Comprehensive Master Integration: Information Ecosystem Theory (IET) & Applied Persistence Architecture

Egidijus Kasiulevičius, Azuolas Kasiulevicius, Saule Kasiuleviciute, AUSRA KasiuleviciENE

1. Summary The master integration framework unifies Information Ecosystem Theory (IET) with applied computer science, cognitive sociology, and network topology. It establishes that computational systems, networks, and physical reality operate as self-calculating persistence ecosystems. By converting cosmological principles into operational code, the framework solves traditional bottlenecks such as binary routing limits, database memory bloat, neural network training saturation, and institutional peer-review gatekeeping. 2. Key Findings Dynamic Base-Radix Scaling: Replacing rigid binary architectures ($Base-2$) with recursive multi-base transitions ($8 \to 9 \to 10 \to 16 \to 32 \to \infty$) compresses routing paths and eliminates distributed network congestion. Information Mass-Equivalence ($m_{eff}$): Indexing data based on structural retention priority rather than uniform byte cost optimizes storage engines and tiers cold data into topological friction pools. Algorithmic Inoculation Thresholds ($\mathcal{T}_{in}$): Triggering asynchronous topological retention cycles when data influx exceeds processing capacity prevents catastrophic forgetting and training saturation in deep neural networks. Human Incentive Optimization ($I_{incentive}$): Quantifying career survival, grant politics, and peer-review tribalism explains institutional resistance to paradigm shifts and dictates the necessity of open-access bypass channels. Decentralized Propagation ($\Psi_{diff}$) & Semantic Compression ($\Theta_{comp}$): Maximizing open-access distribution repositories (such as Zenodo) while stripping unnecessary linguistic noise ensures rapid cross-disciplinary adoption. 3. Core Formulas Information Mass-Equivalence ($m_{eff}$): $$m_{eff} = \lim_{B \to \infty} \left( \frac{\hbar}{c^2} \right) \cdot \left( \frac{\log_B(B_b)}{B} \right) \cdot \rho_I \cdot \chi_{rec}$$ The Inoculation Threshold ($\mathcal{T}_{in}$): $$\mathcal{T}_{in} = \frac{\Delta\rho_I}{\Delta t} \cdot \frac{1}{\Phi_{max}} > 1$$ The Incentive Tensor Operator ($I_{incentive}$): $$I_{incentive} = \frac{\text{Funding Security} \times \text{Peer Validation}}{\text{Paradigm Disruption Cost}} \cdot \tau_{career}$$ The Propagation Diffusion Matrix ($\Psi_{diff}$): $$\Psi_{diff} = \frac{N_{open} \times \Gamma_{cross}}{\mathcal{R}_{friction}} \cdot e^{\lambda_{network}}$$ The Semantic Compression Efficiency Operator ($\Theta_{comp}$): $$\Theta_{comp} = \frac{\Omega_{signal}}{\Sigma_{noise} \times \Lambda_{lexicon}}$$ 4. License Terms: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Archival Registry: Linked directly to master Zenodo DOI ledgers (10.5281/zenodo.21505394 / 10.5281/zenodo.21552236).