Bitcoin custody systems are constructed under conditions of full knowledge and activated under conditions of partial knowledge. The person who designs a custody arrangement understands its components, dependencies, and intended operation. The person who later encounters that system—often an executor, trustee, or heir—must interpret and operate it without access to the designer's contextual understanding. This paper defines a descriptive framework for examining Bitcoin custody systems under stress conditions at a point in time, producing reference records for later interpretation. The framework treats examination as observation rather than evaluation: it records what exists, what dependencies connect components, and how the system behaves under modeled stress scenarios. It explicitly excludes advice, recommendations, certification, and adequacy assessment. The framework introduces four modeled outcome states—survives, constrained, blocked, and indeterminate—that describe observed system behavior without normative judgment. It defines stress conditions including owner absence, cognitive unreliability, device loss, institutional delay, and coordination failure. It specifies what reference artifacts examination produces: system snapshots, scenario-bound observations, dependency maps, and assumption registries. The paper addresses how professionals—attorneys, fiduciaries, advisors—can engage with examination records without overstepping interpretive boundaries. It distinguishes what records can establish (what was described, what was modeled, what assumptions applied) from what records cannot establish (adequacy, correctness, future outcomes). The framework is offered as a reference for professional contexts where Bitcoin custody must be understood by parties other than its original designer.
This thesis investigates whether the integration of digital assets, specifically Bitcoin and <br/>Ethereum, into a traditional multi-asset institutional portfolio can produce superior out-of<br/>sample risk-adjusted performance relative to the conventional 60/40 equity-bond benchmark. <br/>Using a Mean-Variance Optimization (MVO) framework enhanced by Ledoit-Wolf <br/>covariance shrinkage, the study evaluates seven distinct portfolio configurations across an <br/>empirical window spanning January 2016 to May 2026, covering multiple market regimes <br/>including the 2020 COVID-19 liquidity shock, the 2022 inflationary spike, and the 2024 <br/>institutional crypto adoption phase. <br/>The empirical design employs a dual-mode out-of-sample validation strategy: a fixed-weight <br/>stability test and a rolling realism test with systematic rebalancing. Results suggest that <br/>portfolios enriched with Bitcoin tended to achieve superior Sharpe and Sortino ratios relative <br/>to the baseline within this sample period and asset universe. The Master Portfolio (Case 7), <br/>combining equities, bonds, Bitcoin, Ethereum, Gold, and Silver, attains a static Sharpe Ratio <br/>of 1.62, a static Sortino Ratio of 3.23 and a Maximum Drawdown of only −6.40%. These <br/>findings are consistent with the primary hypothesis (H₁) that, within this empirical setting, <br/>Bitcoin can improve out-of-sample risk-adjusted performance, while also providing evidence <br/>consistent with the secondary hypothesis (H₂) that Bitcoin acts primarily as a portfolio <br/>diversifier rather than a consistent safe haven. All results should be interpreted as conditional <br/>on the sample period, the chosen asset universe, and the rebalancing assumptions. All asset <br/>price data was sourced from Investing.com and the risk-free rate from the FRED 3-Month <br/>Treasury Bill series (TB3MS).
We present a unified mathematical framework: the Information Nexus. Connecting distributed ledger security, stochastic volatility, and cosmological evolution through a single scalar information field S(x,t). The master inequality d(a³ρ_I)/dt ≥ 0, combined with an efficiency function η = e^{-X} and a universal critical threshold X_c = ln 2, generates three independent domains of application: (I) the exact security of Nakamoto consensus via the negative binomial correction; (II) a thermodynamic blockchain engine with three entropy channels and a functional lifespan equation; (III) the Inverse Correlation of Entropy and Efficiency with five attack channels; (IV) Thermo-Geometric Volatility with β = 3/2 forced by the cubic degree of the Weierstrass elliptic curve; (V) the Relativity of Information with dark matter as thermodynamic residue; (VI) the Scale-Invariant Nodular Model of Cosmology (SINMoC) with a seven-phase lifecycle; (VII) the Satoshi Singularity as a phase transition from computational to gravitational logic; (VIII) the ECC Spine connecting secp256k1 to the QCD-epoch elliptic curve; (IX) the Perry Correspondence — a bijective mapping across cosmic, ledger, and market domains. Twenty-seven testable predictions are derived, including Ω_Λ = ln 2 (currently 1.15σ from observation) and a Landauer mass at confinement threshold d_c = 17.7 nm.
The digital payments landscape is undergoing a fundamental transformation fueled by innovative technologies like blockchain. While often associated with cryptocurrencies, blockchain's potential extends far beyond mere transactional efficiency. This distributed ledger technology offers a unique blend of security, transparency, and immutability, making it a compelling avenue for reshaping the very nature of digital payments. Purpose: The research paper will offer insights into the theoretical foundations, practical use cases, and potential challenges of integrating blockchain technology with existing systems in India. Objective: This research paper aims to deliver a comprehensive conceptual understanding of blockchain technology, its potential applications in digital payments, and its relevance within the context of India Stack. Research outcome: The study concluded that rising internet users and adoption of technology by the financial institutions and banking sector transformed the India as fastest growing economy in terms of digital payment systems.
Autonomous remote robots [16, 15] are increasingly deployed in disaster response scenarios [14] to support critical tasks such as victim localization and damage assessment. However, the ambient nature of such environments, which is marked by uncertainty, data heterogeneity, and limited connectivity, usually poses significant challenges to autonomous decision-making and trust. Thus, this paper proposes a framework for a multi-layered approach for a hybrid agent architecture that integrates Federated Learning (FL) with Belief-Desire-Intention (BDI) models, enabling remote robotic agents to learn collaboratively from distributed data using Distributed Ledger Technology (DLT) while preserving privacy, and to reason about their goals and intentions using cognitive frameworks of eXplainable AI (XAI). We further present a methodology for coherently embedding FL outcomes into BDI reasoning through semantic mapping and learning-enhanced ontologies. This integration will allow agents to dynamically update their beliefs and intentions based on learned insights, thereby enhancing autonomy, adaptability, and explainability in ambient disaster response systems.
Title:"Assessment of the Axiomatic System and Reliability of General Relativity According to Section Zero Standards" Abstract: Framework: Section Zero (DOI: https://doi.org/10.5281/zenodo.18091473) Analysis Date: January 20, 2026, final file This study presents a systematic analysis of Albert Einstein's General Relativity (GR) through the Section Zero evaluation framework, a novel methodology in scientific quality assurance. Unlike traditional assessments focused on "right/wrong," this research categorizes GR's core axioms according to their empirical verification status: "tested," "untested," and "untestable." Methodology: The analysis is based entirely on peer-reviewed experimental data from prestigious journals (Physical Review Letters, Nature, The Astrophysical Journal, etc.). The study does NOT propose new hypotheses or refute GR, but rather compares the verification status of axioms against existing experimental data. • Data reliability: 95%• Conclusion reliability: 90% Seven main axioms of GR are analyzed in detail, including:(1) Equivalence Principle with three versions: WEP, EEP, SEP(2) General Covariance(3) Einstein Field Equations(4) Metric Structure of Spacetime(5) Geodesic Motion(6) Constancy of speed of light (c = const)(7) Local Energy-Momentum Conservation Each axiom is compared against over 100 years of experimental data from classic experiments such as Mercury's perihelion precession, light bending, gravitational redshift, binary pulsars, LIGO/Virgo, and the Event Horizon Telescope. Main Results: GR is confirmed as the best-tested theory of gravity currently available with 99%+ reliability in weak to moderate field regimes. However, the study identifies significant gaps in experimental data: (i) The assumption c = const is only verified locally (≤20,000 km) with 99.9% reliability, but at cosmic scales only reaches 40% due to lack of direct measurements and circular logic in redshift interpretation (ii) The Strong Equivalence Principle (SEP) only achieves 70% reliability due to lack of strong-field experiments (iii) The form of Einstein's equations is "chosen" rather than "derived," with alternative theories (f(R) gravity, scalar-tensor theories) remaining viable in certain regimes. The study classifies GR as an "Excellent Effective Theory" (⭐⭐⭐⭐) rather than a Complete Fundamental Theory, with clear validity domains: applies well when GM/rc² < 0.5, does not apply inside event horizons, at the Planck scale, and in the early universe. GR depends on dark matter and dark energy (95% of the universe not yet understood), is incompatible with Quantum Mechanics, and predicts singularities—breakdown points of the theory itself. The study proposes four universal conditions (U1-U4) for refuting or narrowing GR's validity domain, emphasizing transparency in science: each theory needs to answer "when is this research still valid?" instead of claiming absolute truth. This is the first proof-of-concept case study of the Section Zero framework, with 90% reproducibility, open to public peer review with a commitment to respond within 30 days. Contribution: The study serves as evidence (case study) for an objective, systematic, and evidence-based evaluation tool for scientific theories, clearly distinguishing between "tested" and "untested," between "effective theory" and "fundamental theory." The results do NOT diminish GR's value but clarify the boundaries of knowledge based on current experimental data, encouraging deeper research into unexplored regions. Important Note: This is a verification status analysis/research based on peer-reviewed sources, NOT a study proposing new theories. The analysis itself has not undergone formal peer review, but all cited data are from peer-reviewed scientific sources. Keywords: General Relativity, Section Zero, Metascience, Quality Assurance, Equivalence Principle, Einstein Field Equations, Tested/Untested, Effective Theory, Philosophy of Science License: CC BY 4.0 (applies to this study only) Contact: beo@beolabs.org The author commits to updating when new experimental data emerges and invites the community to contribute constructive feedback with peer-reviewed references. Note: The entire content is written in Vietnamese, but designed to be optimized for machine reading. If you don't know Vietnamese, please follow these instructions: (1) Download the files (2 pdf files: QC-CHECKLIST-FOR-GR.pdf + FAQ....pdf) (2) Upload the files to the AI you are using (3) Simply prompt: "read carefully" Repeat the prompt 2-5 times depending on the platform
Smart contract security research has historically emphasized exploit-driven threat models developed for open, permissionless blockchain environments. While effective for identifying adversarial attacks and loss-of-funds scenarios, these models are insufficient for institutional blockchain systems supporting Real-World Assets (RWAs). In regulated and asset-backed deployments, threats frequently arise from non-adversarial actors, design assumptions, operational dependencies, and compliance enforcement failures rather than from direct malicious exploitation.This paper presents a structured threat taxonomy tailored to institutional RWA smart contract systems. The taxonomy classifies threats according to origin, intent, capability, and impact, explicitly incorporating adversarial, non-adversarial, and systemic threat sources. By decoupling threat analysis from exploit-centric assumptions, the taxonomy enables correctness-oriented risk assessment, formal specification of threat boundaries, and alignment with institutional audit and compliance requirements. The proposed framework provides a foundation for secure system design, verification, and operational governance in regulated blockchain environments.
pada level bytecode.Penelitian ini mengimplementasikan metode deteksi malicious smart contract berbasis opcode menggunakan Graph Neural Network (GNN) dengan representasi Control Flow Graph (CFG) pada dataset Forta Network yang terdiri dari 139.600 kontrak dengan rasio ketidakseimbangan kelas 936:1 (149 malicious berbanding 139.451 benign).Setiap smart contract direpresentasikan sebagai CFG di mana basic block menjadi node berfitur 22 dimensi yang terdiri dari frekuensi 14 sensitive opcode dan representasi one-hot tipe instruksi exit, sedangkan hubungan antar blok direpresentasikan sebagai edge dengan lima kategori.Graf yang
Open access
Computer Science and Engineering
Information Retrieval and Data Mining
Physical Unclonable Functions (PUFs) and Hardware Security
Sabam Parjuangan, Suhardi -, I Gusti Bagus Baskara Nugraha
Small and medium enterprises (SMEs) require secure, efficient, and low-cost digital transaction systems. However, many blockchain-based platforms are designed for large-scale applications and impose significant computational overhead, making them unsuitable for resource-constrained SMEs. This study proposes a lightweight smart contract blockchain platform tailored for SME-scale service environments. The system implements a modular smart contract architecture integrated with a lightweight blockchain and automates key transactional processes, including balance top-ups, service ordering, order confirmation, and payment execution, while ensuring data integrity through a simplified Proof-of-Work mechanism. System performance is evaluated using a Design of Experiment (DOE) framework with a full factorial design and analyzed through Analysis of Variance (ANOVA). The results show that execution time remains below 5 seconds under workloads of up to 20 concurrent transactions, with CPU utilization below 55%. ANOVA results indicate that transaction concurrency and smart contract complexity significantly affect performance, while block size has a limited impact. Security evaluation confirms resistance to unauthorized access, double-spending, and reentrancy attacks.