Tools for successful NRM in the Lake Eyre Basin : achieving effective engagement

This chapter was developed as part of the ‘People, communities and economies of the Lake Eyre Basin’ project. It has been written for communities, government agencies and interface organisations involved in natural resource management (NRM) in the Lake Eyre Basin (LEB). Its purpose is to identify the key factors for successful community engagement processes relevant to the LEB and present tools and principles for successful engagement processes. The term ‘interface organisation’ is used here to refer to the diverse range of local and regional organisations (such as Catchment Committees or NRM Regional Bodies) that serve as linkages, or translators, between local communities and broader Australian and State Governments. The importance of fostering and harnessing effective processes of community engagement has been identified as crucial to building a prosperous future for rural and remote regions in Australia. The chapter presents an overview of the literature on successful community engagement processes for NRM, as well as an overview of the current NRM arrangements in the LEB. The main part of the chapter presents findings of the series of interviews conducted with the government liaison officers representing both state and federal organisations who are responsible for coordinating and facilitating regional NRM in the LEB, and with the members of communities of the LEB.

Optimization of an integrated model for automatic reduction and expansion of long queries

A long query provides more useful hints for searching relevant documents, but it is likely to introduce noise which affects retrieval performance. In order to smooth such adverse effect, it is important to reduce noisy terms, introduce and boost additional relevant terms. This paper presents a comprehensive framework, called Aspect Hidden Markov Model (AHMM), which integrates query reduction and expansion, for retrieval with long queries. It optimizes the probability distribution of query terms by utilizing intra-query term dependencies as well as the relationships between query terms and words observed in relevance feedback documents. Empirical evaluation on three large-scale TREC collections demonstrates that our approach, which is automatic, achieves salient improvements over various strong baselines, and also reaches a comparable performance to a state of the art method based on user’s interactive query term reduction and expansion.

QUT Library advances the use of open public tools to navigate genetic inventions and builds research networks

Many emerging economies are dangling the patent system to stimulate bio-technological innovations with the ultimate premise that these will improve their economic and social growth. The patent system mandates full disclosure of the patented invention in exchange of a temporary exclusive patent right. Recently, however, patent offices have fallen short of complying with such a mandate, especially for genetic inventions. Most patent offices provide only static information about disclosed patent sequences and even some do not keep track of the sequence listing data in their own database. The successful partnership of QUT Library and Cambia exemplifies advocacy in Open Access, Open Innovation and User Participation. The library extends its services to various departments within the university, builds and encourages research networks to complement skills needed to make a contribution in the real world.

Discussion of : constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

This is a discussion of the journal article: "Construcing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation". The article and discussion have appeared in the Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Improved molecular tools for sugar cane biotechnology

Sugar cane is a major source of food and fuel worldwide. Biotechnology has the potential to improve economically-important traits in sugar cane as well as diversify sugar cane beyond traditional applications such as sucrose production. High levels of transgene expression are key to the success of improving crops through biotechnology. Here we describe new molecular tools that both expand and improve gene expression capabilities in sugar cane. We have identified promoters that can be used to drive high levels of gene expression in the leaf and stem of transgenic sugar cane. One of these promoters, derived from the Cestrum yellow leaf curling virus, drives levels of constitutive transgene expression that are significantly higher than those achieved by the historical benchmark maize polyubiquitin-1 (Zm-Ubi1) promoter. A second promoter, the maize phosphonenolpyruvate carboxylate promoter, was found to be a strong, leaf-preferred promoter that enables levels of expression comparable to Zm-Ubi1 in this organ. Transgene expression was increased approximately 50-fold by gene modification, which included optimising the codon usage of the coding sequence to better suit sugar cane. We also describe a novel dual transcriptional enhancer that increased gene expression from different promoters, boosting expression from Zm-Ubi1 over eightfold. These molecular tools will be extremely valuable for the improvement of sugar cane through biotechnology.

Automatic de-identification of electronic health records : an Australian perspective

We present an approach to automatically de-identify health records. In our approach, personal health information is identified using a Conditional Random Fields machine learning classifier, a large set of linguistic and lexical features, and pattern matching techniques. Identified personal information is then removed from the reports. The de-identification of personal health information is fundamental for the sharing and secondary use of electronic health records, for example for data mining and disease monitoring. The effectiveness of our approach is first evaluated on the 2007 i2b2 Shared Task dataset, a widely adopted dataset for evaluating de-identification techniques. Subsequently, we investigate the robustness of the approach to limited training data; we study its effectiveness on different type and quality of data by evaluating the approach on scanned pathology reports from an Australian institution. This data contains optical character recognition errors, as well as linguistic conventions that differ from those contained in the i2b2 dataset, for example different date formats. The findings suggest that our approach compares to the best approach from the 2007 i2b2 Shared Task; in addition, the approach is found to be robust to variations of training size, data type and quality in presence of sufficient training data.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Perceptions of online learning in an Australian university : Malaysian students’ perspectives-usability of the online learning tools

This study set out to investigate the kinds of learning difficulties encountered by the Malaysian students and how they actually coped with online learning. The modified Online Learning Environment Survey (OLES) instrument was used to collect data from the sample of 40 Malaysian students at a university in Brisbane, Australia. A controlled group of 35 Australian students was also included for comparison purposes. Contrary to assumptions from previous researches, the findings revealed that there were only a few differences between the international Asian and Australian students with regards to their perceptions of online learning. Recommendations based on the findings of this research study were applicable for Australian universities which have Asian international students enrolled to study online.

Sharpening our tools – translating research into practice

Providing culturally appropriate health communication tools at a community level, whilst meeting funding objectives set by Government led initiatives, can be challenging. Literature states that a translational research framework fostering community communication can encourage the development of appropriate communication tools to facilitate transfer of health information between community and researchers. Reflections from initial Need for Feed cooking and nutrition education program trials in remote Indigenous communities across Cape York indicated program resources were neither meeting community nor researchers needs. In response, a translational research framework was modelled with collaborative partnerships formed between researchers and community with the aim of modifying current resources. Local working groups were established to facilitate communication and guide continual remodelling and retrial of resources for successive programs. Feedback from working groups indicated community members wanted resources with more pictures and less words. Partnership with Chronic Disease Resources Online (CDRO) led to the development of pictorial resources including 3 evaluation tools, 27 recipe sets and 10 education support materials. Between June to December 2012 resources were trialled across 4 Cape York communities with 69 school aged children and 4 community elders. Qualitative data has indicated high satisfaction with modified pictorial resources, proving pictorial resources to be an effective and culturally appropriate method to both communicate health messages to community and facilitate flow of evaluation data to researchers. A translational research framework fostering communication between community and researchers can potentially enhance the quality of health communication tools.

Making YAWL and SmartPM interoperate : managing highly dynamic processes by exploiting automatic adaptation features

In the last years, the trade-o between exibility and sup- port has become a leading issue in work ow technology. In this paper we show how an imperative modeling approach used to de ne stable and well-understood processes can be complemented by a modeling ap- proach that enables automatic process adaptation and exploits planning techniques to deal with environmental changes and exceptions that may occur during process execution. To this end, we designed and imple- mented a Custom Service that allows the Yawl execution environment to delegate the execution of subprocesses and activities to the SmartPM execution environment, which is able to automatically adapt a process to deal with emerging changes and exceptions. We demonstrate the fea- sibility and validity of the approach by showing the design and execution of an emergency management process de ned for train derailments.

A Matlab toolbox for parametric identification of radiation-force models of ships and offshore structures

This article describes a Matlab toolbox for parametric identification of fluid-memory models associated with the radiation forces ships and offshore structures. Radiation forces are a key component of force-to-motion models used in simulators, motion control designs, and also for initial performance evaluation of wave-energy converters. The software described provides tools for preparing non-parmatric data and for identification with automatic model-order detection. The identification problem is considered in the frequency domain.

Development of descriptor tools for the characterisation of Australian sugar mill evaporator scale

Cleaning of sugar mill evaporators is an expensive exercise. Identifying the scale components assists in determining which chemical cleaning agents would result in effective evaporator cleaning. The current methods (based on x-ray diffraction techniques, ion exchange/high performance liquid chromatography and thermogravimetry/differential thermal analysis) used for scale characterisation are difficult, time consuming and expensive, and cannot be performed in a conventional analytical laboratory or by mill staff. The present study has examined the use of simple descriptor tests for the characterisation of Australian sugar mill evaporator scales. Scale samples were obtained from seven Australian sugar mill evaporators by mechanical means. The appearance, texture and colour of the scale were noted before the samples were characterised using x-ray fluorescence and x-ray powder diffraction to determine the compounds present. A number of commercial analytical test kits were used to determine the phosphate and calcium contents of scale samples. Dissolution experiments were carried out on the scale samples with selected cleaning agents to provide relevant information about the effect the cleaning agents have on different evaporator scales. Results have shown that by simply identifying the colour and the appearance of the scale, the elemental composition and knowing from which effect the scale originates, a prediction of the scale composition can be made. These descriptors and dissolution experiments on scale samples can be used to provide factory staff with an on-site rapid process to predict the most effective chemicals for chemical cleaning of the evaporators.

Tools for Mobile Multimedia Programming and Development

Mobile devices are rapidly developing into the primary technology for users to work, socialize, and play in a variety of settings and contexts. Their pervasiveness has provided researchers with the means to investigate innovative solutions to ever more complex user demands. Tools for Mobile Multimedia Programming and Development investigates the use of mobile platforms for research projects, focusing on the development, testing, and evaluation of prototypes rather than final products, which enables researchers to better understand the needs of users through image processing, object recognition, sensor integration, and user interactions. This book benefits researchers and professionals in multiple disciplines who utilize such techniques in the creation of prototypes for mobile devices and applications. This book is part of the Advances in Wireless Technologies and Telecommunication series collection.

Designing intelligent litigation support tools : The IKBALS perspective

In the legal domain, it is rare to find solutions to problems by simply applying algorithms or invoking deductive rules in some knowledge‐based program. Instead, expert practitioners often supplement domain‐specific knowledge with field experience. This type of expertise is often applied in the form of an analogy. This research proposes to combine both reasoning with precedents and reasoning with statutes and regulations in a way that will enhance the statutory interpretation task. This is being attempted through the integration of database and expert system technologies. Case‐based reasoning is being used to model legal precedents while rule‐based reasoning modules are being used to model the legislation and other types of causal knowledge. It is hoped to generalise these findings and to develop a formal methodology for integrating case‐based databases with rule‐based expert systems in the legal domain.