CAJA CUSA: Certificación estructural de viabilidad clínica mediante proyección de dominio y estabilidad hessiana

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Authors: Jos Manuel De la calle Amaya
Cited by: 0

Abstract

This work introduces CAJA CUSA (Consciousness–Universal Structural Auditor), a formal mathematical framework for structural viability, stability certification, and automatic reconstruction of non-viable configurations in complex systems, with direct application to pharmacological design and biomedical optimization. The model defines a configuration space X in R^n endowed with a potential V(X), whose second-order structure is characterized by the Hessian matrix H(X) = ∇²V(X). Structural stability is formally certified when H(X) is positive semidefinite, verified through its eigenvalue spectrum.A structural domain D is introduced as the intersection of multiple subdomains (physical, biological, toxicological, clinical, regulatory, or others), defined by constraint functions g_i(X) ≤ 0. The distance to the admissible domain is quantified by:d(X, D) = sum over i of max(0, g_i(X))^2providing a continuous measure of structural violation. Beyond diagnosis, CAJA CUSA implements a reconstruction operator (Regroctudie):Π_D(X) = argmin over Y in D of ||Y − X||^2which projects any non-viable configuration onto the nearest admissible one. A corrective dynamic:dX/dt = −∇V(X) − α∇d(X)drives the system toward stable and domain-compliant solutions.The framework outputs a standardized certification report including final configuration, Hessian eigenvalues, stability flags, domain distance, projected solution, and global viability verdict. Validation examples, including pharmacological compounds such as cisplatin, demonstrate that CAJA CUSA not only detects structural infeasibility but automatically reconstructs clinically admissible configurations. This establishes CAJA CUSA as a general-purpose certifier and redesign engine for stability, coherence, and viability across scientific and engineering domains.

Fields of Study & Subjects

Computational Drug Discovery Methods Statistical Methods in Clinical Trials Advanced Optimization Algorithms Research

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
15 January 2026
Language
EN
Document type
Preprint