A method for risk analysis across governance systems : a great barrier reef case study

Healthy governance systems are key to delivering sound environmental management outcomes from global to local scales. There are, however, surprisingly few risk assessment methods that can pinpoint those domains and sub-domains within governance systems that are most likely to influence good environmental outcomes at any particular scale, or those if absent or dysfunctional, most likely to prevent effective environmental management. This paper proposes a new risk assessment method for analysing governance systems. This method is then tested through its preliminary application to a significant real-world context: governance as it relates to the health of Australia's Great Barrier Reef (GBR). The GBR exists at a supra-regional scale along most of the north eastern coast of Australia. Brodie et al (2012 Mar. Pollut. Bull. 65 81-100) have recently reviewed the state and trend of the health of the GBR, finding that overall trends remain of significant concern. At the same time, official international concern over the governance of the reef has recently been signalled globally by the International Union for the Conservation of Nature (IUCN). These environmental and political contexts make the GBR an ideal candidate for use in testing and reviewing the application of improved tools for governance risk assessment. © 2013 IOP Publishing Ltd.

Innovations in dietetic training in foodservice management : can early exposure to practice enhance final year competency development?

Student engagement in the delivery of theoretical course materials is a current challenge in the tertiary sector including for dietetic training. In 2011 with the creation of a new nutritionist position for Queensland Meals on Wheels (QMOW), a service learning approach to support this organisation was used with third year dietetic students undertaking two days of structured activities at various QMOW sites in South East Queensland, aligned with coursework in Foodservice Management (FSM). This cohort of students was then followed in their final year post successful completion of five weeks professional practice in FSM to see if this experience supported readiness for placement and competency development. Evaluation was undertaken of eligible students (n = 50) via paper based survey (response rate 94%) with all participating in targeted focus groups. Findings showed that students acknowledged the QMOW experience (on reflection 14 months later) providing opportunity for participation and/or observation in 5 of 12 FSM areas taught in third year, including food safety, meal production, assembly, delivery and dishwashing. Over half the students identified good exposure to FSM competency areas during the QMOW experience, with 83% satisfied with their competency exposure and subsequent practice during final year placements. A consistent theme emerged from focus groups supporting inclusion of practical opportunities with the theoretical component of the FSM subject to highlight relevance to learning. These findings highlight the importance of such teaching initiatives to met student learning preferences, linking theory with practice and supporting competency development in the final year of training programs.

Pushing cultural competence boundaries in nutrition and dietetics training

A health workforce ready for safe practice is a government priority, and particularly critical to support indigenous communities closing ‘the gap’. Increased pressure exists on dietetic training programs for quality placements, with fewer opportunities for immersion in Aboriginal and Torres Strait Islander communities to demonstrate cultural competence. In 2012, Queensland University of Technology established a partnership with Apunipima Cape York Health Council with 56 weeks of dietetic placement for 8 students provided to achieve these aims. Clinical practice in Community Public Health Nutrition (CPHN) was structured in a standard 6 week placement, with Individual Case Management (ICM) and Foodservice Management (FSM) integrated across 8 weeks (4 each), with an additional 2 weeks ICM prior in a metropolitan indigenous health service. Students transitioned from urban to rural then remote sites, with new web-based technologies used for support. Strong learning opportunities were provided, with CPHN projects in antenatal and child health, FSM on standardisation of procedures in a 22 bed health facility, and ICM exposing students to a variety of cases via hospital in/outpatients, general clinics and remote community outreach. Supervisor focus group evaluation was positive, with CPHN and FSM enhancing capacity of service. Student focus group evaluation revealed placements exceeded expectations, with rating high, and strong confidence in cultural competence described. Students debriefed final and third year cohorts on their experiences, with increased awareness and enthusiasm for work with indigenous communities indicated by groups. With the success of this partnership, placements are continuing 2013, and new boundaries in dietetic training established.

Application of machine learning technique in wind turbine fault diagnosis

In this study, a machine learning technique called anomaly detection is employed for wind turbine bearing fault detection. Basically, the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase. Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method, and a test to failure data of bearings from the NSF I/UCR Center for Intelligent Maintenance Systems (IMS). The latter data set was used to compare anomaly detection with SVM, a previously well-known applied method, in rapidly finding the incipient faults.

Mindfulness-meditation on the move : implementation of an ACT-based mindfulness practice intervention and training within a University Dance program

As part of the introduction of a broader dance medicine and science related health and wellbeing program, a 9 week mindfulness-meditation ACT-based program was delivered to all students undertaking full-time University dance training (N = 106). The aim of the program was to assist students in the further development of performance psychology skills that could be applied in both performance and non-performance settings. Participant groups were comprised of both male (N = 12) and female (N = 94) students from across all three year levels of two undergraduate dance courses, divided into three groups by mixed year levels due to timetable scheduling requirements. Pre- and post-testing was undertaken utilising the Mindful Attention Awareness Scale (MAAS-15), a uni-dimensional measure of mindfulness, in addition to qualitative questions checking the current level of awareness and understanding of mindfulness practice and its application. Weekly sessions were conducted by qualified sport and exercise psychologists and covered key practices such as: Mindfulness of Body, Mindfulness of Breathing, Mindfulness of Sounds, ACT-based and general Imagery exercises, Developing Open Awareness, Mindfulness of Emotions, and Developing Inner Stillness. Students were required to maintain a reflective journal that was utilised at the end of each weekly session, in addition to completion of a mid-Semester reflective debrief. Teaching staff additionally attended the weekly sessions and linked the mindfulness practice learnings into the student’s practical dance and academic classes where appropriate. Anecdotal feedback indicates that participation in the mindfulness-meditation sessions and the development of these mental skills has resulted in positive performance and personal outcomes. Observations collated from staff and students, results from the data collection phases and recommendations regarding future applications within dance training settings will be discussed within the presentation.

A meshfree-based local Galerkin method with condensation of degree of freedom for elastic dynamic analysis

Condensation technique of degree of freedom is first proposed to improve the computational efficiency of meshfree method with Galerkin weak form for elastic dynamic analysis. In the present method, scattered nodes without connectivity are divided into several subsets by cells with arbitrary shape. Local discrete equation is established over each cell by using moving Kriging interpolation, in which the nodes that located in the cell are used for approximation. Then local discrete equations can be simplified by condensation of degree of freedom, which transfers equations of inner nodes to equations of boundary nodes based on cells. The global dynamic system equations are obtained by assembling all local discrete equations and are solved by using the standard implicit Newmark’s time integration scheme. In the scheme of present method, the calculation of each cell is carried out by meshfree method, and local search is implemented in interpolation. Numerical examples show that the present method has high computational efficiency and good accuracy in solving elastic dynamic problems.

CoRE : a context-aware relation extraction method for relation completion

We identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic relation, RC attempts at linking entity pairs between two entity lists under the relation. To accomplish the RC goals, we propose to formulate search queries for each query entity α based on some auxiliary information, so that to detect its target entity β from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC.

Mini-scale method for nuclear run-on transcription assay in plants

We present a mini-scale method for nuclear run-on transcription assay. In our method, all the centrifuge steps can be carried out by using micro-tubes for short time (5 min each) throughout the process, including isolation of transcriptionally active nuclei and purification of labeled RNA after synthesis of RNA in isolated nuclei. The assay can be performed using a small amount of plant tissue, which enables analysis of developmental changes in transcriptional status of given genes in a single individual plant. Successful results were obtained using the tissues of flower and leaf of petunia and embryo of pea, suggesting that the method is potentially applicable to a variety of plant tissues.

DUTCH : teaching method-based design

All design classes followed a systematic design approach, that, in an abstract way, can be characterized by figure 1. This approach is based on our design approach [1] that we labeled DUTCH (design for users and tasks, from concepts to handles).Consequently, each course starts with collecting, modeling, and analyzing an existing situation. The next step is the development of a vision on a future domain world where new technology and / or new representations have been implemented. This second step is the first tentative global design that will be represented in scenarios or prototypes and can be assessed. This second design model is based on both the client’s requirements and technological possibilities and challenges. In an iterative way multiple instantiations of detail design may follow, that each can be assessed and evaluated again...

Protocol : A highly sensitive RT-PCR method for detection and quantification of microRNAs

MicroRNAs (miRNAs) are a class of small non-coding RNAs with a critical role in development and environmental responses. Efficient and reliable detection of miRNAs is an essential step towards understanding their roles in specific cells and tissues. However, gel-based assays currently used to detect miRNAs are very limited in terms of throughput, sensitivity and specificity. Here we provide protocols for detection and quantification of miRNAs by RT-PCR. We describe an end-point and real-time looped RT-PCR procedure and demonstrate detection of miRNAs from as little as 20 pg of plant tissue total RNA and from total RNA isolated from as little as 0.1 l of phloem sap. In addition, we have developed an alternative real-time PCR assay that can further improve specificity when detecting low abundant miRNAs. Using this assay, we have demonstrated that miRNAs are differentially expressed in the phloem sap and the surrounding vascular tissue. This method enables fast, sensitive and specific miRNA expression profiling and is suitable for facilitation of high-throughput detection and quantification of miRNA expression.

The ranking based constrained document clustering method and its application to social event detection

With the growing size and variety of social media files on the web, it’s becoming critical to efficiently organize them into clusters for further processing. This paper presents a novel scalable constrained document clustering method that harnesses the power of search engines capable of dealing with large text data. Instead of calculating distance between the documents and all of the clusters’ centroids, a neighborhood of best cluster candidates is chosen using a document ranking scheme. To make the method faster and less memory dependable, the in-memory and in-database processing are combined in a semi-incremental manner. This method has been extensively tested in the social event detection application. Empirical analysis shows that the proposed method is efficient both in computation and memory usage while producing notable accuracy.

Matching image sets via adaptive multi convex hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

Enhancing preservice teacher learning through slowmation animation

This paper is an exploration of conceptual change. It reports on a study which utilizes conceptual status elements, and explores the unique contribution of Slowmation Animation in the conceptual learning of preservice science teachers. 15 short animations were created by 55 participants in a single two hour tutorial class as a part of their methods training. Conceptual change was found to occur when their animation topic challenged their understandings of the processes within the scientific concept. The preservice science teachers reported an enthusiasm for Slowmation Animation as a method for learning how to learn, as well as for highlighting what they thought they knew, but didn’t really know.

Pressure state response-based method for evaluating social benefits from smart grid development

In this paper, the inherent mechanism of social benefits associated with smart grid development is examined based on the pressure state response (PSR) model from resource economics. The emerging types of technology brought up by smart grid development are regarded as pressures. The improvements of the performance and efficiency of power system operation, such as the enhanced capability of accommodating renewable energy generation, are regarded as states. The effects of smart grid development on society are regarded as responses. Then, a novel method for evaluating social benefits from smart grid development is presented. Finally, the social benefits from smart grid development in a province in northwest China are carried out by using the developed evaluation system, and reasonable evaluation results are attained.

A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter

Interior permanent-magnet synchronous motors (IPMSMs) become attractive candidates in modern hybrid electric vehicles and industrial applications. Usually, to obtain good control performance, the electric drives of this kind of motor require one position, one dc link, and at least two current sensors. Failure of any of these sensors might lead to degraded system performance or even instability. As such, sensor fault resilient control becomes a very important issue in modern drive systems. This paper proposes a novel sensor fault detection and isolation algorithm based on an extended Kalman filter. It is robust to system random noise and efficient in real-time implementation. Moreover, the proposed algorithm is compact and can detect and isolate all the sensor faults for IPMSM drives. Thorough theoretical analysis is provided, and the effectiveness of the proposed approach is proven by extensive experimental results.