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Perturbation methods can efficiently deal with the consequent enlargement of the mechanical parameter space.

In this article, we argue that real-time heuristic search methods can efficiently solve nondeterministic planning tasks.

This can be explained in part by the fact that convex optimization methods can efficiently compute global solutions to large-scale problems in polynomial time.

Because imputation methods can efficiently predict multiple response variables simultaneously, they may be usefully applied to map several structural attributes at the species-level.

Although both types of methods can efficiently solve the problem, they have several shortcomings.

Unfortunately, the above methods can efficiently evaluate changes in steady state concentrations only for low-dimensional parameter variations.

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Meanwhile, the HOE method can efficiently deal with the special noise present in HJ-1A data.

Furthermore, the proposed method can efficiently tackle the computational complexity using a parallel programming paradigm.

However, the MRHSD method can efficiently compute the examples when C is asymmetric.

The results show that our proposed method can efficiently detect the bots despite their varying behaviors.

A mixed fracture example demonstrates that the present method can efficiently simulate the fracture propagation.

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