AUTO DZ ACT – Scientific Algorithm for Experimental Logic Validation in STOE Framework (Version 2)

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Autore principale: OMRAN, Abdelkader
Natura: Recurso digital
Lingua:inglese
Pubblicazione: Zenodo 2025
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author OMRAN, Abdelkader
author_facet OMRAN, Abdelkader
contents <p> </p> <p>Description (Zenodo – Version 2)</p> <p> </p> <p>This release presents Version 2 of the AUTO DZ ACT algorithm, now implemented as a fully executable Python-based system for validating theoretical predictions against experimental results within the framework of the Simplified Theory of Everything (STOE).</p> <p> </p> <p>It includes:</p> <p> </p> <ul> <li>A working Python script (run_batch.py) that applies the AUTO DZ ACT logic to tabular CSV data.</li> <li>Input (example_input.csv) and output (output_result.csv) datasets demonstrating tolerance-based logic classification into categories: 0/0, D0/DZ, and DZ.</li> <li>A structured directory format for reproducibility, with GitHub Codespace support.</li> </ul> <p> </p> <p> </p> <p>This version marks a critical milestone in making the algorithm reproducible, verifiable, and open-source, offering a general tool for experimental logic validation. It supports scientific workflows, FAIR principles, and broader adoption of STOE in computational physics.</p> <p> </p> <p>Author: Dr. Abdelkader Omran</p> <p>Institution: HONGKONG TRIZEL INTERNATIONAL GROUP LIMITED</p> <p> </p> <p>GitHub Repository:</p> <p> https://github.com/trizel-ai/auto-dz-act</p> <p>DOI (this version): 10.5281/zenodo.16336645</p>
format Recurso digital
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language eng
publishDate 2025
publisher Zenodo
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spellingShingle AUTO DZ ACT – Scientific Algorithm for Experimental Logic Validation in STOE Framework (Version 2)
OMRAN, Abdelkader
STOE, AUTO DZ ACT, Theoretical Physics, Experimental Logic, Scientific Algorithm, Symbolic Activation, TRIZEL AI, Quantum Comparison, Physics Model Validation
<p> </p> <p>Description (Zenodo – Version 2)</p> <p> </p> <p>This release presents Version 2 of the AUTO DZ ACT algorithm, now implemented as a fully executable Python-based system for validating theoretical predictions against experimental results within the framework of the Simplified Theory of Everything (STOE).</p> <p> </p> <p>It includes:</p> <p> </p> <ul> <li>A working Python script (run_batch.py) that applies the AUTO DZ ACT logic to tabular CSV data.</li> <li>Input (example_input.csv) and output (output_result.csv) datasets demonstrating tolerance-based logic classification into categories: 0/0, D0/DZ, and DZ.</li> <li>A structured directory format for reproducibility, with GitHub Codespace support.</li> </ul> <p> </p> <p> </p> <p>This version marks a critical milestone in making the algorithm reproducible, verifiable, and open-source, offering a general tool for experimental logic validation. It supports scientific workflows, FAIR principles, and broader adoption of STOE in computational physics.</p> <p> </p> <p>Author: Dr. Abdelkader Omran</p> <p>Institution: HONGKONG TRIZEL INTERNATIONAL GROUP LIMITED</p> <p> </p> <p>GitHub Repository:</p> <p> https://github.com/trizel-ai/auto-dz-act</p> <p>DOI (this version): 10.5281/zenodo.16336645</p>
title AUTO DZ ACT – Scientific Algorithm for Experimental Logic Validation in STOE Framework (Version 2)
topic STOE, AUTO DZ ACT, Theoretical Physics, Experimental Logic, Scientific Algorithm, Symbolic Activation, TRIZEL AI, Quantum Comparison, Physics Model Validation
url https://doi.org/10.5281/zenodo.16336645