Elements of Mathematical Connections theory, Mathematics of format-numbers, Some format-numbers for s- morphology, Fundamentally New approach to elementary particles by format-numbers
Illia Danilishyn, Oleksandr Danilishyn
Connection is fundamental to understanding our world. Everything is expressed through connections: an object (matter) may be understood as a self-connection in the form of energy closed in on itself, while energy, information, and thoughts are forms of connection. We therefore proceed from connections to connection structures. In this approach, connections are more comprehensive than energy, and energy is more comprehensive than objects. Dynamic science is thus proposed as broader than traditional science because it is based on more general foundations. A central instrument of this approach is the format-number, which defines hierarchical structures of interpretations, energies, actions, and processes. Digitization of interpretation forms by format-numbers makes their use in digital technologies possible through appropriate programming. The format-number (2(1, 2, 1), (1, 2, 1)) characterizes the transition of quantum numbers through the tensor product to entanglement. The (1, 2)- and (2, 1)-interpretations are simplified models of a more complex reality. Beyond classical science, two approaches are distinguished: quantum mechanics, conditionally associated with the (1, 2)-interpretation, and dynamic mathematics, associated with the (2, 1)-interpretation. In contrast to the probabilistic approach of quantum mechanics, dynamic mathematics proposes a deterministic approach through an energy hierarchy. These approaches have a common “root” but operate from opposite positions and may lead to similar conclusions. The proposed framework extends the use of hierarchical structures beyond calculations to the manipulation of energies, objects, actions, and processes through higher levels. Interpretation formats are represented by format-numbers and quantum levels of connections. Traditional science, corresponding to the (1, 1)-interpretation, can be used for manipulating such numbers, thereby providing a bridge between dynamic and traditional science. The first task of the framework is to understand the hierarchy of energies in the Universe and the functioning principles of living energy, including living organisms and subtle energies. On this basis, artificial “pseudo-living energies” could be constructed. Dynamic science studies Energy-space with scalar product and norms of its elements described through format-numbers. Its mathematical basis emphasizes uncertainty and the dynamics of energy rather than objectivity alone. Mathematical uncertainties are used to form new structures for describing processes and objects that cannot be determined by conventional deterministic methods. Many energies are treated as indeterminate from the perspective of traditional science because they arise from uncertainties and may represent large concentrations of specific energy in chaotic states. Dynamic mathematics is designed to work with such uncertainties and proposes their manipulation using direct-accumulative, direct-parallel neural networks. Within this concept, a singular science realized through a neural network analogous to the human central nervous system would be more natural than ordinary computational approaches. Quantum computers are considered only an initial stage. The proposed SmnSprt network is intended to use format-numbers to construct or access levels of subtle energies, including transformations or “shifts” of ordinary energy into subtle energies. The practical development of such a network requires an instrumental mathematical basis and experimental verification. The aim of the work is therefore to introduce concepts and methods for developing new technologies based on this framework. Unlike classical mathematics, which focuses primarily on results, dynamic mathematics places the action itself at the center. It is intended not only to obtain results but also to directly control actions, especially within direct-parallel neural networks. The authors argue that any action has greater potential than its result and that this process-oriented view may be useful not only in mathematics and physics but also in other complex systems. The proposed apparatus is intended for studying living and subtle energies that are not directly expressed through ordinary objectivity and conventional forms of energy, although they may manifest themselves through lower levels. The significance of the work lies in offering a qualitatively different approach to complex processes through new mathematical, hierarchical, and dynamic structures, including processes studied in synergetics. The authors propose a presumptive mathematical structure and classification of subtle energies and introduce new classes of mathematical structures and singularities. The methodological distinction between classical and dynamic mathematics also concerns their application in technical devices. Classical mathematics operates indirectly through numbers, whereas dynamic mathematics is proposed to act directly through energies, including specific energies that determine numerical values at particular moments in technical systems. The uniqueness of a given energy is treated as a key feature for constructing and manipulating subtle energies. The authors do not claim that traditional science currently recognizes dynamic mathematics, since the two approaches occupy different perceptual positions. Traditional science and dynamic science are nevertheless regarded as intersecting domains. Dynamic models identified with traditional mathematics are termed dynamic mathematics, and the corresponding broader framework is termed dynamic science. Concepts such as self, oself, pself and other singularities are presented as instruments for creating order from chaos, while intention is treated as a mechanism for switching the assemblage point to a desired position.
Primedia eLaunch LLC eBooks•Sep 14, 2026•0 citations