IntroductionOracle-dependent DeFi protocols execute collateral valuations, liquidations, and settlements based on reported data.MethodsWe model oracle integrity and attack pressure as coupled positive states in a controlled jump-diffusion. Multiplicative shocks preserve the integrity state space while separating continuous variation from discrete attack windows. Scenario inputs are anchored to 24 documented oracle-related incidents, an oracle-consumer exposure panel, and Chainlink metadata covering 1,669 feeds across 29 networks. A positivity-preserving log-Euler scheme simulates Baseline, Stress, and Controlled configurations.ResultsThe corresponding 10-year collapse probabilities are 10.1%, 93.0%, and 0% across 4,000 paths, with a one-sided 95% upper bound of 0.09% for the Controlled configuration. Within the sampled parameter ranges, pressure decay has the strongest rank association with simulated collapse probability.DiscussionThese results are scenario diagnostics, not causal estimates of protocol controls.

The convergence of Metaverse technologies and Generative AI (GenAI) is reshaping immersive digital education. This study presents a scoping review of Metaverse and GenAI-enabled education following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search of ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, and SpringerLink identified 114 primary studies published or indexed from January 2022 up to the respective database search dates in 2026. Based on the synthesized evidence, we propose a comprehensive taxonomy that classifies existing research across application domains, Metaverse technologies, GenAI categories, target user groups, degrees of freedom (DoF), pedagogical foundations, evaluation practices, technical limitations, and future research directions. The reviewed studies show that Virtual Reality (VR) is the predominant immersive modality (61%), while OpenAI-based models represent the most widely adopted GenAI ecosystem (60%). Applications are concentrated primarily in STEM, higher education, and healthcare, where immersive learning environments increasingly incorporate intelligent tutoring, adaptive feedback, virtual agents, and automated content generation. Although 68% (77 articles) of the reviewed studies reported user perceptual evaluations, these predominantly focused on short-term usability and engagement, with comparatively limited attention to longitudinal learning effectiveness, knowledge retention, cognitive outcomes, AI trustworthiness, accessibility, inclusivity, and real-world deployment. This imbalance reveals a central maturity gap: technological sophistication is advancing faster than the educational evidence needed to establish sustained learning effectiveness and real-world readiness. Commonly reported challenges include limited empirical validation, system maturity, scalability, interoperability, AI trustworthiness, immersive performance, accessibility, and ethical governance. Based on these findings, we identify key priorities for future research, including longitudinal and pedagogically grounded evaluation, scalable and interoperable system architectures, trustworthy AI, inclusive and ecologically valid validation, and stronger integration of pedagogical and governance considerations. The proposed taxonomy provides an evidence-based framework to support future research, comparative analysis, and the design of Metaverse and GenAI-enabled educational systems.

IntroductionModern financial systems are becoming increasingly digital, automated and data-driven. Yet the infrastructures linking trusted representations of real-world social and ecological performance to financial decision-making remain underdeveloped. This challenge is particularly acute within sustainable, impact and development finance, where capital allocation continues to rely heavily on retrospective reporting, proxy indicators and fragmented information flows.MethodsThis study addresses this gap through a design science approach, developing an Impact-Finance Protocol as a conceptual systems architecture for Digital Sustainable Finance. Drawing on systems mapping, comparative protocol analysis and the Integrated Capitals Assessment (ICA) framework, the research identifies the functional requirements for translating verified non-financial performance into adaptive financial processes.ResultsThe resulting architecture comprises six interdependent protocol layers encompassing data, logic, value, governance, community and assessment. Together, these establish an integrated infrastructure through which verified impact information can inform financial decision-making, capital allocation and institutional governance within continuous feedback systems.DiscussionRather than proposing a new financial instrument, the protocol reconceptualises finance as an adaptive information system in which trusted digital infrastructure enables financial flows to respond dynamically to verified real-world conditions. The paper contributes a novel systems architecture for Digital Sustainable Finance and provides a conceptual foundation for future empirical research into blockchain-enabled, AI-enabled and other programmable financial infrastructures supporting sustainable, impact and development finance.

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 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.

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 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.

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.