Author’s Note: Declaration and Disclaimer This manuscript is a hypothesis-generating, speculative, and preliminary research work spanning multiple scientific disciplines. The core ideas are solely those of the author. The whole content of this manuscript was generated using Artificial Intelligence (AI) including Grok, ChatGpt under the full conceptual guidance and supervision of the author. This AI assisted and generated work has not undergone peer review and is shared as preprint exclusively for the purposes of scientific discussion, critical evaluation, and prospective validation by the research community. Formal publication processes, including plagiarism assessment, completion of the reference list, and other academic formalities, are currently pending. All content presented herein should be regarded as exploratory, provisional, and speculative. The ideas, interpretations, and proposed theoretical connections do not represent established scientific knowledge or consensus and require rigorous peer review, empirical testing, and independent verification before any scientific, practical, or applied use. Abstract Active matter systems, spanning scales from subcellular organelles and neural circuits to ecosystems and artificial neural architectures, operate far from thermodynamic equilibrium and are sustained by finite, shared energy fluxes. Empirical observations across diverse domains consistently reveal persistent heterogeneity in energy partitioning among constituent units, even under nominally identical macroscopic conditions. Documented examples include 3–6× variations in mitochondrial ATP production, 8–20× spreads in isogenic bacterial growth rates, 3–5× flux differences in hepatic metabolic zonation, 20–50% discordance in monozygotic twin metabolic profiles, and 2–5× variations in net primary productivity among comparable forest plots. In this preprint the author proposes for the first time Energy Modulation Theory (EMT) as a unifying conceptual framework to account for this pervasive phenomenon. EMT posits that each active unit modulates its share of the incoming energy flux via a nonlinear, history-dependent function dependent on its internal state. The framework is grounded in three minimal assumptions: (1) shared finite energy flux across the ensemble, (2) active, nonlinear modulation characterized by strict convexity or curvature in the allocation response, and (3) non-contracting evolution of internal states over time. Employing tools from measure theory, stochastic calculus (Itô integration, Fokker–Planck equations, Girsanov transformations satisfying Novikov’s condition), and quantum stochastic differential equations (Hudson–Parthasarathy formalism), EMT derives: Theorem 1: a strict lower bound on energetic inequality (max eᵢ – min eᵢ ≥ ε · total flux, ε > 0 independent of ensemble size and time horizon), demonstrating the impossibility of perfect equality in finite-time, flux-driven active systems; Theorem 2: quantifiable lower bounds on variance amplification arising from history dependence and intrinsic noise; Theorem 3: the statistical emergence of several established phenomena—including Darwinian natural selection, metabolic allometry, ecological storage effects, the maximum power principle, Vicsek-model collective motion, ergodicity breaking, and sparse attention mechanisms in transformer architectures—as natural consequences of the proposed modulation dynamics. Supporting evidence draws from published datasets (2020–2025) spanning more than 18 orders of magnitude in spatial and temporal scale, with observed inequalities compatible with the derived bounds. Numerical simulations with moderate chaoticity (Lyapunov exponent ≈ 0. 5), diffusion, and cooperative nonlinearity (Hill coefficient ≈ 4) reproduce variance growth and spread magnitudes consistent with empirical reports. Relationship to Prigogine’s Dissipative Structures Prigogine’s theory of dissipative structures represents one of the most profound achievements of 20th-century physics, demonstrating that order can emerge spontaneously far from equilibrium through irreversible processes and energy dissipation. His 1977 Nobel Prize was richly deserved, and his work continues to underpin modern non-equilibrium thermodynamics. EMT does not contradict, diminish, or supplant Prigogine’s dissipative structures; rather, it is advanced as a natural and complementary extension of his vision. Prigogine elucidated the thermodynamic and dynamical mechanisms by which order arises via flux-driven amplification of fluctuations beyond bifurcation points (e. g. , in convection cells, chemical oscillators, and living organisms). However, real dissipative structures—particularly in biological and ecological contexts—exhibit persistent heterogeneity rather than perfect uniformity. While idealized models (e. g. , mean-field chemical oscillators or Turing patterns) permit homogeneous solutions, nature shows no such perfect equality in active systems. EMT addresses this precise gap by proving that, once units are active (possessing nonlinear, history-dependent modulation functions μᵢ), perfect energetic equality becomes ontologically impossible (Theorems 1 and 2). In Prigogine’s terms, EMT demonstrates that dissipative structures composed of active matter are necessarily unequal dissipative structures. Living systems, which Prigogine famously described as dissipative structures, thus represent the ultimate illustration: energy partitioning among active units is intrinsically unequal—and this inequality is physically required for persistence, diversity, and evolution. Recent advances in active matter research have extended Prigogine’s legacy by incorporating self-propelled particles, local energy injection, and collective dynamics, yet questions persist regarding the origins and functional significance of intrinsic heterogeneity. EMT offers a rigorous, scale-free mechanism that bridges classical dissipative structures to contemporary active matter frameworks, deriving inequality as an inevitable consequence of active modulation. By reframing energetic heterogeneity as an intrinsic, functionally significant feature of flux-driven far-from-equilibrium organization, EMT is proposed as a candidate complementary principle to the second law of thermodynamics—one that may help explain the maintenance of structured diversity, adaptive capacity, and hierarchical complexity in biological, ecological, and engineered systems. The framework suggests testable predictions, including minimum coefficients of variation in low-noise isogenic populations, amplified heterogeneity under fluctuations, and potential biosignature implications for astrobiology. Practical applications are explored in robust biological neural computation, energy-aware artificial intelligence design, and fluctuation-driven ecosystem transitions. Published evidence (2018–2025) provides strong convergent support. In yeast cAMP-PKA lineages, growth-rate CV reaches 0. 41–0. 62 with heritable slow tails; linearization of curvature collapses variance by 20–40% (Levy et al. , 2018). In human T2DM cohorts, UACR CV is 41–48. 8% and independently predicts eGFR decline (HR 3. 33; Rasaratnam 2024, Lin 2022). Macrophage, CAR-T, NK, and DC systems show metabolic CV floors of 20–80%, with γ estimates consistently 2. 0–3. 5; HIF/PDK inhibition collapses variance 30–55% and improves function (Bleriot 2023, Chen 2024, Sarhan 2024). Recognizing the interdisciplinary scope of the proposal and its connections to established research in non-equilibrium statistical mechanics, active matter physics, theoretical ecology, and machine learning, the author respectfully submits this work for open evaluation by the global scientific community. Constructive discussion, independent verification of the mathematical derivations, comparisons with related frameworks (e. g. , stochastic thermodynamics, hydrodynamic descriptions, fluctuation theorems, and information-theoretic approaches), additional simulations, and targeted experimental tests are warmly welcomed and regarded as essential for assessing the framework’s robustness, scope, and potential contributions.
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DR SEIKH JAHANGIR ALAM
Rice Institute
PricewaterhouseCoopers (Canada)
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DR SEIKH JAHANGIR ALAM (Mon,) studied this question.
www.synapsesocial.com/papers/69e866416e0dea528ddeaa6d — DOI: https://doi.org/10.5281/zenodo.18257521
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