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Liver transplantation (LT) has considerably improved the outcome of patients with end-stage liver disease, especially in children. The first pediatric LT in Vietnam was performed in 2004. To assess the current need for pediatric LT in Southern Vietnam, a total of 280 patients with chronic liver disease followed at Children's Hospital 2 (Ho Chi Minh City), the only pediatric LT center in this region, were evaluated from January 2009 to June 2014. Sixty-seven patients satisfied criteria for LT but only one transplant surgery occurred since 2009. Parental consent for LT was obtained only in 28.4% of patients. The main reasons for the small number of LTs were financial costs, far distance from home, lifelong follow-up and treatment, and shortage of organ donors. We conclude that the current need for pediatric LT in Southern Vietnam is high. Efforts should be made to develop the liver transplant program in this developing country.

An ion polymer metal composite (IPMC) is an electro-active polymer that bends in response to a small applied electrical field as a result of the mobility of cations in the polymer network and vice versa. The aim of this paper is the identification of a novel accurate nonlinear black-box model (NBBM) for IPMC actuators with self-sensing behavior based on a recurrent multi-layer perceptron neural network (RMLPNN) and a self-adjustable learning mechanism (SALM).Firstly, an IPMC actuator is investigated. Driving voltage signals are applied to the IPMC in order to identify the IPMC characteristics. Secondly, the advanced NBBM for the IPMC is built with suitable inputs and output to estimate the IPMC tip displacement. Finally, the model parameters are optimized by the collected input/output training data.Modeling results show that the proposed self-sensing methodology based on the optimized NBBM model can well describe the bending behavior of the IPMC actuator corresponding to its applied power without using any measuring sensor.

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