An ecological assessment of secondary poisoning risk in the Australian sugarcane industry

Rodenticide use in agriculture can lead to the secondary poisoning of avian predators. Currently the Australian sugarcane industry has two rodenticides, Racumin® and Rattoff®, available for in-crop use but, like many agricultural industries, it lacks an ecologically-based method of determining the potential secondary poisoning risk the use of these rodenticides poses to avian predators. The material presented in this thesis addresses this by: a. determining where predator/prey interactions take place in sugar producing districts; b. quantifying the amount of rodenticide available to avian predators and the probability of encounter; and c. developing a stochastic model that allows secondary poisoning risk under various rodenticide application scenarios to be investigated. Results demonstrate that predator/prey interactions are highly constrained by environmental structure. Rodents used crops that provided high levels of canopy cover and therefore predator protection and poorly utilised open canopy areas. In contrast, raptors over-utilised areas with low canopy cover and low rodent densities, but which provided high accessibility to prey. Given this pattern of habitat use, and that industry baiting protocols preclude rodenticide application in open canopy crops, these results indicate that secondary poisoning can only occur if poisoned rodents leave closed canopy crops and become available for predation in open canopy areas. Results further demonstrate that after in-crop rodenticide application, only a small proportion of rodents available in open areas are poisoned and that these rodents carry low levels of toxicant. Coupled with the low level of rodenticide use in the sugar industry, the high toxic threshold raptors have to these toxicants and the low probability of encountering poisoned rodents, results indicate that the risk of secondary poisoning events occurring is minimal. A stochastic model was developed to investigate the effect of manipulating factors that might influence secondary poisoning hazard in a sugarcane agro-ecosystem. These simulations further suggest that in all but extreme scenarios, the risk of secondary poisoning is also minimal. Collectively, these studies demonstrate that secondary poisoning of avian predators associated with the use of the currently available rodenticides in Australian sugar producing districts is minimal. Further, the ecologically-based method of assessing secondary poisoning risk developed in this thesis has broader applications in other agricultural systems where rodenticide use may pose risks to avian predators.

Synthesis and modifications of metal oxide nanostructures and their applications

Transition metal oxides are functional materials that have advanced applications in many areas, because of their diverse properties (optical, electrical, magnetic, etc.), hardness, thermal stability and chemical resistance. Novel applications of the nanostructures of these oxides are attracting significant interest as new synthesis methods are developed and new structures are reported. Hydrothermal synthesis is an effective process to prepare various delicate structures of metal oxides on the scales from a few to tens of nanometres, specifically, the highly dispersed intermediate structures which are hardly obtained through pyro-synthesis. In this thesis, a range of new metal oxide (stable and metastable titanate, niobate) nanostructures, namely nanotubes and nanofibres, were synthesised via a hydrothermal process. Further structure modifications were conducted and potential applications in catalysis, photocatalysis, adsorption and construction of ceramic membrane were studied. The morphology evolution during the hydrothermal reaction between Nb2O5 particles and concentrated NaOH was monitored. The study demonstrates that by optimising the reaction parameters (temperature, amount of reactants), one can obtain a variety of nanostructured solids, from intermediate phases niobate bars and fibres to the stable phase cubes. Trititanate (Na2Ti3O7) nanofibres and nanotubes were obtained by the hydrothermal reaction between TiO2 powders or a titanium compound (e.g. TiOSO4·xH2O) and concentrated NaOH solution by controlling the reaction temperature and NaOH concentration. The trititanate possesses a layered structure, and the Na ions that exist between the negative charged titanate layers are exchangeable with other metal ions or H+ ions. The ion-exchange has crucial influence on the phase transition of the exchanged products. The exchange of the sodium ions in the titanate with H+ ions yields protonated titanate (H-titanate) and subsequent phase transformation of the H-titanate enable various TiO2 structures with retained morphology. H-titanate, either nanofibres or tubes, can be converted to pure TiO2(B), pure anatase, mixed TiO2(B) and anatase phases by controlled calcination and by a two-step process of acid-treatment and subsequent calcination. While the controlled calcination of the sodium titanate yield new titanate structures (metastable titanate with formula Na1.5H0.5Ti3O7, with retained fibril morphology) that can be used for removal of radioactive ions and heavy metal ions from water. The structures and morphologies of the metal oxides were characterised by advanced techniques. Titania nanofibres of mixed anatase and TiO2(B) phases, pure anatase and pure TiO2(B) were obtained by calcining H-titanate nanofibres at different temperatures between 300 and 700 °C. The fibril morphology was retained after calcination, which is suitable for transmission electron microscopy (TEM) analysis. It has been found by TEM analysis that in mixed-phase structure the interfaces between anatase and TiO2(B) phases are not random contacts between the engaged crystals of the two phases, but form from the well matched lattice planes of the two phases. For instance, (101) planes in anatase and (101) planes of TiO2(B) are similar in d spaces (~0.18 nm), and they join together to form a stable interface. The interfaces between the two phases act as an one-way valve that permit the transfer of photogenerated charge from anatase to TiO2(B). This reduces the recombination of photogenerated electrons and holes in anatase, enhancing the activity for photocatalytic oxidation. Therefore, the mixed-phase nanofibres exhibited higher photocatalytic activity for degradation of sulforhodamine B (SRB) dye under ultraviolet (UV) light than the nanofibres of either pure phase alone, or the mechanical mixtures (which have no interfaces) of the two pure phase nanofibres with a similar phase composition. This verifies the theory that the difference between the conduction band edges of the two phases may result in charge transfer from one phase to the other, which results in effectively the photogenerated charge separation and thus facilitates the redox reaction involving these charges. Such an interface structure facilitates charge transfer crossing the interfaces. The knowledge acquired in this study is important not only for design of efficient TiO2 photocatalysts but also for understanding the photocatalysis process. Moreover, the fibril titania photocatalysts are of great advantage when they are separated from a liquid for reuse by filtration, sedimentation, or centrifugation, compared to nanoparticles of the same scale. The surface structure of TiO2 also plays a significant role in catalysis and photocatalysis. Four types of large surface area TiO2 nanotubes with different phase compositions (labelled as NTA, NTBA, NTMA and NTM) were synthesised from calcination and acid treatment of the H-titanate nanotubes. Using the in situ FTIR emission spectrescopy (IES), desorption and re-adsorption process of surface OH-groups on oxide surface can be trailed. In this work, the surface OH-group regeneration ability of the TiO2 nanotubes was investigated. The ability of the four samples distinctively different, having the order: NTA > NTBA > NTMA > NTM. The same order was observed for the catalytic when the samples served as photocatalysts for the decomposition of synthetic dye SRB under UV light, as the supports of gold (Au) catalysts (where gold particles were loaded by a colloid-based method) for photodecomposition of formaldehyde under visible light and for catalytic oxidation of CO at low temperatures. Therefore, the ability of TiO2 nanotubes to generate surface OH-groups is an indicator of the catalytic activity. The reason behind the correlation is that the oxygen vacancies at bridging O2- sites of TiO2 surface can generate surface OH-groups and these groups facilitate adsorption and activation of O2 molecules, which is the key step of the oxidation reactions. The structure of the oxygen vacancies at bridging O2- sites is proposed. Also a new mechanism for the photocatalytic formaldehyde decomposition with the Au-TiO2 catalysts is proposed: The visible light absorbed by the gold nanoparticles, due to surface plasmon resonance effect, induces transition of the 6sp electrons of gold to high energy levels. These energetic electrons can migrate to the conduction band of TiO2 and are seized by oxygen molecules. Meanwhile, the gold nanoparticles capture electrons from the formaldehyde molecules adsorbed on them because of gold’s high electronegativity. O2 adsorbed on the TiO2 supports surface are the major electron acceptor. The more O2 adsorbed, the higher the oxidation activity of the photocatalyst will exhibit. The last part of this thesis demonstrates two innovative applications of the titanate nanostructures. Firstly, trititanate and metastable titanate (Na1.5H0.5Ti3O7) nanofibres are used as intelligent absorbents for removal of radioactive cations and heavy metal ions, utilizing the properties of the ion exchange ability, deformable layered structure, and fibril morphology. Environmental contamination with radioactive ions and heavy metal ions can cause a serious threat to the health of a large part of the population. Treatment of the wastes is needed to produce a waste product suitable for long-term storage and disposal. The ion-exchange ability of layered titanate structure permitted adsorption of bivalence toxic cations (Sr2+, Ra2+, Pb2+) from aqueous solution. More importantly, the adsorption is irreversible, due to the deformation of the structure induced by the strong interaction between the adsorbed bivalent cations and negatively charged TiO6 octahedra, and results in permanent entrapment of the toxic bivalent cations in the fibres so that the toxic ions can be safely deposited. Compared to conventional clay and zeolite sorbents, the fibril absorbents are of great advantage as they can be readily dispersed into and separated from a liquid. Secondly, new generation membranes were constructed by using large titanate and small ã-alumina nanofibres as intermediate and top layers, respectively, on a porous alumina substrate via a spin-coating process. Compared to conventional ceramic membranes constructed by spherical particles, the ceramic membrane constructed by the fibres permits high flux because of the large porosity of their separation layers. The voids in the separation layer determine the selectivity and flux of a separation membrane. When the sizes of the voids are similar (which means a similar selectivity of the separation layer), the flux passing through the membrane increases with the volume of the voids which are filtration passages. For the ideal and simplest texture, a mesh constructed with the nanofibres 10 nm thick and having a uniform pore size of 60 nm, the porosity is greater than 73.5 %. In contrast, the porosity of the separation layer that possesses the same pore size but is constructed with metal oxide spherical particles, as in conventional ceramic membranes, is 36% or less. The membrane constructed by titanate nanofibres and a layer of randomly oriented alumina nanofibres was able to filter out 96.8% of latex spheres of 60 nm size, while maintaining a high flux rate between 600 and 900 Lm–2 h–1, more than 15 times higher than the conventional membrane reported in the most recent study.

Visual localisation in outdoor industrial building environments

This paper presents a vision-based method of vehicle localisation that has been developed and tested on a large forklift type robotic vehicle which operates in a mainly outdoor industrial setting. The localiser uses a sparse 3D edgemap of the environment and a particle filter to estimate the pose of the vehicle. The vehicle operates in dynamic and non-uniform outdoor lighting conditions, an issue that is addressed by using knowledge of the scene to intelligently adjust the camera exposure and hence improve the quality of the information in the image. Results from the industrial vehicle are shown and compared to another laser-based localiser which acts as a ground truth. An improved likelihood metric, using peredge calculation, is presented and has shown to be 40% more accurate in estimating rotation. Visual localization results from the vehicle driving an arbitrary 1.5km path during a bright sunny period show an average position error of 0.44m and rotation error of 0.62deg.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Using decision trees in economizer repair decision making

The economiser is a critical component for efficient operation of coal-fired power stations. It consists of a large system of water-filled tubes which extract heat from the exhaust gases. When it fails, usually due to erosion causing a leak, the entire power station must be shut down to effect repairs. Not only are such repairs highly expensive, but the overall repair costs are significantly affected by fluctuations in electricity market prices, due to revenue lost during the outage. As a result, decisions about when to repair an economiser can alter the repair costs by millions of dollars. Therefore, economiser repair decisions are critical and must be optimised. However, making optimal repair decisions is difficult because economiser leaks are a type of interactive failure. If left unfixed, a leak in a tube can cause additional leaks in adjacent tubes which will need more time to repair. In addition, when choosing repair times, one also needs to consider a number of other uncertain inputs such as future electricity market prices and demands. Although many different decision models and methodologies have been developed, an effective decision-making method specifically for economiser repairs has yet to be defined. In this paper, we describe a Decision Tree based method to meet this need. An industrial case study is presented to demonstrate the application of our method.

Research project for managing skid resistance is on the road

In December 2006, the Engineering and Technology Group of Queensland’s Department of Main Roads entered into a three-year skid resistance management research project with QUT Faculty of Built Environment and Engineering researchers and the QUT-based CRC for Integrated Engineering Asset Management (CIEAM). CIEAM undertakes a broad range of asset management research in the areas of defence, utilities, transportation and industrial processes. “The research project is an important activity of Main Roads’ Skid Resistance Management Plan published in June 2006.” said Main Roads project leader Mr Justin Weligamage. “The intended project output is a decision-support model for use by Road Asset Managers throughout a road network. The research objective is to enable road asset managers to better manage the surfacing condition of the road asset with specific focus on skid resistance,” said QUT project leader Professor Arun Kumar. The research project will review existing skid resistance investigatory levels, develop a risk-based method to establish skid resistance investigatory levels and improve the decision support methodology in order to minimise crashes. The new risk-based approach will be used to identify locations on the Queensland state-controlled road network that may have inadequate skid resistance. Once a high risk site is identified, the appropriate remedial action will be decided on. This approach will allow road asset managers to target optimal remedial actions, reducing the incidence and severity of crashes where inadequate skid resistance is a contributing cause.

Quantifying axial deformation of columns using vibration characteristics

Column elements at a certain level in building are subjected to loads from different tributary areas. Consequently, differential axial deformation among these elements occurs. Adverse effects of differential axial deformation increase with building height and geometric complexity. Vibrating wire, electronic strain and external mechanical strain gauges are used to measure the axial deformations to take adequate provisions to mitigate the adverse effects. These gauges require deploying in or on the elements during their construction in order to acquire necessary measurements continuously. The use of these gauges is therefore inconvenient and uneconomical. This highlights the need for a method to quantify the axial deformation using ambient measurements. This paper proposes a comprehensive vibration based method. The unique capabilities of the proposed method present through an illustrative example.

Super-resolved faces for improved face recognition from surveillance video advances in biometrics

Characteristics of surveillance video generally include low resolution and poor quality due to environmental, storage and processing limitations. It is extremely difficult for computers and human operators to identify individuals from these videos. To overcome this problem, super-resolution can be used in conjunction with an automated face recognition system to enhance the spatial resolution of video frames containing the subject and narrow down the number of manual verifications performed by the human operator by presenting a list of most likely candidates from the database. As the super-resolution reconstruction process is ill-posed, visual artifacts are often generated as a result. These artifacts can be visually distracting to humans and/or affect machine recognition algorithms. While it is intuitive that higher resolution should lead to improved recognition accuracy, the effects of super-resolution and such artifacts on face recognition performance have not been systematically studied. This paper aims to address this gap while illustrating that super-resolution allows more accurate identification of individuals from low-resolution surveillance footage. The proposed optical flow-based super-resolution method is benchmarked against Baker et al.’s hallucination and Schultz et al.’s super-resolution techniques on images from the Terrascope and XM2VTS databases. Ground truth and interpolated images were also tested to provide a baseline for comparison. Results show that a suitable super-resolution system can improve the discriminability of surveillance video and enhance face recognition accuracy. The experiments also show that Schultz et al.’s method fails when dealing surveillance footage due to its assumption of rigid objects in the scene. The hallucination and optical flow-based methods performed comparably, with the optical flow-based method producing less visually distracting artifacts that interfered with human recognition.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

QoS-aware web service composition using genetic algorithms

Web service technology is increasingly being used to build various e-Applications, in domains such as e-Business and e-Science. Characteristic benefits of web service technology are its inter-operability, decoupling and just-in-time integration. Using web service technology, an e-Application can be implemented by web service composition — by composing existing individual web services in accordance with the business process of the application. This means the application is provided to customers in the form of a value-added composite web service. An important and challenging issue of web service composition, is how to meet Quality-of-Service (QoS) requirements. This includes customer focused elements such as response time, price, throughput and reliability as well as how to best provide QoS results for the composites. This in turn best fulfils customers’ expectations and achieves their satisfaction. Fulfilling these QoS requirements or addressing the QoS-aware web service composition problem is the focus of this project. From a computational point of view, QoS-aware web service composition can be transformed into diverse optimisation problems. These problems are characterised as complex, large-scale, highly constrained and multi-objective problems. We therefore use genetic algorithms (GAs) to address QoS-based service composition problems. More precisely, this study addresses three important subproblems of QoS-aware web service composition; QoS-based web service selection for a composite web service accommodating constraints on inter-service dependence and conflict, QoS-based resource allocation and scheduling for multiple composite services on hybrid clouds, and performance-driven composite service partitioning for decentralised execution. Based on operations research theory, we model the three problems as a constrained optimisation problem, a resource allocation and scheduling problem, and a graph partitioning problem, respectively. Then, we present novel GAs to address these problems. We also conduct experiments to evaluate the performance of the new GAs. Finally, verification experiments are performed to show the correctness of the GAs. The major outcomes from the first problem are three novel GAs: a penaltybased GA, a min-conflict hill-climbing repairing GA, and a hybrid GA. These GAs adopt different constraint handling strategies to handle constraints on interservice dependence and conflict. This is an important factor that has been largely ignored by existing algorithms that might lead to the generation of infeasible composite services. Experimental results demonstrate the effectiveness of our GAs for handling the QoS-based web service selection problem with constraints on inter-service dependence and conflict, as well as their better scalability than the existing integer programming-based method for large scale web service selection problems. The major outcomes from the second problem has resulted in two GAs; a random-key GA and a cooperative coevolutionary GA (CCGA). Experiments demonstrate the good scalability of the two algorithms. In particular, the CCGA scales well as the number of composite services involved in a problem increases, while no other algorithms demonstrate this ability. The findings from the third problem result in a novel GA for composite service partitioning for decentralised execution. Compared with existing heuristic algorithms, the new GA is more suitable for a large-scale composite web service program partitioning problems. In addition, the GA outperforms existing heuristic algorithms, generating a better deployment topology for a composite web service for decentralised execution. These effective and scalable GAs can be integrated into QoS-based management tools to facilitate the delivery of feasible, reliable and high quality composite web services.

Product labelling can confer sustained increases in expected and actual satiety

Expected satiety has been shown to play a key role in decisions around meal size. Recently it has become clear that these expectations can also influence the satiety that is experienced after a food has been consumed. As such, increasing the expected and actual satiety a food product confers without increasing its caloric content is of importance. In this study we sought to determine whether this could be achieved via product labelling. Female participants (N=75) were given a 223-kcal yoghurt smoothie for lunch. In separate conditions the smoothie was labelled as a diet brand, a highly-satiating brand, or an ‘own brand’ control. Expected satiety was assessed using rating scales and a computer-based ‘method of adjustment’, both prior to consuming the smoothie and 24 hours later. Hunger and fullness were assessed at baseline, immediately after consuming the smoothie, and for a further three hours. Despite the fact that all participants consumed the same food, the smoothie branded as highly-satiating was consistently expected to deliver more satiety than the other ‘brands’; this difference was sustained 24 hours after consumption. Furthermore, post-consumption and over three hours, participants consuming this smoothie reported significantly less hunger and significantly greater fullness. These findings demonstrate that the satiety that a product confers depends in part on information that is present around the time of consumption. We suspect that this process is mediated by changes to expected satiety. These effects may potentially be utilised in the development of successful weight-management products.

Building information modeling in the Australian architecture engineering and construction industry

Building Information Modeling (BIM) is a modern approach to the design, documentation, delivery, and life cycle management of buildings through the use of project information databases coupled with object-based parametric modeling. BIM has the potential to revolutionize the Architecture, Engineering and Construction (AEC) industry in terms of the positive impact it may have on information flows, working relationships between project participants from different disciplines and the resulting benefits it may achieve through improvements to conventional methods. This chapter reviews the development of BIM, the extent to which BIM has been implemented in Australia, and the factors which have affected the up-take of BIM. More specifically, the objectives of this chapter are to investigate the adoption of BIM in the Australian AEC industry and factors that contribute towards the uptake (or non uptake) of BIM. These objectives are met by a review of the related literature in the first instance, followed by the presentation of the results of a 2007 postal questionnaire survey and telephone interviews of a random sample of professionals in the Australian AEC industry. The responses suggest that less than 25 percent of the sample had been involved in BIM – rather less than might be expected from reading the literature. Also, of those who have been involved with BIM, there has been very little interdisciplinary collaboration. The main barriers impeding the implementation of BIM widely across the Australian AEC industry are also identified. These were found to be primarily a lack of BIM expertise, lack of awareness and resistance to change. The benefits experienced as a result of using BIM are also discussed. These include improved design consistency, better coordination, cost savings, higher quality work, greater productivity and increased speed of delivery. In terms of conclusion, some suggestions are made concerning the underlying practical reasons for the slow up-take of BIM and the successes for those early adopters. Prospects for future improvement are discussed and proposals are also made for a large scale worldwide comparative study covering industry-wide participants

'Online' geriatric assessment procedure for older adults referred for geriatric assessment during an acute care episode for consideration of reliability of triage decisions

Background Comprehensive geriatric assessment has been shown to improve patient outcomes, but the geriatricians who deliver it are in short-supply. A web-based method of comprehensive geriatric assessment has been developed with the potential to improve access to specialist geriatric expertise. The current study aims to test the reliability and safety of comprehensive geriatric assessment performed “online” in making geriatric triage decisions. It will also explore the accuracy of the procedure in identifying common geriatric syndromes, and its cost relative to conventional “live” consultations. Methods/Design The study population will consist of 270 acutely hospitalized patients referred for geriatric consultation at three sites. Paired assessments (live and online) will be conducted by independent, blinded geriatricians and the level of agreement examined. This will be compared with the level of agreement between two independent, blinded geriatricians each consulting with the patient in person (i.e. “live”). Agreement between the triage decision from live-live assessments and between the triage decision from live-online assessments will be calculated using kappa statistics. Agreement between the online and live detection of common geriatric syndromes will also be assessed using kappa statistics. Resource use data will be collected for online and live-live assessments to allow comparison between the two procedures. Discussion If the online approach is found to be less precise than live assessment, further analysis will seek to identify patient subgroups where disagreement is more likely. This may enable a protocol to be developed that avoids unsafe clinical decisions at a distance. Trial registration Trial registration number: ACTRN12611000936921