Evaluation of the Suchey-Brooks Method of age estimation in an Australian subpopulation using computed tomography of the pubic symphyseal surface

Despite the prominent use of the Suchey-Brooks (S-B) method of age estimation in forensic anthropological practice, it is subject to intrinsic limitations, with reports of differential inter-population error rates between geographical locations. This study assessed the accuracy of the S-B method to a contemporary adult population in Queensland, Australia and provides robust age parameters calibrated for our population. Three-dimensional surface reconstructions were generated from computed tomography scans of the pubic symphysis of male and female Caucasian individuals aged 15–70 years (n = 195) in Amira® and Rapidform®. Error was analyzed on the basis of bias, inaccuracy and percentage correct classification for left and right symphyseal surfaces. Application of transition analysis and Chi-square statistics demonstrated 63.9% and 69.7% correct age classification associated with the left symphyseal surface of Australian males and females, respectively, using the S-B method. Using Bayesian statistics, probability density distributions for each S-B phase were calculated, providing refined age parameters for our population. Mean inaccuracies of 6.77 (±2.76) and 8.28 (±4.41) years were reported for the left surfaces of males and females, respectively; with positive biases for younger individuals (<55 years) and negative biases in older individuals. Significant sexual dimorphism in the application of the S-B method was observed; and asymmetry in phase classification of the pubic symphysis was a frequent phenomenon. These results recommend that the S-B method should be applied with caution in medico-legal death investigations of Queensland skeletal remains and warrant further investigation of reliable age estimation techniques.

Students’ beliefs about willingness to access complementary and alternative therapies (CAT) training for future integration into psychology practice.

It is suggested that all psychologists gain basic training in the types of complementary and alternative therapies (CAT) their clients may be using. As psychology students are the next cohort of health professionals who will inform future initiatives in the field, it is important to first understand the factors which influence their decisions about CAT integration. Drawing on the Theory of Planned Behavior, we investigated the beliefs that differentiate between psychology students who are high or low on willingness to access training in CAT for future practice use. Psychology students (N = 106) completed a questionnaire assessing the likelihood of both positive and negative consequences of accessing training and utilizing CAT within a psychological practice, important others approval, and barriers preventing them from this integration behavior. Those students high compared to low on willingness more likely to endorse positive outcomes (e.g., offering a more holistic approach to therapy) of accessing CAT training for future practice use and to believe that important others (e.g., clients) would support this behavior. We identified important beliefs of student psychologists related to decisions about undertaking CAT training for future professional use and can inform educators and policy-makers about CAT training and integration in psychology practice.

Characterization of sky-region morphological-temporal airborne collision detection

Automated airborne collision-detection systems are a key enabling technology for facilitat- ing the integration of unmanned aerial vehicles (UAVs) into the national airspace. These safety-critical systems must be sensitive enough to provide timely warnings of genuine air- borne collision threats, but not so sensitive as to cause excessive false-alarms. Hence, an accurate characterisation of detection and false alarm sensitivity is essential for understand- ing performance trade-offs, and system designers can exploit this characterisation to help achieve a desired balance in system performance. In this paper we experimentally evaluate a sky-region, image based, aircraft collision detection system that is based on morphologi- cal and temporal processing techniques. (Note that the examined detection approaches are not suitable for the detection of potential collision threats against a ground clutter back- ground). A novel collection methodology for collecting realistic airborne collision-course target footage in both head-on and tail-chase engagement geometries is described. Under (hazy) blue sky conditions, our proposed system achieved detection ranges greater than 1540m in 3 flight test cases with no false alarm events in 14.14 hours of non-target data (under cloudy conditions, the system achieved detection ranges greater than 1170m in 4 flight test cases with no false alarm events in 6.63 hours of non-target data). Importantly, this paper is the first documented presentation of detection range versus false alarm curves generated from airborne target and non-target image data.

Benefits of using modern cementing techniques in the acetabulum : the rim cutter

Purpose. To compare radiological records of 90 consecutive patients who underwent cemented total hip arthroplasty (THA) with or without use of the Rim Cutter to prepare the acetabulum. Methods. The acetabulum of 45 patients was prepared using the Rim Cutter, whereas the device was not used in the other 45 patients. Postoperative radiographs were evaluated using a digital templating system to measure (1) the positions of the operated hips with respect to the normal, contralateral hips (the centre of rotation of the socket, the height of the centre of rotation from the teardrop, and lateralisation of the centre of rotation from the teardrop) and (2) the uniformity and width of the cement mantle in the 3 DeLee Charnley acetabular zones, and the number of radiolucencies in these zones. Results. The study group showed improved radiological parameters and were closer to the anatomic centre of rotation both vertically (1.5 vs. 3.7 mm, p<0.001) and horizontally (1.8 vs. 4.4 mm, p<0.001) and had consistently thicker and more uniform cement mantles (p<0.001). There were 2 radiolucent lines in the control group but none in the study group. Conclusion. The Rim Cutter resulted in more accurate placement of the centre of rotation of a cemented prosthetic socket, and produced a thicker, more congruent cement mantle with fewer radiolucent lines.

A survey of tagging techniques for music, speech and environmental sound

Sound tagging has been studied for years. Among all sound types, music, speech, and environmental sound are three hottest research areas. This survey aims to provide an overview about the state-of-the-art development in these areas.We discuss about the meaning of tagging in different sound areas at the beginning of the journey. Some examples of sound tagging applications are introduced in order to illustrate the significance of this research. Typical tagging techniques include manual, automatic, and semi-automatic approaches.After reviewing work in music, speech and environmental sound tagging, we compare them and state the research progress to date. Research gaps are identified for each research area and the common features and discriminations between three areas are discovered as well. Published datasets, tools used by researchers, and evaluation measures frequently applied in the analysis are listed. In the end, we summarise the worldwide distribution of countries dedicated to sound tagging research for years.

A hybrid simulation framework to assess the impact of renewable generators on a distribution network

With an increasing number of small-scale renewable generator installations, distribution network planners are faced with new technical challenges (intermittent load flows, network imbalances…). Then again, these decentralized generators (DGs) present opportunities regarding savings on network infrastructure if installed at strategic locations. How can we consider both of these aspects when building decision tools for planning future distribution networks? This paper presents a simulation framework which combines two modeling techniques: agent-based modeling (ABM) and particle swarm optimization (PSO). ABM is used to represent the different system units of the network accurately and dynamically, simulating over short time-periods. PSO is then used to find the most economical configuration of DGs over longer periods of time. The infrastructure of the framework is introduced, presenting the two modeling techniques and their integration. A case study of Townsville, Australia, is then used to illustrate the platform implementation and the outputs of a simulation.

Eigenvoice modeling for cross likelihood ratio based speaker clustering : a Bayesian approach

This paper proposes the use of Bayesian approaches with the cross likelihood ratio (CLR) as a criterion for speaker clustering within a speaker diarization system, using eigenvoice modeling techniques. The CLR has previously been shown to be an effective decision criterion for speaker clustering using Gaussian mixture models. Recently, eigenvoice modeling has become an increasingly popular technique, due to its ability to adequately represent a speaker based on sparse training data, as well as to provide an improved capture of differences in speaker characteristics. The integration of eigenvoice modeling into the CLR framework to capitalize on the advantage of both techniques has also been shown to be beneficial for the speaker clustering task. Building on that success, this paper proposes the use of Bayesian methods to compute the conditional probabilities in computing the CLR, thus effectively combining the eigenvoice-CLR framework with the advantages of a Bayesian approach to the diarization problem. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 33.5% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system.

Students’ approaches to learning a new mathematical model

In this article, we report on the findings of an exploratory study into the experience of undergraduate students as they learn new mathematical models. Qualitative and quanti- tative data based around the students’ approaches to learning new mathematical models were collected. The data revealed that students actively adopt three approaches to under- standing a new mathematical model: gathering information for the task of understanding the model, practising with and using the model, and finding interrelationships between elements of the model. We found that the students appreciate mathematical models that have a real world application and that this can be used to engage students in higher level learning approaches.

Improving asset management process modelling and integration

Asset management (AM) processes play an important role in assisting enterprises to manage their assets more efficiently. To visualise and improve AM processes, the processes need to be modelled using certain process modelling methodologies. Understanding the requirements for AM process modelling is essential for selecting or developing effective AM process modelling methodologies. However, little research has been done on analysing the requirements. This paper attempts to fill this gap by investigating the features of AM processes. It is concluded that AM process modelling requires intuitive representation of its processes, ‘fast’ implementation of the process modelling, effective evaluation of the processes and sound system integration.

Practical insights into curricula integration for primary science

As indicated in a previous Teaching Science article, effective planning for curricula integration requires using standards from two (or more) subject areas (e.g., science and English, science and art or science and mathematics), which also becomes the assessment foci for teaching and learning. Curricula integration of standards into an activity necessitates pedagogical knowledge for developing students’ learning in both subject areas. For science education, the skills and tools for curricula integration include the use of other key learning areas (KLAs). A balance between teacher and student-centred science education programs that draw on democratic processes (e.g., Beane, 1997) can be used to make real-world links to target students’ individual needs. This article presents practical ways to commence thinking about curricula integration towards using Australian curriculum standards.

Bayesian algorithms with applications

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

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

Australian learning and teaching council projects in the mathematical sciences : a retrospective

Since 2004, the Australian Learning and Teaching Council (ALTC) and its predecessor, the Carrick Institute for Learning and Teaching in Higher Education, have funded numerous teaching and educational research-based projects in the Mathematical Sciences. In light of the Commonwealth Government’s decision to close the ALTC in 2011, it is appropriate to take account of the ALTCs input into the Mathe- matical Sciences in higher education. Here we present an overview of ALTC projects in the Mathematical Sciences, as well as report on the contributions they have made to the Discipline.