| ai.viXra.org > Mathematical Physics > 2504 |
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| ai.viXra.org > Mathematical Physics > 2504 |
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[2] ai.viXra.org:2504.0129 [pdf] submitted on 2025-04-30 19:46:43
Authors: Hamed Mehrabi
Comments: 33 Pages.
The universe presents us with a profound paradox: while the second law of thermodynamicspredicts increasing disorder, complex organized systems—from living cellsto galactic structures—emerge and persist throughout cosmic history. We resolve thisparadox by proposing a fundamental extension to Einstein’s mass—energy equivalence,incorporating organizational information as an intrinsic physical quantity that carriesmeasurable energy. Our central principle establishes that the total energy of a systemcomprises both its rest energy (Erest = mc2) and an additional energy associatedwith maintaining organizational information (Eorg = kBT ln(2)Ω), where Ω quantifiesnon-random correlations in bits. Together, these energy components form a completeaccounting of the system’s energetic requirements: Etotal = Erest + Eorg. This frameworkmakes three remarkable quantitative predictions: (1) human brain power consumptionof 20.1W (measured: 20W), (2) E. coli basal metabolic rate of 4.1 × 10−12W(measured: 3.8-4.5 × 10−12W), and (3) quantum decoherence times within 8% of experimentalvalues across different qubit technologies. Through rigorous mathematicalderivation and cross-scale validation, we demonstrate that organizational informationrequires continuous energy expenditure against entropic decay. This fundamental insightnot only resolves longstanding puzzles in bioenergetics and quantum foundations,but also suggests a new understanding of cosmic fine-tuning without invoking multiversehypotheses. By recognizing that maintaining order has an irreducible energeticcost, we reveal a deeper symmetry in nature that bridges thermodynamics, informationtheory, and fundamental physics.
Category: Mathematical Physics
[1] ai.viXra.org:2504.0072 [pdf] submitted on 2025-04-19 22:57:37
Authors: Jon Curry
Comments: 6 Pages.
Differential equations underpin countless physical systems, from chaotic pendulums to cosmic fields, yet classical numerical solvers struggle with stiff, nonlinear, or tensorial problems due to their reliance on time-stepping and derivative approximations. We introduce the Four-Dimensional Iterative Prediction (4DIP) framework, a novel symbolic method that uses geometric residual contraction to solve diverse differential systems with high precision. By iteratively refining a guess toward the true solution using a fixed rule, 4DIP eliminates time discretization, achieving residuals below 10−14 across 13 challenging systems—including chaotic triple pendulums, Dirac fields in curved spacetime, and noisy quantum turbulence—using 50-digit precision. This revised paper enhances theoretical rigor, expands comparisonsto modern solvers, and clarifies failure modes, demonstrating 4DIP’s potential to transformcomputational physics, engineering, and beyond.
Category: Mathematical Physics