首页> 外文期刊>Theoretical Biology and Medical Modelling >Dimensional analysis yields the general second-order differential equation underlying many natural phenomena: the mathematical properties of a phenomenon’s data plot then specify a unique differential equation for it
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Dimensional analysis yields the general second-order differential equation underlying many natural phenomena: the mathematical properties of a phenomenon’s data plot then specify a unique differential equation for it

机译:维度分析产生了许多自然现象背后的一般二阶微分方程:现象数据图的数学特性然后为其指定一个唯一的微分方程

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Background This study uses dimensional analysis to derive the general second-order differential equation that underlies numerous physical and natural phenomena described by common mathematical functions. It eschews assumptions about empirical constants and mechanisms. It relies only on the data plot’s mathematical properties to provide the conditions and constraints needed to specify a second-order differential equation that is free of empirical constants for each phenomenon. Results A practical example of each function is analyzed using the general form of the underlying differential equation and the observable unique mathematical properties of each data plot, including boundary conditions. This yields a differential equation that describes the relationship among the physical variables governing the phenomenon’s behavior. Complex phenomena such as the Standard Normal Distribution, the Logistic Growth Function, and Hill Ligand binding, which are characterized by data plots of distinctly different sigmoidal character, are readily analyzed by this approach. Conclusions It provides an alternative, simple, unifying basis for analyzing each of these varied phenomena from a common perspective that ties them together and offers new insights into the appropriate empirical constants for describing each phenomenon.
机译:背景技术这项研究使用维度分析来推导通用的二阶微分方程,该方程以通用数学函数描述的众多物理和自然现象为基础。它避免了有关经验常数和机制的假设。它仅依赖于数据图的数学属性来提供条件和约束,这些条件和约束用于指定不含每种现象的经验常数的二阶微分方程。结果使用基本微分方程的一般形式以及每个数据图的可观察到的独特数学特性(包括边界条件)对每个函数的实际示例进行了分析。这产生了一个微分方程,描述了控制现象行为的物理变量之间的关系。通过这种方法可以轻松分析复杂现象,例如标准正态分布,对数增长函数和希尔配体结合,这些特征以明显不同的S形特征的数据图为特征。结论它提供了一个替代的,简单的,统一的基础,可以从一个共同的角度分析每种现象,将它们联系在一起,并为描述每种现象的适当经验常数提供了新的见解。

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