PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 27, 20260 citationsOpen Access

EET Mathematical Foundations: Dimensional Analysis, Scaling Laws and Cross-Document Invariants

View Full Paper
HYHongpu Yang

Key Points

  • The aim is to establish the mathematical foundations of Energy-Efficiency Theory (EET) aligned with Core Rules v5.2.
  • Provides dimensional analysis of core parameters across foundational texts.
  • Develops graph-theoretic mathematics, including Laplacian and spectral embedding.
  • Derives hierarchical scaling laws and integrates cognitive constitution variables.
  • Defines the universal exponent at approximately 0.6 for scaling laws.
  • Formalizes the dynamics of consciousness and spirit indices in EET.
  • Presents complete equations for cognition-related entropy dynamics.

Abstract

This document establishes the complete mathematical foundations of Energy-Efficiency Theory (EET), fully aligned with Core Rules v5. 2 and the companion ontologies. It provides the dimensional analysis of all core parameters across the mother texts, formalizes the capacity geometry and unified action quantum, develops the graph-theoretic mathematics (Laplacian, spectral gap, Ollivier-Ricci curvature, spectral embedding), derives the hierarchical scaling laws from the universal exponent 0. 6, and presents the response pool dynamics and variational principle. Version 2. 3 integrates all variables from the cognitive constitution—including consciousness indices (I₂₀ₔₒ₀₋, Iₓ₇ₑ₄ₒ₇₎₋₃), spirit indices (M, d, (t) ), cross-level dynamics (ₗ), and cognitive inverse entropy (Ṡ₈₍ₕ^cog) —with full dimensional verification and bridge declarations. The core equations quick reference now includes the complete coupled dynamics of the consciousness-spirit cycle.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Hongpu Yang (2026) studied this question.

synapsesocial.com/papers/69eefd64fede9185760d4193https://doi.org/10.5281/zenodo.19750227
Ask AI
Helpful
Bookmark
Share
View Full Paper