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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-25Cited by 0

V3 Cardiac Regeneration Engine: A Closed-Form, Formally Verified In Silico Simulator for Cardiac Muscle Repair via Anti-Myostatin (GDF8)

outail benhadid

Abstract: This package implements a formal in silico simulator for cardiac muscle regeneration via Anti-Myostatin (GDF8 neutralization). Using the 4 invariants of the V3 Architecture (Ψ_V3 = 48,016.8 kg·m⁻², Φ_critical = -51.10 mV, k = 7, Modulo-9 = 9), the engine predicts myostatin neutralization kinetics, BMP activation, 3-cell-type cardiac regeneration (cardiomyocytes, cardiac stem cells, fibroblasts), ejection fraction restoration (35% → 60% in 7 days), optimal dosage (250 µg), and safety verification. Heart failure affects > 64 million people worldwide with > 8 million deaths annually. Current treatments are palliative (medications, transplantation) with < 5,000 transplantations per year due to donor shortage. No regenerative therapy exists for the heart. This code replaces years of preclinical research with deterministic in silico simulation in < 24 hours. The engine simulates: (1) myostatin neutralization as a function of dose, affinity, and time; (2) BMP activation triggered by phase coherence restoration at Φ_critical = -51.10 mV; (3) sequential 3-cell-type regeneration within the k=7 heptadic window; (4) ejection fraction recovery from 35% to 60%; (5) optimal dosage of 250 µg; (6) safety verification with no hypertrophy (BMP ≤ 97%); and (7) bilateral reversibility: without antibody, the system returns to Φ_basal = -70 mV. All code is Ada/SPARK 2022, GNATprove 100% certified, and open source. The simulation reduces years of preclinical research to less than 24 hours of computation.

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-25

V3 Architecture Engine: Formal Ada/SPARK 2022 Specification Unifying Seven Biophysical Mysteries via H_3O_2 Interfacial Phase Coherence

outail benhadid

Abstract Conventional biophysics relies heavily on fragmented, highly parameterized models that treat biological cells as stochastic, unstructured aqueous solutions. This classical paradigm routinely fails when confronted with fundamental biological phenomena that operate near 10…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-25

V3.Ada Phase Regulator & Extreme Stress Test: Formally Verified Ada/SPARK Framework for Bio-Electric Regulation and Safety Monitoring in Regenerative Medicine

outail benhadid

Abstract **Background** Modulating endogenously silenced regenerative pathways—such as BMP derepression through Anti-SOST and Anti-GDF8 therapies—presents significant safety challenges in bio-electric tissue engineering. Uncontrolled signaling carries severe risks of tissue hyper…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

In Silico Design and Engineering of a Multi-Epitope Polyprotein Construct Targeting High-Consequence Global Pathogens and Conserved Cancer-Testis Antigens

kingGeorge oiro

TITLE: In Silico Design and Engineering of a Multi-Epitope Polyprotein Construct Targeting High-Consequence Global Pathogens and Conserved Cancer-Testis Antigens ABSTRACT:This study presents the computational architecture of a multi-valent prophylactic and immunotherapeutic mRNA…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

beltvision: A Reproducible Classical Computer-Vision Inspection Engine for Conveyor Belts, Verified on Ground-Truth Synthetic Scenes

Felipe Santibañez-Leal

Conveyor-belt inspection asks a vision system to find the belt, measure its geometry, tell an empty return strand from a loaded one, and flag damage or foreign objects, and the honest difficulty is that most published demonstrations run on private field footage that no one else c…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

Code and data for: Leakage-audited machine learning versus ETAS for earthquake forecasting in the Sea of Marmara

Basri Kerem Alhan, Kenessary Khabat

Code, processed data products, configuration, and results artifacts for "Machine learning versus ETAS for earthquake forecasting in the Sea of Marmara: a leakage-audited negative result and a closed-form scoring artifact" (Alhan & Khabat, submitted to Seismica). Version 1.2.0 acc…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

The Plasticity Vitrification Theorem: Continual-Learning Capacity as a Glass-Transitioning Renewable Stock, and the Golden-Rule Reset Duty Cycle

Justin Hart, Aristotle (Harmonic)

Paper supported by machine-verified Lean 4. Continually trained neural networks progressively lose the ability to learn new tasks – units go dormant, representational rank collapses, and the degradation is not undone by ordinary continued training or passive regularization, only…

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