Thrust allocation with linear constrained quadratic cost function

In this paper, a method of thrust allocation based on a linearly constrained quadratic cost function capable of handling rotating azimuths is presented. The problem formulation accounts for magnitude and rate constraints on both thruster forces and azimuth angles. The advantage of this formulation is that the solution can be found with a finite number of iterations for each time step. Experiments with a model ship are used to validate the thrust allocation system.

Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover

Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making

Decreasing menopausal symptoms in women undertaking a web-based multi-modal lifestyle intervention: The Women's Wellness Program

Menopausal transition can be challenging for many women. This study tested the effectiveness of an intervention delivered in different modes in decreasing menopausal symptoms in midlife women. The Women's Wellness Program (WWP) intervention was delivered to 225 Australian women aged between 40 and 65 years through three modes (i.e., on-line independent, face-to-face with nurse consultations, and on-line with virtual nurse consultations). All women in the study were provided with a 12-week Program Book outlining healthy lifestyle behaviors while women in the consultation groups were supported by a registered nurse who provide tailored health education and assisted with individual goal setting for exercise, healthy eating, smoking and alcohol consumption. Pre- and post-intervention data were collected on menopausal symptoms (Greene Climacteric Scale), health related quality of life (SF12), and modifiable lifestyle factors. Linear mixed-effect models showed an average 0.87 and 1.23 point reduction in anxiety (p < 0.01) and depression scores (p < 0.01) over time in all groups. Results also demonstrated reduced vasomotor symptoms (β = −0.19, SE = 0.10, p = 0.04) and sexual dysfunction (β = −0.17, SE = 0.06, p < 0.01) in all participants though women in the face-to-face group generally reported greater reductions than women in the other groups. This lifestyle intervention embedded within a wellness framework has the potential to reduce menopausal symptoms and improve quality of life in midlife women thus potentially enhancing health and well-being in women as they age. Of course, study replication is needed to confirm the intervention effects.

Back to normal: A retrospective, cross-sectional study of the multi-factorial determinants of normal birth in Queensland, Australia

Background Currently, care providers and policy-makers internationally are working to promote normal birth. In Australia, such initiatives are being implemented without any evidence of the prevalence or determinants of normal birth as a multidimensional construct. This study aimed to better understand the determinants of normal birth (defined as without induction of labour, epidural/spinal/general anaesthesia, forceps/vacuum, caesarean birth, or episiotomy) using secondary analyses of data from a population survey of women in Queensland, Australia. Methods Women who birthed in Queensland during a two-week period in 2009 were mailed a survey approximately three months after birth. Women (n=772) provided retrospective data on their pregnancy, labour and birth preferences and experiences, socio-demographic characteristics, and reproductive history. A series of logistic regressions were conducted to determine factors associated with having labour, having a vaginal birth, and having a normal birth. Findings Overall, 81.9% of women had labour, 66.4% had a vaginal birth, and 29.6% had a normal birth. After adjusting for other significant factors, women had significantly higher odds of having labour if they birthed in a public hospital and had a pre-existing preference for a vaginal birth. Of women who had labour, 80.8% had a vaginal birth. Women who had labour had significantly higher odds of having a vaginal birth if they attended antenatal classes, did not have continuous fetal monitoring, felt able to ‘take their time’ in labour, and had a pre-existing preference for a vaginal birth. Of women who had a vaginal birth, 44.7% had a normal birth. Women who had a vaginal birth had significantly higher odds of having a normal birth if they birthed in a public hospital, birthed outside regular business hours, had mobility in labour, did not have continuous fetal monitoring, and were non-supine during birth. Conclusions These findings provide a strong foundation on which to base resources aimed at increasing informed decision-making for maternity care consumers, providers, and policy-makers alike. Research to evaluate the impact of modifying key clinical practices (e.g., supporting women׳s mobility during labour, facilitating non-supine positioning during birth) on the likelihood of a normal birth is an important next step.

Multi-rotor with suspended load: System dynamics and control toolbox

There is an increasing demand for Unmanned Aerial Systems (UAS) to carry suspended loads as this can provide significant benefits to several applications in agriculture, law enforcement and construction. The load impact on the underlying system dynamics should not be neglected as significant feedback forces may be induced on the vehicle during certain flight manoeuvres. The constant variation in operating point induced by the slung load also causes conventional controllers to demand increased control effort. Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored. To this end, we present a novel controller for safe and precise operation of multi-rotors with heavy slung load in three dimensions. The paper describes a System Dynamics and Control Simulation Toolbox for use with MATLAB/SIMULINK which includes a detailed simulation of the multi-rotor and slung load as well as a predictive controller to manage the nonlinear dynamics whilst accounting for system constraints. It is demonstrated that the controller simultaneously tracks specified waypoints and actively damps large slung load oscillations. A linear-quadratic regulator (LQR) is derived and control performance is compared. Results show the improved performance of the predictive controller for a larger flight envelope, including aggressive manoeuvres and large slung load displacements. The computational cost remains relatively small, amenable to practical implementations.

Structural identification of a laboratory-based steel through truss cantilevered bridge with suspended span using a layered genetic algorithm with patternsearch step (GAPS)

Structural identification (St-Id) can be considered as the process of updating a finite element (FE) model of a structural system to match the measured response of the structure. This paper presents the St-Id of a laboratory-based steel through-truss cantilevered bridge with suspended span. There are a total of 600 degrees of freedom (DOFs) in the superstructure plus additional DOFs in the substructure. The St-Id of the bridge model used the modal parameters from a preliminary modal test in the objective function of a global optimisation technique using a layered genetic algorithm with patternsearch step (GAPS). Each layer of the St-Id process involved grouping of the structural parameters into a number of updating parameters and running parallel optimisations. The number of updating parameters was increased at each layer of the process. In order to accelerate the optimisation and ensure improved diversity within the population, a patternsearch step was applied to the fittest individuals at the end of each generation of the GA. The GAPS process was able to replicate the mode shapes for the first two lateral sway modes and the first vertical bending mode to a high degree of accuracy and, to a lesser degree, the mode shape of the first lateral bending mode. The mode shape and frequency of the torsional mode did not match very well. The frequencies of the first lateral bending mode, the first longitudinal mode and the first vertical mode matched very well. The frequency of the first sway mode was lower and that of the second sway mode was higher than the true values, indicating a possible problem with the FE model. Improvements to the model and the St-Id process will be presented at the upcoming conference and compared to the results presented in this paper. These improvements will include the use of multiple FE models in a multi-layered, multi-solution, GAPS St-Id approach.

Stochastic linear multistep methods for the simulation of chemical kinetics

In this paper, we introduce the Stochastic Adams-Bashforth (SAB) and Stochastic Adams-Moulton (SAM) methods as an extension of the tau-leaping framework to past information. Using the theta-trapezoidal tau-leap method of weak order two as a starting procedure, we show that the k-step SAB method with k >= 3 is order three in the mean and correlation, while a predictor-corrector implementation of the SAM method is weak order three in the mean but only order one in the correlation. These convergence results have been derived analytically for linear problems and successfully tested numerically for both linear and non-linear systems. A series of additional examples have been implemented in order to demonstrate the efficacy of this approach.

Estimation of the time of a linear trend in monitoring survival time

Change point estimation is recognized as an essential tool of root cause analyses within quality control programs as it enables clinical experts to search for potential causes of change in hospital outcomes more effectively. In this paper, we consider estimation of the time when a linear trend disturbance has occurred in survival time following an in-control clinical intervention in the presence of variable patient mix. To model the process and change point, a linear trend in the survival time of patients who underwent cardiac surgery is formulated using hierarchical models in a Bayesian framework. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. We use Markov Chain Monte Carlo to obtain posterior distributions of the change point parameters including the location and the slope size of the trend and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time cumulative sum control chart (CUSUM) control charts for different trend scenarios. In comparison with the alternatives, step change point model and built-in CUSUM estimator, more accurate and precise estimates are obtained by the proposed Bayesian estimator over linear trends. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

Subsampling multi-species trawl catches from tropical northern Australia: Does it matter which part of the catch is sampled?

Subsampling is a common method for estimating the abundance of species in trawl catches. However, the accuracy of subsampling in representing the total catch has not been assessed. To estimate one possible source of bias due to subsampling, we tested whether the position on trawler sorting trays from which subsamples were taken affected their ability to represent species in catches. This was done by sorting catches into 10 kg subsamples and comparing subsamples taken from different positions on the sorting tray. Comparisons were made after species were grouped into three categories of abundance, either 'rare', 'common' or 'abundant'. A generalised linear model analysis showed that taking subsamples from different positions on the sorting tray had no major effect on estimating the total numbers or weights of fish or invertebrates, or the total number of fish or invertebrate taxa, recorded in each position. Some individual taxa showed differences between positions on the sorting tray (11.5% of taxa ina three-position design; 25% in a five-position design). But consistent and meaningful patterns in the position of these taxa on the sorting tray could only be seen for the pony fish Leiognathus moretoniensis and the saucer scallop Amusium pleuronectes. Because most bycatch laxa are well mixed throughout the catch, subsamples can be taken from any position on trawler sorting trays without introducing bias.

Effect of an interactive therapeutic robotic animal on engagement, mood states, agitation and psychotropic drug use in people with dementia: A cluster-randomised controlled trial protocol

Introduction: Apathy, agitated behaviours, loneliness and depression are common consequences of dementia. This trial aims to evaluate the effect of a robotic animal on behavioural and psychological symptoms of dementia in people with dementia living in long-term aged care. Methods and analysis: A cluster-randomised controlled trial with three treatment groups: PARO (robotic animal), Plush-Toy (non-robotic PARO) or Usual Care (Control). The nursing home sites are Australian Government approved and accredited facilities of 60 or more beds. The sites are located in South-East Queensland, Australia. A sample of 380 adults with a diagnosis of dementia, aged 60 years or older living in one of the participating facilities will be recruited. The intervention consists of three individual 15 min non-facilitated sessions with PARO or Plush- Toy per week, for a period of 10 weeks. The primary outcomes of interest are improvement in agitation, mood states and engagement. Secondary outcomes include sleep duration, step count, change in psychotropic medication use, change in treatment costs, and staff and family perceptions of PARO or Plush-Toy. Video data will be analysed using Noldus XT Pocket Observer; descriptive statistics will be used for participants’ demographics and outcome measures; cluster and individual level analyses to test all hypotheses and Generalised Linear Models for cluster level and Generalised Estimation Equations and/or Multi-level Modeling for individual level data. Ethics and dissemination: The study participants or their proxy will provide written informed consent. The Griffith University Human Research Ethics Committee has approved the study (NRS/03/14/HREC). The results of the study will provide evidence of the efficacy of a robotic animal as a psychosocial treatment for the behavioural and psychological symptoms of dementia. Findings will be presented at local and international conference meetings and published in peer-reviewed journals.

Multi-GNSS precise point positioning with raw single-frequency and dual-frequency measurement models

The emergence of multiple satellite navigation systems, including BDS, Galileo, modernized GPS, and GLONASS, brings great opportunities and challenges for precise point positioning (PPP). We study the contributions of various GNSS combinations to PPP performance based on undifferenced or raw observations, in which the signal delays and ionospheric delays must be considered. A priori ionospheric knowledge, such as regional or global corrections, strengthens the estimation of ionospheric delay parameters. The undifferenced models are generally more suitable for single-, dual-, or multi-frequency data processing for single or combined GNSS constellations. Another advantage over ionospheric-free PPP models is that undifferenced models avoid noise amplification by linear combinations. Extensive performance evaluations are conducted with multi-GNSS data sets collected from 105 MGEX stations in July 2014. Dual-frequency PPP results from each single constellation show that the convergence time of undifferenced PPP solution is usually shorter than that of ionospheric-free PPP solutions, while the positioning accuracy of undifferenced PPP shows more improvement for the GLONASS system. In addition, the GLONASS undifferenced PPP results demonstrate performance advantages in high latitude areas, while this impact is less obvious in the GPS/GLONASS combined configuration. The results have also indicated that the BDS GEO satellites have negative impacts on the undifferenced PPP performance given the current “poor” orbit and clock knowledge of GEO satellites. More generally, the multi-GNSS undifferenced PPP results have shown improvements in the convergence time by more than 60 % in both the single- and dual-frequency PPP results, while the positioning accuracy after convergence indicates no significant improvements for the dual-frequency PPP solutions, but an improvement of about 25 % on average for the single-frequency PPP solutions.

Experiences with an Object-Oriented, Multi-Stage Language

Metaphor is a multi-stage programming language extension to an imperative, object-oriented language in the style of C# or Java. This paper discusses some issues we faced when applying multi-stage language design concepts to an imperative base language and run-time environment. The issues range from dealing with pervasive references and open code to garbage collection and implementing cross-stage persistence.