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Focuses on the migration of two pandas from China to Atlanta, Georgia. Part of a joint research program between Chinese and American scientists; Goal of improving the breeding of pandas.

This paper addresses the control design for an upper limb exoskeleton in the presence of input saturation. An adaptive controller employing the neural network technology is proposed to approximate the uncertain robotic dynamics. Also, an auxiliary system is designed to deal with the effect of input saturation. Furthermore, we develop both the state feedback and the output feedback control strategies, which effectively estimates the uncertainties online from the measured feedback errors, instead of the model-based control. In addition to the proposed control, a disturbance observer is designed to reject the unknown disturbance online for achieving the trajectory tracking. The method requires a minimal amount of a priori knowledge of system dynamics. Subsequently, the principle of Lyapunov synthesis ensures the stability of the closed-loop system. Finally, the experimental studies are carried out on this robotic exoskeleton. [ABSTRACT FROM AUTHOR]

Abstract: A Hàjek–Rènyi-type inequality for associated random variables was obtained by Prakasa Rao [Prakasa Rao, B.L.S., 2002. Hàjek–Rènyi-type inequality for associated sequences. Statist. Probab. Lett. 57, 139–143]. Recently, Sung [Sung, H.S., 2008. A note on the Hàjek–Rènyi inequality for associated random variables. Statist. Probab. Lett. 78, 885–889] improved the inequality of Prakasa Rao in the above-mentioned reference. In the paper, we use different methods from Sung’s and improve the results of Sung in the second reference cited above. [Copyright &y& Elsevier]