HSN in adults is a serious relapsing disease, causing renal failure as frequently as in small-vessel ANCA-positive vasculitides. Prognosis and risks differed in this series from those in other countries, including a higher risk of ESRF than in previous series. Distinct groups developed either ESRF, or remitted. The absence of clear benefit suggests that corticosteroids should be reserved for patients with serious disease, and that cytotoxics may not be merited for those at high risk of renal failure.

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In this paper we present an approach to decoupled force/position control of the end-effector along the same direction for redundant

This paper presents a Hidden Markov Model (HMM) approach for real-time activity classification using signals from wearable wireless sensor networks. A wearable wireless sensor network can be used to continuously monitor the daily activities of a subject in real time. However, the wireless sensor nodes are constrained by limited battery and computing resources. The proposed HMM framework has been applied to find the most probable activity states series with low data transmission rate, which makes it highly suitable for daily activity classification applications. The performance was evaluated using a small sensor network consisting of three accelerometers. The activity detection rate is 95.82%, using a test set of 5 subjects with 11 activity series.

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Highlight A novel epidemic model is proposed to represent the epidemic spread under control. Weibull distribution is introduced into the epidemic model. A two-step iterative optimization is designed to estimate the model parameters. The characteristic parameters of COVID-19 are estimated with the novel model. The spread process of COVID-19 in Wuhan is reproduced and the unreported data is estimated.

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