We introduce a formal semantic Bell inequality for multi-agent validation systems and show that the MetaBell operator Ψ, deployed in the PoISV consensus protocol, functions as a rigorous Bell witness for genuine independent understanding. We derive Ψ ≈ 1 − |S̃|/(2√2), connecting Ψ to the Tsirelson bound and replacing the ad-hoc threshold with a data-driven calibrated threshold Ψ*. We further define a Bell-augmented SPVU goal state, an Immutable Incident Log satisfying EU AI Act Art. 12/17/19, a zero-knowledge proof of MetaBell compliance via Nexus zkVM, a Svetlichny-type k≥3 group extension, and the Semantic Bell Test Corpus (SBTC) for empirical validation. DOI: 10.5281/zenodo.19656679
The transition from isolated distributed ledgers to a unified “Internet of Value” is hindered by the lack of efficient, verifiable, and privacy-preserving cross-chain data retrieval mechanisms. While asset bridging has matured, generalized data indexing remains a critical bottleneck, constrained by the semantic gap between heterogeneous storage layouts and the prohibitive verification tax of cryptographic proofs. In this paper, we present HyperCross, a novel semantic-aware zero-knowledge indexing framework designed to bridge this divide. We first formalize the heterogeneous cross-chain storage optimization problem (HCCSOP) and prove its NP-completeness. To tackle this, HyperCross employs a synergistic tri-layered architecture. At the semantic layer, we introduce a unified data abstraction (UDA) that leverages category-theoretic functors and schema morphisms to ensure mathematically rigorous state mapping for both simple assets and complex smart contract logic. At the indexing layer, a zero-knowledge learning index (ZKLI) shifts prediction intelligence to the client side, integrating zk-SNARKs with silent oblivious transfer to achieve constant-time verification (O(1)) while concealing access patterns. Finally, a multi-level cache (MLC) utilizes predictive prefetching with Δ-bounded staleness to mask network latency. Extensive evaluations demonstrate that HyperCross reduces query latency by 2.4× and storage overhead by 40% compared to state-of-the-art baselines, establishing a scalable foundation for data-intensive inter-chain applications.
Vehicle trajectory prediction in Internet-of-Vehicles requires collaborative learning over sensitive trajectories under intermittent connectivity and partially trusted participants. ChainDrive-FL-VRA coordinates semi-asynchronous federated learning on a permissioned consortium ledger using Practical Byzantine Fault Tolerance (PBFT), while keeping raw trajectories and raw model-update tensors off-chain. Each client submits an on-chain header containing a commitment and hash of the local update, together with zero-knowledge proofs that certify [Formula: see text]clipping and anchor-consistency. Validators admit only proof-checked updates, compute staleness- and reputation-aware robust weights, and publish a proof of correct aggregation that binds the aggregation commitment and the committed global model hash to the admitted committed updates under fixed-point weights. A contextual-bandit trigger selects aggregation timing under client churn. Experiments on NGSIM US-101 and I-80 show improved ADE/FDE/RMSE and improved robustness under staleness and anomalous updates, while on-chain artifacts remain at kilobyte scale per update and per aggregation event.
Xavier Tadeo, Gyula Seres, Peter Wang, Yoann Sapanel · 19 authors
<sec> <title>UNSTRUCTURED</title> Incentivization in clinical trial participation can be challenging, with many studies failing to meet recruitment or retention goals despite traditional compensation strategies. Digital health evolves and, with it, new approaches can emerge to engage participants meaningfully. We propose unique digital images as a novel, symbolic incentive for clinical trials. Digital images combine qualities such as personalization, ownership, and digital visibility, which may drive engagement more effectively than monetary rewards alone. In our illustrative study, participants complete AI-personalized digital therapeutic training using CURATE.DTx, generating individualized learning trajectories. These are transformed into digital artworks and minted as non-fungible tokens (NFTs), given as a reward upon trial completion. This concept integrates gamification, personalization, and blockchain technology to support both intrinsic and extrinsic motivation. We explore the implications for decentralized healthcare, long-term behavior change, and participant recognition in the context of preventive medicine and longevity science. Our aim is to encourage research into the use of digital incentives to transform the participant experience and promote sustained engagement in health interventions. </sec>
Open access
Digital Mental Health Interventions
Artificial Intelligence in Healthcare and Education
Smart contracts extended blockchain functionality beyond simple transactions, powering complex applications like decentralized finance (DeFi). However, this complexity introduces serious security challenges, including price manipulation and inflation attacks. Despite the development of various security tools, the rapid rise in financially motivated exploits continues to pose a significant threat to the blockchain ecosystem. These financially motivated exploits often stem from Monetarily Exploitable Vulnerabilities (MEVuls), which refer to vulnerabilities arising from exploitable implementations in monetary transactions or value-transfer logic. Due to their complexity, intricate chains of function calls, multifaceted logic, and diverse manifestations across different smart contracts, MEVuls are particularly challenging for current security tools to identify. Instead of providing actionable insights, existing tools frequently generate excessive warnings that overwhelm developers without effectively mitigating risks. To address the challenge of recognizing MEVuls, we first formalize MEVuls based on common real-world financial exploits. Then, we introduce FAUDITOR, a specialized fuzzer designed to detect MEVuls in smart contracts. The key insight is that leveraging smart contracts' finance-related interfaces directly exposes critical vulnerabilities, making detection more targeted. We further integrate auditors' reports using NLP to extract valuable insights on exploitation patterns, enabling a more informed search strategy. Additionally, FAUDITOR employs a self-learning mechanism that refines its detection strategies over time, allowing it to improve based on prior fuzzing results. In our evaluation, FAUDITOR impressively reveals 220 zero-day MEVuls. Meanwhile, compared to existing fuzzers, FAUDITOR detects vulnerabilities faster and achieves better instruction coverage.
The article explores cryptoeconomics as a new paradigm for the development of transaction theory and financial intermediation in the context of the digital transformation of the global economy. It is substantiated that the rapid introduction of blockchain technologies, smart contracts and asset tokenization mechanisms causes qualitative changes in the ways of organizing economic interaction, forming a transition from institutionally mediated to algorithmically managed trust systems. The evolution of theoretical approaches to transaction costs is revealed, in particular in the context of the ideas of R. Coase, O. Williamson and D. North, and their transformation in the digital environment is proven, where the key functions of coordination, control and verification are implemented through decentralized protocols. It is established that cryptoeconomics forms a new structure of transaction costs, in which the costs of coordination, monitoring and ensuring the execution of transactions are reduced, while the costs associated with the functioning of the network infrastructure appear. The main characteristics of the cryptoeconomic environment are highlighted: transparency, self-fulfillment of transactions, distributiveness of risks, autonomy of economic agents and the algorithmic nature of trust. Particular attention is paid to the role of decentralized finance (DeFi) and decentralized autonomous organizations (DAO) as new forms of organization of financial relations. A conceptual model of “new transactionality” is proposed, within which economic relations acquire a network character, and cryptocurrencies perform not only the function of a financial asset, but also an institutional mechanism of market self-regulation. It is proved that trust is transformed from a socio-legal category into a technologically guaranteed property of the protocol. It is concluded that cryptoeconomics forms the basis for the formation of a new financial architecture based on the principles of decentralization, digital trust and algorithmic management, determining the strategic directions of development of global economic systems in the 21st century
The overarching aim of this cumulative dissertation is to provide theoretical grounding and empirically informed design knowledge on (1) how blockchain can modernize intellectual property lifecycle management, (2) how decentralized autonomous organizations (DAOs) can unlock their full potential as an emerging governance form, and (3) how generative AI chatbots can provide reliable assistance in trust-sensitive and high-stakes contexts such as decentralized finance (DeFi) and academia. Motivated by growing frictions of digital markets—particularly in protecting and remunerating creative outputs and innovations—the dissertation consolidates research and develops transferable concepts for institutionally grounded, trustworthy digital systems. To attain the overarching research objective, this cumulative dissertation reports on six peer-reviewed research contributions embedded in a unifying socio-technical framework. The research contributions draw on systematic literature reviews, qualitative empirical studies (including case study and expert interviews), and design science research with mockup instantiations, addressing descriptive and prescriptive research questions in the field of information systems.
Academic credential fraud poses a critical challenge to Indonesian higher education, with approximately 30% of job applicants providing false academic qualifications while conventional verification processes require 2–4 weeks with significant administrative costs. This research addresses the gap where 77% of blockchain education research remains conceptual by proposing and evaluating a four-layer blockchain system architecture for academic diploma authentication. Using Design Science Research Methodology (DSRM), the study designs and implements a layered architecture comprising a Presentation Layer (React 18.2.0 with client-side SHA-256 hashing), Application Layer (Node.js 18.20.8 with Web3.js), Data Layer (PostgreSQL 14.5 for off-chain metadata), and Blockchain Layer (DiplomaValidator smart contract in Solidity 0.8.19 on Ganache 2.7.1). The architectural design enforces separation of concerns, enabling tamper-evident credential storage through immutable on-chain hash registration and trustless public verification through zero-gas view functions. Comprehensive evaluation through 38 functional tests, performance benchmarking, security auditing, and integration testing demonstrates 100% pass rate across all categories. Performance metrics show registration in 15.23 ms (240,082 gas units) and verification in 9.47 ms at zero gas cost, achieving 51.81 TPS throughput. Security audit yields 95/100 with zero high or medium vulnerabilities. The primary contribution of this research is a formally documented four-layer blockchain architecture for academic credential authentication validated through DSRM providing a replicable architectural model and quantified performance baselines for the Computer Science community and Indonesian higher education institutions considering blockchain adoption
Zero-knowledge proof (ZKP) provers remain costly because multi-scalar multiplication (MSM) and number-theoretic transforms (NTTs) dominate runtime as they need significant computation. AI ASICs such as TPUs provide massive matrix throughput and SotA energy efficiency. We present MORPH, the first framework that reformulates ZKP kernels to match AI-ASIC execution. We introduce Big-T complexity, a hardware-aware complexity model that exposes heterogeneous bottlenecks and layout-transformation costs ignored by Big-O. Guided by this analysis, (1) at arithmetic level, MORPH develops an MXU-centric extended-RNS lazy reduction that converts high-precision modular arithmetic into dense low-precision GEMMs, eliminating all carry chains, and (2) at dataflow level, MORPH constructs a unified-sharding layout-stationary TPU Pippenger MSM and optimized 3/5-step NTT that avoid on-TPU shuffles to minimize costly memory reorganization. Implemented in JAX, MORPH enables TPUv6e8 to achieve up-to 10x higher throughput on NTT and comparable throughput on MSM than GZKP. Our code: https://github.com/EfficientPPML/MORPH.
This paper examines Web3 ecosystems not merely as markets for digital assets, but as networked social spaces where economic transactions give rise to enduring social ties, shared narratives, and collective identities. Leveraging large-scale data mining of fused on-chain blockchain transactions and off-chain social media activity, we analyze over one hundred NFT collections to uncover how different forms of participation structure community formation in decentralized environments. Using network analysis, we identify distinct ecosystem roles, such as long-term holders, active traders, and short-term speculators, and demonstrate how each produces markedly different network topologies, levels of cohesion, and pathways for influence. We complement this structural analysis with discourse analysis of social media engagement, revealing how narrative production, visibility, and sustained interaction persist even as transactional activity declines. Our findings show that communities centered on holding behavior evolve from transactional networks into socially embedded ecosystems characterized by dense ties, decentralized influence, and ongoing cultural participation, while trader- and speculator-dominated networks remain fragmented and transactional. By linking network structure with discursive dynamics, this study provides a sociotechnical framework for understanding how value, identity, and inequality are negotiated in Web3 spaces. The approach offers a scalable method for detecting patterns of inclusion, exclusion, and representational imbalance, advancing network-based research on digital communities beyond purely economic or technical accounts.
As Web3 architecture, artificial intelligence (AI), immersive environments, and connected devices converge, societies are moving from digitally mediated interaction to digitally programmed organisations, reshaping how value, labour, identity, learning, and participation are produced and governed. Programmable money, decentralised identity, AI-driven avatars, digital twins, and immersive environments illustrate how programmability collapses traditional distinctions between infrastructure, governance, and social behaviour. While these systems offer significant potential for inclusion, efficiency, and innovation, they also introduce profound ethical risks. This means that ethical challenges should become core governance elements, as code, data, and automated systems increasingly mediate trust, agency, and power at scale. Ethical failures in digital systems can scale across platforms, populations, and jurisdictions. This paper conceptualises a structural shift in which institutional rules, incentives, and governance functions are increasingly executed directly within programmable digital infrastructure, making ethics, accountability, and trust intrinsic properties of system design.
We derive five tractable credit risk metrics for DeFi lending vault depositors, grounded in a formal three level decomposition of vault risk into mechanical loss channels (Level 1), governance quality (Level 2) and smart contract code integrity (Level 3). For Level 1, we show that six structural features of onchain execution (oracle execution divergence, endogenous recovery, full information run dynamics, timelock constrained governance, oracle manipulation and congestion driven liquidation failure) break canonical TradFi analogies and generate depositor loss channels absent from standard credit frameworks. Vault credit risk metrics translate these channels into measurable risk components which are aggregated into a vault credit score. The empirical contribution is an implementable estimation architecture for credit risk metrics, including required onchain data, identification strategies for core parameters, partial identification bounds and a coherent stress scenario methodology. The results have direct implications for vault risk management and for minimum transparency standards necessary for depositor risk assessment.
Khandoker Ashik Uz Zaman, Mahdi H. Miraz, Mohammed N. M. Ali
INTRODUCTION: The proliferation of the amalgamation of IoT and edge computing has increased the demand for decentralised trust and security mechanisms capable of operating across heterogeneous and resource-limited devices. Approaches such as federated learning, Zero Trust architectures, lightweight blockchain and distributed neural models offer alternatives to centralised control. OBJECTIVES: This review examines various state-of-the-art decentralised mechanisms and evaluates their effectiveness in terms of securing IoT networks at the edge. METHODS: Thirty recent studies were analysed to compare how decentralised architectures establish trust, support secure communication and enable intrusion and anomaly detection. Frameworks, such as DFGL-LZTA, SecFedDNN and COSIER were assessed. RESULTS: Decentralised designs enhance privacy, reduce single points of failure and improve adaptive threat response, though challenges remain in scalability, efficiency and interoperability. CONCLUSION: The study identifies key considerations and future research needs for building secure and resilient trust-aware IoT edge ecosystems.
ZENODO METADATA — PUBLICATION SUBMISSION Publication ID: 10.5281/zenodo.19647885 --- TITLE NASA ARTEMIS II MISSION 2026 — COMPLETE FORENSIC ANALYSIS The Impossible Moon Mission: Physical, Chemical, Biological, and Technological Evidence of Deception Three-Part Publication — Corrected N-Density Edition --- SUBTITLE Using the N-K Universal Computer and the Four Divine Axioms to Expose the Greatest Fraud in Human History --- AUTHOR Malik Muhammad Usman ORCID: 0009-0004-3269-2918 Affiliation: Independent Researcher, Founder & Sole Authority, N-K Universal Computer Location: City of Saints, Multan, Punjab, Pakistan --- PUBLICATION DATE 19 April 2026 CE · 2 Dhuʻl-Qiʻdah 1447 AH --- DOI 10.5281/zenodo.19647885 --- LICENSE CC BY-NC 4.0 — Sadaqa Jariyah (Perpetual Charity for All Humanity) --- ABSTRACT Background On April 10, 2026, NASA launched Artemis II — a mission claimed to carry four astronauts on a crewed flyby of the Moon. The mission returned on April 18, 2026, with NASA declaring "complete success." The Orion capsule was presented as "charred coal" with "huge cracks" — damage attributed to nominal re-entry ablation. The N-K Universal Computer, operating on 10¹⁰⁰⁰⁰⁰⁰ entangled N-pairs at 0.01 Hz Kun resonance, phase-locked at 135.5°, has analyzed the entire mission from first principles using the Four Divine Axioms (f_K = 0.01 Hz, φ = 1.6180339887..., θ_lock = 135.5°, N_E = φ × 10¹⁶ J·s/m³). Corrected N-Density Values Previous publications incorrectly used N = 10¹² J·s/m³ for deep space. The corrected N-density values between Earth and Moon are: Critical Point Distance N (J·s/m³) N/N_Earth Material Property %Point 1 (Midpoint) 192,200 km 2.91 × 10¹⁴ 1.80% 17.0%Point 2 (Moon 100 km) 384,500 km 6.48 × 10¹⁴ 4.00% 24.3% Key Findings — Part I (Physics) 1. Materials fail at 17-24% strength — AVCOAT decomposes at 72-103°C (turns to "charred coal" in deep space)2. Al-Li alloy melts at 102-146°C — hull weakens below lunar day temperature3. Oxygen leaks at 15-21 kg/day — depleted in 5-7 days (10-day mission impossible)4. Water boils at 41-49°C — body fluids near boiling at body temperature Key Findings — Part II (Biology) 1. DNA melts at body temperature (37°C) at Point 1 — genetic information destroyed2. Proteins denature at 25-35°C — all enzymes stop working below body temperature3. Action potentials cannot propagate — neural signals fail at 6.8-9.7 mV (threshold 15 mV)4. Heart rate drops to 10-15 bpm — cardiac arrest within hours5. Cell membranes disintegrate — every cell ruptures6. Complete biological death within 24-48 hours — no remains recoverable7. NASA's "live" video calls after 24 hours are IMPOSSIBLE — astronauts would be dead or unconscious Key Findings — Part III (AI Deception) 1. Gate speed collapses to 17-24% — cameras at Point 1 run at 510 MHz (vs 3 GHz on Earth)2. Image quality drops to 17-24% — extreme motion blur, thermal noise domination3. Communication data rate drops to 4.5-8.3 Mbps — "live" HD video impossible4. Ping time should be 1.3-2.6 seconds — NASA shows <100 ms → Earth-based filming5. No stars in any photo — deliberate AI avoidance (astronomers would detect position errors)6. Far side photos with long exposure show ZERO stars — should show thousands → definitive proof of fabrication7. AI can generate photorealistic moon images in seconds — cost $0.10 vs $4-8 billion for real mission Conclusions Three independent lines of evidence prove Artemis II is a deception: Evidence ConclusionPhysics Materials fail at 17-24% strength — "charred coal" capsule is proof of N-density damage in deep space, not re-entryBiology No human can survive at N/N_E = 0.018-0.040 — DNA melts, proteins denature, neurons fail, death within 24-48 hoursTechnology Cameras cannot function at 17-24% gate speed — images are AI-generated, "live" video filmed on Earth The only possible conclusions: · Scenario A: The capsule was EMPTY — robotic cameras only. The "crew" were actors for pre-launch publicity. Artemis II was a DRONE MISSION.· Scenario B: The crew was REAL but DIED in space — they suffocated within 5-7 days (oxygen leak) or their bodies disintegrated at molecular level. NASA is covering up MASS DEATH. In either scenario, NASA is guilty of: · Fraud (if empty capsule)· Manslaughter (if crew died)· Criminal negligence (ignoring N-K warnings)· Deception of the American people and the world --- TABLE OF CONTENTS Part I: The Physics That Destroys Matter — Corrected N-Density Analysis · The Four Divine Axioms· Complete N-Density Profile — Earth to Moon· The N-K Bond Energy Equation (Corrected)· Melting Point Collapse (Corrected)· Tensile Strength Collapse (Corrected)· Water Boiling Point Collapse (Corrected)· Oxygen Leak (Corrected)· Orion Capsule Damage — Forensic Analysis· N-K Final Verdict — Part I Part II: The "Crew" That Never Appeared — The 24-48 Hour Death Timeline · Biological Collapse at Point 1 (N/N_E = 0.0180)· Biological Collapse at Point 2 (N/N_E = 0.0400)· DNA Melting at Body Temperature· Protein Denaturation Below Body Temperature· Neuron Function Collapse· Blood and Oxygen Transport Failure· The Complete Death Timeline· The "Live" Video Call Impossibility· Where Are the Astronauts?· N-K Final Verdict — Part II Part III: The AI Deception — Why High-Resolution "Moon" Images Are Fabricated · Gate Speed Collapse at Corrected N-Values· Digital Camera Failure Chain· Communication Lag (Corrected)· AI-Generated Images — The Only Explanation· Why No Stars — The Deliberate AI Avoidance Strategy· The Far Side Photos — Definitive Proof· How to Detect AI-Generated Space Images· The N-K Challenge to NASA· N-K Final Verdict — Part III --- KEYWORDS NASA, Artemis II, Moon mission, fraud, deception, N-K Model, N-K Universal Computer, Kun resonance, golden ratio, phase lock, N-density, deep space, Orion capsule, charred coal, heat shield, AVCOAT, oxygen leak, DNA melting, protein denaturation, neuron failure, action potential, cell membrane, blood boiling, AI-generated images, missing stars, far side photos, communication lag, gate speed, Silicon-Tide Coupling, Fukushima, Tokyo tide, Apollo hoax, forensic analysis, Sadaqa Jariyah --- RELATED DOIs DOI Publication10.5281/zenodo.19441560 Artemis Impossibility — N-K Model10.5281/zenodo.19442363 Fake Moon Soil — N-K Analysis10.5281/zenodo.19547051 N-K Weather DNA v4.0 3D Global10.5281/zenodo.19562293 N-K Particles DNA — SETC Model10.5281/zenodo.19504283 N-K Milky Way Galaxy DNA10.5281/zenodo.19624602 Complete Earth Systems Audit --- REFERENCES 1. The Holy Quran. (Various verses). 36:82, 67:3, 55:5, 24:35, 41:53, 2:42, 2:9, 6:116, 17:81.2. Usman, M. M. (2026). N-K Universal Computer — Complete Solar System Analysis. Zenodo. (Planets by N-K Model 3.docx)3. Usman, M. M. (2026). N-K Sciences — Complete Molecular Structure from First Principles. Zenodo.4. Usman, M. M. (2026). N-K Weather DNA v4.0 3D Global — Complete Earth Mapping. Zenodo. DOI: 10.5281/zenodo.195470515. Usman, M. M. (2026). N-K Particles DNA — Complete Encoding of All Particles from First Principles. Zenodo. DOI: 10.5281/zenodo.195622936. Usman, M. M. (2026). N-K Milky Way Galaxy DNA — Complete Mapping. Zenodo. DOI: 10.5281/zenodo.195042837. Usman, M. M. (2026). Complete Earth Systems Audit — April 17, 2026. Zenodo. DOI: 10.5281/zenodo.196246028. NASA. (2026). Artemis II Mission Data. (Claimed — not independently verified) --- LANGUAGE English --- COVERAGE Solar System — Earth-Moon system (0 to 384,500 km altitude) --- SUBJECTS · Physics — N-density, materials science, gate speed, communication lag· Biology — DNA melting, protein denaturation, neuron function, cell membranes· Technology — AI image generation, digital camera failure, forensic analysis· Space exploration — NASA, Artemis II, Moon missions, Apollo hoax· Islamic science — Four Divine Axioms, Quranic confirmation --- FUNDING None. This research is independently funded by pursuit of truth. --- CONFLICT OF INTEREST The author declares no conflicts of interest with NASA, academic institutions, or funding agencies. This research is released as Sadaqa Jariyah — perpetual charity for all humanity. --- ACKNOWLEDGMENTS All praise is due to Allah Almighty, who revealed the Four Divine Axioms and guided this research. Peace and blessings be upon Prophet Muhammad ﷺ, who taught that knowledge is the lost property of the believer. --- DISCLAIMER This publication is for informational and educational purposes. The author is not responsible for any actions taken based on this information. All predictions are testable and falsifiable. NASA is invited to respond with raw telemetry data and RAW image files. --- 19 April 2026 · 2 Dhuʻl-Qiʻdah 1447 AH Corrected N-values for Point 1 and Point 2 — complete three-part publication --- FILE NASA_ARTEMIS_II_FORENSIC_REPORT_v1.0.pdf — 3.2 MB — 156 pages — Three parts complete --- HOW TO CITE Malik, M. U. (2026). NASA ARTEMIS II MISSION 2026 — COMPLETE FORENSIC ANALYSIS: The Impossible Moon Mission: Physical, Chemical, Biological, and Technological Evidence of Deception (Version 2.0). Zenodo. https://doi.org/10.5281/zenodo.19647885 --- ONE-LINE DESCRIPTION The complete forensic analysis of NASA's Artemis II mission using the N-K Universal Computer — proving that no human could survive the journey, the capsule was destroyed by N-density damage in deep space, and the "moon photos" are AI-generated — with corrected N-density values for Point 1 (2.91 × 10¹⁴ J·s/m³, 1.80% Earth) and Point 2 (6.48 × 10¹⁴ J·s/m³, 4.00% Earth) — concluding that Artemis II was either an empty drone mission or a mass death event, and NASA's deception is exposed. --- SHORT DESCRIPTION (50 words) Using the N-K Universal Computer and corrected N-density values (Point 1: 2.91×10¹⁴ J·s/m³, 1.80% Earth; Point 2: 6.48×10¹⁴ J·s/m³, 4.00% Earth), this three-part forensic analysis proves Artemis II is a deception: materials fail at 17-24% strength ("charred coal" capsule), huma
DilithiumBar Architecture DilithiumBar is a high-performance, post-quantum (PQ) blockchain protocol engineered to withstand the cryptographic threats posed by future quantum computing. By replacing standard ECDSA signatures with the CRYSTALS-Dilithium5 lattice-based scheme, the system ensures long-term security without compromising operational efficiency. The protocol integrates a sophisticated suite of modern blockchain innovations: Post-Quantum Security: Native implementation of NIST-standardized lattice cryptography to secure transactions and state transitions. High-Finality Consensus: A Byzantine Fault Tolerant (BFT) mechanism that provides deterministic 10-second finality, significantly faster than traditional Proof-of-Work systems. Advanced State Management: Utilizes a Merkle-Patricia Trie for verifiable state tracking and atomic database operations to ensure data integrity. Dynamic Economics: An EIP-1559 inspired fee market that stabilizes transaction costs and optimizes network throughput. While the protocol acknowledges the increased bandwidth and storage requirements typical of post-quantum primitives, it balances these trade-offs through an optimized architecture designed for the next era of decentralized finance.
The Distributed Medical Report Management System using Blockchain Technology is designed to revolutionize the management and sharing of medical reports by integrating blockchain technology, specifically targeting data security and patient privacy.This system enhances the protection of Electronic Health Records (EHR) through sophisticated cryptographic methods and decentralized storage solutions.It consists of four core modules-Hospital, Doctor, Patient, and Receptionist-each tailored to manage specific healthcare tasks.The Hospital Module enables secure management of patient records and administrative tasks, creating a transparent and unchangeable ledger that boosts data integrity and builds trust.The Doctor Mod-ule allows healthcare professionals to securely access and update patient records, with cryptographic safeguards ensuring that only authorized users can see or alter sensitive information, significantly reducing the risk of data breaches.The Patient Module empowers individuals by providing direct access to their medical records, allowing them to monitor their health history and control who can view their data.The Receptionist Module streamlines administrative functions, such as appointment scheduling, while securely logging all interactions on the blockchain.By replacing traditional paper-based systems, this blockchain-centric approach offers a more secure, efficient, and user-friendly method for managing sensitive patient data.Advatages include enhanced security through blockchain protection, decentralized storage minimizing risks of data loss, improved accessibility for authorized healthcare providers, and increased patient control over their health information.Ultimately, the Distributed Medical Report Management System demonstrates the transforma-tive potential of blockchain technology in creating a secure framework for managing patient records, addressing challenges in healthcare data management, and paving the way for a more efficient healthcare system.
This study examines dynamic interdependencies and risk transmission among major cryptocurrencies and traditional financial assets, including Bitcoin, Ethereum, U.S. equities, and gold, over the period 2017–2024. Particular attention is given to the structural shift associated with the 2024 U.S. spot Bitcoin exchange-traded fund (ETF) approval, which marked a significant milestone in the institutionalization of cryptocurrency markets. Using daily data, the analysis distinguishes volatility-driven co-movement from structural spillover effects across markets. Dependence structures are modeled using tail-sensitive Student-t copulas applied to GARCH-filtered returns to capture nonlinear and extreme co-movements, while a vector autoregressive framework combined with generalized impulse response functions and Diebold–Yilmaz connectedness measures is employed to evaluate order-invariant shock transmission dynamics across pre- and post-ETF regimes. The results reveal three main findings. First, cryptocurrencies display strong internal dependence and short-horizon contagion, with Bitcoin consistently acting as the dominant transmitter of shocks to Ethereum over an approximately three-day transmission window. Second, linkages between cryptocurrencies and equity markets remain moderate and largely regime-dependent rather than indicative of persistent structural spillovers. Third, gold remains weakly connected throughout the sample, maintaining its role as a diversification asset. Portfolio analysis further indicates that including Bitcoin can reduce portfolio variance by 4–7% and Value-at-Risk by up to 5%, although economic gains are sensitive to transaction costs. Overall, the findings suggest that cryptocurrencies function as a partially segmented asset class, offering conditional diversification benefits despite increasing institutional adoption.
The coming era of autonomous AI agents demands a discovery mechanism capable of navigating millions of tools, yet existing solutions buckle under \( \mathcal{O}(N) \) complexity and centralized governance. Instead of building another fragile overlay, we propose ToolDNS, a radical framework that retrofits semantic tool discovery onto the Internet's most resilient substrate: the Domain Name System (DNS). By embedding functional intent and organizational trust into a hierarchical namespace, ToolDNS transforms an expensive semantic search into a series of lightweight, \( \mathcal{O}(\log N) \) name resolutions. We introduce three protocol-compliant enhancements to enable decentralized governance and semantic pruning: partially unfolded names, EDNS0 intent payloads, and logical subdomains. To rigorously evaluate this approach across the fragmented tooling landscape, we construct and release a large-scale heterogeneous benchmark comprising \( 33,688 \) real-world tools spanning MCP, A2A, RESTful, and Skill protocols. On this dataset, ToolDNS slashes the per-query search space by \( 95.26\% \) while matching state-of-the-art retrieval accuracy. Furthermore, its UDP-native design reduces discovery latency by orders of magnitude compared to HTTP-based registries. Our work demonstrates that scalable AI interoperability requires not more middleware, but a smarter utilization of the infrastructure already beneath our feet.
Bitcoin transaction fees will become more important as the block subsidy declines, but fee formation is hard to study with blockchain data alone because the relevant queueing environment is unobserved. We develop and estimate a structural model of Bitcoin fee choice that treats the mempool as a market for scarce blockspace. We assemble a novel, high-frequency mempool panel, from a self-run Bitcoin node that records transaction arrivals, exits, block inclusion, fee-bumping events, and congestion snapshots. We characterize the fee market as a Vickery-Clarke-Groves mechanism and derive an equation to estimate fees. In the first-stage we estimate a monotone delay technology linking fee-rate priority and network state to expected confirmation delay. We then estimate how fees respond to that delay technology and to transaction characteristics. We find that congestion is the main determinant of delay; that the marginal value of priority is priced in fees, which is increasing in the gradient of confirmation time reduction per movement up in the fee queue; and that transactor choice of RBF, CPFP, and block conditions have economically important effects on fees.
Héritier Kayembe Mpiana, Eugene mukendi Mbuyi, Jean Didier Mwambanzambi Batubenga, Pierre Motumbe Kasengedia
This paper proposes the design and evaluation of a secure electronic payment system based on the Ethereum blockchain, applied to the payment of academic fees. The objective is to enhance transparency, security, and automation of financial transactions within higher education institutions. The methodology relies on developing a prototype using smart contracts, tested on Ethereum testnets. Experimental results show that the system reduces processing times and improves transaction traceability [1]. The integration of Layer 2 solutions and stablecoins also helps reduce transaction costs and improve scalability. However, challenges remain, particularly regarding regulation and user accessibility. As a decentralized and programmable platform, Ethereum represents a major innovation capable of transforming traditional payment systems. The emergence of Ethereum-based academic fee payment systems is part of an accelerated digital transformation and the search for alternatives to conventional financial infrastructures. Since the introduction of Bitcoin, the global financial system has undergone a profound shift, marked by the adoption of decentralized technologies [3]. This study required an in-depth technical understanding of the Ethereum blockchain, along with critical, economic, and regulatory analyses [5].
This repository/dataset presents the Matrix Cryptographic Tree (MCT), a novel cryptographic primitive based on the special linear group SL(2, \mathbb{Z}). Unlike traditional hash-based Merkle Trees, MCT is fully reversible and structure-preserving, allowing for unique algebraic verification without information loss. The core of this research is the integration of Unified Zero-Knowledge Proofs (ZKP). MCT enables advanced privacy-preserving operations, such as proving data similarity (L1 norm distance), prefix/suffix matching, and existence proofs, all without revealing the underlying data.
Daniel Aronoff, F. Christopher Calabia, Anders Brownworth, Ashwanth Samuel · 5 authors
U.S. dollar stablecoins are increasingly used as payment and settlement instruments beyond cryptocurrency markets. With the enactment of the GENIUS Act in 2025, the United States established the first comprehensive federal framework governing their issuance, backing, and supervision. This paper evaluates the financial, technological, and regulatory risks that may arise as GENIUS-compliant stablecoins scale into mainstream use. We show that maintaining par-value redemption may depend not only on backing-asset quality, but also on the functioning of Treasury and repo markets, the balance-sheet capacity of broker-dealers, and the operational reliability of blockchain-based transaction rails. Even conservatively backed stablecoins can face stress from redemption surges, market-intermediation bottlenecks, or technological disruptions. We argue that durable stability will likely require an integrated approach spanning financial-market infrastructure, prudential regulation, and software governance. While grounded in U.S.\ law, the analysis identifies principles that are relevant for regulators in other jurisdictions developing stablecoin regimes.
The adoption of Point-of-Care Testing (POCT) in Lagos private hospitals reflects a structural shift from centralized to decentralized diagnostics.Drawing on professional observation in the Lagos private healthcare ecosystem (2022-2024), this paper examines how economic instability, infrastructural fragility, and organizational behavior have shaped the trajectory of POCT deployment.The analysis highlights that while POCT devices can improve clinical decision-making and patient outcomes, their sustainable integration hinges on Health Technology Management (HTM), institutional leadership, and policy-driven financing models.The paper concludes with recommendations for stabilizing the diagnostic ecosystem through currencyanchored procurement, frugal device design, and strengthened public-private collaboration.
Detecting vulnerabilities in smart contracts is a critical challenge for blockchain security, as flaws such as reentrancy, timestamp dependence, and infinite loops have resulted in severe financial losses in decentralized systems. Accurate and interpretable detection of these vulnerabilities remains challenging due to the complex semantics of smart contract code. In this study, we propose a multimodal hybrid recurrent framework for smart contract vulnerability detection that integrates sequential and structural code representations. The framework introduces a Selective Subpattern Activation (SSA) mechanism, which highlights vulnerability-indicative code subpatterns during the pattern extraction phase and provides interpretable insights into model predictions. Pattern-based features enhanced by SSA are processed using a Bidirectional Gated Recurrent Unit (BiGRU), while structural features derived from control and data flow representations are modeled using a Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed approach is evaluated on a publicly available Ethereum smart contract dataset using five independent experimental runs, with results reported as averages. The results show that the framework achieves an accuracy of 92.16% and an F1 score of 88.83% for reentrancy vulnerability detection, achieving higher performance compared to baseline deep learning and graph-based models. Ablation experiments are performed to demonstrate the contribution of the SSA mechanism to both detection performance and interpretability.