Protocol for a randomised blocked design study using telephone and text-messaging to support cardiac patients with diabetes : a cross cultural international collaborative project

Background The prevalence of type 2 diabetes is rising internationally. Patients with diabetes have a higher risk of cardiovascular events accounting for substantial premature morbidity and mortality, and health care expenditure. Given healthcare workforce limitations, there is a need to improve interventions that promote positive self-management behaviours that enable patients to manage their chronic conditions effectively, across different cultural contexts. Previous studies have evaluated the feasibility of including telephone and Short Message Service (SMS) follow up in chronic disease self-management programs, but only for single diseases or in one specific population. Therefore, the aim of this study is to evaluate the feasibility and short-term efficacy of incorporating telephone and text messaging to support the care of patients with diabetes and cardiac disease, in Australia and in Taiwan. Methods/design A randomised controlled trial design will be used to evaluate a self-management program for people with diabetes and cardiac disease that incorporates the use of simple remote-access communication technologies. A sample size of 180 participants from Australia and Taiwan will be recruited and randomised in a one-to-one ratio to receive either the intervention in addition to usual care (intervention) or usual care alone (control). The intervention will consist of in-hospital education as well as follow up utilising personal telephone calls and SMS reminders. Primary short term outcomes of interest include self-care behaviours and self-efficacy assessed at baseline and four weeks. Discussion If the results of this investigation substantiate the feasibility and efficacy of the telephone and SMS intervention for promoting self management among patients with diabetes and cardiac disease in Australia and Taiwan, it will support the external validity of the intervention. It is anticipated that empirical data from this investigation will provide valuable information to inform future international collaborations, while providing a platform for further enhancements of the program, which has potential to benefit patients internationally.

Text messaging to support off-campus clinical nursing facilitators : a descriptive survey

Background Managing large student cohorts can be a challenge for university academics, coordinating these units. Bachelor of Nursing programmes have the added challenge of managing multiple groups of students and clinical facilitators whilst completing clinical placement. Clear, time efficient and effective communication between coordinating academics and clinical facilitators is needed to ensure consistency between student and teaching groups and prompt management of emerging issues. Methods This study used a descriptive survey to explore the use of text messaging via a mobile phone, sent from coordinating academics to off-campus clinical facilitators, as an approach to providing direction and support. Results The response rate was 47.8% (n = 22). Correlations were found between the approachability of the coordinating academic and clinical facilitator perception that, a) the coordinating academic understood issues on clinical placement (r = 0.785, p < 0.001), and b) being part of the teaching team (r = 0.768, p < 0.001). Analysis of responses to qualitative questions revealed three themes: connection, approachability and collaboration. Conclusions This study demonstrates that use of regular text messages improves communication between coordinating academics and clinical facilitators. Findings suggest improved connection, approachability and collaboration between the coordinating academic and clinical facilitation staff.

Rough set based approach to text classification

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.

Strolling the coastline: criminology in everyday life : through ‘landscape’ from gaol to ‘badlands’

Part travelogue, part flight of fancy, this paper recounts a coastline stroll from Maroubra Beach to Bondi in Sydney’s eastern suburbs. The author as ‘travel guide’ points out features of potential interest to two visiting criminological colleagues as they ‘pass by’ scenery of great beauty shadowed by acts of spectacular violence. The everyday acts of walking and talking while passing through a ‘landscape’ serve to constitute a criminology of everyday life, illustrating the way in which a consciousness of crime, crime sites, analyses and theories permeates the ways a ‘tourist trail’ might be experienced and seen, myths made and histories forged. The walk starts with the unseen lines of penal force radiating from Long Bay Gaol, before skirting through surfing and its regulation; the ‘brotherhood’ of the BRA Boys; the Hines killing and the politics of self defence; the shark arm case, the Virgin Mary and the Bali bombing memorial at Coogee; zones of the beach and Jock Young’s Vertigo at Bronte and Tamarama; before finishing at the Marks Park ‘badlands’ at Bondi, scene of a series of mostly unsolved and unpunished homophobic killings, giving rise to reflections on ‘ungrievable lives’, memory, mourning and forgetting.

An Interview with Sylvère Lotringer, Jean Baudrillard Chair at the European Graduate School and Professor Emeritus of French Literature and Philosophy at Columbia University, on the Architectural Contribution to Semiotext(e), Schizoculture, and the Early Deleuze and Guattari Scene at Columbia University

An Interview with Sylvère Lotringer, Jean Baudrillard Chair at the European Graduate School and Professor Emeritus of French Literature and Philosophy at Columbia University, on the Architectural Contribution to Semiotext(e), Schizoculture, and the Early Deleuze and Guattari Scene at Columbia University, which took place at the Department of French, Columbia University, New York City, July 2003. This interview exists as an audio cassette tape recording.

Can we text you? A qualitative exploration of young unemployed job-seekers’ attitudes to receiving resilience-building SMS messages

A significant minority of young job-seekers remain unemployed for many months, and are at risk of developing depression. Both empirical studies and theoretical models suggest that cognitive, behavioural and social isolation factors interact to increase this risk. Thus, interventions that reduce or prevent depression in young unemployed job-seekers by boosting their resilience are required. Mobile phones may be an effective medium to deliver resilience-boosting support to young unemployed people by using SMS messages to interrupt the feedback loop of depression and social isolation. Three focus groups were conducted to explore young unemployed job-seekers’ attitudes to receiving and requesting regular SMS messages that would help them to feel supported and motivated while job-seeking. Participants reacted favourably to this proposal, and thought that it would be useful to continue to receive and request SMS messages for a few months after commencing employment as well.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Interstellar molecules : Conversion or C3O-. to C3O in the gas phase

Neutral C3O has been prepared by collision induced neutralisation of the precursor radical anion formed by the reaction C-=C-CO-OEt --> C3O-. +EtO. . The similar neutralisaaion reionisation (-NR+) and charge reversal (CR) spectra of C3O-. indicate that the potential surfaces of C3O and C3O+. show favourable vertical Franck-Condon overlap, This suggests that the bond connectivities of anion, neutral and cation C3O are the same. Copyright (C) 1999 John Wiley & Sons, Ltd.

Interstellar molecules. Conversion or C3O-. to C3O in the gas phase

Neutral C3O has been prepared by collision induced neutralisation of the precursor radical anion formed by the reaction C-=C-CO-OEt --> C3O-. +EtO. The similar neutralisaaion reionisation (-NR+) and charge reversal (CR) spectra of C3O-. indicate that the potential surfaces of C3O and C3O+. show favourable vertical Franck-Condon overlap, This suggests that the bond connectivities of anion, neutral and cation C3O are the same. Copyright (C) 1999 John Wiley & Sons, Ltd.

Methanesulfonic acid-catalyzed conversion of glucose and xylose mixtures to levulinic acid and furfural

Methanesulfonic acid (MSA) was compared with sulfuric acid for the conversion of glucose and xylose mixtures to produce levulinic acid and furfural. The interactions of glucose and xylose, the predominant sugars found in biomass, were found to influence product yields with furfural degradation reactions enhanced under higher reactant loadings. Fast heating rates allowed maximal yields (>60 mol%) of levulinic acid and furfural to be achieved under short reaction times. Under the range of conditions examined, sulfuric acid produced a slight increase in levulinic acid yield by 6% (P = 0.02), although there was no significant difference (P = 0.11) between MSA and sulfuric acid in levulinic acid formed from glucose alone. The amount and type of the solid residue is similar between MSA and sulfuric acid. As such, MSA is a suitable alternative because its use minimizes corrosion and disposal issues associated with mineral acid catalysts. The heating value of the residue was 22 MJ/kg implying that it is a suitable source of fuel. On the basis of these results, a two-stage processing strategy is proposed to target high levulinic acid and furfural yields, and other chemical products (e.g., lactic acid, xylitol, acetic acid and formic acid). This will result in full utilization of bagasse components.

The Credit Act Advisory System (CAAS) : conversion from an expert system prototype to a C++ commercial system

CAAS is a rule-based expert system, which provides advice on the Victorial Credit Act 1984. It is currently in commercial use, and has been developed in conjunction with a law firm. It uses an object-oriented hybrid reasoning approach. The system was initially prototyped using the expert system shell NExpert Object, and was then converted into the C++ language. In this paper we describe the advantages that this methodology has, for both commercial and research development.

Conversion of linear to rhombic C-4 in the gas phase: A joint experimental and theoretical study

Charge reversal (CR) and neutralization reionization (NR) experiments carried out on a 4-sector mass spectrometer demonstrate that isotopically labeled, linear C-4 anion rearranges upon collisional oxidation. The cations and neutrals formed in these experiments exhibit differing degrees of isotopic scrambling in their fragmentation patterns, indicative of (at least) partial isomerization of both states. Theoretical studies, employing the CCSD(T)/aug-cc-pVDZ//B3LYP/6-31G(d) level of theory, favor conversion to the rhombic C-4 isomer on both cationic and neutral potential-energy surfaces with the rhombic structures predicted to be slightly more stable than the linear forms in each case. The combination of experiment with theory indicates that the elusive rhombic C-4 is formed as a cation and as a neutral following charge stripping of linear C-4(-)

An orientation to early childhood education for sustainability and research : framing the text

This publication arose from the interests of the chapter authors, ‘a small group of thoughtful people’ almost all of whom participated in one or both Transnational Dialogues in Research in Early Childhood Education for Sustainability, held in Stavanger, Norway in 2010 and Brisbane, Australia in 2011 (Refer Appendix 1 for list of participants). These meetings were the first time that a critical mass of researchers from vastly different parts of the globe - Norway, Sweden, Australia and New Zealand at the inaugural meeting, with additional participants from Korea, Japan and Singapore attending the second - had come together to debate, discuss and share ideas about research and theory in the emerging field of Early Childhood Education for Sustainability (ECEfS. Some of the researchers who joined these Transnational Dialogues, had met serendipitously at earlier conferences and meetings, or corresponded via email, but many had never met face-to-face. Now a significant number are contributing authors in this text. It is a testament to these researchers’ interest in this agenda that they mostly self-funded their travel and other costs to attend the Transnational Dialogues research meetings. While most chapter authors come from the field of early childhood education, a few are more aligned with education for sustainability/environmental education, while a much smaller number are already working at the intersection of early childhood education and education for sustainability. What we share as a group is a range of perspectives and orientations to research and to the research focus at the heart of this book - young children and their actual and potential capabilities as agents of change for sustainability. As researchers, regardless of experience and perspectives, participants knew they had something extra to offer - their expertise as researchers - providing scholarly insights into the work of practitioners, applying critically reflective lenses to curricula, pedagogies and assumptions, testing of ideas and theories, and presenting a sense for where ECEfS might fit or, indeed, go beyond norms and orthodoxies. This is a text, then, for both researchers and those whose primary interests lie in daily interactions with children, families and communities.

Using automated text analysis to evaluate students’ conceptual understanding

Background: A major challenge for assessing students’ conceptual understanding of STEM subjects is the capacity of assessment tools to reliably and robustly evaluate student thinking and reasoning. Multiple-choice tests are typically used to assess student learning and are designed to include distractors that can indicate students’ incomplete understanding of a topic or concept based on which distractor the student selects. However, these tests fail to provide the critical information uncovering the how and why of students’ reasoning for their multiple-choice selections. Open-ended or structured response questions are one method for capturing higher level thinking, but are often costly in terms of time and attention to properly assess student responses. Purpose: The goal of this study is to evaluate methods for automatically assessing open-ended responses, e.g. students’ written explanations and reasoning for multiple-choice selections. Design/Method: We incorporated an open response component for an online signals and systems multiple-choice test to capture written explanations of students’ selections. The effectiveness of an automated approach for identifying and assessing student conceptual understanding was evaluated by comparing results of lexical analysis software packages (Leximancer and NVivo) to expert human analysis of student responses. In order to understand and delineate the process for effectively analysing text provided by students, the researchers evaluated strengths and weakness for both the human and automated approaches. Results: Human and automated analyses revealed both correct and incorrect associations for certain conceptual areas. For some questions, that were not anticipated or included in the distractor selections, showing how multiple-choice questions alone fail to capture the comprehensive picture of student understanding. The comparison of textual analysis methods revealed the capability of automated lexical analysis software to assist in the identification of concepts and their relationships for large textual data sets. We also identified several challenges to using automated analysis as well as the manual and computer-assisted analysis. Conclusions: This study highlighted the usefulness incorporating and analysing students’ reasoning or explanations in understanding how students think about certain conceptual ideas. The ultimate value of automating the evaluation of written explanations is that it can be applied more frequently and at various stages of instruction to formatively evaluate conceptual understanding and engage students in reflective

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.