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ABSTRACT Elements are the basic substances that make up living organisms, and the element composition in plants quantitatively reflect the adaptation of plants to environment. However, the drivers that constitute the species‐specific plant elementome, as well as the bivariate bioelemental correlations in determining the stability of different bioelements are yet unclear. Based on 1058 leaf observations of 84 plant species from 232 wetlands across large environmental gradients, we found that bioelements with higher concentration were more stable and evolutionary constrained. We proposed a stability of well‐coordinated elements hypothesis, suggesting that bioelements that coordinate well in driving certain physiological functions constrain each other, thus maintaining relatively stable ratios in plants. In contrast, those functionally independent bioelements fluctuate greatly with environmental nutrient availability. Cold and saline stresses decreased plant stoichiometric network connectivity, complexity, and stability. Our research filled the gap in study of wetland plant elementome, and provided new evidences of plant–environment interactions in regions sensitive to climate change. 

This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynam- ical. This problem is subject to the ‘curse of dimension- ality’ associated with the dynamic programming method. This paper proposes a novel decoupled data-based con- trol (D2C) algorithm that addresses this problem using a decoupled, ‘open-loop - closed-loop’, approach. First, an open-loop deterministic trajectory optimization problem is solved using a black-box simulation model of the dynamical system. Then, closed-loop control is developed around this open-loop trajectory by linearization of the dynamics about this nominal trajectory. By virtue of linearization, a linear quadratic regulator based algorithm can be used for this closed-loop control. We show that the performance of D2C algorithm is approximately optimal. Moreover, simulation performance suggests a significant reduction in training time compared to other state of the art algorithms.