The importance of use and end-of-life phases to the life cycle greenhouse gas (GHG) emissions of concrete : a review

Global climate change is one of the most significant environmental impacts at the moment. One central issue for the building and construction industry to address global climate change is the development of credible carbon labelling schemes for building materials. Various carbon labelling schemes have been developed for concrete due to its high contribution to global greenhouse gas (GHG) emissions. However, as most carbon labelling schemes adopt cradle-to-gate as system boundary, the credibility of the eco-label information may not be satisfactory because recent studies show that the use and end-of-life phases can have a significant impact on the life cycle GHG emissions of concrete in terms of carbonation, maintenance and rehabilitation, other indirect emissions, and recycling activities. A comprehensive review on the life cycle assessment of concrete is presented to holistically examine the importance of use and end-of-life phases to the life cycle GHG quantification of concrete. The recent published ISO 14067: Carbon footprint of products – requirements and guidelines for quantification and communication also mandates the use of cradle-to-grave to provide publicly available eco-label information when the use and end-of-life phases of concrete can be appropriately simulated. With the support of Building Information Modelling (BIM) and other simulation technologies, the contribution of use and end-of-life phases to the life cycle GHG emissions of concrete should not be overlooked in future studies.

Information literacy programs and research: reflections on 'Information literacy programs and research: An international review' by Christine Bruce. 2000. The Australian Library Journal 49, 3: 209-218

Although in the late 1990s there was much discussion as to whether the idea of information literacy was necessary or had longevity, global interest in the phenomenon has increased rather than diminished. In the midst of all this activity, what has happened to the way in which we interpret the idea of information literacy in the last decade or more? The label of information literacy has certainly become widely applied, especially to library based programs and remains more popular in formal learning environments.Ultimately information literacy is about peoples’ experience of using information wherever they happen to be. Information literacy is about people interacting, engaging, working with information in many contexts, either individually or in community. Emerging technologies may transform the kinds of information available and how it is engaged with. Nevertheless, we continue to need to understand the experience of information use in order to support people in their information environments. We continue to need to develop programs which reflect and enhance peoples’ experiences of using information to learn in ever widening and more complex settings (Bruce, 2008; Bruce & Hughes, 2010).

Unsupervised multi-label text classification using a world knowledge ontology

The development of text classification techniques has been largely promoted in the past decade due to the increasing availability and widespread use of digital documents. Usually, the performance of text classification relies on the quality of categories and the accuracy of classifiers learned from samples. When training samples are unavailable or categories are unqualified, text classification performance would be degraded. In this paper, we propose an unsupervised multi-label text classification method to classify documents using a large set of categories stored in a world ontology. The approach has been promisingly evaluated by compared with typical text classification methods, using a real-world document collection and based on the ground truth encoded by human experts.

Automatic population HARDI white matter tract clustering by label fusion of multiple tract atlases

Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our "label fusion" method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults. © 2012 Springer-Verlag.

Label-free isolation of a prostate cancer cell among blood cells and the single-cell measurement of drug accumulation using an integrated microfluidic chip

Circulating tumor cells (CTCs) are found in the blood of patients with cancer. Although these cells are rare, they can provide useful information for chemotherapy. However, isolation of these rare cells from blood is technically challenging because they are small in numbers. An integrated microfluidic chip, dubbed as CTC chip, was designed and fabricated for conducting tumor cell isolation. As CTCs usually show multidrug resistance (MDR), the effect of MDR inhibitors on chemotherapeutic drug accumulation in the isolated single tumor cell is measured. As a model of CTC isolation, human prostate tumor cells were mixed with mouse blood cells and the labelfree isolation of the tumor cells was conducted based on cell size difference. The major advantages of the CTC chip are the ability for fast cell isolation, followed by multiple rounds of single-cell measurements, suggesting a potential assay for detecting the drug responses based on the liquid biopsy of cancer patients.

Learning with symmetric label noise: The importance of being unhinged

Convex potential minimisation is the de facto approach to binary classification. However, Long and Servedio [2008] proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing. This ostensibly shows that convex losses are not SLN-robust. In this paper, we propose a convex, classification-calibrated loss and prove that it is SLN-robust. The loss avoids the Long and Servedio [2008] result by virtue of being negatively unbounded. The loss is a modification of the hinge loss, where one does not clamp at zero; hence, we call it the unhinged loss. We show that the optimal unhinged solution is equivalent to that of a strongly regularised SVM, and is the limiting solution for any convex potential; this implies that strong l2 regularisation makes most standard learners SLN-robust. Experiments confirm the unhinged loss’ SLN-robustness.

Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.

Health information management : What business are we in?

Developments in information technology will drive the change in records management; however, it should be the health information managers who drive the information management change. The role of health information management will be challenged to use information technology to broker a range of requests for information from a variety of users, including he alth consumers. The purposes of this paper are to conceptualise the role of health information management in the context of a technologically driven and managed health care environment, and to demonstrat e how this framework has been used to review and develop the undergraduate program in health information management at the Queensland University of Technology.