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Titlebook: Non Coding RNAs in Plants; Volker A. Erdmann,Jan Barciszewski Book 2011 Springer-Verlag Berlin Heidelberg 2011 RNA interference.microRNAs.

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Shang Gao,Hailing Jin and non cylindrical PDE analysis. We apply here this setting to the morphic measure between shape or images. We consider both regular and non smooth situations and we derive complete shape metric space with characterization of geodesic as being solution to Euler fluid-like equation. By the way, thi
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Christopher D. Rock,Qing-Jun Luo,Fangqian Ou-Yang-point algorithm for linear multicommodity network flows overcame this drawback, and was able to efficiently solve large and difficult instances. In this work we perform a computational evaluation of an extension of that specialized algorithm for multicommodity problems with convex and separable qua
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Kyohei Arita,Tatsuo Kanno,Manabu Yoshikawa,Yoshiki Habuctice, but have to be modelled as random variables with a certain joint probability distribution. Hence, problems from limit (collapse) load analysis or plastic analysis and from plastic and elastic design of structures are treated in the framework of stochastic optimization. Using especially reliab
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Julieta L. Mateos,Nicolás G. Bologna,Javier F. Palatnikctice, but have to be modelled as random variables with a certain joint probability distribution. Hence, problems from limit (collapse) load analysis or plastic analysis and from plastic and elastic design of structures are treated in the framework of stochastic optimization. Using especially reliab
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Yijun Meng,Ping Wu,Ming Chenctice, but have to be modelled as random variables with a certain joint probability distribution. Hence, problems from limit (collapse) load analysis or plastic analysis and from plastic and elastic design of structures are treated in the framework of stochastic optimization. Using especially reliab
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Zhu Benzhongds, etc. Both design and planning of these networks involve optimization problems. The first part of this paper introduces the common characteristics of a nonlinear network (the network may be linear, the objective function may be non linear, or both may be nonlinear). The second part develops a mat
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