Patterns of support in an online community for smoking cessation

Social support offers various benefits for health and behaviour change. However, previous work has shown that individuals are typically reluctant to ask for support on social network sites, unless they can present a changed, healthier identity. To examine the relationship between stage of change and social support we conducted a thematic analysis of messages posted in a public Facebook support group for people trying to quit smoking. Our findings show that the kind of support exchanged online is related to participants' stage of change. Contrary to our expectations, supportive responses and leadership in the support group came mainly from users who just started their change process rather than people who had already changed. We discuss contributions to theories of online participation and impression management as well as implications for practitioners who seek to establish support groups.

Expanding the research area of behavior change support systems

The First International Workshop on Behavior Change Support Systems attracted a great research interest. The selected papers focused on abstraction, implementation and evaluation of Behavior Change Support Systems. The workshop is an evidence of how researchers from around the globe have their own perspective of behavior change interventions. In this abstract, we have attempted to outline core issues that can enhance persuasiveness of such support systems. Finally, we highlight important research questions relating to the development of effective Behavior Change Support Systems.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

Kohti leikkaussalin tuloksellista toiminnanohjausta

Background: The aging population is placing increasing demands on surgical services, simultaneously with a decreasing supply of professional labor and a worsening economic situation. Under growing financial constraints, successful operating room management will be one of the key issues in the struggle for technical efficiency. This study focused on several issues affecting operating room efficiency. Materials and methods: The current formal operating room management in Finland and the use of performance metrics and information systems used to support this management were explored using a postal survey. We also studied the feasibility of a wireless patient tracking system as a tool for managing the process. The reliability of the system as well as the accuracy and precision of its automatically recorded time stamps were analyzed. The benefits of a separate anesthesia induction room in a prospective setting were compared with the traditional way of working, where anesthesia is induced in the operating room. Using computer simulation, several models of parallel processing for the operating room were compared with the traditional model with respect to cost-efficiency. Moreover, international differences in operating room times for two common procedures, laparoscopic cholecystectomy and open lung lobectomy, were investigated. Results: The managerial structure of Finnish operating units was not clearly defined. Operating room management information systems were found to be out-of-date, offering little support to online evaluation of the care process. Only about half of the information systems provided information in real time. Operating room performance was most often measured by the number of procedures in a time unit, operating room utilization, and turnover time. The wireless patient tracking system was found to be feasible for hospital use. Automatic documentation of the system facilitated patient flow management by increasing process transparency via more available and accurate data, while lessening work for staff. Any parallel work flow model was more cost-efficient than the traditional way of performing anesthesia induction in the operating room. Mean operating times for two common procedures differed by 50% among eight hospitals in different countries. Conclusions: The structure of daily operative management of an operating room warrants redefinition. Performance measures as well as information systems require updating. Parallel work flows are more cost-efficient than the traditional induction-in-room model.

IASDR 2015 Interplay Exhibition 2-5 NOV 2015

It has been said that we are living in a golden age of innovation. New products, systems and services aimed to enable a better future, have emerged from novel interconnections between design and design research with science, technology and the arts. These intersections are now, more than ever, catalysts that enrich daily activities for health and safety, education, personal computing, entertainment and sustainability, to name a few. Interactive functions made possible by new materials, technology, and emerging manufacturing solutions demonstrate an ongoing interplay between cross-disciplinary knowledge and research. Such interactive interplay bring up questions concerning: (i) how art and design provide a focus for developing design solutions and research in technology; (ii) how theories emerging from the interactions of cross-disciplinary knowledge inform both the practice and research of design and (iii) how research and design work together in a mutually beneficial way. The IASDR2015 INTERPLAY EXHIBITION provides some examples of these interconnections of design research with science, technology and the arts. This is done through the presentation of objects, artefacts and demonstrations that are contextualised into everyday activities across various areas including health, education, safety, furniture, fashion and wearable design. The exhibits provide a setting to explore the various ways in which design research interacts across discipline knowledge and approaches to stimulate innovation. In education, Designing South African Children’s Health Education as Generative Play (A Bennett, F Cassim, M van der Merwe, K van Zijil, and M Ribbens) presents a set of toolkits that resulted from design research entailing generative play. The toolkits are systems that engender pleasure and responsibility, and are aimed at cultivating South African’s youth awareness of nutrition, hygiene, disease awareness and prevention, and social health. In safety, AVAnav: Avalanche Rescue Helmet (Jason Germany) delivers an interactive system as a tool to contribute to reduce the time to locate buried avalanche victims. Helmet-mounted this system responds to the contextual needs of rescuers and has since led to further design research on the interface design of rescuing devices. In apparel design and manufacturing, Shrinking Violets: Fashion design for disassembly (Alice Payne) proposes a design for disassembly through the use of beautiful reversible mono-material garments that interactively responds to the challenges of garment construction in the fashion industry, capturing the metaphor for the interplay between technology and craft in the fashion manufacturing industry. Harvest: A biotextile future (Dean Brough and Alice Payne), explores the interplay of biotechnology, materiality and textile design in the creation of sustainable, biodegradable vegan textile through the process of a symbiotic culture of bacteria and yeast (SCOBY). SCOBY is a pellicle curd that can be harvested, machine washed, dried and cut into a variety of designs and texture combinations. The exploration of smart materials, wearable design and micro-electronics led to creative and aesthetically coherent stimulus-reactive jewellery; Symbiotic Microcosms: Crafting Digital Interaction (K Vones). This creation aims to bridge the gap between craft practitioner and scientific discovery, proposing a move towards the notion of a post-human body, where wearable design is seen as potential ground for new human-computer interactions, affording the development of visually engaging multifunctional enhancements. In furniture design, Smart Assistive chair for older adults (Chao Zhao) demonstrates how cross-disciplinary knowledge interacting with design strategies provide solution that employed new technological developments in older aged care, and the participation of multiple stakeholders: designers, health care system and community based health systems. In health, Molecular diagnosis system for newborns deafness genetic screening (Chao Zhao) presents an ambitious and complex project that includes a medical device aimed at resolving a number of challenges: technical feasibility for city and rural contexts, compatibility with standard laboratory and hospital systems, access to health system, and support the work of different hospital specialists. The interplay between cross-disciplines is evident in this work, demonstrating how design research moves forward through technology developments. These works exemplify the intersection between domains as a means to innovation. Novel design problems are identified as design intersects with the various areas. Research informs this process, and in different ways. We see the background investigation into the contextualising domain (e.g. on-snow studies, garment recycling, South African health concerns, the post human body) to identify gaps in the area and design criteria; the technologies and materials reviews (e.g. AR, biotextiles) to offer plausible technical means to solve these, as well as design criteria. Theoretical reviews can also inform the design (e.g. play, flow). These work together to equip the design practitioner with a robust set of ‘tools’ for design innovation – tools that are based in research. The process identifies innovative opportunity and criteria for design and this, in turn, provides a means for evaluating the success of the design outcomes. Such an approach has the potential to come full circle between research and design – where the design can function as an exemplar, evidencing how the research-articulated problems can be solved. Core to this, however, is the evaluation of the design outcome itself and identifying knowledge outcomes. In some cases, this is fairly straightforward that is, easily measurable. For example the efficacy of Jason Germany’s helmet can be determined by measuring the reduced response time in the rescuer. Similarly the improved ability to recycle Payne’s panel garments can be clearly determined by comparing it to those recycling processes (and her identified criteria of separating textile elements!); while the sustainability and durability of the Brough & Payne’s biotextile can be assessed by documenting the growth and decay processes, or comparative strength studies. There are however situations where knowledge outcomes and insights are not so easily determined. Many of the works here are open-ended in their nature, as they emphasise the holistic experience of one or more designs, in context: “the end result of the art activity that provides the health benefit or outcome but rather, the value lies in the delivery and experience of the activity” (Bennet et al.) Similarly, reconfiguring layers of laser cut silk in Payne’s Shrinking Violets constitutes a customisable, creative process of clothing oneself since it “could be layered to create multiple visual effects”. Symbiotic Microcosms also has room for facilitating experience, as the work is described to facilitate “serendipitous discovery”. These examples show the diverse emphasis of enquiry as on the experience versus the product. Open-ended experiences are ambiguous, multifaceted and differ from person to person and moment to moment (Eco 1962). Determining the success is not always clear or immediately discernible; it may also not be the most useful question to ask. Rather, research that seeks to understand the nature of the experience afforded by the artefact is most useful in these situations. It can inform the design practitioner by helping them with subsequent re-design as well as potentially being generalizable to other designers and design contexts. Bennett et. al exemplify how this may be approached from a theoretical perspective. This work is concerned with facilitating engaging experiences to educate and, ultimately impact on that community. The research is concerned with the nature of that experience as well, and in order to do so the authors have employed theoretical lenses – here these are of flow, pleasure, play. An alternative or complementary approach to using theory, is using qualitative studies such as interviews with users to ask them about what they experienced? Here the user insights become evidence for generalising across, potentially revealing insight into relevant concerns – such as the range of possible ‘playful’ or experiences that may be afforded, or the situation that preceded a ‘serendipitous discovery’. As shown, IASDR2015 INTERPLAY EXHIBITION provides a platform for exploration, discussion and interrogation around the interplay of design research across diverse domains. We look forward with excitement as IASDR continues to bring research and design together, and as our communities of practitioners continue to push the envelope of what is design and how this can be expanded and better understood with research to foster new work and ultimately, stimulate innovation.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

MAC design for heterogeneous application support in OFDM based wireless systems

With the increasing adoption of wireless technology, it is reasonable to expect an increase in file demand for supporting both real-time multimedia and high rate reliable data services. Next generation wireless systems employ Orthogonal Frequency Division Multiplexing (OFDM) physical layer owing, to the high data rate transmissions that are possible without increase in bandwidth. Towards improving file performance of these systems, we look at the design of resource allocation algorithms at medium-access layer, and their impact on higher layers. While TCP-based clastic traffic needs reliable transport, UDP-based real-time applications have stringent delay and rate requirements. The MAC algorithms while catering to the heterogeneous service needs of these higher layers, tradeoff between maximizing the system capacity and providing fairness among users. The novelly of this work is the proposal of various channel-aware resource allocation algorithms at the MAC layer. which call result in significant performance gains in an OFDM based wireless system.

Water management capacity building to support rapidly developing mining economies

Many developing countries are experiencing rapid expansion in mining with associated water impacts. In most cases mining expansion is outpacing the building of national capacity to ensure that sustainable water management practices are implemented. Since 2011, Australia's International Mining for Development Centre (IM4DC) has funded capacity building in such countries including a program of water projects. Five projects in particular (principally covering experiences from Peru, Colombia, Ghana, Zambia, Indonesia, Philippines and Mongolia) have provided insight into water capacity building priorities and opportunities. This paper reviews the challenges faced by water stakeholders, and proposes the associated capacity needs. The paper uses the evidence derived from the IM4DC projects to develop a set of specific capacity-building recommendations. Recommendations include: the incorporation of mine water management in engineering and environmental undergraduate courses; secondments of staff to suitable partner organisations; training to allow site staff to effectively monitor water including community impacts; leadership training to support a water stewardship culture; training of officials to support implementation of catchment management approaches; and the empowerment of communities to recognise and negotiate solutions to mine-related risks. New initiatives to fund the transfer of multi-disciplinary knowledge from nations with well-developed water management practices are called for.

HealthMap: A cluster randomised trial of interactive health plans and self-management support to prevent coronary heart disease in people with HIV

Background The leading causes of morbidity and mortality for people in high-income countries living with HIV are now non-AIDS malignancies, cardiovascular disease and other non-communicable diseases associated with ageing. This protocol describes the trial of HealthMap, a model of care for people with HIV (PWHIV) that includes use of an interactive shared health record and self-management support. The aims of the HealthMap trial are to evaluate engagement of PWHIV and healthcare providers with the model, and its effectiveness for reducing coronary heart disease risk, enhancing self-management, and improving mental health and quality of life of PWHIV. Methods/Design The study is a two-arm cluster randomised trial involving HIV clinical sites in several states in Australia. Doctors will be randomised to the HealthMap model (immediate arm) or to proceed with usual care (deferred arm). People with HIV whose doctors are randomised to the immediate arm receive 1) new opportunities to discuss their health status and goals with their HIV doctor using a HealthMap shared health record; 2) access to their own health record from home; 3) access to health coaching delivered by telephone and online; and 4) access to a peer moderated online group chat programme. Data will be collected from participating PWHIV (n = 710) at baseline, 6 months, and 12 months and from participating doctors (n = 60) at baseline and 12 months. The control arm will be offered the HealthMap intervention at the end of the trial. The primary study outcomes, measured at 12 months, are 1) 10-year risk of non-fatal acute myocardial infarction or coronary heart disease death as estimated by a Framingham Heart Study risk equation; and 2) Positive and Active Engagement in Life Scale from the Health Education Impact Questionnaire (heiQ). Discussion The study will determine the viability and utility of a novel technology-supported model of care for maintaining the health and wellbeing of people with HIV. If shown to be effective, the HealthMap model may provide a generalisable, scalable and sustainable system for supporting the care needs of people with HIV, addressing issues of equity of access. Trial registration Universal Trial Number (UTN) U111111506489; ClinicalTrial.gov Id NCT02178930 submitted 29 June 2014

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.

Mixed pixel analysis for flood mapping using extended support vector machine

This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the 'wet' areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.

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.

Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.

Decision support requirements for intelligent operation of southern grid in India

Control centers (CC) play a very important role in power system operation. An overall view of the system with information about all existing resources and needs is implemented through SCADA (Supervisory control and data acquisition system) and an EMS (energy management system). As advanced technologies have made their way into the utility environment, the operators are flooded with huge amount of data. The last decade has seen extensive applications of AI techniques, knowledge-based systems, Artificial Neural Networks in this area. This paper focuses on the need for development of an intelligent decision support system to assist the operator in making proper decisions. The requirements for realization of such a system are recognized for the effective operation and energy management of the southern grid in India The application of Petri nets leading to decision support system has been illustrated considering 24 bus system that is a part of southern grid.

Minimum enclosing spheres formulations for support vector ordinal regression

We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. Both approaches guarantee that the radii of the spheres are properly ordered at the optimal solution. The size of the optimization problem is linear in the number of training samples. The popular SMO algorithm is adapted to solve the resulting optimization problem. Numerical experiments on some real-world data sets verify the usefulness of our approaches for data mining.