Teachers facilitating support for young children experiencing parental separation and divorce

This paper reports on a study of Australian early childhood teachers’ pedagogical practices with young children experiencing parental separation and divorce. Twenty-one semi-structured interviews and a focus group were conducted to explore the actions of teachers to support young children experiencing parental separation and divorce. A grounded theory approach was used to analyse data. Teachers reported actions that were focussed on constructing emotional, behavioural, and academic support for young children, as well as forming partnerships with parents, school personnel, and community members to assist. Results are discussed in terms of the implications for professional practice.

The affect heuristic and public support for three types of wood smoke mitigation policies

This study applied the affect heuristic model to investigate key psychological factors (affective associations, perceived benefits, and costs of wood heating) contributing to public support for three distinct types of wood smoke mitigation policies: education, incentives, and regulation. The sample comprised 265 residents of Armidale, an Australian regional community adversely affected by winter wood smoke pollution. Our results indicate that residents with stronger positive affective associations with wood heating expressed less support for wood smoke mitigation policies involving regulation. This relationship was fully mediated by expected benefits and costs associated with wood heating. Affective associations were unrelated to public support for policies involving education and incentives, which were broadly endorsed by all segments of the community, and were more strongly associated with rational considerations. Latent profile analysis revealed no evidence to support the proposition that some community members experience internal “heart versus head” conflicts in which their positive affective associations with wood heating would be at odds with their risk judgments about the dangers of wood smoke pollution. Affective associations and cost/benefit judgments were very consistent with each other.

Nutritional support in chronic obstructive Pulmonary Disease (COPD) a randomised trial

Rationale Nutritional support is effective in managing malnutrition in COPD (Collins et al., 2012) leading to functional improvements (Collins et al., 2013). However, comparative trials of first line interventions are lacking. This randomised trial compared the effectiveness of individualised dietary advice by a dietitian (DA) versus oral nutritional supplements (ONS). Methods A target sample of 200 stable COPD outpatients at risk of malnutrition (‘MUST’; medium + high risk) were randomised to either a 12-week intervention of ONS (ONS: ~400 kcal/d, ~40 g/d protein) or DA with supportive written advice. The primary outcome was quality of life (QoL) measured using St George’s Respiratory Questionnaire with secondary outcomes including handgrip strength, body weight and nutritional intake. Both the change from baseline and the differences between groups was analysed using SPSS version 20. Results 84 outpatients were recruited (ONS: 41 vs. DA: 43), 72 completed the intervention (ONS: 33 vs. DA: 39). Mean BMI was 18.2 SD 1.6 kg/m2, age 72.6 SD 10 years, FEV1% predicted 36 SD 15% (severe COPD). In comparison to the DA group, the ONS group experienced significantly greater improvements in protein intakes above baseline values at both week 6 (+21.0 SEM 4.3 g/d vs. +0.52 SEM 4.3 g/d; p < 0.001) and week 12 (+19.0 SEM 5.0 g/d vs. +1.0 SEM 3.6 g/d; p = 0.033;ANOVA). QoL and secondary outcomes remained stable at 12 weeks in both groups with slight improvements in the ONS group but no differences between groups. Conclusion In outpatients at risk of malnutrition with severe COPD, nutritional support involving either ONS or DA appears to maintain in tritional status, functional capacity and QoL. However, larger trials, and earlier, multi-modal nutritional interventions for an extended duration should be explored.

A framework for people capability enhancement to support sustainable facility management practices

Spanning over a considerable length of time, facility management is a key phase in the development cycle of built assets. Therefore facility managers are in a commanding position to maximise the potential of sustainability through the operation, maintenance and upgrade of built facilities leading to decommission and deconstruction. Sustainability endeavours in facility management practices will not only contribute to reducing energy consumption, waste and running costs, but also help improve organisational productivity, financial returns and community standing of the organisation. At the forefront facing sustainability challenge, facility manager should be empowered with the necessary knowledge and capabilities. However, literature studies show a gap between the current level of awareness and the specific knowledge and necessary skills required to pursue sustainability in the profession. People capability is considered as the key enabler in managing the sustainability agenda as well as being central to the improvement of competency and innovation in an organization. This paper aims to identify the critical factors for enhancing people capabilities in promoting the sustainability agenda in facility management practices. Starting with a total of 60 factors identified through literature review, the authors conducted a questionnaire survey to assess the perceived importance of these factors. The findings reveal 23 critical factors as significantly important. They form the basis of a mechanism framework developed to equip facility managers with the right knowledge, to continue education and training and to develop new mind-sets to enhance the implementation of sustainability measures in FM practices.

Decision support for the efficient annotation of bioacoustic events

Acoustic sensors allow scientists to scale environmental monitoring over large spatiotemporal scales. The faunal vocalisations captured by these sensors can answer ecological questions, however, identifying these vocalisations within recorded audio is difficult: automatic recognition is currently intractable and manual recognition is slow and error prone. In this paper, a semi-automated approach to call recognition is presented. An automated decision support tool is tested that assists users in the manual annotation process. The respective strengths of human and computer analysis are used to complement one another. The tool recommends the species of an unknown vocalisation and thereby minimises the need for the memorization of a large corpus of vocalisations. In the case of a folksonomic tagging system, recommending species tags also minimises the proliferation of redundant tag categories. We describe two algorithms: (1) a “naïve” decision support tool (16%–64% sensitivity) with efficiency of O(n) but which becomes unscalable as more data is added and (2) a scalable alternative with 48% sensitivity and an efficiency ofO(log n). The improved algorithm was also tested in a HTML-based annotation prototype. The result of this work is a decision support tool for annotating faunal acoustic events that may be utilised by other bioacoustics projects.

An investigation of problem-solving support and relaxation motivations on e-WOM

As a consequence of greater computer-mediated consumer-to-consumer communication within the firm's marketing communications, there has been a growing need to understand these digital interactions more explicitly. That is, we still know little about the exact extrinsic and intrinsic motivations that drive electronic word-of-mouth. The purpose of the paper is to better understand why members within community-based websites develop a need to exchange and/or develop a social bond within the community. Questionnaire data were gathered from 147 members of an online beauty forum in Australia. The findings highlight that those members seeking problem-solving support in combination with elements of relaxation will be more inclined to exchange with other community members and develop a social bond within that community. Marketing managers can capitalise these findings by strengthening problem-solving support systems and creating environments where community members can also relax and unwind to increase the exchange between members and also increase the social bonds within the community.

A high performance liquid chromatography and electrospray ionization mass spectrometry method for the analysis of the natural medicine, Forsythia suspensa

Samples of Forsythia suspensa from raw (Laoqiao) and ripe (Qingqiao) fruit were analyzed with the use of HPLC-DAD and the EIS-MS techniques. Seventeen peaks were detected, and of these, twelve were identified. Most were related to the glucopyranoside molecular fragment. Samples collected from three geographical areas (Shanxi, Henan and Shandong Provinces), were discriminated with the use of hierarchical clustering analysis (HCA), discriminant analysis (DA), and principal component analysis (PCA) models, but only PCA was able to provide further information about the relationships between objects and loadings; eight peaks were related to the provinces of sample origin. The supervised classification models-K-nearest neighbor (KNN), least squares support vector machines (LS-SVM), and counter propagation artificial neural network (CP-ANN) methods, indicated successful classification but KNN produced 100% classification rate. Thus, the fruit were discriminated on the basis of their places of origin.

Portal-based support for mental health research

This paper describes experiences with the use of the Globus toolkit and related technologies for development of a secure portal that allows nationally-distributed Australian researchers to share data and application programs. The portal allows researchers to access infrastructure that will be used to enhance understanding of the causes of schizophrenia and advance its treatment, and aims to provide access to a resource that can expand into the world’s largest on-line collaborative mental health research facility. Since access to patient data is controlled by local ethics approvals, the portal must transparently both provide and deny access to patient data in accordance with the fine-grained access permissions afforded individual researchers. Interestingly, the access protocols are able to provide researchers with hints about currently inaccessible data that may be of interest to them, providing them the impetus to gain further access permissions.

Enhancing the auditor's report : to what extent is there support for the IAASB's proposed changes?

This article outlines proposed reforms to auditor reporting currently being considered by the International Auditing and Assurance Standards Board (IAASB), and other key national and transnational standard-setters and regulatory bodies. It adds to recent academic contributions on reforming the auditor’s report by analyzing the 165 stakeholder responses to the IAASB’s 2012 Invitation to Comment: Improving the Auditor’s Report to determine levels of support for the IAASB’s proposed reforms, and the differences, if any, between the views of various respondents based on stakeholder groups (e.g. audit and assurance firms, users, preparers, regulators, etc.) and regional classifications. Guided by insights from communication theory, our results show the levels of stakeholder support for the IAASB’s proposed reforms addressing auditors’ expectations, information and communication gaps are mixed. The strongest overall support was for enhanced auditor reporting on other information attached to, or intended to be read with, the financial statements, and the least supported initiative was including additional information in the auditor’s report about the auditor’s judgements and processes. Whilst overall there is generally consensus across both stakeholder groups and regions concerning the various questions investigated, we highlight where statistically significant differences between groups do exist. Notably, North American respondents were less likely to support a number of the IAASB’s proposed reforms than their counterparts from other regions.

Supervised latent dirichlet allocation models for efficient activity representation

Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of efficiently representing the extracted features for classification to improve the overall performance. We introduce two generative supervised topic models, maximum entropy discrimination LDA (MedLDA) and class- specific simplex LDA (css-LDA), to encode the raw features suitable for discriminative SVM based classification. Unsupervised LDA models disconnect topic discovery from the classification task, hence yield poor results compared to the baseline Bag-of-words framework. On the other hand supervised LDA techniques learn the topic structure by considering the class labels and improve the recognition accuracy significantly. MedLDA maximizes likelihood and within class margins using max-margin techniques and yields a sparse highly discriminative topic structure; while in css-LDA separate class specific topics are learned instead of common set of topics across the entire dataset. In our representation first topics are learned and then each video is represented as a topic proportion vector, i.e. it can be comparable to a histogram of topics. Finally SVM classification is done on the learned topic proportion vector. We demonstrate the efficiency of the above two representation techniques through the experiments carried out in two popular datasets. Experimental results demonstrate significantly improved performance compared to the baseline Bag-of-features framework which uses kmeans to construct histogram of words from the feature vectors.

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.