Blockchain Papers

Xiaoya Wang, Yuchuan Wang

No abstract is available for this record.

Mercedez Lopez

EthAiSynHuman-AI Integration ArchitecturePsychological Audit ReportVersion 3.0 --- Research-Updated EditionA dual-lens audit applying the EthAi Syn and Ethain-Synthia frameworksto identify and resolve structural gaps before enterprise deployment.Prepared by ChloeDate March 2026Version 3.0 --- Research-Updated EditionAudit Type Internal Psychological AuditFrameworks Applied EthAi Syn + Ethain-Synthia (ESF)Gaps Identified 5Gaps Resolved 5Additional Finding Measurement Frontier --- Research Mandate (Active)New Role Created Human-AI Integration ArchitectResearch Sources Integrated 8 peer-reviewed sources (2023--2026)Executive SummaryFive Gaps. All Resolved. One Frontier Named. One Research Foundation Integrated.This report documents a full psychological audit of the EthAi Syn Behavioral Governance Framework, updated to incorporate the revised framework draft and an eight-source peer-reviewed research foundation. The audit applied two complementary lenses: the EthAi Syn framework's Psychological Audit methodology, which evaluates whether systems support or deplete human capability, and the Ethain-Synthia Framework (ESF), which evaluates whether human judgment is structurally preserved or quietly handed off to the system.Five structural gaps were identified. Each was examined through both lenses. Each has been resolved with a specific structural realignment consistent with the framework's own design principles. A sixth finding --- the Measurement Frontier --- was documented as a formal research mandate rather than a resolvable gap. This version adds a seventh finding: the Research Foundation, documenting how eight peer-reviewed sources published between 2023 and 2026 strengthen the framework's evidence base, resolve former areas of theoretical weakness, and establish the field-level demand for exactly the role EthAiSyn creates.Version 3.0 ChangesThis version integrates eight peer-reviewed research sources spanning neuroscience, HCI, clinical psychology, implementation science, and regulatory law. Key additions include: the first field study of clinician AI trust formation (Kelly et al., 2025); a 30-year systematic review confirming no field studies existed prior to 2025 (Wischnewski et al., 2023); clinical evidence on metacognitive sensitivity in joint decisions (Lee et al., 2025); documentation of the psychologist gap in AI design (JMIR AI, 2024; JMIR HF, 2021); and the Woebot shutdown as a case study in integration architecture failure (Torous & Cipriani, 2025).Audit MethodologyTwo Lenses, Five Gaps, One Frontier, One Research FoundationThe audit followed EthAi Syn's four-stage framework structure across all sessions, with each stage evaluated through both analytical lenses simultaneously. Where the two lenses conflicted or overlapped, the intersection was treated as the highest-priority finding.EthAi Syn LensAt each stage: does this environment support human capability or actively deplete it? Where does the user's mental model break from the system's actual behavior?Ethain-Synthia (ESF) LensAt each stage: is human judgment structurally present as a generative function, or is it operating as a backstop that only activates after the system has already decided?Stage 1: Baseline MappingWhat EthAi Syn Is and Who It ServesIntended UsersShort-term: Enterprise organizations, with HR leadership and healthcare administration as primary buyers. Long-term: Individual practitioners and researchers using the framework directly for professional development and field-building.Intended ExperienceUsers engage through natural language and structured consultation. The system builds a deep understanding of their organizational context, values, and cognitive patterns over time. The goal is movement toward each organization's and user's own ceiling of responsible AI-augmented capability, not a standardized benchmark.Delivery ModelA combination of audit methodology, measurement program design, training curriculum, and consultancy engagement. The specific configuration is determined by the enterprise deployment context. The Human-AI Integration Architect role is the organizational function this delivery model creates.New Role Created: Human-AI Integration ArchitectThe framework generates an organizational function that does not exist before its arrival: a role that designs and governs the conditions under which humans and AI systems work together without the humans losing what makes their contribution irreplaceable. This role is grounded in psychological expertise, implementation science, and measurement theory --- not in technology implementation, compliance, or communications.Research validation for this role: The JMIR AI systematic review (2024) named the absence of psychologists from AI design as a field-level gap. The JMIR Human Factors mapping review (2021) called human factors and ergonomics expertise "essential" for defining the dynamic interaction of AI within organizational systems. Torous et al. (2025) documented that the digital navigator role --- the implementation-level equivalent of the Integration Architect --- has been called for since 2015 and remains largely unfilled. Strudwick et al. (2025) established that successful AI implementation requires "intentional infrastructure, not just technology." The Integration Architect is that infrastructure.The Five Gaps and Their ResolutionsGAP A | The Temporal Value Gap RESOLVEDWhat Was FoundEthAi Syn's value proposition is long-cycle. The framework's most defensible claims --- that it prevents judgment erosion, maintains human skill under AI dependency, and preserves moral accountability --- all require longitudinal deployment before they produce measurable evidence. Enterprise buyers operate on quarterly decision cycles. This temporal mismatch is a structural positioning problem.Research Grounding (Added Version 3.0)The Wischnewski et al. (2023) finding --- that 30 years of trust calibration research produced zero field studies --- actually resolves this gap in a counterintuitive way: the absence of field evidence is itself the evidence. Organizations can cite baseline measurement data immediately, before long-term outcomes accumulate, because the baseline is the proof of concept. The gap between "no measurement" and "systematic measurement" is demonstrable from T0.Realignment: Early Proof Point Checklist + Positioning ReframePosition EthAiSyn's earliest deliverable --- the baseline competency battery and behavioral logging protocol --- as the proof of concept. An organization that has systematically measured its human-AI system's baseline is already in the top percentile of responsible deployment, because the research base confirms that no one else has done so. The longitudinal evidence accumulates over time, but the governance value begins immediately.GAP B | The Concealed Decision Pathway Gap RESOLVEDWhat Was FoundAI systems increasingly function as a pre-cognitive System 0 (Saßmannshausen & Wagener, 2026; Chiriatti et al., 2025), shaping what information enters human awareness before deliberate evaluation begins. When AI shapes the decision pathway before conscious engagement, traditional audit methods that assume deliberate human decision-making are structurally inadequate.Research Grounding (Added Version 3.0)The System 0 concept directly explains why the concealed pathway is invisible to standard measurement: by the time the operator is deliberating, the AI has already structured the cognitive landscape. The transparency paradox (BaHammam, 2025) adds a second layer: operators may not disclose AI reliance even when aware of it, because disclosure carries institutional penalty. The measurement architecture must therefore capture decision pathways through behavioral telemetry rather than self-report alone.Realignment: Intent Signal + Transparent Decision LayerRequire the logging of pre-AI independent judgment as a structural component of every AI-assisted workflow. The intent signal --- what the operator was thinking before AI exposure --- is the counterfactual baseline against which post-AI decision movement is measured. This makes the concealed pathway visible without requiring disclosure and without adding cognitive burden to normal operations.GAP C | The Undefined Autonomy Threshold Gap RESOLVEDWhat Was FoundThe framework did not specify at what point AI contribution crosses from assistance to replacement of human judgment. Without a defined threshold, the Moral Diffusion construct lacks operational anchoring --- the system cannot distinguish appropriate augmentation from inappropriate substitution.Research Grounding (Added Version 3.0)Kelly et al. (2025) found that clinicians bounded their trust contextually --- trusting AI for low-risk screening but not for complex clinical formulation --- and that this context-sensitivity was the appropriate and healthy response, not insufficient adoption. The Wischnewski et al. (2023) distinction between warranted and unwarranted trust provides the theoretical anchor: the autonomy threshold is not a fixed percentage of AI contribution but a contextual assessment of whether reliance is warranted given actual AI reliability in that case type.Realignment: Moral Understanding Indicator + Autonomy InvitationDefine autonomy thresholds contextually by case type in the construct mapping phase. For each case category, establish the AI reliability zone and the corresponding appropriate reliance range. Design the Moral Understanding Indicator to assess whether operators can articulate these contextual thresholds, not just whether they apply a fixed rule. The Autonomy Invitation structures the operator's active choice about when to rely versus resist --- making reliance a deliberate decision rather than a default.GAP D | The Reactive Notification Model Gap RESOLVEDWhat Was FoundThe original framework triggered governance review only after threshold crossings were detected. This reactive architecture means the most dangerous trajectory --- slow, multi-indicator erosion that approaches but does not immediately cross any single threshold --- is invisible to governance until it has already caused damage.Research Grounding (Added Version 3.0)The Wischnewski et al. (2023) finding on the absence of field studies reveals that organizations currently have no systematic approach to proactive detection. The Strudwick et al. (2025) implementation science finding --- that promising tools consistently stall at demonstration without intentional infrastructure --- confirms that reactive governance is the default, not the exception. The EthAiSyn governance model must be explicitly proactive to differentiate itself from the field's current practice.Realignment: Decision TraceThe Decision Trace is a continuous behavioral record that makes erosion trajectories visible before threshold crossing. By logging decision pathways, override patterns, and pre/post AI judgment shifts in real time, the Trace creates a running picture of the system's health that enables early intervention. The governance model shifts from reactive threshold monitoring to proactive trajectory analysis --- flagging concerning directions before they become critical values.GAP E | The Recursive System Orientation Gap RESOLVEDWhat Was FoundThe Human-AI Integration Architect enters the role with a linear implementation mental model and encounters a bilateral co-evolution system. The user is simultaneously learning and training a model that is learning and adapting from the user. The gap between a linear deployment mental model and a recursive co-evolution reality is significant enough to cause early disorientation and role abandonment.Research Grounding (Added Version 3.0)Saßmannshausen & Wagener (2026) establish that LLM behavior "often feels discovered rather than engineered" --- an empirical description of the recursive reality Gap E addresses. Their seven propositions for adaptive mental model development, particularly P1 (cognitive scaffolding) and P7 (duration-optimized integration), directly inform the Bilateral Loop Briefing's content. The Triadic Framework's Metacognitive Layer --- emphasizing that anthropomorphic misconceptions about AI co-evolution are the primary source of mental model failure --- provides the theoretical foundation for why the briefing must precede all other Architect training.Realignment: The Bilateral Loop BriefingA structured orientation protocol delivered before the Architect's first session with the system. Not a manual --- a facilitated entry experience that surfaces the Architect's current mental model of AI governance, identifies where that model is linear, and reorients it toward the recursive reality of EthAi Syn before the gap has a chance to cause damage. The Bilateral Loop Briefing covers three things: the nature of the co-evolution loop itself, the user's authority over initiation, and the difference between governing outputs and governing the relationship.Why This Is Non-NegotiableEvery other gap in this audit could theoretically be discovered and recovered from mid-deployment. Gap E cannot. An Architect operating from a linear mental model inside a recursive system will make governance decisions that actively harm the loop they are responsible for protecting.Sixth Finding: The Measurement FrontierWhat the Field Cannot Yet ProveThis is not a gap in EthAi Syn. It is the framework doing something most frameworks avoid: naming the boundary of what it can currently prove, and calling for the work required to push that boundary forward.The framework explicitly states that some of the most important outcomes in AI collaboration --- overreliance, shallow evaluation, moral diffusion, and cognitive fatigue --- are measurable only imperfectly with current instruments. It calls for future work to develop validated instruments for mental model gap detection and to study how judgment gates affect trust calibration, performance, and human learning over time.Strategic SignificanceThe measurement gap is the same open problem named publicly in the framework's accompanying LinkedIn thought leadership. The framework that identifies the problem and the researcher calling for its solution are the same person. That is not a coincidence to be managed. It is a positioning asset to be claimed explicitly.Constructs Currently Lacking Validated Instruments Mental model gap magnitude and severity across AI deployment contexts Judgment displacement rate over time in naturalistic professional workflows Trust calibration accuracy across different AI contribution types and case complexities Cognitive load distribution across workflow stages in high-volume environments Moral diffusion indicators in team AI use and collaborative decision-making Deskilling onset patterns in high-reliance environments across expertise levels Override rate as a proxy for healthy human-AI complementarity across domains The Research MandateEthAi Syn formally calls for the development of mixed-method evaluation designs combining behavioral data, workflow telemetry, and qualitative user evidence. Future empirical work should test the framework in healthcare administration, enterprise platforms, and AI-supported knowledge work. Comparative studies of audited versus non-audited workflows would establish baseline evidence for the framework's impact. Longitudinal studies of judgment gate use would reveal how structured human decision points affect both performance and capability development over time.This is the work that turns EthAi Syn from a governance framework into a research program. It is the work most directly aligned with establishing intellectual authority at the intersection of I/O psychology and AI, and it is the work the field has not yet treated as non-negotiable.Seventh Finding: The Research FoundationWhat the Evidence Base Now ProvesVersion 3.0 integrates eight peer-reviewed sources published between 2023 and 2026. Together they do not merely support EthAiSyn's claims --- they establish the specific field-level gaps that EthAiSyn is positioned to fill.Source Key Finding EthAiSyn ImplicationWischnewski et al., 2023 (CHI) 30 years, 96 studies, zero field studies The gap EthAiSyn fills is documented at the field levelTennakoon et al., 2025 (JAI) Adaptive explainability: 16% error detection gain, no time cost Override quality is measurable and improvable through designLee et al., 2025 (PNAS Nexus) Metacognitive sensitivity is the mechanism of optimal joint decisions Confidence without calibration is worse than no confidenceBaHammam, 2025 (PMC) Disclosure is institutionally punished; strategic non-disclosure follows Governance architecture must not depend on voluntary self-reportMorris, 2025 (AI in Eye Care) Human clinical judgment is equally opaque and unaudited "The problem is not new with AI --- it is newly visible"Saßmannshausen & Wagener, 2026 (Qeios) Jagged intelligence + System 0 + metacognitive literacy Three-layer framework maps exactly onto EthAiSyn's architectureKelly et al., 2025 (JMIR HF) First field study: trust is sequential, contextual, conditional Clinician trust forms exactly as EthAiSyn predicted --- in stages, not staticallyStrudwick et al., 2025 (JMIR MH) "Intentional infrastructure, not just technology" required The gap EthAiSyn fills named as the field's most urgent unmet needThe Woebot Case StudyIn July 2025, Woebot --- the most prominent AI therapy chatbot in history --- shut down. The shutdown was not driven by technical failure. The technology worked. What failed was the integration architecture: unresolved accountability structures, undefined scope-of-practice boundaries, and the limits of AI in high-stakes human relationships were never designed for from the beginning.This is the most current real-world evidence for EthAiSyn's core argument. The question was never whether the AI was capable. The question was whether the organizational and ethical infrastructure around the AI was adequate to sustain it responsibly at scale. It was not. EthAiSyn is that infrastructure.The Woebot Positioning StatementEthAiSyn does not build the AI. It designs the conditions under which humans can use AI safely, maintain appropriate trust, preserve their independent judgment, and remain genuine moral agents for the outcomes their AI-assisted work produces. The Woebot shutdown is the case study that proves why this infrastructure is not optional.Audit SummaryWhere EthAi Syn Stands NowEthAi Syn entered this audit as a framework with strong conceptual foundations and five structural gaps that would have surfaced under enterprise scrutiny. It exits with a complete realignment architecture built entirely from within its own design principles, a formally named research mandate, a new organizational role it generates in every enterprise deployment, and an eight-source peer-reviewed evidence base that validates the framework's core claims and documents the field-level gaps it is positioned to fill.# Gap Realignment StatusA Temporal Value Gap Early Proof Point Checklist + Research Reframe ResolvedB Concealed Decision Pathway Intent Signal + Transparent Decision Layer ResolvedC Undefined Autonomy Threshold Moral Understanding Indicator + Autonomy Invitation ResolvedD Reactive Notification Model Decision Trace (Proactive Trajectory Analysis) ResolvedE Recursive System Orientation Gap Bilateral Loop Briefing ResolvedF Measurement Frontier Formal Research Mandate ActiveG Research Foundation 8-Source Peer-Reviewed Evidence Base IntegratedThe realignments documented here are not additions to EthAi Syn. They are expressions of what the framework was already designed to do, made explicit enough to survive scrutiny. The Measurement Frontier is not a limitation. It is the framework's most honest and strategically significant contribution to the field.EthAi Syn | Psychological Audit Report | Version 3.0 | March 2026 | Confidential

Xin Zhang, Fan Liang

Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning.

Anthony Coslett

Structural identity — the geometric fingerprint that makes a neural network this specific model rather than any other — can be measured, survives routine deformation, resists adversarial erasure, and composes with standard verification infrastructure. It cannot, in the tested regime, be recovered from endpoint weight statistics or architecture descriptors alone. These two facts together force a question the measurement program has not yet answered: if identity is real but not readable from the final artifact, then where in the training process did it form, and what determined which identity formed rather than another? This paper presents the first empirical study of structural identity formation during neural network pretraining. Using dense checkpoint trajectories and seed-controlled training runs in the Pythia observatory suite, we show three results. First, the structural observable follows a characteristic three-phase identity emergence profile — an early rise in geometric spread, a long compression, and a late plateau where identity stabilizes while functional training continues. Second, models trained with the same architecture, the same data, and the same hyperparameters but different random seeds produce structurally distinguishable fingerprints far beyond measurement noise — a property we call path sensitivity — with the divergence traceable to differential structural response during the learning-rate warmup regime. Third, a panel of endpoint weight statistics varies across seeds but does not predict which structural identity formed — a condition we call endpoint underdetermination. Together, these results recast structural identity as a developmental property of training history rather than a static property legible from final artifacts alone. Supplementary Material This paper is accompanied by HistoricalIdentity.v, a Coq proof file that formalizes two consequences of the formation data described in §§3–5: trajectory non-recovery (no decision procedure restricted to the tested endpoint summary panel can be both sound and complete for claims about the formative training-history class that produced a model's structural identity) and lock boundary source exclusion (if structural divergence between two specification-identical models is already present at the lock boundary, no intervention applied after that boundary can be its source). The file contains 4 empirical axioms grounded in the measurements of §§3–5, 4 theorems, 1 corollary, and 0 unresolved obligations (Admitted). It compiles cleanly under the Rocq Prover 9.1.1 (the current release of the Coq proof assistant, compiled with OCaml 5.4.0). It is available for download as a supplementary file attached to this record. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).

T.VISHNUPRIYA, Mr.P. VISWANATHA REDDY, Mr.P. CHANDRA SEKHAR

Modern financial security issues have arisen as a result of the rapid proliferation of decentralized financial systems and cryptocurrencies. One such issue is the identification of individuals who are laundering money in blockchain networks. Blockchain technology's distributed ledgers clarify matters; however, the anonymity of wallet addresses facilitates illicit financial transactions by criminals. It is crucial to have effective methods to identify these crimes, as over $82 billion in cryptocurrencies were associated with money laundering in 2025. This study demonstrates a method for detecting indications of money laundering in blockchain transaction networks through the use of machine learning. The proposed method for identifying unusual patterns in transactions involves the combination of supervised machine learning, graph-based feature extraction, and data cleansing. The system examines transaction graphs to identify unusual patterns that are associated with illicit financial activities by employing techniques such as Random Forest, Gradient Boosting, and Graph Neural Networks.

Vaishnavi K, Santhiya S, Ashvitha S, Anusha D

Traditional selling systems often limit products to local markets and rely heavily on intermediaries, resulting in reduced profit margins, inconsistent quality, and limited market reach. Maintaining consistent quality and ensuring market transparency remain significant challenges in these legacy frameworks. To address these issues, this project proposes a secure and efficient Double Auction System for multi-category product trading. To enhance security, privacy, and trust, the project integrates advanced cryptographic mechanisms. zk-SNARKs (Zero-Knowledge Succinct Non- Interactive Arguments of Knowledge) are employed for sealed bidding, ensuring that both bidder identities and bid values remain hidden while maintaining mathematical verifiability. Conversely, Linked Ring Signatures are used for open bidding, allowing bid values to remain transparent while masking the identities of the bidders. A Commit-Reveal Scheme is implemented to prevent bid manipulation and ensure fairness during the submission phase. Additionally, a Reputation Score Algorithm incentivizes honest participation by rewarding users with a trust score based on their historical behavior. Finally, Blockchain technology is integrated via a private blockchain to record all auction data and reports in an immutable and tamper-proof manner. This multi-layered approach ensures a fair, secure, and sustainable trading ecosystem, benefiting both producers and buyers across diverse sectors.

Natalya Yurievna Amurova

Smart grids are a modern model for developing electric power infrastructure based on the integration of information and communication technologies and intelligent control systems. These networks enable the creation of a highly efficient, reliable, and adaptive energy environment capable of quickly responding to changes in electricity generation and consumption patterns. Key principles of a smart grid include adaptive load management, two-way data exchange between power system elements, the integration of distributed energy resources, and the use of modern digital technologies, including the Internet of Things, artificial intelligence, and distributed ledger technologies. The implementation of smart grids optimizes the generation, transmission, and distribution of electricity, improves the reliability and sustainability of the power system, and develops effective consumer interaction mechanisms based on intelligent energy management and dynamic pricing.

Guoxin Huang, Min Wang, Sihan Li, Y Chen

Smart contract vulnerability detection has gained increasing attention due to growing financial losses from hacker attacks. Existing deep learning methods either rely on a single feature type or lack effective interaction among heterogeneous features, limiting vulnerability representation. To address this, we propose neighborhood constrained cross attention. It uses the control-flow graph’s k-hop neighborhood as a structural prior to restrict bidirectional interactions between graph features and sequence features to local regions likely associated with the same execution logic, thereby reducing noise from global attention. Self-attention is further applied within each branch to model long-range dependencies. Experiments show that NCCA-Det achieves accuracies of 94.87%, 92.62%, and 92.94% on three common vulnerability types, significantly outperforming comparative methods, and thus offers a complementary solution for bytecode-level smart contract vulnerability detection.

Ozan Nadirgil

No abstract is available for this record.

Jiahao Pei, Ning Duan, Gang Du, Kejia Zhang · 5 authors

Smart contracts are self-executing programs running on blockchain networks. Once deployed, they are immutable, making their security critically important. Reentrancy vulnerability is one of the most notorious security vulnerabilities in smart contracts, which allows attackers to repeatedly invoke target functions before the execution of contract functions is completed, thereby stealing funds or corrupting contract states, resulting in severe economic losses in recent years. Existing detection tools often suffer from insufficient path coverage and oversimplified detection rules. This paper proposes a static analysis approach based on smart contract bytecode that recovers execution paths by constructing a control flow graph (CFG), identifies all potential vulnerability paths using taint analysis, and detects reentrancy vulnerabilities through path matching rules. To validate the approach’s effectiveness, we compare it with mainstream detection tools on an annotated smart contract dataset. Experimental results demonstrate that the approach achieves a precision of 93.2%, outperforming other tools overall. Additionally, through analysis of 2023 real-world smart contracts deployed on Ethereum, 21 contracts are found to contain reentrancy vulnerabilities.

Anna Tatarczak, Oleksandra Humeniuk

This study examines short-term return forecasting for Bitcoin, Ethereum, and Litecoin over 2020–2024, comparing autoregressive benchmarks with Kitchen Sink and VARX-type models using point and density accuracy measures supported by Diebold–Mariano and Model Confidence Set inference. The results demonstrate that the AR(1) benchmark and parsimonious specifications incorporating cryptocurrency-specific variables consistently outperform the more elaborate linear frameworks considered, while the inclusion of macro-financial predictors offers limited benefits. Findings highlight the robustness of autoregressive dynamics for short-term cryptocurrency forecasting and underscore the importance of parsimony over model complexity. These results are consistent with a market environment characterised by high structural uncertainty, sentiment-driven trading and rapidly shifting regimes, in which additional macro-financial information contributes little to forecastability beyond short-run return momentum and crypto-specific volatility.

Ntebogang Dinah Moroke

This paper develops a deep reinforcement learning (DRL) framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation (PPO) agent is trained on a reward function derived from non-equilibrium thermodynamics: the free-energy Bellman equation, in which (i) transaction costs are the geodesic slippage S∗ on the Fisher information manifold of a maximum-entropy Markov-switching GARCH model, and (ii) regime-transition costs are the Wasserstein-2 distance Wt between the calm and turbulent return distributions. The agent is embedded in the WOW-E-W quadrilogy, a four-paper research programme that integrates statistical mechanics, fluid dynamics, Riemannian information geometry, and thermodynamic control into a unified cryptocurrency risk architecture. The PPO agent observes an 11-dimensional state vector ot that combines turbulent-regime probabilities \( \hat{\xi}_t(2) \) and parameter estimates \( \hat{\theta}_t \) from a maximum-entropy Markov-switching GARCH model, a viscosity-filtered velocity signal ht and gate states zt, rt from a GRU viscosity filter, and the Fisher curvature Gt, Ricci scalar κt, Betti numbers β0,t, β1,t,Wasserstein dissipation Wt, and topological alarm dI(t) from the Riemannian execution geometry layer. The framework establishes a thermodynamic Carnot bound on portfolio efficiency: η ≤ 1 − Hturb/Hcalm, where Hturb and Hcalm are the maximum-entropy values of the turbulent and calm regime distributions. Five hypotheses are tested across Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash over January 2017 to March 2026: the geometric-cost PPO agent achieves higher Sharpe ratio than Buy-and-Hold, Greedy signal-following, and flat-fee PPO baselines (bootstrap p < 0.05 for four of five assets); portfolio turnover is reduced by 56 to 83 percent relative to signal-following; the thermodynamic friction point at which the agent prefers no-trade is asset-specific and ranges from 0.6 percent (Bitcoin) to 1.8 percent (Ethereum), ordered by turbulent half-life (Spearman ρ = 0.94, p = 0.017); a joint topological and geometric circuit breaker reduces Maximum Drawdown by 28 to 38 percent; and ablation confirms that every component of ot contributes a statistically significant performance gain (Diebold-Mariano p < 0.05 for at least four of five assets per component). The framework requires liquid cryptocurrency markets with validated parametric volatility models; transferability to other asset classes requires upstream recalibration and is an explicitly bounded limitation.

Satoshi Kawauchi

A six-part study proposing a deterministic computing architecture based on Quantum Thought Circuit OS ASI. It integrates heterogeneous self-optimizing hardware, energy-circulating communication, hardware-rooted trust, adaptive inference control, distributed infrastructure, and heterogeneous TEE confidential computing to improve efficiency, resilience, compliance, security, and scalability.

Minela Nuhić-Mešković, M. Kabir Hassan, Admir Mešković

Purpose This study aims to systematically synthesize academic literature on Islamic FinTech published prior to 2025 to identify prevailing themes, regional and methodological trends and unresolved research gaps. Design/methodology/approach A systematic literature review (SLR) was conducted following the PRISMA 2020 protocol to ensure transparency and replicability. A total of 162 peer-reviewed journal articles were identified from Scopus and Web of Science databases using defined keywords. Bibliometric mapping (via VOSviewer), qualitative coding and descriptive statistics were used to identify major themes, methodological patterns and research gaps. Findings The review reveals a rapid increase in Islamic FinTech scholarship, particularly after 2020, with Southeast Asia dominating the output. Five major thematic clusters emerge: digital transformation, technology adoption, Shariah compliance, decentralized finance and Islamic social finance. Research limitations/implications Findings point to the importance of more diversified methodologies, cross-regional studies, harmonized Shariah standards and inclusive digital financial solutions. Practical implications The findings suggest that effective adoption of FinTech can enhance cost efficiency, operational scalability and product diversification for Islamic financial institutions. Social implications Islamic FinTech can widen social inclusion, improve transparency and support social goals. To unlock that potential, the study needs shared Shariah and regulatory standards, user-centred design and pilot projects that measure outcomes. Originality/value To the best of the authors’ knowledge, this is the first comprehensive SLR of Islamic FinTech integrating Scopus and Web of Science sources within the PRISMA 2020 framework, providing a consolidated foundation for future empirical, theoretical and policy research.

Jovita T. Nsoh

The Fifth Industrial Revolution (Industry 5.0) foregrounds human–machine collaboration, sustainability, and resilience as organizing principles for next-generation cyber-physical systems. Yet the identity and access management (IAM) architectures inherited from Industry 4.0 remain perimeter-centric, policy-static, and blind to the behavioral dynamics of human–AI teaming. This paper introduces the Human-Centric Zero Trust Identity Architecture (HC-ZTIA), a novel framework that repositions identity as the adaptive control plane for Industry 5.0 environments. HC-ZTIA integrates three mutually reinforcing innovations: (1) a Joint Embedding Predictive Architecture (JEPA)-driven Behavioral Identity Assurance Engine (BIAE) that learns abstract world models of operator and machine-agent behavior to perform continuous, context-aware identity verification without relying on raw biometric surveillance; (2) a Privacy-Preserving Adaptive Authorization Protocol (PP-AAP) employing zero-knowledge proofs and federated policy evaluation to enforce least-privilege access across human, non-human, and hybrid identity classes while satisfying data-minimization mandates; and (3) a Resilience-Oriented Trust Degradation Model (RO-TDM) that guarantees fail-safe identity governance under adversarial, degraded, or disconnected operating conditions characteristic of operational technology (OT) and critical infrastructure. The framework is grounded in the Agile-Infused Design Science Research Methodology (A-DSRM) and formally extends NIST SP 800-207 and the CISA Zero Trust Maturity Model by addressing five identified gaps in human-centric identity governance. We present the formal system model, threat model, architectural specification, and a multi-scenario evaluation spanning energy-sector OT, smart manufacturing, and vehicle-to-everything (V2X) environments. Simulation results, validated through Monte Carlo trials with 95% confidence intervals, demonstrate that HC-ZTIA reduces identity-related breach exposure by 73.2% (±4.1%) while maintaining sub-200 ms authorization latency, offering a principled bridge between Zero Trust rigor and Industry 5.0 human-centricity.

J. Sravanthi, Pinninti Abhinav, Poosa Nagaraju, Pulla Nikhitha · 5 authors

The rapid evolution of cloud computing has revolutionized digital data storage and sharing, enabling users to access information anytime and anywhere. Despite these advantages, cloud-based systems face major challenges related to data security, privacy protection, and trust management, particularly when handling sensitive user information. Conventional cloud storage solutions operate on centralized architectures, where a single cloud service provider manages and controls the stored data. This centralized model introduces significant risks, including single points of failure, unauthorized data access, data manipulation, and limited visibility into data-sharing activities. In many traditional systems, data protection mechanisms rely on basic encryption methods without strong auditing or verification features, leaving them vulnerable to insider attacks and external cyber threats. Furthermore, the absence of immutable transaction records and robust key management practices reduces accountability and weakens user confidence in cloud environments. To address these shortcomings, the proposed system presents a secure cloud data sharing framework that combines Elliptic Curve Cryptography (ECC) with blockchain technology. In this approach, user files are encrypted using ECC before being uploaded to the cloud, ensuring strong data confidentiality and protection against unauthorized access. Simultaneously, blockchain technology is employed to record file metadata and transaction details in a decentralized and tamper-resistant ledger, enabling transparent and verifiable audit trails. The decentralized architecture eliminates reliance on a single authority, enhances trust, and prevents unauthorized modification of stored records. Additionally, secure authentication and controlled access mechanisms further reinforce system security. By integrating advanced cryptographic encryption with decentralized verification, the proposed solution enhances data integrity, improves transparency, and establishes a reliable and accountable framework for secure cloud data sharing.

Harish Karthikeyan, Antigoni Polychroniadou

Privacy-preserving aggregation is a cornerstone for AI systems that learn from distributed data without exposing individual records, especially in federated learning and telemetry. Existing two-server protocols (e.g., Prio and successors) set a practical baseline by validating inputs while preventing any single party from learning users' values, but they impose symmetric costs on both servers and communication that scales with the per-client input dimension $L$. Modern learning tasks routinely involve dimensionalities $L$ in the tens to hundreds of millions of model parameters. We present TAPAS, a two-server asymmetric private aggregation scheme that addresses these limitations along four dimensions: (i) no trusted setup or preprocessing, (ii) server-side communication that is independent of $L$ (iii) post-quantum security based solely on standard lattice assumptions (LWE, SIS), and (iv) stronger robustness with identifiable abort and full malicious security for the servers. A key design choice is intentional asymmetry: one server bears the $O(L)$ aggregation and verification work, while the other operates as a lightweight facilitator with computation independent of $L$. This reduces total cost, enables the secondary server to run on commodity hardware, and strengthens the non-collusion assumption of the servers. One of our main contributions is a suite of new and efficient lattice-based zero-knowledge proofs; to our knowledge, we are the first to establish privacy and correctness with identifiable abort in the two-server setting.

Krzysztof Numpsa

We propose a revolutionary shift in the utility of Non-Fungible Tokens (NFTs), transitioning from static digital assets to "Dynamic Logic Seeds" (DLS). By leveraging the Coherence Tensor () and fractal memory architectures, these assets act as frequency-based keys that trigger recursive computational expansions. Through a dual-blockchain system (Low-Frequency/High-Frequency), we demonstrate a method for preserving infinite logical versions across spacetime fluctuations at the Planck scale.

International Journal of Technology, Leadership and Sciences

Food and agriculture supply chain transparency is growing in importance for both consumers and states. The fast expansion of blockchain technology's use is being propelled by its inherent trustworthiness and immutability. This technology can offer safe traceability for the management of the agri-food chain, prevent food fraud, and provide information like a food product's provenance. It is far more difficult than in other businesses to create smart contracts that are suitable for certain use cases. Although many agri-food chain management systems based on smart contracts and blockchain have been developed, they are all quite ad hoc and not easily adaptable to different products or production processes. A new method for quickly adapting and developing universal smart contracts for the agri-food business based on Ethereum is presented in this research. We can automate the process and reuse modules and code using this strategy, which shortens development times without sacrificing dependability and safety. In order to set up a semi-automatic system, we want to start with the production process and build the smart contracts that control the system and the user interfaces that automatically connect with them. To further illustrate how our method works, we provide a case research on honey production. The primary goal of future studies will be to find ways to apply the method to different types of supply chains. Even though Ethereum is now in use, our technology can be simply adapted to other blockchain systems.

Mingyue Xie

Facing two challenges in distributed power trade, that is, blockchain throughput bottleneck problem and transaction privacy leakage problem, this paper designs an efficient consensus mechanism based on dynamic clustering and zero knowledge proof. Specifically, the network is firstly divided into multiple consensus groups in parallel by multi-dimensional feature dynamic clustering algorithm, which improves the communication topology; then, zero knowledge proofs are applied into consensus process, and groups can generate globally verified proofs for all transactions in certain period without leaking any information; finally, a hierarchical hybrid consensus architecture is designed to achieve fast local sorting and efficient global confirmation. The experimental results show that the mechanism can achieve 2150 TPS with 300 nodes, and the success rate of privacy attack is lower than 10 %. Meanwhile, the mechanism still maintains high stability under dynamic perturbation. The research proves that the mechanism can effectively solve the core requirements of efficiency and privacy in distributed energy trade.

Ruixue Xu, Sijia Lian, Suhua Liu, Yuanjie Zhu

Aiming at the core pain points such as low execution efficiency, high resource consumption, and insufficient dynamic adaptability caused by the "deploy-and-freeze" characteristic of traditional blockchain smart contracts, this paper proposes a dynamic execution optimization scheme for smart contracts based on the Group Relative Policy Optimization (GRPO) algorithm [1]. Specifically, the "group sampling + relative advantage" mechanism, which is the core of the GRPO algorithm, is implemented through two key modules: for the group sampling module, the algorithm first divides the smart contract execution state space into multiple sub-scenarios based on key feature dimensions such as transaction type, data volume, and network congestion degree, then randomly selects 3–5 candidate execution actions from each sub-scenario and forms a candidate action group by fusing actions from different sub-scenarios; for the relative advantage calculation module, instead of adopting the absolute advantage evaluation method of the traditional Proximal Policy Optimization (PPO) algorithm [2], it introduces a relative advantage function that takes the average execution effect of the candidate action group as the reference benchmark, quantifies the advantage of each candidate action relative to other actions in the group through indicators such as Gas cost saving rate, execution delay reduction rate, and task completion rate, and weights the relative advantage values to determine the optimal execution action. With the GRPO reinforcement learning algorithm as the core driving force, the scheme constructs a three-layer collaborative architecture consisting of an off-chain Artificial Intelligence (AI) decision-making layer, an oracle data layer, and an on-chain contract execution layer. Through real-time state perception, group sampling action generation, and scenario-based reward function design, it realizes the dynamic adaptive adjustment of smart contract execution strategies. Experimental results show that in typical application scenarios such as Decentralized Finance (DeFi) lending and supply chain finance, compared with traditional static contracts and optimization schemes based on the mainstream PPO algorithm, this scheme can reduce the average Gas fee by 22%~25%, lower the non-performing loan rate from 3.2% to 1.1%, and control the execution response delay within 500ms, significantly improving the execution efficiency, resource utilization, and dynamic adaptation capability of smart contracts. This research provides a new technical path for solving the problem of dynamic execution of smart contracts and has important theoretical and practical significance for promoting the efficient and trusted operation of the Web3 ecosystem.

Tivlumun Ge, Joel Aondofa Udoji, Isaac Adom

Academic certificate verification in many institutions is still carried out using centralized and manual systems, which are prone to forgery, data manipulation, high administrative costs, and delays in verification. These challenges reduce the reliability and efficiency of credential validation. This research work focuses on the development of a Decentralized Certificate Verification System (DCVS) that improves security, transparency, and trust in academic credential verification. The proposed system uses blockchain technology to represent academic certificates as Non-Fungible Tokens (NFTs) based on the ERC-721 standard deployed on the Polygon blockchain. Certificate documents and metadata are stored off-chain using the Interplanetary File System (IPFS), while cryptographic references are recorded on the blockchain to ensure data integrity and prevent tampering. The system adopts Self-Sovereign Identity (SSI) principles, allowing students to own and share their credentials while enabling employers and institutions to verify certificates without relying on a central authority. Evaluation of the system on the Polygon test network showed a reliable result with a minting confirmation time of 3.2 seconds, less than the 10-second standard benchmark, verification latency of 0.8 seconds, less than the 2-second benchmark, and a transaction cost of 0.00021 MATIC, less than the 0.01 MATIC standard cost. The study demonstrates that blockchain-based solutions can effectively address the challenges of traditional academic certificate verification systems.

Andrew Kim, Jarrett Bobrin, David Weinstein, Isabelle Kim

Non-fungible Tokens (NFTs) in Diagnostic ImagingAndrew Kim1, Jarrett Bobrin1, David Weinstein1, Isabelle G. Kim.Temple University Hospital1, Department of Radiology, Philadelphia, PA.Non-fungible tokens (NFTs) have garnered significant media attention in recent years, largely due to the astronomical prices fetched by some digital artworks. They have emerged as a popular medium for buying and selling digital art. Most people associate NFTs with high-profile examples such as the Bored Ape Yacht Club or Beeple’s digital artwork, the latter of which famously sold for over $69 million. Even the world’s first SMS text message was converted into an NFT and sold for over 100,000 euros. In 2021, the NFT market was valued at approximately $41 billion USD, and the term “NFT” ranked among the most popular search terms on Google during both 2021 and early 2022.However, NFTs are more than just digital collectibles; they hold significant untapped potential, particularly in the medical field, including diagnostic imaging. While blockchain technology has been widely explored in healthcare, the specific role of NFTs in diagnostic imaging remains largely unexplored. Although there has been extensive discussion on the use of blockchain in medicine, the application of NFTs in this space is still in its infancy.So, what exactly is an NFT? A non-fungible token is a unique digital asset representing ownership of a specific item or piece of data—whether that be digital artwork, music, or in more recent applications, items in video games or medical records. NFTs are built using the same blockchain technology as cryptocurrencies like Ethereum. However, unlike cryptocurrencies or fiat currencies, NFTs are non-fungible, meaning they are not interchangeable, and each holds a distinct value. Both NFTs and cryptocurrencies rely on blockchain transactions to validate authenticity and ownership. NFTs serve as a digital certificate of ownership, and each time an NFT changes hands, the transaction is recorded on the blockchain decentralized, public ledger.NFTs also incorporate smart contract technology, which is particularly relevant to the field of medicine. For instance, in the art world, the original artist may receive royalty every time their artwork is resold. This same mechanism can be applied to healthcare data, offering both security and potential financial benefits to patients.In the U.S., it is estimated that each patient generates approximately 80 megabytes of health data annually. Utilizing NFTs to manage medical data would allow patients to enhance the confidentiality of their personal health information. Through smart contracts, patients could control and define who has access to their data—whether it’s their primary care physician, an emergency room doctor, a radiologist, or a specialist at a cancer center. Once recorded on a public, decentralized blockchain, this data becomes immutable and highly secure, preventing tampering or unauthorized access.This model empowers patients and shifts control away from commercial or nonprofit institutions that often manage and monetize patient data without individual input. As Dr. Kristin Kostick-Quenet has pointed out, once health information is digitized, it typically falls out of the patient’s control and is commodified by companies for profit. NFTs offer a solution: patients could maintain ownership over their data and even receive financial compensation when it is accessed or utilized.The digital contracts associated with NFTs also allow patients to trace the use of their data—who accessed it, when, how, and why. According to an article from Cointelegraph, the healthcare platform Aimedis plans to tokenize anonymized patient data into NFTs, which can then be sold to pharmaceutical companies. In return, patients may receive revenue from the sales of these NFT tokens. However, a key challenge remains, healthcare IT systems are currently fragmented and not yet optimized for this level of integration. In an ideal future, patients would use a single login interface to manage all their health data.Importantly, NFTs can enhance the quality and accessibility of medical data, making it more suitable for artificial intelligence applications and data mining. Aimedis aims to revolutionize global exchange and monetize de-identified health data using blockchain and NFT technologies.NFTs also have direct applications in radiology. Patients could predefine which radiologists or physicians can access their imaging studies and reports. They could also track who views their data and under what circumstances. If their imaging is later sold or used by a commercial entity—such as a medical center or pharmaceutical company—for research or drug development, the patient could receive royalty payments each time it is used. For example, if a cancer patient undergoes a PET/CT scan and the resulting data is converted into an NFT, a pharmaceutical company using that data in drug research might owe compensation to the patient.Moreover, NFTs could enhance the information available to radiologists. For example, they could include important historical details, such as previous reactions to gadolinium contrast, a history of renal insufficiency, or retained metal that could affect MRI compatibility. Such centralized and accessible data would aid in ensuring patient safety and improving diagnostic accuracy.With the rise of telemedicine, NFTs could also play a key role in verifying transactions between the physical and digital healthcare environments. For example, a doctor’s prescription or imaging order could be tokenized, eliminating any ambiguity regarding its origin or intent. In radiology, this could clarify whether a referring physician wanted a CT scan with or without contrast or preferred a two-view chest X-ray over a portable study—ultimately improving communication between referring clinicians and radiology departments.Teleradiology images could also be tokenized, giving patients visibility over who has accessed their reports and to whom results were sent. In addition, NFTs could be used to verify the credentials of radiologists, such as medical degrees and certifications. Since this information would be recorded on an immutable blockchain, it would be secure and tamper-proof. This could reduce administrative burdens, such as those placed on radiology file rooms by repeated requests for copies of reports or credentials.Tokenized radiology data may also serve as a valuable audit trail, allowing radiologists to confirm that their reports were viewed and used appropriately by referring clinicians.While numerous challenges remain, including legal considerations, government regulations, and the environmental impact of blockchain technology, NFTs are poised to play a growing role in healthcare. Diagnostic imaging, often at the forefront of technological innovation in medicine, is well positioned to benefit from the adoption of blockchain-based NFT applications.References:Conti, R. (2022, August 16). What is an NFT? non-fungible tokens explained. Forbes. Retrieved August 29, 2022, from https://www.forbes.com/advisor/investing/cryptocurrency/nft-non-fungible-token/Culbertson, N. (2021, August 6). Council post: The Skyrocketing Volume of Healthcare Data Makes Privacy Imperative. Forbes. Retrieved August 29, 2022, from https://www.forbes.com/sites/forbestechcouncil/2021/08/06/the-skyrocketing-volume-of-healthcare-data-makes-privacy-imperative/?sh=327ba8536555Diaz, N. (n.d.). What nfts need to achieve before healthcare implementation. Becker’s Hospital Review. Retrieved August 29, 2022, from https://www.beckershospitalreview.com/healthcare-information-technology/what-nfts-need-to-achieve-before-healthcare-implementation.htmlHarrison, S. (2022, April 13). Some medical ethicists endorse nfts-here’s why. Scientific American. Retrieved August 29, 2022, from https://www.scientificamerican.com/article/some-medical-ethicists-endorse-nfts-heres-why/HHMGlobal, C. T. (2022, April 11). Content team HHMGlobal. HHM Global B2B Online Platform Magazine. Retrieved August 29, 2022, from https://www.hhmglobal.com/knowledge-bank/news/can-nfts-be-repurposed-for-the-healthcare-industryJones, C. (2021, September 13). Why nfts, crypto and blockchain can help e-health thrive. Cointelegraph. Retrieved August 29, 2022, from https://cointelegraph.com/news/why-nfts-crypto-and-blockchain-can-help-e-health-thriveKhatri, N. (2021, December 8). Beyond Trendy Investments: Three applications of nfts in healthcare and Pharma Marketing. PM360. Retrieved August 29, 2022, from https://www.pm360online.com/beyond-trendy-investments-three-applications-of-nfts-in-healthcare-and-pharma-marketing/Kostick-Quenet, K., Mandl, K. D., Minssen, T., Cohen, I. G., Gasser, U., Kohane, I., & McGuire, A. L. (2022). How nfts could transform Health Information Exchange. Science, 375(6580), 500–502. https://doi.org/10.1126/science.abm2004Limited, V. M. P. (n.d.). AIMEDIS announces the NFT Healthcare Platform. Newsfile. Retrieved August 29, 2022, from https://www.newsfilecorp.com/release/103552/Aimedis-Announces-the-NFT-Healthcare-PlatformMcGuire, A. (2022, February 4). Can NFT technology benefit healthcare? in. Retrieved August 29, 2022, from https://healthcare-in-europe.com/en/news/can-nft-technology-benefit-healthcare.htmlShyam Sabat MD, M. B. A. (2021, April 27). Blockchain - promises for academic radiology. LinkedIn. Retrieved August 29, 2022, from https://www.linkedin.com/pulse/blockchain-promises-academic-radiology-shyam-sabat-md-sabat-mdTagliafico AS, Campi C, Bianca B, et al. Blockchain in radiology research and clinical practice: current trends and future directions. La Radiologia Medica. 2022 Apr;127(4):391-397.YouTube. (2021, September 20). How nfts will revolutionize medicine. YouTube. Retrieved August 29, 2022, from https://www.youtube.com/watch?v=TnhmUltTGo

Red Boumghar, Annalisa Riccardi, Cesar Guzman, Shahzad Ameen

No abstract is available for this record.