Improved estimation of size-transition matrices using tag-recapture data

We derive a new method for determining size-transition matrices (STMs) that eliminates probabilities of negative growth and accounts for individual variability. STMs are an important part of size-structured models, which are used in the stock assessment of aquatic species. The elements of STMs represent the probability of growth from one size class to another, given a time step. The growth increment over this time step can be modelled with a variety of methods, but when a population construct is assumed for the underlying growth model, the resulting STM may contain entries that predict negative growth. To solve this problem, we use a maximum likelihood method that incorporates individual variability in the asymptotic length, relative age at tagging, and measurement error to obtain von Bertalanffy growth model parameter estimates. The statistical moments for the future length given an individual’s previous length measurement and time at liberty are then derived. We moment match the true conditional distributions with skewed-normal distributions and use these to accurately estimate the elements of the STMs. The method is investigated with simulated tag–recapture data and tag–recapture data gathered from the Australian eastern king prawn (Melicertus plebejus).

The effectiveness of SMS reminders on appointment attendance: A meta-analysis

Objective To identify the efficacy of short message service (SMS) reminders in health care appointment attendance. Materials and Methods A systematic review was undertaken to identify studies published between 2005 and 2015 that compared the attendance rates of patients receiving SMS reminders compared to patients not receiving a reminder. Each article was examined for information regarding the study design, sample size, population demographics and intervention methods. A meta-analysis was used to calculate a pooled estimate odds ratio. Results Twenty-eight (28) studies were included in the review, including 13 (46%) randomized controlled trials. The pooled odds ratio of the randomized control trials was 1.62 (1.35 – 1.94). Half of the studies reviewed sent the reminder within 48 hour prior to the appointment time, yet no significant subgroups differences with respect to participant age, SMS timing, rate or type, setting or specialty was detectable. Discussion All studies, except one with a small sample size, demonstrated a positive OR, indicating SMS reminders were an effective means of improving appointment attendance. There was no significant difference in OR when controlling for when the SMS was sent, the frequency of the reminders or the content of the reminder. Conclusion SMS appointment reminders are an effective and operative method in improving appointment attendance in a health care setting and this effectiveness has improved over the past five years. Further research is required to identify the optimal SMS reminder timing and frequency, specifically in relation to the length of time since the appointment.

Nonparametric traversability estimation in partially occluded and deformable terrain

Terrain traversability estimation is a fundamental requirement to ensure the safety of autonomous planetary rovers and their ability to conduct long-term missions. This paper addresses two fundamental challenges for terrain traversability estimation techniques. First, representations of terrain data, which are typically built by the rover’s onboard exteroceptive sensors, are often incomplete due to occlusions and sensor limitations. Second, during terrain traversal, the rover-terrain interaction can cause terrain deformation, which may significantly alter the difficulty of traversal. We propose a novel approach built on Gaussian process (GP) regression to learn, and consequently to predict, the rover’s attitude and chassis configuration on unstructured terrain using terrain geometry information only. First, given incomplete terrain data, we make an initial prediction under the assumption that the terrain is rigid, using a learnt kernel function. Then, we refine this initial estimate to account for the effects of potential terrain deformation, using a near-to-far learning approach based on multitask GP regression. We present an extensive experimental validation of the proposed approach on terrain that is mostly rocky and whose geometry changes as a result of loads from rover traversals. This demonstrates the ability of the proposed approach to accurately predict the rover’s attitude and configuration in partially occluded and deformable terrain.

The effectiveness of the Precautionary Principle in protecting Australia's endangered species

This report is the result of a small-scale experiment looking at improving methods for evaluating environmental laws. The objective in this research was to evaluate the effectiveness of the precautionary principle – an accepted principle of international environmental law – in the context of Australia’s endangered species. Two case studies were selected by our team: the (Great) White Shark and an endangered native Australian plant known as Tylophora Linearis.

Analysing the Impact of Learning Inputs - Application to Terrain Traversability Estimation

Data-driven approaches such as Gaussian Process (GP) regression have been used extensively in recent robotics literature to achieve estimation by learning from experience. To ensure satisfactory performance, in most cases, multiple learning inputs are required. Intuitively, adding new inputs can often contribute to better estimation accuracy, however, it may come at the cost of a new sensor, larger training dataset and/or more complex learning, some- times for limited benefits. Therefore, it is crucial to have a systematic procedure to determine the actual impact each input has on the estimation performance. To address this issue, in this paper we propose to analyse the impact of each input on the estimate using a variance-based sensitivity analysis method. We propose an approach built on Analysis of Variance (ANOVA) decomposition, which can characterise how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We apply the proposed approach to a terrain-traversability estimation method we proposed in prior work, which is based on multi-task GP regression, and we validate this implementation experimentally using a rover on a Mars-analogue terrain.

Estimation of cosmological parameters using adaptive importance sampling

We present a Bayesian sampling algorithm called adaptive importance sampling or population Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to considerably reduce the wall-clock time required for sampling, along with providing other benefits. To assess the performance of the approach for cosmological problems, we use simulated and actual data consisting of CMB anisotropies, supernovae of type Ia, and weak cosmological lensing, and provide a comparison of results to those obtained using state-of-the-art Markov chain Monte Carlo (MCMC). For both types of data sets, we find comparable parameter estimates for PMC and MCMC, with the advantage of a significantly lower wall-clock time for PMC. In the case of WMAP5 data, for example, the wall-clock time scale reduces from days for MCMC to hours using PMC on a cluster of processors. Other benefits of the PMC approach, along with potential difficulties in using the approach, are analyzed and discussed.

Bayesian mixture model estimation of aerosol particle size distributions

In this paper, we examine approaches to estimate a Bayesian mixture model at both single and multiple time points for a sample of actual and simulated aerosol particle size distribution (PSD) data. For estimation of a mixture model at a single time point, we use Reversible Jump Markov Chain Monte Carlo (RJMCMC) to estimate mixture model parameters including the number of components which is assumed to be unknown. We compare the results of this approach to a commonly used estimation method in the aerosol physics literature. As PSD data is often measured over time, often at small time intervals, we also examine the use of an informative prior for estimation of the mixture parameters which takes into account the correlated nature of the parameters. The Bayesian mixture model offers a promising approach, providing advantages both in estimation and inference.

A novel method for the age estimation of Saddletail snapper (Lutjanus malabaricus) using Fourier Transform-near infrared (FT-NIR) spectroscopy

Near infrared (NIR) spectroscopy was investigated as a potential rapid method of estimating fish age from whole otoliths of Saddletail snapper (Lutjanus malabaricus). Whole otoliths from 209 Saddletail snapper were extracted and the NIR spectral characteristics were acquired over a spectral range of 800–2780 nm. Partial least-squares models (PLS) were developed from the diffuse reflectance spectra and reference-validated age estimates (based on traditional sectioned otolith increments) to predict age for independent otolith samples. Predictive models developed for a specific season and geographical location performed poorly against a different season and geographical location. However, overall PLS regression statistics for predicting a combined population incorporating both geographic location and season variables were: coefficient of determination (R2) = 0.94, root mean square error of prediction (RMSEP) = 1.54 for age estimation, indicating that Saddletail age could be predicted within 1.5 increment counts. This level of accuracy suggests the method warrants further development for Saddletail snapper and may have potential for other fish species. A rapid method of fish age estimation could have the potential to reduce greatly both costs of time and materials in the assessment and management of commercial fisheries.

The utility of near infrared spectroscopy for age estimation of deepwater sharks

Reliable age information is vital for effective fisheries management, yet age determinations are absent for many deepwater sharks as they cannot be aged using traditional methods of growth bands counts. An alternative approach to ageing using near infrared spectroscopy (NIRS) was investigated using dorsal fin spines, vertebrae and fin clips of three species of deepwater sharks. Ages were successfully estimated for the two dogfish, Squalus megalops and Squalus montalbani, and NIRS spectra were correlated with body size in the catshark, Asymbolus pallidus. Correlations between estimated-ages of the dogfish dorsal fin spines and their NIRS spectra were good, with S. megalops R2=0.82 and S. montalbani R2=0.73. NIRS spectra from S. megalops vertebrae and fin clips that have no visible growth bands were correlated with estimated-ages, with R2=0.89 and 0.76, respectively. NIRS has the capacity to non-lethally estimate ages from fin spines and fin clips, and thus could significantly reduce the numbers of sharks that need to be lethally sampled for ageing studies. The detection of ageing materials by NIRS in poorly calcified deepwater shark vertebrae could potentially enable ageing of this group of sharks that are vulnerable to exploitation.

Reducing falls in hospital through education to change patient and staff behaviour: A stepped wedge cluster randomised controlled effectiveness trial

Background: Falls remain the most frequent adverse event reported in hospitals, particularly geriatric rehabilitation wards. Randomised trials reducing fall injuries in hospitals have been elusive. Our previous randomised trial (n = 1206) demonstrated that multimedia education with physiotherapist falls educator support reduced falls among patients with higher cognition levels, but this benefit was offset by a potential increase in falls rates among patients with poor cognition. In the previous trial, hospital staff were blinded to the allocation of individual patients, and only delivered usual care.

Remote sensing of boreal land cover: estimation of forest attributes and extent

Remote sensing provides methods to infer land cover information over large geographical areas at a variety of spatial and temporal resolutions. Land cover is input data for a range of environmental models and information on land cover dynamics is required for monitoring the implications of global change. Such data are also essential in support of environmental management and policymaking. Boreal forests are a key component of the global climate and a major sink of carbon. The northern latitudes are expected to experience a disproportionate and rapid warming, which can have a major impact on vegetation at forest limits. This thesis examines the use of optical remote sensing for estimating aboveground biomass, leaf area index (LAI), tree cover and tree height in the boreal forests and tundra taiga transition zone in Finland. The continuous fields of forest attributes are required, for example, to improve the mapping of forest extent. The thesis focus on studying the feasibility of satellite data at multiple spatial resolutions, assessing the potential of multispectral, -angular and -temporal information, and provides regional evaluation for global land cover data. Preprocessed ASTER, MISR and MODIS products are the principal satellite data. The reference data consist of field measurements, forest inventory data and fine resolution land cover maps. Fine resolution studies demonstrate how statistical relationships between biomass and satellite data are relatively strong in single species and low biomass mountain birch forests in comparison to higher biomass coniferous stands. The combination of forest stand data and fine resolution ASTER images provides a method for biomass estimation using medium resolution MODIS data. The multiangular data improve the accuracy of land cover mapping in the sparsely forested tundra taiga transition zone, particularly in mires. Similarly, multitemporal data improve the accuracy of coarse resolution tree cover estimates in comparison to single date data. Furthermore, the peak of the growing season is not necessarily the optimal time for land cover mapping in the northern boreal regions. The evaluated coarse resolution land cover data sets have considerable shortcomings in northernmost Finland and should be used with caution in similar regions. The quantitative reference data and upscaling methods for integrating multiresolution data are required for calibration of statistical models and evaluation of land cover data sets. The preprocessed image products have potential for wider use as they can considerably reduce the time and effort used for data processing.

On the orthogonality of suboptimum estimate in the combined estimation and control problem

By deriving the equations for an error analysis of modeling inaccuracies for the combined estimation and control problem, it is shown that the optimum estimation error is orthogonal to the actual suboptimum estimate.

Estimation and evaluation of multi-decadal fire severity patterns using Landsat sensors

Retrospective identification of fire severity can improve our understanding of fire behaviour and ecological responses. However, burnt area records for many ecosystems are non-existent or incomplete, and those that are documented rarely include fire severity data. Retrospective analysis using satellite remote sensing data captured over extended periods can provide better estimates of fire history. This study aimed to assess the relationship between the Landsat differenced normalised burn ratio (dNBR) and field measured geometrically structured composite burn index (GeoCBI) for retrospective analysis of fire severity over a 23 year period in sclerophyll woodland and heath ecosystems. Further, we assessed for reduced dNBR fire severity classification accuracies associated with vegetation regrowth at increasing time between ignition and image capture. This was achieved by assessing four Landsat images captured at increasing time since ignition of the most recent burnt area. We found significant linear GeoCBI–dNBR relationships (R2 = 0.81 and 0.71) for data collected across ecosystems and for Eucalyptus racemosa ecosystems, respectively. Non-significant and weak linear relationships were observed for heath and Melaleuca quinquenervia ecosystems, suggesting that GeoCBI–dNBR was not appropriate for fire severity classification in specific ecosystems. Therefore, retrospective fire severity was classified across ecosystems. Landsat images captured within ~ 30 days after fire events were minimally affected by post burn vegetation regrowth.

The effectiveness of the teach-back method on adherence and self-management in health education for people with chronic disease: A systematic review

- BACKGROUND Chronic diseases are increasing worldwide and have become a significant burden to those affected by those diseases. Disease-specific education programs have demonstrated improved outcomes, although people do forget information quickly or memorize it incorrectly. The teach-back method was introduced in an attempt to reinforce education to patients. To date, the evidence regarding the effectiveness of health education employing the teach-back method in improved care has not yet been reviewed systematically. - OBJECTIVES This systematic review examined the evidence on using the teach-back method in health education programs for improving adherence and self-management of people with chronic disease. - INCLUSION CRITERIA Types of participants: Adults aged 18 years and over with one or more than one chronic disease. Types of intervention: All types of interventions which included the teach-back method in an education program for people with chronic diseases. The comparator was chronic disease education programs that did not involve the teach-back method. Types of studies: Randomized and non-randomized controlled trials, cohort studies, before-after studies and case-control studies. Types of outcomes: The outcomes of interest were adherence, self-management, disease-specific knowledge, readmission, knowledge retention, self-efficacy and quality of life. - SEARCH STRATEGY Searches were conducted in CINAHL, MEDLINE, EMBASE, Cochrane CENTRAL, Web of Science, ProQuest Nursing and Allied Health Source, and Google Scholar databases. Search terms were combined by AND or OR in search strings. Reference lists of included articles were also searched for further potential references. - METHODOLOGICAL QUALITY Two reviewers conducted quality appraisal of papers using the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument. - DATA EXTRACTION Data were extracted using the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument data extraction instruments. - DATA SYNTHESIS There was significant heterogeneity in selected studies, hence a meta-analysis was not possible and the results were presented in narrative form. - RESULTS Of the 21 articles retrieved in full, 12 on the use of the teach-back method met the inclusion criteria and were selected for analysis. Four studies confirmed improved disease-specific knowledge in intervention participants. One study showed a statistically significant improvement in adherence to medication and diet among type 2 diabetics patients in the intervention group compared to the control group (p < 0.001). Two studies found statistically significant improvements in self-efficacy (p = 0.0026 and p < 0.001) in the intervention groups. One study examined quality of life in heart failure patients but the results did not improve from the intervention (p = 0.59). Five studies found a reduction in readmission rates and hospitalization but these were not always statistically significant. Two studies showed improvement in daily weighing among heart failure participants, and in adherence to diet, exercise and foot care among those with type 2 diabetes. - CONCLUSION Overall, the teach-back method showed positive effects in a wide range of health care outcomes although these were not always statistically significant. Studies in this systematic review revealed improved outcomes in disease-specific knowledge, adherence, self-efficacy and the inhaler technique. There was a positive but inconsistent trend also seen in improved self-care and reduction of hospital readmission rates. There was limited evidence on improvement in quality of life or disease related knowledge retention.

Computationally Efficient Methods for MDL-Optimal Density Estimation and Data Clustering

The Minimum Description Length (MDL) principle is a general, well-founded theoretical formalization of statistical modeling. The most important notion of MDL is the stochastic complexity, which can be interpreted as the shortest description length of a given sample of data relative to a model class. The exact definition of the stochastic complexity has gone through several evolutionary steps. The latest instantation is based on the so-called Normalized Maximum Likelihood (NML) distribution which has been shown to possess several important theoretical properties. However, the applications of this modern version of the MDL have been quite rare because of computational complexity problems, i.e., for discrete data, the definition of NML involves an exponential sum, and in the case of continuous data, a multi-dimensional integral usually infeasible to evaluate or even approximate accurately. In this doctoral dissertation, we present mathematical techniques for computing NML efficiently for some model families involving discrete data. We also show how these techniques can be used to apply MDL in two practical applications: histogram density estimation and clustering of multi-dimensional data.