Blockchain, decentralized finance (DeFi), and artificial intelligence are converging into an economic ecosystem whose concentration, risk, and mechanism design have rarely been examined within a unified empirical framework. This study integrates five publicly available data sources: Uniswap decentralization indices, the Elliptic Bitcoin dataset, a multi-year sample of ERC-20 trading activity, Bitcoin incentive series, and DeFiLlama Total Value Locked (TVL) data. We processed four sources through a common audit and canonicalization protocol, while adopting the Uniswap decentralization indices as published derived series and harmonizing them within the same analytical framework. The results reveal extreme inequality in the protocol-level distribution of Total Value Locked (Gini =0.985 for aggregated TVL; Gini excluding CEX =0.978), although the corresponding Herfindahl–Hirschman Index does not indicate high protocol-level market concentration under conventional thresholds. A Random Forest classifier identifies illicit Bitcoin transactions with a precision of 0.988 and a recall of 0.692, consistent with the original Elliptic benchmark. SHAP analysis assigns 74.4% of the aggregate mean absolute attribution to transaction-level features and 25.6% to neighborhood features. In addition, Aave V3 exhibits a higher borrowed-to-deposited ratio than Compound V3 (0.410 vs. 0.300), while the observed utilization–volatility associations do not establish a uniform relationship across protocols. Taken together, these findings show that inequality, risk, and mechanism-design characteristics remain jointly observable across the blockchain and DeFi systems examined. Beyond the empirical findings, this study introduces a reproducible audit protocol for evaluating blockchain-based economic systems through explicit cross-source verification, result traceability, and scope-calibrated inference.

IntroductionThe rapid growth of the Internet of Things (IoT) and smart environments has led to an unprecedented increase in the storage and communication of heterogeneous and sensitive data, creating significant challenges in ensuring confidentiality, integrity, privacy, trust, and secure data sharing. Although blockchain, cryptography, and artificial intelligence (AI) have demonstrated potential for addressing these challenges, existing approaches often employ these technologies separately, limiting their ability to provide intelligent, scalable, and comprehensive security.MethodsThis study proposes an AI-driven blockchain framework integrated with AES–RSA hybrid encryption for secure data sharing in smart environments. Lightweight AI models are employed to classify heterogeneous files and detect anomalous activities before encryption and blockchain registration. AES is used for efficient data encryption, while RSA provides secure encryption and distribution of AES session keys. Blockchain technology is employed to support decentralized integrity verification, traceability, trusted data management, and immutable auditing.ResultsThe proposed framework was empirically evaluated using heterogeneous datasets containing files of different sizes and types. The experimental results achieved an average encryption time of 1.68 ms, an average decryption time of 0.63 ms, and an average blockchain verification time of 2.75 ms. The anomaly detection model achieved an accuracy of 99%, while blockchain-based integrity verification achieved 100%. Comparative analysis demonstrated that the proposed framework provides an improved balance between cryptographic protection, decentralized trust, intelligent threat detection, and computational overhead.DiscussionThe results indicate that the proposed AI-driven blockchain framework with AES–RSA hybrid encryption provides a scalable and reliable approach for secure heterogeneous data sharing in IoT-enabled smart environments. The integration of AI, hybrid cryptography, and blockchain enhances security and intelligent decision-making while maintaining computational efficiency, supporting its potential for next-generation cybersecurity applications.

The increasing occurrence of structural breaks, financial crises, and economic uncertainty has significantly weakened the predictive performance of traditional macroeconomic forecasting models. In response to these limitations, this study proposes a blockchain-enabled causal machine learning framework designed to improve forecasting robustness, transparency, and interpretability under unstable economic conditions. The proposed framework integrates blockchain-based data governance mechanisms, structural break detection techniques, machine learning algorithms, causal inference methodologies, and Explainable Artificial Intelligence (XAI) tools within a unified forecasting architecture. More specifically, the study combines Bai–Perron structural break analysis, Markov-Switching models, Double Machine Learning Causal Forest estimation, and SHAP-based explainability techniques to capture nonlinear and regime-dependent macroeconomic dynamics. The empirical analysis relies on a large macroeconomic dataset covering major crisis episodes, including the 2008 global financial crisis, the COVID-19 pandemic, and the 2022 inflationary shock period. The forecasting performance of the proposed framework is compared with conventional econometric models and standard machine learning algorithms such as VAR, TVP-VAR, Random Forest, XGBoost, and LSTM networks. The results demonstrate that the Double Machine Learning framework significantly outperforms benchmark models across different forecasting horizons and uncertainty regimes. The findings further reveal that uncertainty indicators, oil prices, financial volatility, and monetary policy variables exert strong regime-dependent effects on inflation dynamics. In addition, the proposed framework incorporates a blockchain-based governance layer designed to enhance data provenance, auditability, reproducibility, and decentralized governance throughout the forecasting pipeline. While the empirical evaluation focuses on forecasting performance, the blockchain layer provides the architectural foundation for secure and transparent model lifecycle management. Overall, the study contributes to the emerging literature at the intersection of blockchain technologies, causal artificial intelligence, and macroeconomic forecasting by developing a decentralized and explainable forecasting framework capable of supporting adaptive policy analysis under uncertain economic environments.

IntroductionWhile blockchain integration within Electronic Healthcare Record (EHR) platforms has strengthened data integrity and patient privacy, practical deployment continues to suffer from elevated latency and inflexible processing mechanisms. This work presents the AI-Accelerated Blockchain Architecture (AIBA-EHR), a framework engineered to accelerate transaction processing and enable adaptive security measures suitable for real-world clinical environments.MethodsOur approach couples a lightweight Python-based blockchain implementation with asynchronous parallel block validation and machine-learning-driven consensus parameter optimization. A Proximal Policy Optimization (PPO) reinforcement learning agent anticipates network congestion and dynamically calibrates block size alongside validation difficulty. A risk estimation module built on Temporal Convolutional Networks with Attention Mechanisms (TCN + AM) prioritizes trustworthy nodes and identifies anomalous access behavior. The TCN + AM module employs dilated causal convolutions with dilation rates d ∈ {1,2,4,8}, yielding a receptive field of 31 time steps that fully covers the 20-step access sequences used in this study.ResultsExperimental evaluation using synthetic EHR datasets comprising 120,000 access events across 15 features reveals a mean confirmation latency of 25.7 s and an average throughput of 209.7 transactions per second (TPS), representing a 40% reduction in confirmation latency and a 25% improvement in throughput relative to fixed-parameter blockchain baselines. The TCN + AM anomaly detector achieves an F1-score of 0.9942, precision of 0.9953, recall of 0.9931, and AUC of 0.9950 across five-fold cross-validation (mean ± std: 0.9935 ± 0.0018), outperforming all sequential and tabular baselines on classification metrics.DiscussionThese findings indicate that the strategic fusion of artificial intelligence with distributed ledger technology can simultaneously achieve operational efficiency and robust security, enabling real-time healthcare data exchange without compromising patient privacy across decentralized infrastructures.

The tokenization of financial instruments—the conversion of assets into digital representations on distributed ledgers—represents a structural transformation of capital markets. While it promises a “compliance by design” paradigm where algorithmic code guarantees rights and minimizes trust costs, the academic literature often overlooks the fundamental paradox at its core: blockchain technology cannot independently resolve the legal uncertainty surrounding the nature of tokens or their enforceability against third parties. This study fills a critical gap by integrating technological protocols and comparative law through a Law and Economics lens to determine if tokenization truly heralds an autonomous “on-chain” financial law or remains an evolution strictly conditioned by sovereign norms. By evaluating the regulatory models of Switzerland, the European Union, the United Arab Emirates, and the United States, this research identifies the institutional transaction costs inherent in each system. The methodology views these legal frameworks as dynamic incentive systems, evaluating their capacity to minimize institutional transaction costs and meet the requirements of global financial stability. The results demonstrate that systemic adoption remains hampered by two structural “locks”: the absence of a wholesale central bank digital currency (wCBDC) to eliminate counterparty risk, and the persistent fragility of contractual protections for holders in the event of insolvency. Ultimately, the study concludes that the value of tokenization is only fully realized when code and law are explicitly aligned.

The rapid growth of Ethereum’s world state has created increasing pressure on network efficiency and storage requirements for full nodes. Early ideas such as “state rent,” initially proposed by Vitalik Buterin, suggested periodically charging accounts for occupying space in the world state. However, these concepts were presented at a high level and did not address how to implement such mechanisms efficiently, or how to ensure that removing expired state would not increase the computational complexity of operating the network. Likewise, proposals based on time-based state expiry leave key architectural and performance questions unresolved. This paper presents the first comprehensive, implementation-ready solution that operationalizes the idea of state rent while preserving Ethereum’s current performance guarantees. Our approach, Promethon, integrates periodic charging for stored state with a unified hybrid data structure that combines Red-Black Tree ordering with Merkle-Patricia Trie hashing. This design enables efficient pruning and retrieval without introducing additional time or storage complexity. A pilot implementation in Go demonstrates the practical feasibility of our design and highlights its advantages over prior conceptual proposals.

Blockchain can transform emerging energy markets through decentralised transactions, peer-to-peer trading, smart contracts, and renewable-energy tokenisation. However, knowledge remains fragmented across technology, economics, digital finance, and public policy. This conceptual paper develops an integrated framework explaining how blockchain-enabled energy markets may improve efficiency, financial inclusion, energy access, and environmental sustainability. The framework is developed through a structured synthesis of interdisciplinary academic literature, institutional reports, and documented applications, informed by transaction cost economics, institutional theory, and diffusion of innovation theory. The analysis suggests that blockchain may reduce transaction costs, improve price discovery, broaden market participation, and support renewable-energy integration. However, these outcomes depend on regulatory clarity, institutional capacity, digital infrastructure, and technological readiness. Emerging economies should establish adaptive regulations, regulatory sandboxes, public–private pilot projects and supporting infrastructure. Policymakers should develop adaptive regulations, establish regulatory sandboxes, and invest in digital and decentralised energy infrastructure to support inclusive blockchain adoption.

IntroductionSmart contracts are widely used to automate blockchain-based transactions and decentralised services, but vulnerabilities in their code can expose users and systems to financial loss, service disruption, and security compromise. Detecting vulnerable smart contracts remains challenging because reliable labelling often requires expert analysis, which makes fully labelled datasets costly to produce.MethodsThis study investigates whether uncertainty-based active learning can reduce the labelling requirements for smart contract vulnerability detection using machine-learning classifiers. The BCCC-VulSCs-2023 dataset, containing 36,670 smart contract samples with 73 raw feature columns and a binary vulnerability label, was used for model development and evaluation. The dataset was preprocessed by removing constant and irrelevant columns, handling missing values, class balancing via SMOTE, an 80/20 train-test split, SelectKBest feature selection (top 14 features), and numerical feature normalisation. Random Forest, XGBoost, and CatBoost classifiers were evaluated under three conditions: uncertainty-based active learning, a random-sampling baseline with an identical labelling schedule, and a fully supervised baseline trained on the complete training set. LSTM, DNN, and GRU models were implemented as deep learning baselines.ResultsUnder a fixed labelling budget (1,000 initial samples plus 4,000 samples queried per iteration over 10 iterations, totaling 41,000 of 43,062 available training labels), Random Forest and CatBoost with active learning achieved the strongest performance (73.7% and 73.9% test accuracy, respectively), outperforming XGBoost with active learning (69.1% accuracy) and all three deep learning baselines (66.7%–68.2% accuracy). However, under this budget, active learning showed no consistent advantage over the random-sampling baseline: test accuracy differed by less than 0.3 percentage points between active learning and random sampling across all three classifiers, and both approached the accuracy of the fully supervised baseline trained on the complete label set.DiscussionThese findings indicate that ensemble-based classifiers (Random Forest, CatBoost) are better suited to this structured smart contract feature representation than the evaluated deep learning baselines. However, at the labelling budget evaluated in this study, uncertainty-based active learning did not provide a measurable advantage over random sampling, suggesting that its benefit, if present, may only emerge at smaller labelling budgets than those tested here.

Electricity procurement in post-conflict settings faces serious legal and financial pressure, especially when markets operate under sanctions and weak institutional control. Reconstruction requires fast contracting for fuel supply, repair works, and grid recovery, yet these processes often lack strong oversight and trusted payment systems. Existing studies discuss blockchain applications in energy, but they give less attention to how legal recognition and regulatory assurance can be supported in procurement affected by jurisdiction-specific sanctions regimes. This gap creates uncertainty for investors, regulators, and contractors, where compliance risks and payment delays can stop essential projects. The study is motivated by this challenge, focusing on how a structured legal framework can support secure and accountable procurement processes. The aim of this research is to develop a framework that connects blockchain records with legal and regulatory systems so that procurement actions and contract milestones can be documented, reviewed, and more readily relied upon in oversight and dispute processes. The study explains how ledger entries can function as evidence, how responsibilities are assigned, and how dispute processes can rely on these records. This research is important because it offers a clear model to support trust, reduce risks, and improve financial flows in electricity markets recovering from conflict while respecting legal limits, confidentiality requirements, and data-protection concerns. As a conceptual and jurisdiction-neutral framework, the study does not claim uniform legal recognition or empirical effectiveness across jurisdictions; rather, its application requires validation against jurisdiction-specific legal rules, institutional capacities, and sanctions regimes.