On-track to what? Foucauldian analysis of Victorian post-compulsory education policy

Current ideas of adolescent development portray a slow steady movement toward adulthood. These notions developed hand in hand with social practices that evolved in the latter half of the 19th century and contemporaneously with modernisation. During this period conceptions of adolescence included longer stays in school, organised leisure activities, juvenile justice policies and the protection of youth from child labour. Lesko (2001) works from a position that the modern age is defined by time, an understanding that events and change are meaningful in their occurrence in and through time. She examines adolescence as partaking of panoptical time which is condensed and commodified; a time framework that compels us - scholars, educators, parents, and teenagers - to attend to progress, precocity, arrest, or decline" (2001 p.41). Panoptical time can be used to explore how ideas of what is 'normal' development can be used to privilege particular ways of being an adolescent, to monitor who is deemed to be 'at risk' of not conforming to that model and to govern their behaviour. A Foucauldian analysis suggests the formation of 'at risk' identities reflects historically specific discourses. An understanding of how these and other discursive constructions are formed opens the way for resistance. This presentation explores the recent implementation of On-Track and On-Track Connect within Victorian government policy and explores the experience of a Local Learning and Employment Network in implementing the policy.

3-dimensional characterization of membrane with nanoporous structure using TEM tomography and image analysis

The nanoporous structure of membrane varies in 3-dimensional (3-D) space and has remarkable influences on the filtration or desalination achieved, fouling potentials and therefore, the quality of yielded water. Knowledge of the 3-D nanoporous structure is thus vital to understanding and predicting its performance. A novel method by incorporating transmission electronic microtomography, image processing and 3-D reconstruction is introduced to characterize membranes with nano structures. The reconstruction algorithm allows for the visualization of 3-D nanoporous structure in a non-destructive way from any directions. This novel technique Ieads to in-depth understanding and accurate prediction of filtration performance.

3-dimensional characterization of membrane with nanoporous structure using TEM tomography and image analysis

The nanoporous structure of a membrane varies in a 3-dimensional (3-D) space and has remarkable influences on the filtration or desalination achieved, fouling potentials and therefore, the quality of yielded water. Knowledge of the 3-D nanoporous structure is thus vital to understanding and predicting its performance. A novel method by incorporating transmission electronic microtomography, image processing and 3-D reconstruction is introduced to characterize membranes with nano structures. The reconstruction algorithm allows for the visualization of 3-D nanoporous structure in a non-destructive way from any directions. This novel technique leads to in-depth understanding and accurate prediction of filtration performance.

She would say that, he's from Melbourne : fun and games with Dame Edna and Barry Humphries

This article explores the role that urban place and specifically urban comparison play in the public performances of both the comedian Barry Humphries and the character Edna Everage. In developing Claire Colebrook's analysis of satire as a form of humour that is physically and historically located, we argue that the initial success of Humphries’ satire rests on his elaboration of a specific series of geo-social locations. The article then examines the ways in which Edna makes the local her own global, demonstrating how Barry Humphries has progressively modified and internationalised Edna's provincialism so that his satirical cultural project is understandable over five decades and beyond her origins in Melbourne.

Biomedical time series analysis based on bag-of-words model

This research proposes a number of new methods for biomedical time series classification and clustering based on a novel Bag-of-Words (BoW) representation. It is anticipated that the objective and automatic biomedical time series clustering and classification technologies developed in this work will potentially benefit a wide range of applications, such as biomedical data management, archiving, retrieving, and disease diagnosis and prognosis in the future.

Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved.

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management.

Tailored mesoporous silica nanoparticles for controlled drug delivery: platform fabrication, targeted delivery, and computational design and analysis

Mesoporous silica nanoparticles (MSNs) are exceptionally promising drug carriers for controlled drug delivery systems because their morphology, pore structure, pore volume and pore size can be well tailored to obtain certain drug release profiles. Moreover, they possess the ability to specifically transport and deliver anti-cancer drugs when targeting molecules are properly grafted onto their surface. MSNs based drug delivery systems have the potential to revolutionize cancer therapy. This review provides a comprehensive overview of the fabrication, modification of MSNs and their applications in tumour-targeted delivery. In addition, the characterization and analysis of MSNs with computer aided strategies were described. The existing issues and future prospective concerning the applications of MSNs as drug carriers for controlled drug delivery systems were discussed.