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

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 hyperplasia or systemic physiological breakdown. High-assurance, safety-critical software systems are therefore required to strictly bound bio-electric parameters within homeostatic limits. **Objective** This repository provides a formally verified software framework implemented in Ada/SPARK 2022 for closed-loop bio-electric phase regulation and lethal boundary stress testing under the **V3 Regenerative Medicine Framework**. **System Architecture** The release comprises two core packages fully analyzed and validated with 100% GNATprove coverage (SPARK_Mode => On): * **V3.Ada_Phase_Regulator**: A dynamic closed-loop control engine that continuously monitors bio-electric phase potential (\Phi_{\text{critical}} = -51.10\text{ mV}) to trigger an automatic safety lock upon homeostatic restoration, preventing hyperplastic cellular overgrowth. It harmonizes antibody neutralization rates with 7-day immune cycles (k = 7) and applies differential dose adjustments based on tissue severity and viable mass across multi-lesional cases (vascular decalcification and myocardial regeneration). * **V3.Ada_Extreme_Stress_Test**: A physiological boundary watchdog that subjects the regulator to lethal aggression scenarios. It evaluates system stability against defined mortality thresholds, including necrosis (\Phi_{\text{death}} = -15.0\text{ mV}), coherence collapse (< 20\%), organ failure (< 5\% viable mass), and circulatory shock. **Formal Guarantees & Results** The architecture mathematically enforces strict physical and structural invariants, including the invariant constant \Psi_{\text{V3}} = 48,016.8\text{ kg}\cdot\text{m}^{-2} and Modulo-9 checksum integrity. Static verification guarantees absolute freedom from runtime errors (absence of overflows, division-by-zero, and out-of-bound access). Simulations validate dual-lesion therapeutic recovery while ensuring automatic shutdown prior to irreversible biological collapse. **Significance & Applications** This codebase serves as a high-integrity software foundation for safety-critical medical devices (e.g., smart infusion systems), real-time ICU monitoring algorithms, and *in silico* patient digital twins for clinical trial optimization in regenerative biotherapies.

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

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

developing a nature-inspired design framework for self-regulating urban parks: a digital twin-based model for intelligent landscape management

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

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 myosta…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-23

Computational Patient Selection for Extracellular Matrix Therapies: A Machine Learning Framework for Phase II Trial Optimization

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Live Interactive Clinical Interface: https://ventrigelcds.streamlit.app/ Abstract: Phase II cardiovascular trials fail frequently because of patient heterogeneity and high capital costs, with only an estimated 25 percent of cardiovascular drugs successfully transitioning to Phase…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-23

What a Single Decision Token Can and Cannot Reconstruct: The Shape- versus-Phase Boundary of Extreme Signal Compression

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

Artificial Intelligence and Neonatal Longevity: A Conceptual Framework for Reframing Early Physiological Monitoring as a Foundation for Lifelong Health Research

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Background Artificial intelligence (AI) has demonstrated promising performance in neonatal intensive care by supporting early prediction of acute conditions such as late-onset sepsis, necrotizing enterocolitis, apnea, and cardiorespiratory instability. However, existing neonatal…

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