Geometric sensitivity of patient-specific finite element models of the spine to variability in user-selected anatomical landmarks

Software to create individualised finite element (FE) models of the osseoligamentous spine using pre-operative computed tomography (CT) data-sets for spinal surgery patients has recently been developed. This study presents a geometric sensitivity analysis of this software to assess the effect of intra-observer variability in user-selected anatomical landmarks. User-selected landmarks on the osseous anatomy were defined from CT data-sets for three scoliosis patients and these landmarks were used to reconstruct patient-specific anatomy of the spine and ribcage using parametric descriptions. The intra-observer errors in landmark co-ordinates for these anatomical landmarks were calculated. FE models of the spine and ribcage were created using the reconstructed anatomy for each patient and these models were analysed for a loadcase simulating clinical flexibility assessment. The intra-observer error in the anatomical measurements was low in comparison to the initial dimensions, with the exception of the angular measurements for disc wedge and zygapophyseal joint (z-joint) orientation and disc height. This variability suggested that CT resolution may influence such angular measurements, particularly for small anatomical features, such as the z-joints, and may also affect disc height. The results of the FE analysis showed low variation in the model predictions for spinal curvature with the mean intra-observer variability substantially less than the accepted error in clinical measurement. These findings demonstrate that intra-observer variability in landmark point selection has minimal effect on the subsequent FE predictions for a clinical loadcase.

Nonlinear container ship model for the study of parametric roll resonance

Parametric roll is a critical phenomenon for ships, whose onset may cause roll oscillations up to +-40 degrees, leading to very dangerous situations and possibly capsizing. Container ships have been shown to be particularly prone to parametric roll resonance when they are sailing in moderate to heavy head seas. A Matlab/Simulink parametric roll benchmark model for a large container ship has been implemented and validated against a wide set of experimental data. The model is a part of a Matlab/Simulink Toolbox (MSS, 2007). The benchmark implements a 3rd-order nonlinear model where the dynamics of roll is strongly coupled with the heave and pitch dynamics. The implemented model has shown good accuracy in predicting the container ship motions, both in the vertical plane and in the transversal one. Parametric roll has been reproduced for all the data sets in which it happened, and the model provides realistic results which are in good agreement with the model tank experiments.

Measurement equivalence of the dispositional resistance to change scale

Intended to bridge the gap between the latest methodological developments and cross-cultural research, this interdisciplinary resource presents the latest strategies for analyzing cross-cultural data. Techniques are demonstrated through the use of applications that employ cross national data sets such as the latest European Social Survey. With an emphasis on the generalized latent variable approach, internationally?prominent researchers from a variety of fields explain how the methods work, how to apply them, and how they relate to other methods presented in the book. Syntax and graphical and verbal explanations of the techniques are included. [from publisher's website]

Classifying natural aerial scenery for autonomous aircraft emergency landing

In this paper, we present an approach for image-based surface classification using multi-class Support Vector Machine (SVM). Classifying surfaces in aerial images is an important step towards an increased aircraft autonomy in emergency landing situations. We design a one-vs-all SVM classifier and conduct experiments on five data sets. Results demonstrate consistent overall performance figures over 88% and approximately 8% more accurate to those published on multi-class SVM on the KTH TIPS data set. We also show per-class performance values by using normalised confusion matrices. Our approach is designed to be executed online using a minimum set of feature attributes representing a feasible and ready-to-deploy system for onboard execution.

The effect of preferential sampling on sampling variance

We examine some variations of standard probability designs that preferentially sample sites based on how easy they are to access. Preferential sampling designs deliver unbiased estimates of mean and sampling variance and will ease the burden of data collection but at what cost to our design efficiency? Preferential sampling has the potential to either increase or decrease sampling variance depending on the application. We carry out a simulation study to gauge what effect it will have when sampling Soil Organic Carbon (SOC) values in a large agricultural region in south-eastern Australia. Preferential sampling in this region can reduce the distance to travel by up to 16%. Our study is based on a dataset of predicted SOC values produced from a datamining exercise. We consider three designs and two ways to determine ease of access. The overall conclusion is that sampling performance deteriorates as the strength of preferential sampling increases, due to the fact the regions of high SOC are harder to access. So our designs are inadvertently targeting regions of low SOC value. The good news, however, is that Generalised Random Tessellation Stratification (GRTS) sampling designs are not as badly affected as others and GRTS remains an efficient design compared to competitors.

Study Protocol - Alcohol Management Plans (AMPs) in remote indigenous communities in Queensland: their impacts on injury, violence, health and social indicators and their cost-effectiveness

Background In 2002/03 the Queensland Government responded to high rates of alcohol-related harm in discrete Indigenous communities by implementing alcohol management plans (AMPs), designed to include supply and harm reduction and treatment measures. Tighter alcohol supply and carriage restrictions followed in 2008 following indications of reductions in violence and injury. Despite the plans being in place for over a decade, no comprehensive independent review has assessed to what level the designed aims were achieved and what effect the plans have had on Indigenous community residents and service providers. This study will describe the long-term impacts on important health, economic and social outcomes of Queensland’s AMPs. Methods/Design The project has two main studies, 1) outcome evaluation using de-identified epidemiological data on injury, violence and other health and social indicators for across Queensland, including de-identified databases compiled from relevant routinely-available administrative data sets, and 2) a process evaluation to map the nature, timing and content of intervention components targeting alcohol. Process evaluation will also be used to assess the fidelity with which the designed intervention components have been implemented, their uptake and community responses to them and their perceived impacts on alcohol supply and consumption, injury, violence and community health. Interviews and focus groups with Indigenous residents and service providers will be used. The study will be conducted in all 24 of Queensland’s Indigenous communities affected by alcohol management plans. Discussion This evaluation will report on the impacts of the original aims for AMPs, what impact they have had on Indigenous residents and service providers. A central outcome will be the establishment of relevant databases describing the parameters of the changes seen. This will permit comprehensive and rigorous surveillance systems to be put in place and provided to communities empowering them with the best credible evidence to judge future policy and program requirements for themselves. The project will inform impending alcohol policy and program adjustments in Queensland and other Australian jurisdictions. The project has been approved by the James Cook University Human Research Ethics Committee (approval number H4967 & H5241).

Dynamic modelling of long-term care decisions

This paper describes and analyzes research on the dynamics of long-term care and the policy relevance of identifying the sources of persistence in caregiving arrangements (including the effect of dynamics on parameter estimates, implications for family welfare, parent welfare, child welfare, and cost of government programs). We discuss sources and causes of observed persistence in caregiving arrangements including inertia/state dependence (confounded by unobserved heterogeneity) and costs of changing caregivers. We comment on causes of dynamics including learning/human capital accumulation; burnout; and game-playing. We suggest how to deal with endogenous geography; dynamics in discrete and continuous choices; and equilibrium issues (multiple equilibria, dynamic equilibria). We also present an overview of commonly used longitudinal data sets and evaluate their relative advantages/disadvantages. We also discuss other data issues related to noisy measures of wealth and family structure. Finally, we suggest some methods to handle econometric problems such as endogeneous geography. © 2014 Springer Science+Business Media New York.

A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples

A new test of hypothesis for classifying stationary time series based on the bias-adjusted estimators of the fitted autoregressive model is proposed. It is shown theoretically that the proposed test has desirable properties. Simulation results show that when time series are short, the size and power estimates of the proposed test are reasonably good, and thus this test is reliable in discriminating between short-length time series. As the length of the time series increases, the performance of the proposed test improves, but the benefit of bias-adjustment reduces. The proposed hypothesis test is applied to two real data sets: the annual real GDP per capita of six European countries, and quarterly real GDP per capita of five European countries. The application results demonstrate that the proposed test displays reasonably good performance in classifying relatively short time series.

Polarization of forecast densities : a new approach to time series classification

Time series classification has been extensively explored in many fields of study. Most methods are based on the historical or current information extracted from data. However, if interest is in a specific future time period, methods that directly relate to forecasts of time series are much more appropriate. An approach to time series classification is proposed based on a polarization measure of forecast densities of time series. By fitting autoregressive models, forecast replicates of each time series are obtained via the bias-corrected bootstrap, and a stationarity correction is considered when necessary. Kernel estimators are then employed to approximate forecast densities, and discrepancies of forecast densities of pairs of time series are estimated by a polarization measure, which evaluates the extent to which two densities overlap. Following the distributional properties of the polarization measure, a discriminant rule and a clustering method are proposed to conduct the supervised and unsupervised classification, respectively. The proposed methodology is applied to both simulated and real data sets, and the results show desirable properties.

Development of thin layer mortared concrete masonry

This research was a step forward to developing data sets for thin layer mortared concrete masonry through systematic experimental and numerical studies. Since thin layer mortared concrete masonry is relatively new type of masonry construction, methodical research studies have been undertaken to properly address the gaps in understanding of this masonry system. As part of the ARC Linkage research project, this thesis has been developed to extend the knowledge on thin layer mortared concrete masonry.

Large scale read classification for next generation sequencing

Next Generation Sequencing (NGS) has revolutionised molecular biology, resulting in an explosion of data sets and an increasing role in clinical practice. Such applications necessarily require rapid identification of the organism as a prelude to annotation and further analysis. NGS data consist of a substantial number of short sequence reads, given context through downstream assembly and annotation, a process requiring reads consistent with the assumed species or species group. Highly accurate results have been obtained for restricted sets using SVM classifiers, but such methods are difficult to parallelise and success depends on careful attention to feature selection. This work examines the problem at very large scale, using a mix of synthetic and real data with a view to determining the overall structure of the problem and the effectiveness of parallel ensembles of simpler classifiers (principally random forests) in addressing the challenges of large scale genomics.

Exploring the properties of macroscopic fundamental diagram : analysing Brisbane urban network

This thesis explored traffic characteristics at the aggregate level for area-wide traffic monitoring of large urban area. It focused on three aspects: understanding a macroscopic network performance under real-time traffic information provision, measuring traffic performance of a signalised arterial network using available data sets, and discussing network zoning for monitoring purposes in the case of Brisbane, Australia. This work presented the use of probe vehicle data for estimating traffic state variables, and illustrated dynamic features of regional traffic performance of Brisbane. The results confirmed the viability and effectiveness of area-wide traffic monitoring.

Improving the outcomes of foot and ankle surgery through the audit cycle : a case study

The direct costs of managing adverse outcomes from Australian health care are estimated to be $2 billion. The audit cycle is considered an important tool to assist in the preventive management of adverse outcomes.Australian guidelines for audit cycle design allow for comparison of data sets derived from similar surgical specialities. However a lack of data set standardisation inhibits meaningful comparisons of foot and ankle surgical audits. This research will assist development of a best practice model for auditing foot and ankle surgery. Data derived from this model will improve the safety and quality of foot and ankle surgery. The preliminary phase of this process is to identify and understand the attitudes and behaviours of how and why surgeons participate in the audit cycle. A descriptive embedded multiple case study research design is planned to provide an intense focus on a single phenomenon (the audit cycle) within its real life context (clinical governance). The measures to be included in the case study have been identified by the Balanced Patient Safety Measurement Framework. These include: audit and peer review activity, provider attitudes to patient safety, safety learning, action and performance. A purposive sample of 6 to 8 surgeons (units of analysis) from 3 to 4 specialities (cases) will undergo semi-structured interview. This will investigate: current audit tools and processes; attitudes; and behaviours of surgeons to the audit cycle. Similarities in and differences between the units of analysis will indicate which identified measures function as barriers or enablers of the audit cycle. Reliability and validity (external and construct) will be assessed using established methods for case studies. The descriptive embedded multiple case study will reveal how and why foot and ankle surgeons participate in the audit cycle. This will inform further research to improve the outcomes of foot and ankle surgery through development of an audit tool.

An analytical framework for quantifying aquifer response time scales associated with transient boundary conditions

A major challenge in studying coupled groundwater and surface-water interactions arises from the considerable difference in the response time scales of groundwater and surface-water systems affected by external forcings. Although coupled models representing the interaction of groundwater and surface-water systems have been studied for over a century, most have focused on groundwater quantity or quality issues rather than response time. In this study, we present an analytical framework, based on the concept of mean action time (MAT), to estimate the time scale required for groundwater systems to respond to changes in surface-water conditions. MAT can be used to estimate the transient response time scale by analyzing the governing mathematical model. This framework does not require any form of transient solution (either numerical or analytical) to the governing equation, yet it provides a closed form mathematical relationship for the response time as a function of the aquifer geometry, boundary conditions, and flow parameters. Our analysis indicates that aquifer systems have three fundamental time scales: (i) a time scale that depends on the intrinsic properties of the aquifer; (ii) a time scale that depends on the intrinsic properties of the boundary condition, and; (iii) a time scale that depends on the properties of the entire system. We discuss two practical scenarios where MAT estimates provide useful insights and we test the MAT predictions using new laboratory-scale experimental data sets.

Datascape

"Contemporary society is in the midst of the boundless generation and collection of data, data that is produced from almost any measurable act. Be it weather or transport data sets published by government agencies, or the individual and interpersonal data generated by our digital interactions; a server somewhere is collating. With the rise of this digital data phenomenon comes questions of comprehension, purpose, ownership and translation. Without mediation digital data is an immense abstract list of text and numbers and in this abstracted form data sets become detached from the circumstances of their creation. Artists and digital creatives are building works from these constantly evolving data sets to develop a discourse that investigates, appropriates, reveals and reflects upon the society and environment that generates this medium. Datascape presents a range of works that use data as building blocks to facilitate connections and understanding around a range of personal, social and worldly issues. The exhibition is concerned with creating an opportunity for experiential discovery through engaging with work from some of the world’s prominent creatives in this field of practice. Utilising three thematic lenses: Generative Currents, the Anti-Sublime and the Human Context, the works offer a variety of pathways to traverse the Datascape. Lubi Thomas and Rachael Parsons, QUT Creative Industries Precinct"