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March 27, 2026Open Access

The Universal Processing Law (UPL): HOW THE UNIVERSE EXPAND!

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Key Points

This hypothesis aims to explore the implications of the Universal Processing Law for the expansion of the universe.
Presents a logical chain that is internally consistent and physically motivated.
Includes proven equations from past UPL papers alongside new derived results.
Identifies an open problem related to quantum-gravity unification.
Introduces new findings in the derivation of the Universal Processing Law.
Highlights existing proven equations that support the hypothesis.

Implication

Hypothesis proposes new derivations in the Universal Processing Law, suggesting implications for understanding universe expansion.

Abstract

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Cite This Study

Ahmed Lahmidi (2026) studied this question.

synapsesocial.com/papers/69c9c5c5f8fdd13afe0bdc1c https://doi.org/10.5281/zenodo.19265573

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1The Universal Processing Law (UPL): HOW THE UNIVERSE EXPAND!Original Research · 2026
2The Universal Processing Law (UPL): A Computational Ontology for the Unification of General Relativity and Quantum MechanicsOriginal Research · 2026
3Universal Processing Law (UPL) Track 5: Objective Wavefunction Collapse from Computational Buffer Overload: A Discrete Derivation of the Penrose-Diosi Time via the Universal Processing LawOriginal Research · 2026
4Universal Processing Law (UPL) Track 4: Derivation of the Bekenstein-Hawking Entropy from Discrete Pixel Geometry via the Universal Processing LawOriginal Research · 2026
5Universal Processing Law (UPL): A Parameter-Free Derivation of the Cosmological ConstantOriginal Research · 2026

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Universal Processing Law (UPL) — Track 2: Kerr Metric Derivation, Geometric Gradient QNM Prediction, and Development Notes2026
Universal Processing Law (UPL) Track 4: Derivation of the Bekenstein-Hawking Entropy from Discrete Pixel Geometry2026
Underlying Properties Hypothesis (UPH): Regularities Expressed Across Multiple Levels of Complexity2026