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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>
        <language>en-us</language>
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        <pubDate>2026-11-08T00:58:03.422+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.2001090</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.2001090</link>
        <title><![CDATA[Editorial: Ethical issues, challenges and solutions in metaverse and Web3]]></title>
        <pubdate>2026-11-06T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Jane Thomason</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1941258</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1941258</link>
        <title><![CDATA[A machine learning framework for privacy-preserving financial risk assessment using explainable artificial intelligence and blockchain-based trust management]]></title>
        <pubdate>2026-11-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sanjay Agal</author>
        <description><![CDATA[IntroductionFinancial risk assessment requires models that jointly satisfy privacy, explainability, and decentralized trust, yet existing machine-learning approaches usually address these requirements in isolation.MethodsThis paper proposes the Privacy-Preserving Explainable Trust Framework (PP-ETF), integrating federated learning with differential privacy, post hoc SHAP and LIME explanations, and a permissioned distributed ledger for trust management. PP-ETF is evaluated on the German Credit and HELOC datasets against statistical models, centralized ensembles and deep networks, and privacy-preserving baselines.ResultsPP-ETF outperforms privacy-preserving baselines in AUC-ROC while remaining within 1.4%–1.6% of centralized deep-learning models. Reputation-based adversarial detection excludes a malicious participant within two communication rounds with a 3.2% false-positive rate. Explanation stability yields average cosine similarity of at least 0.87, and the ledger processes up to 480 transactions per second with 2.4 s finality latency.DiscussionThe results support the feasibility of jointly implementing privacy, interpretability, and decentralized trust under the evaluated conditions; they do not imply that the underlying trade-offs are eliminated. PP-ETF provides an end-to-end blueprint for effective, scalable, private, interpretable, and trustworthy risk-management systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1895432</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1895432</link>
        <title><![CDATA[Exploring socio-technical facilitators of blockchain adoption in agri-food value chains: an interpretive structural modelling (ISM) approach]]></title>
        <pubdate>2026-11-05T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Ganeshkumar D. Rede</author><author>Pallawi B. Sangode</author>
        <description><![CDATA[Blockchain technology has significant potential to promote transparency, efficiency, and sustainability in the agricultural sector. However, successful implementation depends not only on the availability of technology but also on organizational readiness, infrastructure, and coordinated effort among stakeholders. This paper identifies the key facilitators that support the adoption of blockchain across agri-food value chains spanning farm-level precision-agriculture practices and downstream supply-chain operations and analyzes the interdependencies among them. A literature review identified ten Facilitators of Blockchain Adoption (FBA), which were reviewed and validated by a panel of five experts spanning academia, agro-industry, farming, non-governmental organizations, and information technology. The interrelationships among these facilitators were analyzed using Interpretive Structural Modelling (ISM), a structural method for organizing expert judgments about pairwise influence into a hierarchical model; ISM does not estimate causal effect size or statistical significance. Applying transitivity consistently to the expert-derived reachability matrix revealed that nine of the ten facilitators form a single, tightly interdependent cluster, while enhancing trust among stakeholders emerges as the sole outcome-level factor at the apex of the hierarchy. A complementary MICMAC analysis confirms that these nine facilitators exhibit both high driving power and high dependence power (a “linkage” profile), indicating that the blockchain-adoption system is mutually reinforcing rather than governed by a small set of independent root causes. These findings suggest that ICT infrastructure, stakeholder collaboration, shared responsibility, service quality, auditability, usability, and fast-payment capability function together as a coupled socio-technical system that ultimately builds stakeholder trust. The results can inform extension professionals, educators, policymakers, and technology developers seeking to prioritize interventions that support a more coordinated and sustainable introduction of blockchain into agri-food value chains.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1947022</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1947022</link>
        <title><![CDATA[Blockchain-based PKI infrastructure for efficient management of medical prescriptions]]></title>
        <pubdate>2026-11-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amira Faiza Benkara Mostefa</author><author>Abdelhak Hidouri</author><author>Samia Bouzefrane</author>
        <description><![CDATA[The secure management of electronic prescriptions is a critical challenge in modern healthcare, where medication errors, drug diversion, and fraudulent claims collectively cost billions of dollars annually. Centralized Public Key Infrastructure (PKI), the prevailing e-prescription framework, presents a single point of failure, obstructs interoperability between doctors and pharmacists, and provides no safeguard against prescription duplication. This paper introduces a decentralized cross-chain PKI architecture that integrates four complementary technologies: permissioned blockchains utilizing Practical Byzantine Fault Tolerance (PBFT) consensus, three functionally separated InterPlanetary File System (IPFS) nodes, a formally specified Lock & Mint cross-chain synchronization protocol deployed on a dedicated Ethereum relay chain, and entropy-verified secure QR codes. The proposed solution incorporates a five-state certificate lifecycle with cryptographic chaining and enforces a strict separation between the control plane and the storage plane. We formally prove three security properties of the Lock & Mint protocol: reentrancy freedom, replay idempotency, and atomic rollback. Experimental evaluation conducted on three EVM-compliant Ethereum chains across 200 attack attempts spanning four threat categories (reentrancy, certificate forgery, QR cloning, and QR replay) yields an overall detection rate of 89% at a measured precision of 1.00 (F1 = 0.942), with prescription issuance completing in 74.0 ms and consumption in 70.5 ms on average. These results indicate that the proposed framework achieves robust, formally analysed prescription management with performance margins that are compatible with clinical workflows.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1961818</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1961818</link>
        <title><![CDATA[Operationalising the impact-finance protocol: institutional architecture and the role of DLT in digital sustainable finance]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kate Bennett</author>
        <description><![CDATA[Sustainable, impact and development finance increasingly rely on digital systems to measure and manage real-world social and environmental performance. Yet impact information is still used primarily for reporting and retrospective assessment, rather than as an operational input into financial decision-making. This creates a persistent gap between what financial systems know about real-world outcomes and how capital is actually allocated, released or constrained. This study examines how that gap might be addressed through the Impact-Finance Protocol (IFP), an institutional architecture designed to connect trusted real-world information with financial execution. Building on the conceptual IFP proposed in earlier work, the study evaluates how the protocol behaves when operationalised under real implementation conditions. A comparative Design Science approach was used, combining an initial reference implementation with a longitudinal programme of implementation across multiple institutional, governance and technical contexts. The protocol, rather than any individual organisation, technology or project, was treated as the unit of analysis. Implementation evidence was systematically reconstructed through evidence validation, qualitative coding, implementation-episode reconstruction, comparative analysis and Design Knowledge synthesis. The findings show that the core responsibilities for structuring evidence and verifying its meaning remain comparatively stable across different implementation contexts. By contrast, connecting verified information to financial action requires more extensive governance, institutional authority and operational coordination than the original architecture anticipated. Practical implementation also depends on cross-cutting capabilities such as legal identity, compliance, financial accounts, payment rails and coordination across recurring and overlapping implementation processes. Community participation emerges as an operational capability, while system-level Assessment remains comparatively immature and requires further implementation and evaluation. Where distributed ledger technology (DLT) mechanisms were used, they supported selective integrity, coordination and governed execution, but remained context-dependent rather than necessary architectural components; off-chain verification, institutional authority and governance remained fundamental to financial activation. The study contributes implementation-derived Design Knowledge and transferable Design Principles for digital sustainable finance. It shows that enabling trusted impact information to influence financial decisions requires not only reliable data and verification, but an institutional architecture capable of governing how information becomes actionable financial value across diverse real-world settings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.2002131</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.2002131</link>
        <title><![CDATA[Ethereum value formation as a feedback system: an integrative causal-loop framework]]></title>
        <pubdate>2026-11-03T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Vladimir Kovšca</author><author>Zrinka Lacković Vincek</author><author>Suzana Keglević Kozjak</author>
        <description><![CDATA[The valuation of decentralized blockchain platforms remains challenging because their native tokens do not represent conventional residual claims and may derive value from several interacting economic mechanisms. This study develops an integrative framework of Ethereum value formation through a mechanism-based conceptual synthesis of the relevant literature. The literature is organized into six principal valuation perspectives: fundamental and on-chain, network-based, monetary and tokenomics, scarcity-oriented, security and staking-based, and behavioral and reflexive approaches. The synthesis indicates that the six perspectives emphasize different but partly overlapping mechanisms of value formation and that none independently explains the interaction among productive activity, adoption, monetary demand, token scarcity, network security, and market expectations. These mechanisms are integrated into a causal-loop framework comprising five reinforcing feedback loops, three balancing feedback loops, and additional productive value-creation pathways that do not form a closed feedback loop. The framework analytically distinguishes ecosystem value creation, ETH-related value capture, and market-price formation while conceptualizing Ethereum as a complex adaptive socio-economic system rather than solely as a conventional productive asset or monetary instrument. The resulting framework provides a theory-building foundation for subsequent stock-and-flow model development, empirical calibration, and comparative application to other blockchain platforms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1903194</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1903194</link>
        <title><![CDATA[From multi-sig to DLCs: modern oracle designs on Bitcoin]]></title>
        <pubdate>2026-10-30T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Giulio Caldarelli</author>
        <description><![CDATA[Unlike Ethereum, which was conceived as a general-purpose smart-contract platform, Bitcoin was designed primarily as a transaction ledger for its native currency, which limits programmability for conditional applications. This constraint is particularly evident when considering oracles, mechanisms that enable Bitcoin contracts to depend on exogenous events. This paper investigates whether new oracle designs have emerged for Bitcoin Layer 1 since the 2015 transition to the Ethereum smart contracts era and whether subsequent Bitcoin improvement proposals have expanded oracles’ implementability. Using Scopus and Web of Science searches, complemented by Google Scholar to capture protocol proposals, we observe that the indexed academic coverage remains limited, and many contributions circulate outside journal venues. Within the retrieved corpus, the main post-2015 shift is from multi-sig-style, which envisioned oracles as co-signers, toward attestation-based designs, mainly represented by Discreet Log Contracts (DLCs), which show greater alignment with Bitcoin design constraints, tool support, and evidence of practical implementations in real-world scenarios such as betting and prediction markets.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1960105</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1960105</link>
        <title><![CDATA[Poisoning-resistant signature sharing for hybrid intrusion detection using a permissioned blockchain ledger]]></title>
        <pubdate>2026-10-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sharad Singh</author><author>G. Hanumat Sastry</author>
        <description><![CDATA[Collaborative intrusion detection systems share attack signatures so that an attack detected at one site, such as a roadside operator in an intelligent transportation system, can be blocked at others. Blockchain has been proposed as the sharing medium, but earlier evaluations have neither separated the contribution of the ledger from that of the detector nor examined whether shared signatures can be used to attack the receivers. This paper studies and extends a hybrid blockchain-based intrusion detection system (HBIDS) that combines signature matching with a classifier ensemble and exchanges new signatures through a permissioned ledger; the architecture follows Khonde and Ulagamuthalvi. The contributions are an endorsement rule that rejects poisoned signatures and a leakage-controlled evaluation. Each validator endorses a signature only if it matches at most 0.05% of its own benign traffic, and a signature is committed only with a threshold of signed endorsements, followed by Practical Byzantine Fault Tolerance (PBFT) agreement. HBIDS was evaluated on 6 days of CSE-CIC-IDS2018 with a chronological split per class, after removing 1,847,084, exact duplicate flows (29.4% of the records). The hybrid detector reached an F1-score of 99.43% and a false-positive rate of 0.093%. With each capture day treated as a separate site, sharing signatures raised the recall on attack families unseen at a site from 27.00% to 98.55% (authenticated publish-subscribe) and 93.07% (ledger), so the gain came from sharing, not from the ledger. Under a poisoning attack, publish-subscribe accepted all 50 poisoned signatures and the mean false-positive rate of the honest sites rose to 43.647%, whereas the ledger with an n − f admission threshold committed 0 of them (false-positive rate 0.504%) and still rejected all of them with up to f colluding validators; a 2f + 1 threshold admitted all of them with one colluding validator. Ledger commit latency was 75–435 m per 50-signature block for 4 to 13 validators, against 12–36 m for publish-subscribe. The ledger does not improve detection accuracy by itself; its benefit lies in admission control, ordering and tamper evidence for shared signatures.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1947901</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1947901</link>
        <title><![CDATA[Deployment and empirical validation of a blockchain–IoT framework for carbon credit certification in renewable energy SMEs]]></title>
        <pubdate>2026-10-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Matteo Vaccargiu</author><author>Azmat Ullah</author><author>Pierluigi Gallo</author><author>Giovanni Capizzi</author><author>Francesco Saverio Cannizzaro</author><author>Maria Ilaria Lunesu</author><author>Roberto Tonelli</author>
        <description><![CDATA[IntroductionCarbon credit certification for small and medium-sized enterprises (SMEs) depends on reliable, automated mechanisms that connect measured renewable energy production to verifiable emission-reduction records. Existing approaches are primarily conceptual and lack empirical validation in production environments.MethodsThis paper presents an implementation and validation of a four-layer blockchain–Internet of Things (IoT) architecture at a 100 kWp photovoltaic (PV) installation operated by an agricultural SME in Marrubiu (OR), Sardinia, Italy. The system integrates Shelly Pro 3 EM smart energy meters, two Raspberry Pi data collection units, and a Hyperledger Fabric (HLF) permissioned blockchain running the ControllerContract chaincode.ResultsDeployed on 31 October 2025, the system remained deployed throughout the observation period, with automated recovery from connectivity interruptions, and recorded 42,853.98 kWh of exchanged energy and 11.87 tCO2e of blockchain-certified carbon credits (credits whose computation and provenance are recorded immutably on-chain, pending validation by an accredited voluntary market body) over the period from 1 November 2025 to 26 April 2026. As of 9 August 2026, the blockchain ledger contained 70,620 blocks. During normal operation, the combined design rate was 384 blocks per day (288 from data collection and 96 from the certification controller).DiscussionThe deployment demonstrates the architectural validity of the framework across all four layers, illustrates anomaly detection with retransmission logging in production, and reports field challenges not anticipated in the original design, including electrical panel space constraints and Wi-Fi signal instability in an agricultural setting.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbloc.2026.1845998</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbloc.2026.1845998</link>
        <title><![CDATA[Modeling blockchain adoption in the United Kingdom’s banking: an empirical study using technology-organization-environment framework]]></title>
        <pubdate>2026-10-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohammad Sami Ali</author><author>Sultan Alabdullatif</author><author>Saleh Baqader</author><author>Mohammad Hariri</author>
        <description><![CDATA[Introduction Blockchain technology (BCT) has the ability to significantly change the financial services industry by making transactions faster, cheaper, safer, as well as more transparent. Therefore, the current paper seeks to investigate and empirically validate the variables, which are affecting the adoption of Blockchain in the UK’s banking system.Methods For this purpose the study collected secondary data through reviewing literature for the period January 2015 and June 2025 from well indexed journals, conference proceedings, books, white papers, and industry reports, those are related to blockchain technology and the banking sector. However, primary data for this study were collected from banking and financial services employees in London between the period January 2025 and July 2025 using a structured questionnaire. A non-probability purposive sampling technique was adopted and Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to examine the relationships among the proposed technological, organizational, and environmental determinants of blockchain adoption. The Technology Organizational Environmental framework served as the model for the scale used for measuring BCA enablers.ResultsConsequently, the study found that perceived Compatibility, relative advantage, learning culture, top management, consumer readiness, and competitive pressure significantly affects the adoption of blockchain, whereas perceived cost limits this adoption process. Additionally, the results reveal that perceived compatibility, relative advantage, learning culture, top management support, customer readiness, and competitive pressure positively influence blockchain adoption, whereas perceived cost negatively affects adoption.DiscussionMoreover, the findings indicated that Blockchain might therefore be viewed as an emerging disruptive force in digital technology that is transforming business models and is becoming as an essential element on a global scale.]]></description>
      </item><item>
        <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.2026.1882471</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>
      </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>
      </item><item>
        <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>
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        <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.2026.1883639</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.2026.1905158</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.2026.1837907</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>
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