25 research outputs found

    Enabling Distributed Simulation of OMNeT++ INET Models

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    Parallel and distributed simulation have been extensively researched for a long time. Nevertheless, many simulation models are still executed sequentially. We attribute this to the fact that many of those models are simply not capable of being executed in parallel since they violate particular constraints. In this paper, we analyze the INET model suite, which enables network simulation in OMNeT++, with regard to parallelizability. We uncovered several issues preventing parallel execution of INET models. We analyzed those issues and developed solutions allowing INET models to be run in parallel. A case study shows the feasibility of our approach. Though there are parts of the model suite that we didn't investigate yet and the performance can still be improved, the results show parallelization speedup for most configurations. The source code of our implementation is available through our web site at code.comsys.rwth-aachen.de.Comment: Published in: A. F\"orster, C. Sommer, T. Steinbach, M. W\"ahlisch (Eds.), Proc. of 1st OMNeT++ Community Summit, Hamburg, Germany, September 2, 2014, arXiv:1409.0093, 201

    Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training

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    In this paper, we propose a novel approach to eLearning that makes use of smart wearable sensors. Traditional eLearning supports the remote and mobile learning of mostly theoretical knowledge. Here we discuss the possibilities of eLearning to support the training of manual skills. We employ forearm armbands with inertial measurement units and surface electromyography sensors to detect and analyse the user’s hand motions and evaluate their performance. Hand hygiene is chosen as the example activity, as it is a highly standardized manual task that is often not properly executed. The World Health Organization guidelines on hand hygiene are taken as a model of the optimal hygiene procedure, due to their algorithmic structure. Gesture recognition procedures based on artificial neural networks and hidden Markov modeling were developed, achieving recognition rates of 98 . 30 % ( ± 1 . 26 % ) for individual gestures. Our approach is shown to be promising for further research and application in the mobile eLearning of manual skills

    Collapse and Restoration of MHC Class-I-Dependent Immune Privilege : Exploiting the Human Hair Follicle as a Model

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    The collapse of major histocompatiblity complex (MHC) class-I-dependent immune privilege can lead to autoimmune disease or fetal rejection. Pragmatic and instructive models are needed to clarify the as yet obscure controls of MHC class I down-regulation in situ, to dissect the principles of immune privilege generation, maintenance, and collapse as well as to develop more effective strategies for immune privilege restoration. Here, we propose that human scalp hair follicles, which are abundantly available and easily studied, are ideally suited for this purpose: interferon-γ induces ectopic MHC class I expression in the constitutively MHC class-I-negative hair matrix epithelium of organ-cultured anagen hair bulbs, likely via interferon regulatory factor-1, along with up-regulation of the MHC class I pathway molecules β(2)microglobulin and transporter associated with antigen processing (TAP-2). In the first report to identify natural immunomodulators capable of down-regulating MHC class I expression in situ in a normal, neuroectoderm-derived human tissue, we show that ectopic MHC class I expression in human anagen hair bulbs can be normalized by treatment with α-MSH, IGF-1, or TGF-β1, all of which are locally generated, as well as by FK506. These agents are promising candidates for immune privilege restoration and for suppressing MHC class I expression where this is clinically desired (eg, in alopecia areata, multiple sclerosis, autoimmune uveitis, mumps orchitis, and fetal or allograft rejection)
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