Abstract
Scientists have already undertaken experimental attempts to find a grain of space. In this article, the Bekenstein formula and the information-oriented approach are combined for the first time to theoretically calculate the smallest achievable grain length, as well as the energy and quantity of information. This became possible due to the fact that the information approach is based on the calculation of the amount of information contained in the model of the physical phenomenon. The results show very good agreement between theory and experiment, at least with respect to the scale of the length and the minimum resolution of energy. This concept can be important for a reliable interpretation of the forthcoming cosmological and quantum dimensions.
Highlights
In the age of the Internet, the Big Bang, the colonization of Mars, the pervasive computerization, concepts and methods of information theory are widely used in different varieties of the areas of human activities, such as physics, chemistry, biology, physiology, technology, and so on
This became possible due to the fact that the information approach is based on the calculation of the amount of information contained in the model of the physical phenomenon
System of Units (SI) is a set of dimensional quantities, base and calculated on their derived basis, which are necessary and sufficient to describe the known laws of nature, as in the physical content and quantitatively [4]
Summary
In the age of the Internet, the Big Bang, the colonization of Mars, the pervasive computerization, concepts and methods of information theory are widely used in different varieties of the areas of human activities, such as physics, chemistry, biology, physiology, technology, and so on. Information theory plays a fundamental role in the modelling of various processes and phenomena. This is because modelling is an information process, wherein information about the state and behavior of an observed object is obtained from the developed model. Information is increased, and information entropy is reduced because of the increased knowledge about the object [1]. In the 1980s, a brilliant elegant formula was developed, and the upper limit of the amount of information (called the Bekenstein boundary) was calculated [2].
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