John Alexander Taborda, Cesar Enrique Polo Castro, Miguel Martínez
Just energy transitions in the Global South unfold under conditions of institutional fragmentation, fiscal constraints, and high socio-ecological turbulence, making governance capacity a critical bottleneck for effective decarbonization and climate justice. This study proposes the Cybernetic Environmental Hub (CEH) framework, which extends the Viable System Model (VSM) to sustainability governance by integrating AIoT-enabled environmental monitoring, Early Warning Systems, decentralized data governance, and justice-centered institutional design. Methodologically, the article is primarily a conceptual framework paper accompanied by an illustrative single-site qualitative case study designed to probe the plausibility and diagnostic utility of the proposed architecture rather than to generate statistical generalization. The research combines theoretical development with participatory territorial diagnostics in the Caribbean Mining Corridor, where socio-ecological challenges were collected through participatory innovation workshops, thematically coded, and mapped onto the five VSM subsystems to identify systemic “variety gaps.” The analysis indicates that fragmented operational initiatives coexist with weak meta-systemic coordination, limiting adaptive capacity in energy transition processes. The CEH architecture is proposed to address these deficiencies by embedding AIoT sensing, federated learning, blockchain-based coordination, and Early Warning Systems within recursive governance structures and is grounded in a real cyber-physical deployment of around 90 monitoring stations across Albania, La Jagua de Ibirico and Algarrobo. The study also introduces a Territorial Governance Maturity Model (H1–H3) to diagnose systemic learning capacities and transition readiness across technological, institutional, data governance, and justice dimensions. The findings suggest that cybernetic environmental hubs may function as socio-technical infrastructures supporting coordinated, adaptive, and justice-centered energy transitions in the Global South, while comparative empirical evidence remains an agenda for future work.
This preprint presents Invariant Ontodynamics (IOD), a structural field theory derived from a single minimal geometric primitive with zero continuously adjustable dimensionless fit parameters. To our knowledge, no prior framework derives both the Schrödinger equation and the Einstein field equations from a single uniqueness-selected geometric primitive without continuously adjustable fit parameters. The theory derives quantum dynamics, relativistic field structure, fermion spin-½, general relativity, and gauge symmetry as theorems rather than assumptions. A universal structural law — that the effective complexity of any system is a linear function of its structural curvature k, with a universal slope and fixed point derived from the same primitive — is empirically confirmed at R² = 0.978 across 15 pre-selected independent domains spanning 19 orders of magnitude in physical scale, under a pre-registration protocol with SHA-256 cryptographic locks. New results in this version include: A zero-free-parameter prediction of the Higgs boson mass, m_H = 125.33 GeV (0.06% from the observed 125.25 GeV), via a one-loop renormalization group trajectory anchored at a structurally derived UV scale A complete CPL dark-energy equation-of-state parameter pair (w₀ = −0.858, w_a = −0.411), both pre-registered before DESI DR3 Exact zero-free-parameter black hole thermodynamics: Schwarzschild radius, Hawking temperature, and surface gravity all derived from the primitive alone, with a falsifiable 29% Hawking temperature shift relative to the GR prediction A structural information measure (Heun log-coefficient) connecting the near-horizon field structure to the Brownian fixed-point evaporation endpoint, with exact Page curve endpoint M_Page = M₀/√2 Previously confirmed predictions — solar mixing angle (0.05σ), reactor angle (0.39σ), tau lepton mass (0.91σ), baryon asymmetry (−1.0σ), dark matter ratio (0.2%), inflationary spectral index (1.0σ) — remain confirmed. Three explicit tensions are stated without omission: atmospheric mixing angle (2.2σ, DUNE 2030 decisive), leptonic CP violation (J_CP = 0, DUNE 2030 decisive), and dark energy w₀ (0.4σ from DESI DR2 best fit, DESI DR3 decisive). Priority and legal status: This document is a public technical summary and priority disclosure. Full derivations, exact primitive specification, all coefficient values, and complete proofs are in US Provisional Patent No. 63/963,472 (filed January 2026) and Addenda 1–15 (through April 2026). The non-provisional application will be filed by January 2027.
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Control and Stability of Dynamical Systems
Ecosystem dynamics and resilience
Stability and Controllability of Differential Equations
The stability of global financial markets is increasingly threatened by rapid liquidity cascades, manifesting empirically as instantaneous "Flash Crashes." Current quantitative risk models, such as Value-at-Risk (VaR) and the Efficient Market Hypothesis (EMH), assume continuous liquidity and treat extreme volatility as probabilistic statistical anomalies based on historical distributions. These models fundamentally lack a deterministic, geometric boundary for limit-order book coherence. This paper introduces a strict topo-dynamical framework for financial network scaling. By modeling the market structure as a spatial competition between the geometric propagation of liquidity and localized volatility shocks, we derive a universal square-root geometric invariant (ℓmarket). We provide an intuitive translation of this threshold, explicitly dissect the failure of VaR during the May 2010 Flash Crash, and map the invariant across both traditional equities and Decentralized Finance (DeFi) Automated Market Makers (AMMs). Finally, we present a hardware-aware (FPGA) blueprint for Active Liquidity Throttling (ALT), acknowledging systemic implementation risks and regulatory hurdles.
Shiyu Zhang, Zining Wang, Jin Zheng, John Cartlidge
Systemic risk refers to the overall vulnerability arising from the high degree of interconnectedness and interdependence within the financial system. In the rapidly developing decentralized finance (DeFi) ecosystem, numerous studies have analyzed systemic risk through specific channels such as liquidity pressures, leverage mechanisms, smart contract risks, and historical risk events. However, these studies are mostly event-driven or focused on isolated risk channels, paying limited attention to the structural dimension of systemic risk. Overall, this study provides a unified quantitative framework for ecosystem-level analysis and continuous monitoring of systemic risk in DeFi. From a network-based perspective, this paper proposes the DeFi Correlation Fragility Indicator (CFI), constructed from time-varying correlation networks at the protocol category level. The CFI captures ecosystem-wide structural fragility associated with correlation concentration and increasing synchronicity. Furthermore, we define a Risk Contribution Score (RCS) to quantify the marginal contribution of different protocol types to overall systemic risk. By combining the CFI and RCS, the framework enables both the tracking of time-varying systemic risk and identification of structurally important functional modules in risk accumulation and amplification.
High-yield decentralized finance (DeFi) lending protocols attract capital by offering returns that exceed organically sustainable market demand. This paper defines <b><i>Anchor Protocol Overexposure</i></b><b> </b>as a systemic risk condition in which outsized, subsidy-driven yields concentrate liquidity into a single mechanism, creating hidden leverage, correlated withdrawal behavior, and reflexive collapse dynamics. Using Anchor Protocol as a representative archetype, the paper analyzes how yield subsidies, composability, and perception-driven stability interact to generate unsustainable exposure across interconnected DeFi ecosystems. We further demonstrate why transparency, disclosure, and governance-based controls fail to mitigate this class of risk. Finally, the paper outlines a logic-layer enforcement model capable of constraining yield-induced systemic fragility prior to the onset of collapse dynamics.
Cryptocurrency markets are characterised by high volatility, high returns and comparative immaturity relative to equity and commodity markets. Topological Data Analysis (TDA) persistence norms are effective tools for the analysis of noisy dynamical systems like the cryptocurrency markets. We show how information from the shape of daily return data adds additional inference on activity within the cryptocurrency markets. TDA persistence norms embed volatility and connectedness between coins as well as incorporating information from uncertainty indexes, financial market performance and commodity returns. Our TDA measures are robust to noise and are consistent across a raft of alternative coin selections. Further, we exposit how persistence norms peak to forewarn of crashes and stay low as markets face exogenous shocks. We demonstrate the clear advantages of TDA for the study of cryptocurrency markets and develop the next steps for exploiting the potential of TDA for application to cryptocurrency markets.
Samuel W. Akingbade, Marian Gidea, Matteo Manzi, Vahid Nateghi
We present a heuristic argument for the propensity of Topological Data Analysis (TDA) to detect early warning signals of critical transitions in financial time series. Our argument is based on the Log-Periodic Power Law Singularity (LPPLS) model, which characterizes financial bubbles as super-exponential growth (or decay) of an asset price superimposed with oscillations increasing in frequency and decreasing in amplitude when approaching a critical transition (tipping point). We show that whenever the LPPLS model is fitting with the data, TDA generates early warning signals. As an application, we illustrate this approach on a sample of positive and negative bubbles in the Bitcoin historical price.
We investigate the effects of the recent financial turbulence of 2020 on the market of cryptocurrencies taking into account the hourly price and volume of transactions from December 2019 to April 2020. The data were subdivided into time frames and analyzed the directed network generated by the estimation of the multivariate transfer entropy. The approach followed here is based on a greedy algorithm and multiple hypothesis testing. Then, we explored the clustering coefficient and the degree distributions of nodes for each subperiod. It is found the clustering coefficient increases dramatically in March and coincides with the most severe fall of the recent worldwide stock markets crash. Further, the log-likelihood in all cases bent over a power law distribution, with a higher estimated power during the period of major financial contraction. Our results suggest the financial turbulence induce a higher flow of information on the cryptocurrency market in the sense of a higher clustering coefficient and complexity of the network. Hence, the complex properties of the multivariate transfer entropy network may provide early warning signals of increasing systematic risk in turbulence times of the cryptocurrency markets.
The paper focuses on the study of the effect of long memory and the analysis of the multifractal properties of the time series of the most capitalized cryptocurrencies for the period from 2010 to 2018. To do this, the Hurst exponent is calculated by both R/S analysis and the Detrended Fluctuation Analysis being more stable in the case of non-stationary time series. Our results show that time series of cryptocurrencies to be persistent during almost the whole study period that do not allow accepting the hypothesis concerning the efficiency of the cryptocurrency market. We also found that (i) time series became anti-persistent during the periods of market crisis phenomena and turbulence; (ii) the Hurst exponents showed significant fluctuations about the value of 0.5. In addition, we conduct a multifractal analysis of cryptocurrency time series that allows us to assess the state and stability of the market.The calculated spectrum of multifractality shows that the cryptocurrency market comes out of a crisis state, since the width of the multifractality spectrum has the maximum value for all cryptocurrencies.
Marian Gidea, Daniel Goldsmith, Yuri A. Katz, Pablo Roldan · 5 authors
We analyze the time series of four major cryptocurrencies (Bitcoin, Ethereum,\nLitecoin, and Ripple) before the digital market crash at the end of 2017 -\nbeginning 2018. We introduce a methodology that combines topological data\nanalysis with a machine learning technique -- $k$-means clustering -- in order\nto automatically recognize the emerging chaotic regime in a complex system\napproaching a critical transition. We first test our methodology on the complex\nsystem dynamics of a Lorenz-type attractor, and then we apply it to the four\nmajor cryptocurrencies. We find early warning signals for critical transitions\nin the cryptocurrency markets, even though the relevant time series exhibit a\nhighly erratic behavior.\n
The year 2017 saw the rise and fall of the crypto-currency market, followed by high variability in the price of all crypto-currencies. In this work, we study the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the phenomenon of critical slowing down) or the stochastic transition phenomena. We find that, regardless of the specific crypto-currency or rolling window size, the autocorrelation always fluctuates around a high value, while the standard deviation increases monotonically. Therefore, while the autocorrelation does not display signals of critical slowing down, the standard deviation can be used to anticipate critical or stochastic transitions. In particular, we have detected two sudden jumps in the standard deviation, in the second quarter of 2017 and at the beginning of 2018, which could have served as early warning signals of two majors price collapses that have happened in the following periods. We finally propose a mean-field phenomenological model for the price of crypto-currency to show how the use of the standard deviation of the residuals is a better leading indicator of the collapse in price than the time series' autocorrelation. Our findings represent a first step towards a better diagnostic of the risk of critical transition in the price and/or volume of crypto-currencies.
Marian Gidea, Daniel Goldsmith, Yuri Katz, Pablo Roldan · 5 authors
We analyze the time series of four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple) before the digital market crash at the end of 2017 - beginning 2018. We introduce a methodology that combines topological data analysis with a machine learning technique -- $k$-means clustering -- in order to automatically recognize the emerging chaotic regime in a complex system approaching a critical transition. We first test our methodology on the complex system dynamics of a Lorenz-type attractor, and then we apply it to the four major cryptocurrencies. We find early warning signals for critical transitions in the cryptocurrency markets, even though the relevant time series exhibit a highly erratic behavior.