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• W2080013992 abstract "We give a functional generalization of fractal scaling laws applied to response problems as well as to probability distributions. We consider excitations and responses, which are functions of a given state vector. Based on scaling arguments, we derive a general nonlinear response functional scaling law, which expresses the logarithm of a response at a given state as a superposition of the values of the logarithms of the excitations at different states. Such a functional response law may result from the balance of different growth processes, characterized by variable growth rates, and it is the first order approximation of a perturbation expansion similar to the phase expansion. Our response law is a generalization of the static fractal scaling law and can be applied to the study of various problems from physics, chemistry, and biology. We consider some applications to heterogeneous and disordered kinetics, organ growth (allometry), and population genetics. Kinetics on inhomogeneous reconstructing surfaces leads to rate equations described by our nonlinear scaling law. For systems with dynamic disorder with random energy barriers, the probability density functional of the rate coefficient is also given by our scaling law. The relative growth rates of different biological organs (allometry) can be described by a similar approach. Our scaling law also emerges by studying the variation of macroscopic phenotypic variables in terms of genotypic growth rates. We study the implications of the causality principle for our theory and derive a set of generalized Kramers-Kronig relationships for the fractal scaling exponents." @default.
• W2080013992 created "2016-06-24" @default.
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• W2080013992 date "2007-03-20" @default.
• W2080013992 modified "2023-10-16" @default.
• W2080013992 title "Functional, fractal nonlinear response with application to rate processes with memory, allometry, and population genetics" @default.
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• W2080013992 doi "https://doi.org/10.1073/pnas.0700397104" @default.
• W2080013992 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/1829218" @default.
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