Project 27 : insights into a production-centred study on art direction and love

This paper offers insight into the development of a PhD in advertising art direction. For over half a century art directors within the advertising industry have been adapting to the changes occurring in media, culture and the corporate sector, toward enhancing professional performance and competitiveness. These professionals seldom offer explicit justification about the role images play in effective communication. It is uncertain how this situation affects advertising performance, because advertising has, nevertheless, evolved in parallel to this as an industry able to fabricate new opportunities for itself. However, uncertainties in the formalization of art direction knowledge restrict the possibilities of knowledge transfer in higher education. The theoretical knowledge supporting advertising art direction has been adapted spontaneously from disciplines that rarely focus on specific aspects related to the production of advertising content, like, for example: marketing communication, design, visual communication, or visual art. Meanwhile, in scholarly research, vast empirical knowledge has been generated about advertising images, but often with limited insight into production expertise. Because art direction is understood as an industry practice and not as an academic discipline, an art direction perspective in scholarly contributions is rare. Scholarly research that is relevant to art direction seldom offers viewpoints to help understand how it is that research outputs may specifically contribute to art direction practices. There is a need to formally understanding the knowledge underlying art direction and using it to explore models for visual analysis and knowledge transfer in higher education. This paper provides insight into the development of a thesis that explored this need. The PhD thesis to which this paper refers is Strategic Aesthetics in Advertising Campaigns: Implications for Art Direction Education.

An investigation into factors affecting electron density calibration for a megavoltage cone-beam CT system

There is a growing interest in the use of megavoltage cone-beam computed tomography (MV CBCT) data for radiotherapy treatment planning. To calculate accurate dose distributions, knowledge of the electron density (ED) of the tissues being irradiated is required. In the case of MV CBCT, it is necessary to determine a calibration-relating CT number to ED, utilizing the photon beam produced for MV CBCT. A number of different parameters can affect this calibration. This study was undertaken on the Siemens MV CBCT system, MVision, to evaluate the effect of the following parameters on the reconstructed CT pixel value to ED calibration: the number of monitor units (MUs) used (5, 8, 15 and 60 MUs), the image reconstruction filter (head and neck, and pelvis), reconstruction matrix size (256 by 256 and 512 by 512), and the addition of extra solid water surrounding the ED phantom. A Gammex electron density CT phantom containing EDs from 0.292 to 1.707 was imaged under each of these conditions. The linear relationship between MV CBCT pixel value and ED was demonstrated for all MU settings and over the range of EDs. Changes in MU number did not dramatically alter the MV CBCT ED calibration. The use of different reconstruction filters was found to affect the MV CBCT ED calibration, as was the addition of solid water surrounding the phantom. Dose distributions from treatment plans calculated with simulated image data from a 15 MU head and neck reconstruction filter MV CBCT image and a MV CBCT ED calibration curve from the image data parameters and a 15 MU pelvis reconstruction filter showed small and clinically insignificant differences. Thus, the use of a single MV CBCT ED calibration curve is unlikely to result in any clinical differences. However, to ensure minimal uncertainties in dose reporting, MV CBCT ED calibration measurements could be carried out using parameter-specific calibration measurements.

Engaging complexity through collaborative brand design

In this research we used inductive reasoning through design to understand how stakeholders in the Waterfall Way (New South Wales, Australia) perceive the relationships between themselves and the place they live in. This paper describes a collaborative design methodology used to release information about local identities, which guided the regional brand exercise. The methodology is explicit about the uncertainties and complexities of the design process and of its reception system. As such, it aims to engage with local stakeholders and experts in order to help elicit tacit knowledge and identify system patterns and trends that would possibly not be visible if a top-down expert-based process was used. Through collective design, local people were drawn together in search for a symbol to represent the meaning attached to their places/region in relation to sustainable tourism activity.

Bayesian algorithms with applications

Advances in algorithms for approximate sampling from a multivariable target function have led to solutions to challenging statistical inference problems that would otherwise not be considered by the applied scientist. Such sampling algorithms are particularly relevant to Bayesian statistics, since the target function is the posterior distribution of the unobservables given the observables. In this thesis we develop, adapt and apply Bayesian algorithms, whilst addressing substantive applied problems in biology and medicine as well as other applications. For an increasing number of high-impact research problems, the primary models of interest are often sufficiently complex that the likelihood function is computationally intractable. Rather than discard these models in favour of inferior alternatives, a class of Bayesian "likelihoodfree" techniques (often termed approximate Bayesian computation (ABC)) has emerged in the last few years, which avoids direct likelihood computation through repeated sampling of data from the model and comparing observed and simulated summary statistics. In Part I of this thesis we utilise sequential Monte Carlo (SMC) methodology to develop new algorithms for ABC that are more efficient in terms of the number of model simulations required and are almost black-box since very little algorithmic tuning is required. In addition, we address the issue of deriving appropriate summary statistics to use within ABC via a goodness-of-fit statistic and indirect inference. Another important problem in statistics is the design of experiments. That is, how one should select the values of the controllable variables in order to achieve some design goal. The presences of parameter and/or model uncertainty are computational obstacles when designing experiments but can lead to inefficient designs if not accounted for correctly. The Bayesian framework accommodates such uncertainties in a coherent way. If the amount of uncertainty is substantial, it can be of interest to perform adaptive designs in order to accrue information to make better decisions about future design points. This is of particular interest if the data can be collected sequentially. In a sense, the current posterior distribution becomes the new prior distribution for the next design decision. Part II of this thesis creates new algorithms for Bayesian sequential design to accommodate parameter and model uncertainty using SMC. The algorithms are substantially faster than previous approaches allowing the simulation properties of various design utilities to be investigated in a more timely manner. Furthermore the approach offers convenient estimation of Bayesian utilities and other quantities that are particularly relevant in the presence of model uncertainty. Finally, Part III of this thesis tackles a substantive medical problem. A neurological disorder known as motor neuron disease (MND) progressively causes motor neurons to no longer have the ability to innervate the muscle fibres, causing the muscles to eventually waste away. When this occurs the motor unit effectively ‘dies’. There is no cure for MND, and fatality often results from a lack of muscle strength to breathe. The prognosis for many forms of MND (particularly amyotrophic lateral sclerosis (ALS)) is particularly poor, with patients usually only surviving a small number of years after the initial onset of disease. Measuring the progress of diseases of the motor units, such as ALS, is a challenge for clinical neurologists. Motor unit number estimation (MUNE) is an attempt to directly assess underlying motor unit loss rather than indirect techniques such as muscle strength assessment, which generally is unable to detect progressions due to the body’s natural attempts at compensation. Part III of this thesis builds upon a previous Bayesian technique, which develops a sophisticated statistical model that takes into account physiological information about motor unit activation and various sources of uncertainties. More specifically, we develop a more reliable MUNE method by applying marginalisation over latent variables in order to improve the performance of a previously developed reversible jump Markov chain Monte Carlo sampler. We make other subtle changes to the model and algorithm to improve the robustness of the approach.

Power system sensitivity analysis considering induction motor loads

Power system operation and planning are facing increasing uncertainties especially with the deregulation process and increasing demand for power. Probabilistic power system stability assessment and probabilistic power system planning have been identified by EPRI as one of the important trends in power system operations and planning. Probabilistic small signal stability assessment studies the impact of system parameter uncertainties on system small disturbance stability characteristics. Researches in this area have covered many uncertainties factors such as controller parameter uncertainties and generation uncertainties. One of the most important factors in power system stability assessment is load dynamics. In this paper, composite load model is used to consider the uncertainties from load parameter uncertainties impact on system small signal stability characteristics. The results provide useful insight into the significant stability impact brought to the system by load dynamics. They can be used to help system operators in system operation and planning analysis.

Games for change and transformative learning : an ethnographic case study

This thesis aims to contribute to a better understanding of how serious games/games for change function as learning frameworks for transformative learning in an educational setting. This study illustrates how the meaning-making processes and learning with and through computer gameplay are highly contingent, and are significantly influenced by the uncertainties of the situational context. The study focuses on SCAPE, a simulation game that addresses urban planning and sustainability. SCAPE is based on the real-world scenario of Kelvin Grove Urban Village, an inner city redevelopment area in Brisbane, Queensland, Australia. The game is embedded within an educational program, and I thus account for the various gameplay experiences of different school classes participating in this program. The networks emerging from the interactions between students/players, educators, facilitators, the technology, the researcher, as well as the setting, result in unanticipated, controversial, and sometimes unintended gameplay experiences and outcomes. To unpack play, transformative learning and games, this study adopts an ecological approach that considers the magic circle of gameplay in its wider context. Using Actor-Network Theory as the ontological lens for inquiry, the methods for investigation include an extensive literature review, ethnographic participant observation of SCAPE, as well as student and teacher questionnaires, finishing with interviews with the designers and facilitators of SCAPE. Altogether, these methods address my research aim to better understand how the heterogeneous actors engage in the relationships in and around gameplay, and illustrate how their conflicting understandings enable, shape or constrain the (transformative) learning experience. To disentangle these complexities, my focus continuously shifts between the following modes of inquiry into the aims „h To describe and analyse the game as a designed artefact. „h To examine the gameplay experiences of players/students and account for how these experiences are constituted in the relationships of the network. „h To trace the meaning-making processes emerging from the various relations of players/students, facilitators, teachers, designers, technology, researcher, and setting, and consider how the boundaries of the respective ecology are configured and negotiated. „h To draw out the implications for the wider research area of game-based learning by using the simulation game SCAPE as an example for introducing gameplay to educational settings. Accounting in detail for five school classes, these accounts represent, each in its own right, distinct and sometimes controversial forms of engagement in gameplay. The practices and negotiations of all the assembled human and non-human actors highlight the contingent nature of gameplay and learning. In their sum, they offer distinct but by no means exhaustive examples of the various relationships that emerge from the different assemblages of human and non-human actors. This thesis, hence, illustrates that game-based learning in an educational setting is accompanied by considerable unpredictability and uncertainty. As ordinary life spills and leaks into gameplay experiences, group dynamics and the negotiations of technology, I argue that overly deterministic assertions of the game¡¦s intention, as well as a too narrowly defined understanding of the transformative learning outcome, can constrain our inquiries and hinder efforts to further elucidate and understand the evolving uncertainties around game-based learning. Instead, this thesis posits that playing and transformative learning are relational effects of the respective ecology, where all actors are networked in their (partial) enrolment in the process of translation. This study thus attempts to foreground the rich opportunities for exploring how game-based learning is assembled as a network of practices.

Logan Basin Water Resource Plan : environmental assessment of the southern Moreton Bay islands. Technical Advisory Panel Report: Stage 1.

This report is a technical assessment of the hydrological environment of the southern Moreton Bay islands and follows the terms of reference supplied by the then Queensland Department of Natural Resources and Water. The terms of reference describe stage 1 as a condition assessment and stage 2 as an assessment of the implications of water planning scenarios on future condition. This report is the first stage of a two-stage investigation whose primary purpose is to identify and assess groundwater dependent ecosystems (GDEs) and the groundwater flow regimes necessary to support them. Within this context, the groundwaters themselves are also considered and comment made on their condition. Information provided in this report will inform an amendment to the Logan Basin Water Resource Plan to incorporate the southern Moreton Bay islands. The study area is the water resource plan amendment area, which includes North and South Stradbroke islands and the smaller islands between these and the mainland, including the inhabited smaller rocky islands—namely, Macleay, Russell, Karragarra, Lamb and Coochiemudlo islands. This assessment is largely a desktop study based on existing information, but incorporates some field observations, input from experts in specific areas and community representatives, and the professional experience and knowledge of the authors. This report reviews existing research and information on the southern Moreton Bay area with an emphasis on North Stradbroke Island, as it represents the largest and most regionally significant groundwater resource in southern Moreton Bay. The report provides an assessment of key waterrelated environmental features, their condition and their degree of dependence on groundwater. This report also assesses the condition and status of ecosystems within this region. In addition, the report identifies information gaps, uncertainties and potential impacts; reviews groundwater models that have been developed for North Stradbroke Island; and makes recommendations on monitoring and research needs.

Pricing approaches in the construction industry

Contractors have to bid competitively for most of their work and at the same time deal with the risks and uncertainties connected with bid submission. This article examines the factors involved in tender pricing and how they interrelate. From this, a conceptual model of contractors’ pricing strategy is developed.

Sensitivity of peak positions to flight-path parameters in a deep-inelastic scattering neutron TOF spectrometer

The effects of small changes in flight-path parameters (primary and secondary flight paths, detector angles), and of displacement of the sample along the beam axis away from its ideal position, are examined for an inelastic time-of-flight (TOF) neutron spectrometer, emphasising the deep-inelastic regime. The aim was to develop a rational basis for deciding what measured shifts in the positions of spectral peaks could be regarded as reliable in the light of the uncertainties in the calibrated flight-path parameters. Uncertainty in the length of the primary or secondary flight path has the least effect on the positions of the peaks of H, D and He, which are dominated by the accuracy of the calibration of the detector angles. This aspect of the calibration of a TOF spectrometer therefore demands close attention to achieve reliable outcomes where the position of the peaks is of significant scientific interest and is discussed in detail. The corresponding sensitivities of the position of peak of the Compton profile, J(y), to flight-path parameters and sample position are also examined, focusing on the comparability across experiments of results for H, D and He. We show that positioning the sample to within a few mm of the ideal position is required to ensure good comparability between experiments if data from detectors at high forward angles are to be reliably interpreted.

Robust aerodynamic design optimisation of morphing aerofoil/wing using distributed moga

In this paper, the shape design optimisation using morphing aerofoil/wing techniques, namely the leading and/or trailing edge deformation of a natural laminar flow RAE 5243 aerofoil is investigated to reduce transonic drag without taking into account of the piezo actuator mechanism. Two applications using a Multi-Objective Genetic Algorithm (MOGA)coupled with Euler and boundary analyser (MSES) are considered: the first example minimises the total drag with a lift constraint by optimising both the trailing edge actuator position and trailing edge deformation angle at a constant transonic Mach number (M! = 0.75)and boundary layer transition position (xtr = 45%c). The second example consists of finding reliable designs that produce lower mean total drag (μCd) and drag sensitivity ("Cd) at different uncertainty flight conditions based on statistical information. Numerical results illustrate how the solution quality in terms of mean drag and its sensitivity can be improved using MOGA software coupled with a robust design approach taking account of uncertainties (lift and boundary transition positions) and also how transonic flow over aerofoil/wing can be controlled to the best advantage using morphing techniques.

Coordinated probabilistic optimal decision-making model for multi-area ATC with risk control

In the decision-making of multi-area ATC (Available Transfer Capacity) in electricity market environment, the existing resources of transmission network should be optimally dispatched and coordinately employed on the premise that the secure system operation is maintained and risk associated is controllable. The non-sequential Monte Carlo simulation is used to determine the ATC probability density distribution of specified areas under the influence of several uncertainty factors, based on which, a coordinated probabilistic optimal decision-making model with the maximal risk benefit as its objective is developed for multi-area ATC. The NSGA-II is applied to calculate the ATC of each area, which considers the risk cost caused by relevant uncertainty factors and the synchronous coordination among areas. The essential characteristics of the developed model and the employed algorithm are illustrated by the example of IEEE 118-bus test system. Simulative result shows that, the risk of multi-area ATC decision-making is influenced by the uncertainties in power system operation and the relative importance degrees of different areas.

Rejuvenating lost and disused space within Fortitude Valley : a study into the effectiveness of urban greenery in subtropical public space

An issue gaining prominence in our urban environments in the notion of lost space, the undesirable urban areas that are in need of redesign, commonly caused by a focus on development as individual architectural entities, without a greater view of the urban environment as a holistic entity. Within the context of South East Queensland, the suburb of Fortitude Valley has been earmarked for development as an extension of the current CBD. With lost and disused spaces already existing throughout the suburb due to rapid growth and mismatched developments, recent planning regimes have proposed rejuvenation in the form of proposals that echo typologies from other Australian regions, such as the laneway typology from Melbourne. Opportunities exist in these spaces for design approaches that relate specifically to the individual and unique subtropical character of the area. This research explores the relationship between innovative approaches towards urban greenery as a means to rejuvenate lost and disused public space, and its suitability within a subtropical climate, specifically focused within the suburb of Fortitude Valley. A trend gaining prominence is the notion of biophilic cities; cities that integrate urban greenery as a means to provide vibrant public spaces, and meet the growing aesthetic, social, cultural and economic needs of our cities. Through analysis of case studies showcasing greenery in an inventive way, observations of public using subtropical public space, and a discussion of the current policy frameworks at place within Fortitude Valley, innovative uses of urban greenery is proposed as viable placemaking technique in subtropical urban environments.

Intelligent reliability analysis with incomplete covariates

The reliability analysis is crucial to reducing unexpected down time, severe failures and ever tightened maintenance budget of engineering assets. Hazard based reliability methods are of particular interest as hazard reflects the current health status of engineering assets and their imminent failure risks. Most existing hazard models were constructed using the statistical methods. However, these methods were established largely based on two assumptions: one is the assumption of baseline failure distributions being accurate to the population concerned and the other is the assumption of effects of covariates on hazards. These two assumptions may be difficult to achieve and therefore compromise the effectiveness of hazard models in the application. To address this issue, a non-linear hazard modelling approach is developed in this research using neural networks (NNs), resulting in neural network hazard models (NNHMs), to deal with limitations due to the two assumptions for statistical models. With the success of failure prevention effort, less failure history becomes available for reliability analysis. Involving condition data or covariates is a natural solution to this challenge. A critical issue for involving covariates in reliability analysis is that complete and consistent covariate data are often unavailable in reality due to inconsistent measuring frequencies of multiple covariates, sensor failure, and sparse intrusive measurements. This problem has not been studied adequately in current reliability applications. This research thus investigates such incomplete covariates problem in reliability analysis. Typical approaches to handling incomplete covariates have been studied to investigate their performance and effects on the reliability analysis results. Since these existing approaches could underestimate the variance in regressions and introduce extra uncertainties to reliability analysis, the developed NNHMs are extended to include handling incomplete covariates as an integral part. The extended versions of NNHMs have been validated using simulated bearing data and real data from a liquefied natural gas pump. The results demonstrate the new approach outperforms the typical incomplete covariates handling approaches. Another problem in reliability analysis is that future covariates of engineering assets are generally unavailable. In existing practices for multi-step reliability analysis, historical covariates were used to estimate the future covariates. Covariates of engineering assets, however, are often subject to substantial fluctuation due to the influence of both engineering degradation and changes in environmental settings. The commonly used covariate extrapolation methods thus would not be suitable because of the error accumulation and uncertainty propagation. To overcome this difficulty, instead of directly extrapolating covariate values, projection of covariate states is conducted in this research. The estimated covariate states and unknown covariate values in future running steps of assets constitute an incomplete covariate set which is then analysed by the extended NNHMs. A new assessment function is also proposed to evaluate risks of underestimated and overestimated reliability analysis results. A case study using field data from a paper and pulp mill has been conducted and it demonstrates that this new multi-step reliability analysis procedure is able to generate more accurate analysis results.

Sustainable and affordable housing : a myth or reality

The Australian government has released a draft National Building Framework that will likely tighten the building standard for new houses to meet higher sustainability requirements. There are uncertainties about the impact this could have on the cost of housing and the supply of affordable housing. This paper aims to provide evidence-based conclusions on the possibility of delivering sustainable and affordable housing for low income people. The case studies are gathered from Brisbane and Gold Coast. Case studies are analysed by unpacking the features that were included to meet sustainability and affordability goals for housing. This paper outlines the key factors for their success and also challenges for replication of the projects. The study shows that the key success drivers for delivering sustainable and affordable housing are providing planning incentives, subsidies for increased energy efficiency, supportive regulatory frameworks and appropriate allocation of infrastructure charges. It shows that government can prioritise their resources to support affordable and sustainable housing for low income people.

A multi-objective optimization approach for coordinating available transfer capability with risk control

In the electricity market environment, coordination of system reliability and economics of a power system is of great significance in determining the available transfer capability (ATC). In addition, the risks associated with uncertainties should be properly addressed in the ATC determination process for risk-benefit maximization. Against this background, it is necessary that the ATC be optimally allocated and utilized within relative security constraints. First of all, the non-sequential Monte Carlo stimulation is employed to derive the probability density distribution of ATC of designated areas incorporating uncertainty factors. Second, on the basis of that, a multi-objective optimization model is formulated to determine the multi-area ATC so as to maximize the risk-benefits. Then, the solution to the developed model is achieved by the fast non-dominated sorting (NSGA-II) algorithm, which could decrease the risk caused by uncertainties while coordinating the ATCs of different areas. Finally, the IEEE 118-bus test system is served for demonstrating the essential features of the developed model and employed algorithm.