This paper describes an entropy equation, but one that should be used for measuring energy and not information. In relation to the human brain therefore, both of these quantities can be used to represent the stored information. The human brain makes use of energy efficiency to form its structures, which is likely to be linked to the neuron wiring. This energy efficiency can also be used as the basis for a clustering algorithm, which is described in a different paper. This paper is more of a discussion about global properties, where the rules used for the clustering algorithm can also create the entropy equation E = (mean * variance). This states that work is done through the energy released by the 'change' in entropy. The equation is so simplistic and generic that it can offer arguments for completely different domains, where the journey ends with a discussion about global energy properties in physics and beyond. A comparison with Einstein's relativity equation is made and also the audacious suggestion that a black hole has zero-energy inside.

中文翻译：

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能量的熵方程

这篇论文描述了一个熵方程，但它应该用于测量能量而不是信息。因此，就人脑而言，这两个量都可以用来表示存储的信息。人脑利用能源效率来形成其结构，这很可能与神经元布线有关。这种能量效率也可以用作聚类算法的基础，这在另一篇论文中有所描述。这篇论文更多地是关于全局属性的讨论，其中用于聚类算法的规则也可以创建熵方程 E =（均值 * 方差）。这表明工作是通过熵的“变化”释放的能量完成的。这个方程是如此简单和通用，以至于它可以为完全不同的领域提供论据，旅程以对物理学及其他领域的全球能量特性的讨论结束。与爱因斯坦的相对论方程进行了比较，并大胆地提出了黑洞内部为零能量的建议。