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        <title>Frontiers in Blockchain | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/blockchain</link>
        <description>RSS Feed for Frontiers in Blockchain | New and Recent Articles</description>
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        <pubDate>2026-10-17T20:43:54.450+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1882471</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1882471</link>
        <title><![CDATA[Layered digital money: the complementary roles of regulated stablecoins and CBDCs]]></title>
        <pubdate>2026-10-07T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Maryam Taghiyeva</author>
        <description><![CDATA[Stablecoins and central bank digital currencies (CBDCs) represent two distinct approaches to the digitalisation of money. While CBDCs are designed as sovereign digital cash, fiat-backed stablecoins have rapidly evolved into practical tools for payments, cross-border transfers, and decentralised finance. Although often framed as competitors, their development trajectories suggest that they perform distinct functions and are unlikely to replace one another directly. The existing literature has identified the complementarity thesis in broad terms, yet a significant gap remains: there is no systematic account of the specific regulatory, infrastructural, and macroeconomic conditions under which this complementarity actually materialises in practice, nor a consolidated framework that captures the interaction between these layers in real-world jurisdictions. This paper fills that gap. It introduces a five-layer digital money architecture and evaluates whether complementarity holds empirically across multiple regulatory environments and market contexts. Drawing on comparative analysis of regulatory frameworks—including MiCA, VARA, and developments in the United Kingdom, United States, and Singapore—as well as industry integrations by Visa, Mastercard, and Circle, and granular adoption evidence from Argentina and Nigeria, the paper shows that regulated stablecoins are progressively embedded in mainstream financial systems. Evidence from inflation-affected economies highlights their role in value preservation and payments, in contrast to the limited adoption of retail CBDCs. The findings suggest that stablecoins are best positioned as the programmable, user-facing layer, while CBDCs function as settlement-grade public infrastructure. The paper further identifies tokenised deposits and tokenised real-world assets as an emerging third dimension of this architecture, setting out the conditions under which future research should examine their integration.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1942124</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1942124</link>
        <title><![CDATA[Oracle integrity under predatory attack: a calibrated jump-diffusion model with public-data evidence and a positivity-preserving numerical scheme]]></title>
        <pubdate>2026-10-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Edson Pindza</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1860582</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1860582</link>
        <title><![CDATA[Metaverse and generative AI in education: a scoping review of technologies, applications, evaluation, challenges, and future directions]]></title>
        <pubdate>2026-10-05T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Muhammad Tukur</author><author>Wala Elsharif</author><author>Ahmad Sani Bello</author><author>Babatunde Abiodun Salami</author><author>Isa Ali Ibrahim</author><author>Mowafa Househ</author><author>Jens Schneider</author><author>George Mikros</author><author>Marco Agus</author>
        <description><![CDATA[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.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1947009</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1947009</link>
        <title><![CDATA[Impact-finance protocol for digital sustainable finance: institutional architecture for outcome-based capital allocation]]></title>
        <pubdate>2026-10-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kate Bennett</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1949598</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1949598</link>
        <title><![CDATA[A multi-source empirical audit of blockchain and DeFi: concentration, intelligent agents, and mechanism design]]></title>
        <pubdate>2026-09-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>William Villegas-Ch</author><author>Aracely Mera Navarrete</author><author>Rommel Gutierrez</author><author>Paul Sarango-Lalangui</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1935632</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1935632</link>
        <title><![CDATA[An AI-based blockchain framework with AES–RSA hybrid encryption for secure data sharing in smart environments]]></title>
        <pubdate>2026-09-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sahbaa AbdulKareem Al-Abassy</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1934036</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1934036</link>
        <title><![CDATA[Compliance by design or law by decree? A comparative analysis of the tokenization of financial instruments]]></title>
        <pubdate>2026-09-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hicham Sadok</author><author>Salah Kassimi</author><author>Enas Mohammed AlQodsi</author><author>Mohammed El Hadi El Maknouzi</author><author>Iyad Mohammad Jadalhaq</author><author>Ibrahim Alshawabkeh</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1905158</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1905158</link>
        <title><![CDATA[AI-Accelerated blockchain architecture for efficient and secure electronic healthcare record exchange]]></title>
        <pubdate>2026-09-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nagaraj Segar</author><author>Vijayarajan Vijayan</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1883639</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1883639</link>
        <title><![CDATA[Decentralized blockchain governance for explainable causal AI in macroeconomic forecasting]]></title>
        <pubdate>2026-09-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Oumaima Abouzaid</author><author>Faouzi Boussedra</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1837907</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1837907</link>
        <title><![CDATA[Promethon: efficient reduction of Ethereum’s world state size using red-black trees]]></title>
        <pubdate>2026-09-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Seyed Sepehr Mirnasrollahi Parsa</author><author>AmirHossein Aghajari</author><author>Salman Niksefat</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1872161</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1872161</link>
        <title><![CDATA[Blockchain-enabled energy markets in emerging economies: a conceptual framework for efficiency, inclusion, and sustainability]]></title>
        <pubdate>2026-09-16T00:00:00Z</pubdate>
        <category>Review</category>
        <author>David Mhlanga</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1882027</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1882027</link>
        <title><![CDATA[Comparative evaluation of active learning, random sampling, and deep learning for smart contract vulnerability detection]]></title>
        <pubdate>2026-09-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gabriel Avelino Sampedro</author><author>Cheng Yanhui</author><author>Arlene Caballero</author><author>Made Adi Paramartha Putra</author><author>Nyoman Bogi Aditya Karna</author><author> Indriati</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1887646</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1887646</link>
        <title><![CDATA[Blockchain-based regulatory assurance for sanctions-exposed electricity procurement in post-conflict markets]]></title>
        <pubdate>2026-09-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ahmed Jawdat Ali Ozeri</author><author>Tavga Abbas Towfiq</author><author>Kawar Mohammed Mousa</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1830313</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1830313</link>
        <title><![CDATA[Equity by design as a sustainability multiplier in the citiverse: an empirical proof-of-concept]]></title>
        <pubdate>2026-09-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Paola Cecchi‐Dimeglio</author>
        <description><![CDATA[IntroductionCitiverse deployments and smart-city systems (e.g., converging artificial intelligence, digital twins, extended reality, IoT, and blockchain within urban governance) are scaling rapidly, yet the equity architecture embedded in their design remains unexamined. Whereas sustainability governance scholarship has treated equity chiefly as a normative outcome, this paper theorizes it instead as a structural design property: a performance multiplier embedded in socio-technical architecture. It extends the System Design and Technology Equity Nexus frameworks to the Citiverse and asks whether equity design quality is independently associated with sustainability performance, across dimensions formally independent of equity by design.MethodsThe paper draws on the ITU/UNICC/Digital Dubai Citiverse Use Case Taxonomy, the leading United Nations initiative in this domain. It develops two original instruments: the Citiverse Sustainability Impact Index (CSII) and the Equity Design Quality (EDQ) rubric. These are applied across 44 use cases, with their relationship tested through frequentist OLS using a three-specification identification strategy, together with a Monte Carlo measurement-uncertainty analysis.ResultsThe results offer conditional proof-of-concept support for the central hypothesis. Equity design quality carries independent predictive power over non-equity sustainability performance. This relationship is multiplicative rather than additive once technology richness is accounted for. At this sample size, implementation maturity is the dominant structural predictor.DiscussionThese findings are proof-of-concept rather than causal; causal inference will require longitudinal data. The measurement infrastructure introduced here, the CSII and EDQ rubric, is designed to scale with future documentation and to serve as a replicable basis for tracking the equity design gap over time.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1914193</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1914193</link>
        <title><![CDATA[Blockchain in emerging higher education systems: a TOE-based framework for adoption informed by faculty evidence from Jordan]]></title>
        <pubdate>2026-09-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ibrahim Moh’d Saraireh</author><author>Faten Albtoush</author><author>Mohammed A. K. A. Al-Btoush</author><author>Saleem Alziadin</author>
        <description><![CDATA[IntroductionBlockchain is increasingly discussed in higher education both as curriculum content and as infrastructure for digital credentials and student records. However, empirical evidence from emerging higher education systems remains limited. This study investigates the factors shaping blockchain adoption in Jordanian higher education and develops an empirically informed Technology–Organization–Environment (TOE)-based framework.MethodsA cross-sectional survey was conducted among 170 faculty members from public and private universities in Jordan. The survey examined current and planned blockchain-related teaching, perceived adoption barriers, and institutional support priorities. The resulting framework was subjected to initial content validation by an expert panel of 12 participants.ResultsOnly 11.8% of respondents reported currently teaching blockchain-related content, whereas 64.7% planned to introduce it during the following academic year. The principal barriers were a lack of high-quality textbooks and case studies (41.2%), limited time for specialized training (23.5%), and inadequate laboratory or software infrastructure (17.6%). Faculty most frequently prioritized pedagogical workshops and industry partnerships, with each selected by 35.3% of respondents. Based on these findings, the study developed a five-dimensional TOE-based framework encompassing strategy, governance, infrastructure, curriculum integration, and stakeholder capacity. Expert validation identified governance and regulatory issues, together with stakeholder capacity, as the main near-term bottlenecks.DiscussionThe findings indicate substantial faculty interest in blockchain education but limited institutional readiness to support its adoption. The proposed framework provides an evidence-informed roadmap for aligning faculty development, curriculum planning, infrastructure investment, governance, and industry engagement in Jordanian higher education.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1952615</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1952615</link>
        <title><![CDATA[Editorial: Blockchain and tokenomics for sustainable development, volume II]]></title>
        <pubdate>2026-09-07T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Charalampos Alexopoulos</author><author>Enrico Ferro</author><author>Claudio Schifanella</author>
        <description></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1876539</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1876539</link>
        <title><![CDATA[AI-based context-aware data anonymization with federated adaptive differential privacy for IoT applications]]></title>
        <pubdate>2026-09-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shaik Mahamad Shakeer</author><author>M. Rajasekhara Babu</author>
        <description><![CDATA[The rapid growth of Internet of Things (IoT)-based healthcare systems has raised significant concerns regarding data privacy and security. Ensuring privacy while maintaining the utility of healthcare data remains a major challenge in IoT-enabled healthcare environments. This article proposes a novel data anonymization framework based on federated learning and adaptive differential privacy. Initially, IoT healthcare data are processed using a residual bidirectional gated recurrent unit (Res-BiGRU) model to capture contextual and privacy-related features. Subsequently, an adaptive differential privacy mechanism is applied to minimize information loss while ensuring strong privacy protection. To improve optimization performance, a modified Resilient Adaptive Apiary Organizational Optimization Algorithm (RAOOA) incorporating a fitness-based adaptive factor is introduced to enhance convergence stability and solution quality. The effectiveness of the proposed framework is evaluated using a healthcare dataset. The proposed approach demonstrates superior performance compared with existing methods in terms of computation time, information loss, and privacy risk. The results indicate that the proposed framework provides an efficient and scalable solution for privacy-preserving IoT healthcare data management while achieving an improved balance between data utility, computational efficiency, and privacy protection.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1903202</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1903202</link>
        <title><![CDATA[TOAD-ML: a trust-aware framework for validating oracle price feeds and detecting manipulation in decentralized finance]]></title>
        <pubdate>2026-09-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dhivyalakshmi Venkatraman</author><author>Manikandan Kuppusamy</author>
        <description><![CDATA[IntroductionDecentralized finance (DeFi) applications depend on external oracle networks to obtain asset prices that drive core financial operations, including lending, collateral management, and automated liquidations. Oracle systems remain vulnerable to manipulation through coordinated strategies such as flash loan attacks and temporary price distortions in low-liquidity markets. Although several studies have addressed oracle manipulation, most existing approaches rely on single-source validation or detect attacks only after malicious activity has occurred, and few provide a continuous quantitative measure of oracle data trustworthiness.MethodsThis study proposes TOAD-ML, a trust-aware machine learning framework that evaluates the reliability of decentralized oracle price feeds using multi-source validation. TOAD-ML integrates data from five sources: labeled oracle attack events from DeFiHackLabs, CoinGecko historical market prices, Chainlink on-chain oracle rounds, Uniswap V3 TWAP pool data, and DeFiLlama multi-token aggregated prices. An XGBoost classifier is trained on engineered cross-source deviation and temporal features, augmented by a trust-calibration layer that produces a binary anomaly label and a continuous trust score bounded in [0,1]. The model is trained on 19 months of oracle price data encompassing four distinct, well-documented oracle hacks covering approximately 60 days of attack periods.ResultsExtensive empirical evaluation demonstrates 96.72% accuracy in identifying oracle price anomalies, with an AUC-ROC of 0.97. Ablation studies show performance reductions of 5.27% when multi-source fusion is removed and 3.84% when the trust-scoring layer is removed. TOAD-ML outperforms the baseline models: SVM (88.42%), Random Forest (91.76%), and standalone XGBoost (93.85%). These values are consistent with the reported experimental results.DiscussionIn case-study evaluation on documented oracle attacks, In the documented case studies, TOAD-ML signalled suspicious oracle behaviour before corresponding token price crashes. The framework provides early-warning capability, continuous trust quantification, and interpretable anomaly detection, supporting reliability-aware monitoring of DeFi oracle price feeds. The manuscript does not report an independent quantitative detection-latency benchmark; latency evaluation is identified as future work.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1838214</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1838214</link>
        <title><![CDATA[GasGAT: a graph attention network framework for smart contract gas optimization]]></title>
        <pubdate>2026-09-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Youssef Said</author><author>Al Mahdi Khaddar</author><author>Lahcen Hassine</author><author>Ahmed Eddaoui</author><author>Tarik Chafiq</author>
        <description><![CDATA[IntroductionThe optimization of gas consumption in Ethereum smart contracts is critical for enhancing the economic viability, scalability, and security of blockchain applications. Existing tools, however, are largely limited to static heuristics or local pattern matching, failing to capture the complex, non-local dependencies that drive gas inefficiency.MethodsTo address this limitation, this paper introduces GasGAT, a deep learning framework that models smart contracts as semantic graphs and leverages a Graph Attention Network (GAT) to detect gas-intensive code patterns. By capturing intricate dependencies between functions, state variables, and control flows, GasGAT is able to identify non-local and inter-procedural inefficiencies that are often missed by rule-based or purely local static analysis tools. Unlike existing approaches, which focus primarily on local code patterns or rule-based detection, GasGAT explicitly reasons about long-range execution paths within smart contracts. We evaluate our method on a dataset of 40,000 verified Ethereum smart contracts (Solidity ≥0.8.0, labeled via Slither static analysis), including both a polarized subset (excluding ambiguous cases) and a full, non-filtered setting.ResultsUnder 5-fold cross-validation, GasGAT achieves an accuracy of 94.92% (±1.04%) and a macro F1-score of 88.69% (±2.70%), with a McNemar statistic of 633.12 (p ≪ 0.05) confirming statistical significance. Crucially, GasGAT is the only model whose performance remains stable when correlated node features are removed (Δ = 0.00%), demonstrating that its attention mechanism learns genuine structural patterns rather than label-feature correlations.DiscussionThe primary contribution of GasGAT lies not in marginal accuracy improvements, but in its ability to provide interpretable, structural insights through attention mechanisms. This represents a paradigm shift from traditional detection methods toward explainable, graph-based reasoning for gas optimization, enabling developers to design more efficient, scalable, and secure decentralized applications.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1880173</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1880173</link>
        <title><![CDATA[The effect of blockchain technology on international trade in South Africa]]></title>
        <pubdate>2026-08-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Teboho Charles Mashao</author><author>Ireen Choga</author>
        <description><![CDATA[Blockchain technology has transformed international trade. Despite its beneficial potential in promoting international trade, blockchain technology is not a universal solution for all nations, as it affects global governance of trade activities. It is vital to know how blockchain technology affect international trade in developing nations. Therefore, the main objective of this study is to investigate the impact of blockchain technology on international trade in South Africa using quarterly time series data from 2013Q1 to 2024Q4. The autoregressive distributed lag (ARDL) and nonlinear autoregressive distributed lag (NARDL) approaches were used to analyse. The study found that blockchain technology positively affects international trade. This demonstrates that blockchain technology is effective in facilitating international trade transactions in South Africa. Regarding the nonlinear effects, the study reveals that positive change in blockchain technology has a positive impact on international trade, while negative change has a negative insignificant impact. Moreover, the study found that these effects are nonlinear in the long run. The study also found that inflation, FDI, exchange rate and GDP have a positive impact on international trade in South Africa. This study recommends that policymakers should promote the implementation of blockchain technology in international trade by creating supportive regulatory policies and investments in blockchain infrastructure. The implementation of supportive regulatory policies and investment in blockchain infrastructure could improve the level of South African international trade. The major contribution of this study was to provide a data-driven empirical analysis on the impact of blockchain technology on international trade at the country-specific level.]]></description>
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