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Artificial neural networks (ANN) are employed using
different combinations among the surface friction velocity u*, surface buoyancy flux Bs, free-flow stability N, Coriolis parameter f,
and surface roughness length z0 from large-eddy simulation data as inputs
to investigate which variables are essential in determining the stable boundary
layer(SBL) height h. In addition, the
performances of several conventional linear SBL height parameterizations are
evaluated. ANN results indicate that the surface friction velocity u* is the most predominant
variable in the estimation of SBL height h.
When u* is absent, the
secondly important variable is the surface buoyancy flux Bs. The relevance of N, f, and z0 to
The stability of a kind of cooperative models incorporating harvesting is considered in this paper. By analyzing the characteristic roots of the models and constructing suitable Lyapunov functions, we prove that nonnegative equilibrium points of the models are globally asymptotically stable. Further, the corresponding nonautonomous cooperative models have a unique asymptotically periodic solution, which is uniformly asymptotically stable. An example is given to illustrate the effectiveness of our results.
The AGM axiom system is for the belief
revision (revision by a single belief), and the DP axiom system is for the
iterated revision (revision by a finite sequence of beliefs). Li  gave an R-calculus for R-configurations Δ|Γ, where Δ is a set of atomic formulas or the negations
of atomic formulas, and Γ is a finite set of
formulas. In propositional logic programs, one R-calculus N will
be given in this paper, such that N is sound and complete with respect to operator s(Δ,t), where s(Δ,t)is a pseudo-theory minimal change of t by Δ.