (i.e. task performance and task mastery). It was expected that the proposed stress-buffering effects of control would be evident only for individuals high in DFC. Moreover, it was anticipated that control may have a stress-exacerbating effect for those low in DFC. These hypotheses were tested on a sample of 137 first year psychology students who participated in an in-basket activity under low and high conditions of demand and control. Results revealed that the proposed stress-buffering effect of control was found only for those high in DFC and a stress-exacerbating effect of increased control was evident for those low in DFC on task performance and task mastery perceptions. Future research directions and the implications of these findings to applied settings are discussed.

Explaining psychological distress in the legal profession: the role of overcommitment

The purpose of this study was to explain previously reported high levels of psychological distress in lawyers. General demands (i.e., time pressure and emotional demands) and lawyer demands (i.e., emphasis on profits and competitiveness), as well as three resources (i.e., control, pay, and praise) were examined, along with the moderating role of overcommitment. Participants included 448 Australian lawyers who completed an online questionnaire distributed to them by their state or territory law society. Results revealed significant direct relationships between the demands, resources, and overcommitment on depression and anxiety. Significant two-way interactions indicated that the positive relationship of general demands on depression and anxiety were more marked for high overcommitted lawyers, as was the negative relationship of pay on anxiety. Contrary to predictions for the three-way interaction, in the presence of high general demands, there was a trend to suggest that high control was positively related to psychological distress for high overcommitted lawyers. The theoretical and practical implications of these results, including the importance of identifying overcommitted lawyers and developing preventive interventions to reduce overcommitment prior to the development of strain, are discussed.

The relevance of shared experiences : a multi-level study of collective efficacy as a moderator of job control in the stressor-strain relationship

Karasek's Job Demand-Control model proposes that control mitigates the positive effects of work stressors on employee strain. Evidence to date remains mixed and, although a number of individual-level moderators have been examined, the role of broader, contextual, group factors has been largely overlooked. In this study, the extent to which control buffered or exacerbated the effects of demands on strain at the individual level was hypothesized to be influenced by perceptions of collective efficacy at the group level. Data from 544 employees in Australian organizations, nested within 23 workgroups, revealed significant three-way cross-level interactions among demands, control and collective efficacy on anxiety and job satisfaction. When the group perceived high levels of collective efficacy, high control buffered the negative consequences of high demands on anxiety and satisfaction. Conversely, when the group perceived low levels of collective efficacy, high control exacerbated the negative consequences of high demands on anxiety, but not satisfaction. In addition, a stress-exacerbating effect for high demands on anxiety and satisfaction was found when there was a mismatch between collective efficacy and control (i.e. combined high collective efficacy and low control). These results provide support for the notion that the stressor-strain relationship is moderated by both individual- and group-level factors.

Assembly and disassembly of temporary public spaces : establishing the theoretical needs

Contemporary cities no longer offer the same types of permanent environments that we planned for in the latter part of the twentieth century. Our public spaces are increasingly temporary, transient, and ephemeral. The theories, principles and tactics with which we designed these spaces in the past are no longer appropriate. We need a new theory for understanding the creation, use, and reuse of temporary public space. Moe than a theory, we need new architectural tactics or strategies that can be reliably employed to create successful temporary public spaces. This paper will present ongoing research that starts that process through critical review and technical analysis of existing and historic temporary public spaces. Through the analysis of a number of public spaces, that were either designed for temporary use or became temporary through changing social conditions, this research identifies the tactics and heuristics used in such projects. These tactics and heuristics are then analysed to extract some broader principles for the design of temporary public space. The theories of time related building layers, a model of environmental sustainability, and the recycling of social meaning, are all explored. The paper will go on to identify a number of key questions that need to be explored and addressed by a theory for such developments: How can we retain social meaning in the fabric of the city and its public spaces while we disassemble it and recycle it into new purposes? What role will preservation have in the rapidly changing future; will exemplary temporary spaces be preserved and thereby become no longer temporary? Does the environmental advantage of recycling materials, components and spaces outweigh the removal or social loss of temporary public space? This research starts to identify the knowledge gaps and proposes a number of strategies for making public space in the age of temporary, recyclable, and repurposing of our urban infrastructure; a way of creating lighter, cheaper, quicker, and temporary interventions.

Understanding the effects of complex seasonality on suburban daily transit ridership

Fluctuations in transit ridership pattern over the year have always concerned transport planners, operators and researchers. Predominantly, metrological elements have been specified to explain variability in ridership volume. However, the outcome of this research points to new direction to explain ridership fluctuation in Brisbane. It explored the relationship between daily bus ridership, seasonality and weather variables for a one-year period, 2012. Rather than segregating the entire year’s ridership into the four calendar seasons (summer, autumn, spring, and winter), this analysis distributed the yearly ridership into nine complex seasonality blocks. These represent calendar season, school/university (academic) period and their corresponding holidays, as well as other observant holidays such as Christmas. The dominance of complex seasonality over typical calendar season was established through analysis and using Multiple Linear Regression (MLR). This research identified a very strong association between complex seasonality and bus ridership. Furthermore, an expectation that Brisbane’s subtropical summer is unfavourable to transit usage was not supported by the findings of this study. A nil association of precipitation and temperature was observed in this region. Finally, this research developed a ridership estimation model, capable of predicting daily ridership within very limited error range. Following the application of this developed model, the estimated annual time series data of each suburb was analysed using Fourier Transformation to appreciate whether any cyclical effects remained, compared with the original data.

A principled experimental design approach to Big Data analysis

Big Datasets are endemic, but they are often notoriously difficult to analyse because of their size, heterogeneity, history and quality. The purpose of this paper is to open a discourse on the use of modern experimental design methods to analyse Big Data in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has wide generality and advantageous inferential and computational properties. In particular, the principled experimental design approach is shown to provide a flexible framework for analysis that, for certain classes of objectives and utility functions, delivers near equivalent answers compared with analyses of the full dataset under a controlled error rate. It can also provide a formalised method for iterative parameter estimation, model checking, identification of data gaps and evaluation of data quality. Finally, it has the potential to add value to other Big Data sampling algorithms, in particular divide-and-conquer strategies, by determining efficient sub-samples.

Sampling Methods for Exploring Between Subject Variability in Cardiac Electrophysiology Experiments

Between-subject and within-subject variability is ubiquitous in biology and physiology and understanding and dealing with this is one of the biggest challenges in medicine. At the same time it is difficult to investigate this variability by experiments alone. A recent modelling and simulation approach, known as population of models (POM), allows this exploration to take place by building a mathematical model consisting of multiple parameter sets calibrated against experimental data. However, finding such sets within a high-dimensional parameter space of complex electrophysiological models is computationally challenging. By placing the POM approach within a statistical framework, we develop a novel and efficient algorithm based on sequential Monte Carlo (SMC). We compare the SMC approach with Latin hypercube sampling (LHS), a method commonly adopted in the literature for obtaining the POM, in terms of efficiency and output variability in the presence of a drug block through an in-depth investigation via the Beeler-Reuter cardiac electrophysiological model. We show improved efficiency via SMC and that it produces similar responses to LHS when making out-of-sample predictions in the presence of a simulated drug block.

Investigating variation in bus ridership rates within localised areas of a sub-tropical city

This thesis makes a significant contribution to knowledge and understanding of 'Human Travel Behaviour' in relation to transportation research. It holds some important merits that have not been proposed before. It develops a new, comprehensive and meaningful relationship that includes bus transit ridership change due to weather variables, seasonality and transit quality of service within a single daily ridership rate estimation model. The research incorporated both temporal and spatial influences on ridership within a modelling structure, named as the Nested Model Structure. It provides a complete picture of ridership variation across the sub-tropical city of Brisbane, Australia.

An innovative learning model for teaching architectural technology using building information modelling : a Queensland University of Technology perspective

In architecture courses, instilling a wider understanding of the industry specific representations practiced in the Building Industry is normally done under the auspices of Technology and Science subjects. Traditionally, building industry professionals communicated their design intentions using industry specific representations. Originally these mainly two dimensional representations such as plans, sections, elevations, schedules, etc. were produced manually, using a drawing board. Currently, this manual process has been digitised in the form of Computer Aided Design and Drafting (CADD) or ubiquitously simply CAD. While CAD has significant productivity and accuracy advantages over the earlier manual method, it still only produces industry specific representations of the design intent. Essentially, CAD is a digital version of the drawing board. The tool used for the production of these representations in industry is still mainly CAD. This is also the approach taken in most traditional university courses and mirrors the reality of the situation in the building industry. A successor to CAD, in the form of Building Information Modelling (BIM), is presently evolving in the Construction Industry. CAD is mostly a technical tool that conforms to existing industry practices. BIM on the other hand is revolutionary both as a technical tool and as an industry practice. Rather than producing representations of design intent, BIM produces an exact Virtual Prototype of any building that in an ideal situation is centrally stored and freely exchanged between the project team. Essentially, BIM builds any building twice: once in the virtual world, where any faults are resolved, and finally, in the real world. There is, however, no established model for learning through the use of this technology in Architecture courses. Queensland University of Technology (QUT), a tertiary institution that maintains close links with industry, recognises the importance of equipping their graduates with skills that are relevant to industry. BIM skills are currently in increasing demand throughout the construction industry through the evolution of construction industry practices. As such, during the second half of 2008, QUT 4th year architectural students were formally introduced for the first time to BIM, as both a technology and as an industry practice. This paper will outline the teaching team’s experiences and methodologies in offering a BIM unit (Architectural Technology and Science IV) at QUT for the first time and provide a description of the learning model. The paper will present the results of a survey on the learners’ perspectives of both BIM and their learning experiences as they learn about and through this technology.