Transition of EGA wheat breeding activities to Australian Grain Technologies

Transition of EGA wheat breeding activities to Australian Grain Technologies.

Precision Agriculture and Remote Sensing Technologies in Central Queensland agricultural production systems

Demonstrate potential benefits of various Precision Agricultural technologies to Central Queensland farming community.

How does complex adaptive system theory inform innovation in complex project based organisations?

Innovation enables organisations to endure by responding to emergence and to improve efficiency. Innovation in a complex organisation can be difficult due to complexities contributing to slow decision-making. Complex projects fail due to an inability to respond to emergence which consumes finances and impacts on resources and organisational success. Therefore, for complex organisations to improve on performance and resilience, it would be advantageous to understand how to improve the management of innovation and thus, the ability to respond to emergence. The benefits to managers are an increase in the number of successful projects and improved productivity. This study will explore innovation management in a complex project based organisation. The contribution to the academic literature will be an in-depth, qualitative exploration of innovation in a complex project based organisation using a comparative case study approach.

Remote management technologies: Producer demonstration sites Roma region

Technology demonstration sites for remote water management for Roma region.

CRC20080 Diagnostic technologies for phosphine resistance management

Development of molecular markers for rapid diagnosis of phosphine resistance in insects.

Strength and stiffness assessment technologies for improving grading effectiveness of radiata pine wood.

This work was designed to provide the Australian structural radiata pine processing industry with some indications for improving stress grading methods and/or technologies to give an increase in structural grade yields, and significantly reduce processing costs without compromising product quality. To achieve this, advanced statistical techniques were used in conjunction with state-of-the-art property measurement systems applied to the same sample of sawn timber. Acoustic vibration analyses were conducted on green and dry boards. Raw data from existing in-line systems was captured on the same boards. The Metriguard HCLT stress rating system was used as the "reference" machine grading because of its current common use in the industry. A WoodEye optical scanning system and an X-ray LHG scanner were also able to provide relevant information on knots. The data set was analyzed using classical and advanced statistical tools to provide correlations between data sets, and to develop efficient strength and stiffness prediction equations. Reductions in non-structural dry volumes can be achieved..

The disruptive technologies that will shape business in the years ahead

Regardless of your industry, the marketplace is continually evolving. The reason, increasingly, is the evolution of disruptive technology. Disruptive technologies are enhanced or new technological innovations that essentially displace conventional and established technology, rendering it obsolete. They can create opportunities for new products, new markets, and new ways of conducting business. In 2016, business models will again change as businesses adapt. The enhancement of current technology and the development of new technological innovations will undeniably transform how new businesses are established, and how existing businesses compete. For small and medium-sized firms, technology will also enable significant leaps forward in terms of innovation, efficiency and competitiveness. Adapting quickly will be essential, so here’s the top six we think you should be prepared for.

R-parameter: A local truncation error based adaptive framework for finite volume compressible flow solvers

A residual-based strategy to estimate the local truncation error in a finite volume framework for steady compressible flows is proposed. This estimator, referred to as the -parameter, is derived from the imbalance arising from the use of an exact operator on the numerical solution for conservation laws. The behaviour of the residual estimator for linear and non-linear hyperbolic problems is systematically analysed. The relationship of the residual to the global error is also studied. The -parameter is used to derive a target length scale and consequently devise a suitable criterion for refinement/derefinement. This strategy, devoid of any user-defined parameters, is validated using two standard test cases involving smooth flows. A hybrid adaptive strategy based on both the error indicators and the -parameter, for flows involving shocks is also developed. Numerical studies on several compressible flow cases show that the adaptive algorithm performs excellently well in both two and three dimensions.

A review of the application of molecular genetics for fisheries management and conservation of sharks and rays

Since the first investigation 25 years ago, the application of genetic tools to address ecological and evolutionary questions in elasmobranch studies has greatly expanded. Major developments in genetic theory as well as in the availability, cost effectiveness and resolution of genetic markers were instrumental for particularly rapid progress over the last 10 years. Genetic studies of elasmobranchs are of direct importance and have application to fisheries management and conservation issues such as the definition of management units and identification of species from fins. In the future, increased application of the most recent and emerging technologies will enable accelerated genetic data production and the development of new markers at reduced costs, paving the way for a paradigm shift from gene to genome-scale research, and more focus on adaptive rather than just neutral variation. Current literature is reviewed in six fields of elasmobranch molecular genetics relevant to fisheries and conservation management (species identification, phylogeography, philopatry, genetic effective population size, molecular evolutionary rate and emerging methods). Where possible, examples from the Indo-Pacific region, which has been underrepresented in previous reviews, are emphasized within a global perspective. (C) 2012 The Authors Journal of Fish Biology (C) 2012 The Fisheries Society of the British Isles

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.

Seamless journeys : enhancing accessibility to work through digital technologies for people with mobility-related impairments

Numerous disruptions and barriers are encountered by persons with mobility-related disabilities in their daily’s experience of going to work and the pressure these exert on gaining and maintaining their employment. The nature and extent of their difficulties to workforce participation entails a requirement for extensive planning and also strategies to address problems of being stranded (for example, when the bus they are waiting for is not accessible). This paper presents the conceptualisation and methods of understanding workforce participation as a journey, and a discussion on the role digital technologies play in helping people with mobility-related disabilities in their journeys to work and mitigating disruptions when these occur. This is presented through an initial case study that helped identify the sequence of supports needed to be in place to make the work journey possible. Importantly, the paper also highlights points of intervention for the use of digital technologies and where design can potentially help to enhance accessibility to work for people with mobility-related impairments by making journeys to work seamless.

Designing self-service technologies for e-wellness

In the context of an international economic shift from manufacturing to services and the constant expansion of industries towards online services (Sheth and Sharma, 2008), this study is concerned with the design of self-service technologies (SSTs) for online environments. An industry heavily adopting SSTs across a variety of different services is Health and Wellness, where figures show an ever growing number of health and wellness apps being developed, downloaded and abandoned (Kelley, 2014). Little is known about how to enhance people’s engagement with online wellness SSTs to support self-health management and self-efficacy. This literature review argues that service design of wellness SSTs in online contexts can be improved by developing an enhanced understanding from a people perspective and customer experience point of view. Customer value, quality of service, usability, and self-efficacy all play an important role in understanding how to design SSTs for wellness and keep users engaged. There is a need for further study on how people interact and engage with online services in the context of wellness in order to design engaging wellness services.

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Single network adaptive critic design for power system stabilisers

The recently developed single network adaptive critic (SNAC) design has been used in this study to design a power system stabiliser (PSS) for enhancing the small-signal stability of power systems over a wide range of operating conditions. PSS design is formulated as a discrete non-linear quadratic regulator problem. SNAC is then used to solve the resulting discrete-time optimal control problem. SNAC uses only a single critic neural network instead of the action-critic dual network architecture of typical adaptive critic designs. SNAC eliminates the iterative training loops between the action and critic networks and greatly simplifies the training procedure. The performance of the proposed PSS has been tested on a single machine infinite bus test system for various system and loading conditions. The proposed stabiliser, which is relatively easier to synthesise, consistently outperformed stabilisers based on conventional lead-lag and linear quadratic regulator designs.