Jeppesen Kaas (twigpull76)

REC and of resistant Gram-positive pathogens. This study will explore and understand the experience of doctors volunteering online in managing the boundaries between work and family in health virtual communities (HVC). A qualitative case study approach was used to explore and understand how doctors volunteering online balances between work and family in a Health Virtual Community called DoktorBudak.com (DB). A total of seventeen (17) doctors were interviewed using either face-to-face, Skype, phone interview or through email. The results of this study suggested that doctors perceived the physical border at their workplace as less permeable though the ICT has freed them from the restriction to perform other non-related work (such as online volunteering (OV) works) during working hours. In addition, doctors OV use ICTs to perform work at home or during working hours, they perceive their work and family borders as flexible. Furthermore, the doctors used different strategies when it came to blending, whether to segment or integrate their work and family domains. This study has defined issues on work-family balance and OV. Most importantly this study had discussed the conceptual framework of work-family balance focusing on doctors volunteering online and how they have incorporated ICTs such as Internet technology to negotiate the work-family boundaries, which are permeable, flexible and blending. This study has defined issues on work-family balance and OV. Most importantly this study had discussed the conceptual framework of work-family balance focusing on doctors volunteering online and how they have incorporated ICTs such as Internet technology to negotiate the work-family boundaries, which are permeable, flexible and blending.Machine learning has been used in the past for the auxiliary diagnosis of Alzheimer's Disease (AD). However, most existing technologies only explore single-view data, require manual parameter setting and focus on two-class (i.e., dementia or not) classification problems. Unlike single-view data, multi-view data provide more powerful feature representation capability. Learning with multi-view data is referred to as multi-view learning, which has received certain attention in recent years. In this paper, we propose a new multi-view clustering model called Consensus Multi-view Clustering (CMC) based on nonnegative matrix factorization for predicting the multiple stages of AD progression. check details The proposed CMC performs multi-view learning idea to fully capture data features with limited medical images, approaches similarity relations between different entities, addresses the shortcoming from multi-view fusion that requires manual setting parameters, and further acquires a consensus representation containing shared features and complementary knowledge of multiple view data. It not only can improve the predication performance of AD, but also can screen and classify the symptoms of different AD's phases. Experimental results using data with twelve views constructed by brain Magnetic Resonance Imaging (MRI) database from Alzheimer's Disease Neuroimaging Initiative expound and prove the effectiveness of the proposed model. Retinal blood vessels classification into arterioles and venules is a major task for biomarker identification. Especially, clustering of retinal blood vessels is a challenging task due to factors affecting the images such as contrast variability, non-uniform illumination etc. Hence, a high performance automatic retinal vessel classification system is highly prized. In this paper, we propose a novel unsupervised methodology to classify retinal vessels extracted from fundus camera images into arterioles and venules. The proposed method utilises the homomorphic filtering (HF) to preprocess the input image for non-uniform illumination and denoising. In the next step, an unsupervised multiscale line operator segmentation techn