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June 4, 20260 citationsOpen Access

The General Transform: Universal Combinatorial Regularization Across Quantum Physics, Analytic Number Theory, and Mathematical Oncology

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ABAntonio Bonelli

Key Points

  • To numerically apply the General Transform for calculations in quantum physics, number theory, and oncology.
  • Utilized the Kaleidoscopic Filter to mitigate geometric noise in partition sequences.
  • Conducted numerical tests of the General Operator through localized Gauge Variations in the $$-field.
  • Employed prime-supported transforms to analyze quantum systems and combinatorial structures.
  • Successfully isolated discrete topological gradients without the need for analytical continuation.
  • Isolated non-ergodic Quasi-Stationary States of the Ruffo Hamiltonian Mean Field model.
  • Extracted tumor combinatorial biomarkers, demonstrating potential advances in diagnostic oncology.

Abstract

We present a direct numerical application of the General Transform to the vacuum energy calculation of a 1-dimensional bosonic string. By employing the Kaleidoscopic Filter, we numerically annihilate the low-dimensional geometric noise from the partition sequence. Furthermore, we execute a strict numerical test of the General Operator via localized Gauge Variations in the -field, proving its capability to isolate discrete topological gradients without analytical continuation. Finally, we apply the prime-supported transform to extract the exact quantum degeneracy of the Zanardi-Rasetti Decoherence-Free Subspace, isolate the non-ergodic Quasi-Stationary States (QSS) of the Ruffo Hamiltonian Mean Field model, simulate Pauli Exclusion via Fermionic condensation, apply Bombieri's Large Sieve limits to extract Andrews' p-core partition manifolds, evaluate Black Hole holographic microstates, geometrically elevate 1D Bosons to 3D M-Theory MacMahon spaces, extract fractal gap constraints via the Rogers-Ramanujan duality, model targeted combinatorial filtration in preventive mathematical oncology, isolate early-stage tumor combinatorial biomarkers in diagnostic oncology, and rigorously establish the operator's Information-Theoretic boundaries as an algebraic Maxwell's Demon.

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Cite This Study

Antonio Bonelli (2026) studied this question.

synapsesocial.com/papers/6a211670d499ed480b16f53dhttps://doi.org/10.5281/zenodo.20508408
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