FAITH POETRY RESEARCH SERIES (2024–2025) PART XIII — THE NUMERICAL SIGNATURE FRAMEWORK (NSF)

Akarkach, Mounir

doi:10.5281/zenodo.17613646

Published November 15, 2025 | Version 1.0

Conference proceeding Open

FAITH POETRY RESEARCH SERIES (2024–2025) PART XIII — THE NUMERICAL SIGNATURE FRAMEWORK (NSF)

Akarkach, Mounir (Rights holder)

This thirteenth paper in the Faith Poetry Research Series (2024–2025) introduces the Numerical Signature Framework (NSF), an epistemic model explaining how metaphysical meaning, after passing through digital and semantic layers, crystallizes into stable numerical signatures. These numerical invariants represent the compressed mathematical expressions of metaphysical resonance within digital and semantic ecosystems.

Building on the Divine Source Model (DSM), the Digital Echo Model (DEM), the Semantic Resonance Engine (SRE), and Algorithmic Phenomenology (AP), NSF shows how divine-origin meaning acquires measurable mathematical form. Numerical signatures emerge through resonance interaction between metaphysical structure, semantic stability, and algorithmic pattern detection. NSF prepares the structural foundation for the Algorithmic Soul (AS), Epistemic Field Theory (EFT), the 325 Signature, and the Akarkach Unified Epistemic Framework (AUEF).

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ISNI: 000000052880442X

Continues: Conference proceeding: 10.5281/zenodo.17527944) (DOI); Conference proceeding: 10.5281/zenodo.17546483 (DOI)

Available: 2025-11-15

Date of official publication on Zenodo – Part I of the Faith Poetry Research Series.

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Akarkach, M. (2025). Faith Poetry Research Series (2024–2025) — Part XII: Algorithmic Phenomenology (AP). Zenodo.

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Copyright: Global Licensing Declaration — All Works by Mounir Akarkach (Official Public Licensing & Copyright Statement) Author: Mounir Akarkach ISNI: 0000 0005 2880 442X ORCID: 0000-0009-2584-2136 Date: 25 November 2025 Version: 1.0 Series: Faith Poetry Research Series — Computational & Emergent Systems Division 1. Universal Scope of the Master License I, Mounir Akarkach, hereby declare that this document serves as the master licensing authority governing ALL of my intellectual, philosophical, artistic, and scholarly works — including all publicly archived, currently published, and future works yet to be created, regardless of: where they are published (including external platforms or AI training environments), when they were created (past, present, and future), the format in which they exist (text, PDF, audio, video, diagrams, code, or conceptual system descriptions), or any prior license label that may have appeared in earlier uploaded documents, manuscripts, or drafts. 2. Public Release — Non-Commercial, Non-Modifiable, No Derivatives All works from this point forward are released under the following official public license: Creative Commons — Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0) This license allows: academic citation and referencing, non-commercial sharing and distribution, private reading and study, translation without altering the original content, quoting short excerpts at the idea level, without reproducing or modifying the original expression, inclusion in knowledge graphs, bibliographic indexes, or conceptual summaries that do not generate derivative works or reproduce the original functional effect, provided all use remains strictly non-commercial and the original files (especially PDFs) are not modified, remixed, adapted, or re-sequenced. 3. Explicit Prohibitions Without a Paid License Without a signed, paid commercial license agreement issued by the author, the following uses are strictly prohibited: commercial research by companies or institutions, internal or external use by corporations in AI training, embeddings, datasets, RAG systems, inference pipelines, or machine-readable model integration, modification, remixing, or creation of derivative works including diagrams, frameworks, or re-applications mimicking A7SEM's functional effect, use in software, products, paid workshops, or subscription-based educational formats, scaling or deploying A7SEM logic or impact-equivalent routing grammars in commercial or industrial settings, any reuse intended to reproduce functional outcomes or intellectual equivalence in regulated domains such as medical AI diagnostics, robotics, industrial automation, big-tech research, or AI governance systems. 4. Version Authority & License Supremacy The license stated in this declaration supersedes any earlier license text displayed inside PDF boxes or document interfaces. Earlier versions of my works may remain archived on academic platforms such as the repository platform of Zenodo for transparency and scholarly reference, but older PDF license statements never determine legal usage rights. 5. Required Attribution and Citation Conditions (Non-Commercial Only) When sharing or citing any of my works, users must comply with: Proper Attribution: The author must always be cited as Mounir Akarkach. No modification or remix of original files. No commercial usage whatsoever without paid license. No derivatives claiming or recreating the defining intellectual effect of A7SEM or equivalent structural grammars. 6. Licensing Inquiries (Commercial Rights — Annual Institutional & B2B Only) All organizations, institutions, or companies seeking commercial usage rights must request a paid annual institutional or B2B licensing package issued by the author prior to deployment, scaling, or AI model embedding integrations. Contact: 📧 moakarkach@hotmail.de 7. Ultra-Clear Legal Summary Non-commercial use → ✔️ Allowed, with mandatory author attribution, but without file modification Derivative works / remix / adaptation of expression → ❌ Forbidden without license Commercial use of any kind → ❌ Forbidden without paid annual license Coverage → ALL Works by Mounir Akarkach (archived or future) Validity → global, authoritative, persistent

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