The 2015 IWGDF guidance documents on prevention and management of foot problems in diabetes: Development of an evidence-based global consensus

Foot problems complicating diabetes are a source of major patient suffering and societal costs. Investing in evidence-based, internationally appropriate diabetic foot care guidance is likely among the most cost-effective forms of healthcare expenditure, provided it is goal-focused and properly implemented. The International Working Group on the Diabetic Foot (IWGDF) has been publishing and updating international Practical Guidelines since 1999. The 2015 updates are based on systematic reviews of the literature, and recommendations are formulated using the Grading of Recommendations Assessment Development and Evaluation system. As such, we changed the name from 'Practical Guidelines' to 'Guidance'. In this article we describe the development of the 2015 IWGDF Guidance documents on prevention and management of foot problems in diabetes. This Guidance consists of five documents, prepared by five working groups of international experts. These documents provide guidance related to foot complications in persons with diabetes on: prevention; footwear and offloading; peripheral artery disease; infections; and, wound healing interventions. Based on these five documents, the IWGDF Editorial Board produced a summary guidance for daily practice. The resultant of this process, after reviewed by the Editorial Board and by international IWGDF members of all documents, is an evidence-based global consensus on prevention and management of foot problems in diabetes. Plans are already under way to implement this Guidance. We believe that following the recommendations of the 2015 IWGDF Guidance will almost certainly result in improved management of foot problems in persons with diabetes and a subsequent worldwide reduction in the tragedies caused by these foot problems.

Creating healthy food environments in NSW: A knowledge and evidence synthesis to inform advocacy and action. Seven separate documents across the domains: Food promotion

Reviews and synthesizes evidence to make recommendations on policy actions improve food environments in the area of food promotion for NSW Health

Creating healthy food environments in NSW: A knowledge and evidence synthesis to inform advocacy and action. Seven separate documents across the domains: Food retail

Reviews and synthesizes nutrition policy actions to improve food retail for NSW Health

Creating healthy food environments in NSW: A knowledge and evidence synthesis to inform advocacy and action. Seven separate documents across the domains: Overview

Summaries evidence across seven domains of potential food policy action to improve food environments and food supply to prevent obesity for NSW Health

A comparative study of early childhood curriculum documents focused on education for sustainability in South Korea and Australia

This thesis is about a comparative study of early childhood education (ECE) curriculum documents focused on education for sustainability (EfS) in South Korea and Australia. It examined how the national ECE curriculum documents in two culturally different contexts align with contemporary concepts of sustainability and activist early childhood education for sustainability (ECEfS) principles. Drawing on systems theory, Korean and Australian ECE curriculum documents were used as the primary sources for this study within the framework of critical document analysis (CDA). This study offers a step forward in developing culturally inclusive/holistic understandings of sustainability and more contextualised/localised approaches to ECEfS.

A blind indic script recognizer for multi-script documents

We report a hierarchical blind script identifier for 11 different Indian scripts. An initial grouping of the 11 scripts is accomplished at the first level of this hierarchy. At the subsequent level, we recognize the script in each group. The various nodes of this tree use different feature-classifier combinations. A database of 20,000 words of different font styles and sizes is collected and used for each script. Effectiveness of Gabor and Discrete Cosine Transform features has been independently, evaluated using nearest neighbor linear discriminant and support vector machine classifiers. The minimum and maximum accuracies obtained, using this hierarchical mechanism, are 92.2% and 97.6%, respectively.

Localization of handwritten text in documents using moment invariants and Delaunay triangulation

This paper describes an approach based on Zernike moments and Delaunay triangulation for localization of hand-written text in machine printed text documents. The Zernike moments of the image are first evaluated and we classify the text as hand-written using the nearest neighbor classifier. These features are independent of size, slant, orientation, translation and other variations in handwritten text. We then use Delaunay triangulation to reclassify the misclassified text regions. When imposing Delaunay triangulation on the centroid points of the connected components, we extract features based on the triangles and reclassify the text. We remove the noise components in the document as part of the preprocessing step so this method works well on noisy documents. The success rate of the method is found to be 86%. Also for specific hand-written elements such as signatures or similar text the accuracy is found to be even higher at 93%.

Frechet distance based approach for searching online handwritten documents

We propose a novel, language-neutral approach for searching online handwritten text using Frechet distance. Online handwritten data, which is available as a time series (x,y,t), is treated as representing a parameterized curve in two-dimensions and the problem of searching online handwritten text is posed as a problem of matching two curves in a two-dimensional Euclidean space. Frechet distance is a natural measure for matching curves. The main contribution of this paper is the formulation of a variant of Frechet distance that can be used for retrieving words even when only a prefix of the word is given as query. Extensive experiments on UNIPEN dataset(1) consisting of over 16,000 words written by 7 users show that our method outperforms the state-of-the-art DTW method. Experiments were also conducted on a Multilingual dataset, generated on a PDA, with encouraging results. Our approach can be used to implement useful, exciting features like auto-completion of handwriting in PDAs.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

"Standards are living documents ..."

"Standards are living documents which reflect progress in science, technology and systems" - Standards Australia.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Selective dissemination of XML documents using GAs and SVM

XML has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Self Adaptive Migration Model Genetic Algorithm (SAMCA)[5] and multi class Support Vector Machine (SVM) are used to learn a user model. Based on the feedback from the users the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.