Jump into Number

The DVD, Jump into Number, was a joint project between Independent Schools Queensland, Queensland University of Technology and Catholic Education (Diocese of Cairns) aimed at improving mathematical practice in the early years. Independent Schools Queensland Executive Director Dr John Roulston said the invaluable teaching resource features a series of unscripted lessons which demonstrate the possibilities of learning among young Indigenous students. “Currently there is a lack of teaching resources for numeracy in younger students, especially from pre Prep to Year 3 which is such an important stage of a child’s early education. Jump into Number is a benchmark for all teachers to learn more about the mathematical development of younger students,” Dr Roulston said.

Straightforward biodegradable nanoparticle generation through megahertz-order ultrasonic atomization

Simple and reliable formation of biodegradable nanoparticles formed from poly-ε-caprolactone was achieved using 1.645 MHz piston atomization of a source fluid of 0.5% w/v of the polymer dissolved in acetone; the particles were allowed to descend under gravity in air 8 cm into a 1 mM solution of sodium dodecyl sulfate. After centrifugation to remove surface agglomerations, a symmetric monodisperse distribution of particles φ 186 nm (SD=5.7, n=6) was obtained with a yield of 65.2%. © 2006 American Institute of Physics.

A Fixed Order Model Matching Approach for the Design of Robust Power System Oscillation Damper

This book focuses on how evolutionary computing techniques benefit engineering research and development tasks by converting practical problems of growing complexities into simple formulations, thus largely reducing development efforts. This book begins with an overview of the optimization theory and modern evolutionary computing techniques, and goes on to cover specific applications of evolutionary computing to power system optimization and control problems.

A micro-level investigation of the solid displacement method for porosity determination of dried food

Porosity is one of the key parameters of the macroscopic structure of porous media, generally defined as the ratio of the free spaces occupied (by the volume of air) within the material to the total volume of the material. Porosity is determined by measuring skeletal volume and the envelope volume. Solid displacement method is one of the inexpensive and easy methods to determine the envelope volume of a sample with an irregular shape. In this method, generally glass beads are used as a solid due to their uniform size, compactness and fluidity properties. The smaller size of the glass beads means that they enter into the open pores which have a larger diameter than the glass beads. Although extensive research has been carried out on porosity determination using displacement method, no study exists which adequately reports micro-level observation of the sample during measurement. This study set out with the aim of assessing the accuracy of solid displacement method of bulk density measurement of dried foods by micro-level observation. Solid displacement method of porosity determination was conducted using a cylindrical vial (cylindrical plastic container) and 57 µm glass beads in order to measure the bulk density of apple slices at different moisture contents. A scanning electron microscope (SEM), a profilometer and ImageJ software were used to investigate the penetration of glass beads into the surface pores during the determination of the porosity of dried food. A helium pycnometer was used to measure the particle density of the sample. Results show that a significant number of pores were large enough to allow the glass beads to enter into the pores, thereby causing some erroneous results. It was also found that coating the dried sample with appropriate coating material prior to measurement can resolve this problem.

A follow up sample of first time drink driving offenders : how many drink drive post offence?

Background Drink driving remains an important issue to address in terms of health and injury prevention even though research shows that over time there has been a steady decline in drink driving. This has been attributed to the introduction of countermeasures such as random breath testing (RBT), changing community attitudes and norms leading to less acceptance of the behaviour and, to a lesser degree, the implementation of programs designed to deter offenders from engaging in drink driving. Most of the research to date has focused on the hard core offenders - those with high blood alcohol content at the time of arrest, and those who have more than one offence. Aims There has been little research on differences within the first offender population or on factors contributing to second offences. This research aims to fill the gap by reporting on those factors in a sample of offenders. Methods This paper reports on a study that involved interviewing 198 first offenders in court and following up this group 6-8 months post offence. Of these original participants, 101 offenders were able to be followed up, with 88 included in this paper on the basis that they had driven a vehicle since the offence. Results Interestingly, while the rate of reported apprehended second offences was low in that time frame (3%), a surprising number of offenders reported that they had driven under the influence at a much higher rate (27%). That is a large proportion of first offenders were willing to risk the much larger penalties associated with a second offence in order to engage in drink driving. Discussion and conclusions Key characteristics of this follow up group are examined to inform the development of a evidence based brief intervention program that targets first time offenders with the goal of decreasing the rate of repeat drink driving.

Suppression of cluster ions during rapidly increasing particle number concentration events in the environment

We show that the cluster ion concentration (CIC) in the atmosphere is significantly suppressed during events that involve rapid increases in particle number concentration (PNC). Using a neutral cluster and air ion spectrometer, we investigated changes in CIC during three types of particle enhancement processes – new particle formation, a bushfire episode and an intense pyrotechnic display. In all three cases, the total CIC decreased with increasing PNC, with the rate of decrease being greater for negative CIC than positive. We attribute this to the greater mobility, and hence the higher attachment coefficient, of negative ions over positive ions in the air. During the pyrotechnic display, the rapid increase in PNC was sufficient to reduce the CIC of both polarities to zero. At the height of the display, the negative CIC stayed at zero for a full 10 min. Although the PNCs were not significantly different, the CIC during new particle formation did not decrease as much as during the bushfire episode and the pyrotechnic display. We suggest that the rate of increase of PNC, together with particle size, also play important roles in suppressing CIC in the atmosphere.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Coverage of international and Pacific news in the Fiji Times and The Australian

The debate over the nature and flow of international news has dominated intellectual debate about journalism practice for some time. Developing countries argued there was an imbalance in the nature and amount of international news concerning them. They argued that the Western media rarely reported on developing countries and when they did, reported predominantly negative news about developing countries. The debate led to calls for a New World Information and Communication Order (NWICO). A number of studies examined its arguments, many finding developing countries were indeed disadvantaged by the Western media. This study compared foreign news coverage in The Australian and The Fiji Times, with special attention on news from the Pacific Islands region. It found the coverage of the Pacific Islands was still grossly inadequate in both newspapers. The coverage consisted of only a small number of stories, which were predominantly negative, surprising especially in the case of The Fiji Times.

Toward the adaptive generation of bespoke game content

In this chapter, we explore methods for automatically generating game content—and games themselves—adapted to individual players in order to improve their playing experience or achieve a desired effect. This goes beyond notions of mere replayability and involves modeling player needs to maximize their enjoyment, involvement, and interest in the game being played. We identify three main aspects of this process: generation of new content and rule sets, measurement of this content and the player, and adaptation of the game to change player experience. This process forms a feedback loop of constant refinement, as games are continually improved while being played. Framed within this methodology, we present an overview of our recent and ongoing research in this area. This is illustrated by a number of case studies that demonstrate these ideas in action over a variety of game types, including 3D action games, arcade games, platformers, board games, puzzles, and open-world games. We draw together some of the lessons learned from these projects to comment on the difficulties, the benefits, and the potential for personalized gaming via adaptive game design.

Herds of methane chambers grazing bubbles

Water to air methane emissions from freshwater reservoirs can be dominated by sediment bubbling (ebullitive) events. Previous work to quantify methane bubbling from a number of Australian sub-tropical reservoirs has shown that this can contribute as much as 95% of total emissions. These bubbling events are controlled by a variety of different factors including water depth, surface and internal waves, wind seiching, atmospheric pressure changes and water levels changes. Key to quantifying the magnitude of this emission pathway is estimating both the bubbling rate as well as the areal extent of bubbling. Both bubbling rate and areal extent are seldom constant and require persistent monitoring over extended time periods before true estimates can be generated. In this paper we present a novel system for persistent monitoring of both bubbling rate and areal extent using multiple robotic surface chambers and adaptive sampling (grazing) algorithms to automate the quantification process. Individual chambers are self-propelled and guided and communicate between each other without the need for supervised control. They can maintain station at a sampling site for a desired incubation period and continuously monitor, record and report fluxes during the incubation. To exploit the methane sensor detection capabilities, the chamber can be automatically lowered to decrease the head-space and increase concentration. The grazing algorithms assign a hierarchical order to chambers within a preselected zone. Chambers then converge on the individual recording the highest 15 minute bubbling rate. Individuals maintain a specified distance apart from each other during each sampling period before all individuals are then required to move to different locations based on a sampling algorithm (systematic or adaptive) exploiting prior measurements. This system has been field tested on a large-scale subtropical reservoir, Little Nerang Dam, and over monthly timescales. Using this technique, localised bubbling zones on the water storage were found to produce over 50,000 mg m-2 d-1 and the areal extent ranged from 1.8 to 7% of the total reservoir area. The drivers behind these changes as well as lessons learnt from the system implementation are presented. This system exploits relatively cheap materials, sensing and computing and can be applied to a wide variety of aquatic and terrestrial systems.

Second-order quantile methods for experts and combinatorial games

We aim to design strategies for sequential decision making that adjust to the difficulty of the learning problem. We study this question both in the setting of prediction with expert advice, and for more general combinatorial decision tasks. We are not satisfied with just guaranteeing minimax regret rates, but we want our algorithms to perform significantly better on easy data. Two popular ways to formalize such adaptivity are second-order regret bounds and quantile bounds. The underlying notions of 'easy data', which may be paraphrased as "the learning problem has small variance" and "multiple decisions are useful", are synergetic. But even though there are sophisticated algorithms that exploit one of the two, no existing algorithm is able to adapt to both. In this paper we outline a new method for obtaining such adaptive algorithms, based on a potential function that aggregates a range of learning rates (which are essential tuning parameters). By choosing the right prior we construct efficient algorithms and show that they reap both benefits by proving the first bounds that are both second-order and incorporate quantiles.

Numerical algorithms for time-fractional subdiffusion equation with second-order accuracy

This article aims to fill in the gap of the second-order accurate schemes for the time-fractional subdiffusion equation with unconditional stability. Two fully discrete schemes are first proposed for the time-fractional subdiffusion equation with space discretized by finite element and time discretized by the fractional linear multistep methods. These two methods are unconditionally stable with maximum global convergence order of $O(\tau+h^{r+1})$ in the $L^2$ norm, where $\tau$ and $h$ are the step sizes in time and space, respectively, and $r$ is the degree of the piecewise polynomial space. The average convergence rates for the two methods in time are also investigated, which shows that the average convergence rates of the two methods are $O(\tau^{1.5}+h^{r+1})$. Furthermore, two improved algorithms are constrcted, they are also unconditionally stable and convergent of order $O(\tau^2+h^{r+1})$. Numerical examples are provided to verify the theoretical analysis. The comparisons between the present algorithms and the existing ones are included, which show that our numerical algorithms exhibit better performances than the known ones.