PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 8, 20260 citationsOpen Access

The General Theory of Data Physics: For Industrial Assetization and Cross-Domain Interoperability

HYHAN YAXI

Key Points

  • The aim is to expand the General Theory of Data Physics by introducing new concepts related to data assetization.
  • Introduced the Principle of Physical Constraint-Driven Assetization.
  • Defined Entangled Interaction Data at the intersection of different operational settings.
  • Formalized a deterministic resolution mechanism to ensure mathematical consistency across diverse domains.
  • Identified physical resistances that affect data transition to tradeable assets.
  • Expanded the framework to include a new class of data, enhancing interoperability.
  • Provided a structured approach for coupling digital value with physical reality.

Abstract

The traditional paradigm of information exchange treats data as static entities. This paper extends the General Theory of Data Physics by introducing the Principle of Physical Constraint-Driven Assetization. We posit that the transition of data from a latent signal to a tradeable asset is fundamentally driven by physical resistances (Rphysical), such as geographic fixity, spatial inaccessibility, and domain-specific schema isolation. Furthermore, we define a third class of data—Entangled Interaction Data—which emerges at the interface of divergent operational environments. By formalizing a deterministic resolution mechanism, this framework ensuring mathematical consistency across heterogeneous domains, maintaining the coupling between digital value and physical reality. Note: This work is the first formal expansion of the General Theory of Data Physics, specifically addressing the Axiom of Physical Constraint-Driven Assetization and the discovery of Entangled Interaction Data (δ∈(0,1)).

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

HAN YAXI (2026) studied this question.

synapsesocial.com/papers/698828d90fc35cd7a8848bf9https://doi.org/10.5281/zenodo.18503833
Ask AI
Helpful
Bookmark
Share
View Full Paper