The article examines the role of cryptocurrencies in developing electronic commerce and transforming modern payment infrastructure within the digital economy. Particular attention is paid to the economic nature of cryptocurrencies as innovative financial instruments and their increasing use in online commercial transactions. The study analyzes the key features of applying digital currencies in e-commerce, including decentralization, transaction transparency, the high speed of cross-border payments, and reduced dependence on traditional financial intermediaries. The advantages of cryptocurrency payments over conventional systems are identified, such as lower transaction costs, enhanced security through blockchain technology, and expanded international market access for businesses and consumers. Conversely, the article outlines the main challenges and risks limiting the widespread adoption of cryptocurrencies in electronic commerce. These encompass significant price volatility, technical and infrastructural barriers, cybersecurity threats, and the absence of unified legal regulation across many jurisdictions. Special attention is devoted to analyzing the practical experience of leading international companies–including Amazon, Shopify, PayPal, Microsoft, Expedia, and Rakuten–that have implemented or tested cryptocurrency payment solutions. Results demonstrate that these practices improve payment efficiency, accelerate international settlements, reduce commission fees, and increase overall transaction security. The study concludes that integrating cryptocurrencies into e-commerce represents a natural evolutionary stage of the digital economy. Ultimately, cryptocurrencies possess substantial potential to strengthen electronic commerce and support its ongoing development.
Ezinne Victory Kanu, Charles Chibuisi Ehiemere, Ishaku Adamu Akyala, Eric Terkuma Chia · 5 authors
Despite global commitments under SDG-3, maternal mortality rates remain disproportionately high in Sub-Saharan Africa. This review examines how health policies have shaped outcomes between 2014 and 2024 in Nigeria, Rwanda, South Africa, and Gabon. A comparative narrative review was conducted using WHO, World Bank, UNFPA, DHS, and national policy documents. Guided by the Walt & Gilson Policy Triangle and the WHO Health System Building Blocks, policies were assessed for context, content, actors, process, and health system capacity. Data were synthesized thematically to compare implementation and outcomes. Rwanda achieved substantial declines through decentralized financing, performance-based funding, and community health worker integration. South Africa reduced deaths via integration of HIV and maternal services but still faces equity gaps. Gabon improved financial access but rural infrastructure and workforce limitations constrain outcomes. Nigeria’s fragmented governance and weak PHC financing explain stagnation despite multiple reforms. Implementation quality, not policy presence, drives progress. Strengthening governance, financing transparency, workforce readiness, and community engagement remains crucial for achieving SDG-3. This study highlights cross-country lessons transferable to similar contexts.
Wenhao Zhang, Zhenpeng Tang, Xiaowen Zhuang, Yi Cai · 5 authors
The cryptocurrency market has attracted significant attention from global investors, with Cardano (ADA) ranking among the top cryptocurrencies by market capitalization. However, predicting ADA returns remains challenging due to the complex, multi-scale dynamics influenced by Federal Reserve policies, geopolitical events, and high-frequency trading. This study proposes a “Sliding EMD–Multi Variables” framework for cryptocurrency return prediction, leveraging Empirical Mode Decomposition’s multi-scale fractal properties to capture nonlinear dynamics at different time scales. The sliding window decomposition method addresses data leakage issues while incorporating key economic and policy variables at the component level. The empirical results demonstrate that the Sliding EMD system significantly outperforms univariate and multivariate benchmarks. Compared to the univariate system, it improves MSE, RMSE, SMAPE, and DSTAT by 0.83%, 0.42%, 5.23%, and 0.43%, respectively, while enhancing investment metrics (maximum drawdown, Sharpe ratio, Sortino ratio, Calmar ratio) by 0.19, 0.36, 0.95, and 0.15. Against the multivariate system, improvements reach 5.52%, 3.14%, 5.74%, and 17.62% in prediction accuracy, with investment performance gains of 0.47, 1.69, 4.27, and 0.31. Incorporating economic variables at the component level yields additional improvements of 0.94%, 0.47%, and 0.78% in MSE, RMSE, and MAE. These findings offer valuable insights for cryptocurrency portfolio optimization using fractal-based decomposition methods.
Integrating blockchain into the Industrial Internet of Things (IIoT) has emerged as a promising solution for preserving data privacy and ensuring IoT security. Among various blockchain platforms, Ethereum stands out due to its support for smart contracts and its interoperability with lightweight communication protocols. Despite these advantages, particularly within Ethereum-based networks, IIoT systems remain vulnerable to large-scale threats such as Sybil attacks. These attacks pose a critical security risk because an adversary generates numerous fake entities to infiltrate and compromise the network, ultimately undermining its integrity and availability. Existing approaches utilize Ethereum smart contracts and lightweight protocols such as MQTT to secure IIoT communications, but often overlook sophisticated threats such as Sybil attacks, which introduce fraudulent nodes into the network. Conventional detection methods typically depend on centralized monitoring, undermining scalability and privacy, and there remains a lack of publicly available datasets representing adversarial behaviors in IIoT environments. In this paper, an Ethereum-based IIoT network is first developed, and a publicly available dataset is released through the GitHub repository. An advanced method is then proposed to detect and prevent Sybil attacks in a PoA-based IIoT network using decentralized federated learning. During the detection phase, a convolutional neural network (CNN) is employed within the decentralized federated learning framework, achieving an average detection accuracy and recall of 91.13% and 91.37% among clients, respectively. In the prevention phase, a secure smart contract is designed to manage a dynamic reputation system, effectively preventing Sybil nodes from remaining active on the network.
In this study, it is aimed to compare quantitative forecasting methods (traditional and learning based) in cryptocurrency market. For his purpose the daily prices between 16 September 2017 – 15 September 2022 of Bitcoin, Ethereum, Binance Coin and Monero were analyzed with five different methods: ARIMA, exponential smoothing, artificial neural networks, RNN and LSTM.In the results it is indicated that exponential smoothing method is the most successful method at forecasting daily prices. The method has high performance in forecasting BTC, ETH and BNB daily prices. But at forecasting daily XMR prices, artificial neural networks method was the most successful one.The other point which was detected in this study is deep learning based methods made some unsuccessful forecasts. This is thought to be due to the fact that deep learning methods require more data. In future studies, using other quantitative methods (e.g. GRU, XGBoost, transformer models) on other cryptocurrencies will contribute to the literature.
Bitcoin already faces a quantum threat through Shor attacks on elliptic-curve signatures. This paper isolates the other component that public discussion often conflates with it: mining. Grover's algorithm halves the exponent of brute-force search, promising a quadratic edge to any quantum miner of Bitcoin. Exactly how large that edge grows depends on fault-tolerant hardware. No prior study has costed that hardware end to end. We build an open-source estimator that sweeps the full attack surface: reversible oracles for double-SHA-256 mining and RIPEMD-based address preimages, surface-code factory sizing, fleet logistics under Nakamoto-consensus timing, and Kardashev-scale energy accounting. A parametric sweep over difficulty bits b, runtime caps, and target success probabilities reveals a sharp transition. At the most favourable partial-preimage setting (b = 32, 2^224 marked states), a superconducting surface-code fleet still requires about 10^8 physical qubits and about 10^4 MW. That load is comparable to a large national grid. Tightening to Bitcoin's January 2025 mainnet difficulty (b about 79) explodes the bill to about 10^23 qubits and about 10^25 W, approaching the Kardashev Type II threshold. These numbers settle a narrower question than "Is Bitcoin quantum-secure?" Once Grover mining is lifted from asymptotic query counts to fault-tolerant physical cost, practical quantum mining collapses under oracle, distillation, and fleet overhead. To push mining into non-trivial consensus effects, one must invoke astronomical quantum fleets operating at energy scales that lie far above present-day civilization.
Version: v1.6.4 (June 2026) Major additions in this version: phased migration protocol with cryptographic quarantine (Section 6.4.4), sensitivity boundaries delineating the statistical decoupling threshold up to mu = 1.9% (Section 6.7), and integration of recent empirical MEV findings (Mancino & Rezzoli, 2025). Abstract Contemporary blockchain architectures face a critical impasse defined herein as the "Tetra-Lemma"—a four-dimensional optimization problem encompassing decentralization, security, scalability, and thermodynamic sustainability. Legacy Proof-of-Work networks confront diminishing security budgets due to the exhaustion of block subsidies, while Proof-of-Stake systems inherently risk oligarchic centralization. This paper establishes a Unified Monetary-Supply Framework that resolves these structural conflicts by synthesizing the deterministic Customized Halving schedule with the probabilistic regeneration logic of the Proof of Rinne (PoR). We demonstrate that by enforcing a "Thermodynamic Statute of Limitations" on dormant assets, the protocol functions as a Non-Equilibrium Thermodynamic Engine. This architecture transforms entropic asset attrition—traditionally viewed as systemic loss—into a regenerative security budget. The remainder of the abstract, covering the SDE and Fokker-Planck validation, the ZKP owner recovery model, and the resulting equilibrium, is in the manuscript. Data & Code AvailabilityThe mathematical models and high-precision stochastic simulations (e.g., Monte Carlo paths, SDE convergence, and Fokker-Planck distributions) presented in this manuscript are fully reproducible. The corresponding Python simulation suite and open-source models are made available at the author's GitHub repository (rincoin-regenerative-simulations) to ensure scientific transparency. Integrity & Provenance This document is anchored to the Bitcoin blockchain via OpenTimestamps. The proof file verification_data_v1.6.4.ots, included in the files below, covers the SHA-256 digest of Tokino_Rincoin_v1.6.4.pdf: 5269207ea7e363e8df312ed50c00afc119b43e6fa5d3c717e6a7d8fc9863147b The archived proof is in its as-submitted form: it commits the digest to the public OpenTimestamps calendars and does not itself embed the Bitcoin attestations. Completing it against those calendars — which both verification paths below do automatically — yields three Bitcoin attestations, the earliest in block 952366. An OpenTimestamps proof carries no wall-clock time of its own — any date reported for it is read from a Bitcoin block header. To verify, upload the PDF and the .ots file to opentimestamps.org, or with a Bitcoin node: ots verify -f Tokino_Rincoin_v1.6.4.pdf verification_data_v1.6.4.ots — the -f flag is required because the proof's filename differs from the document's. The provenance of this document is recorded in a separate signed artifact, the Rincoin Provenance Certificate (10.5281/zenodo.21415730), which binds this whitepaper to the digest above and is the reference for the full anchoring detail. That certificate carries its own OpenPGP signature, Bitcoin anchor, and PAdES signature; this whitepaper itself carries the OpenTimestamps proof only. Zenodo archival gives this record a persistent identifier and an independent retrieval path; it is not itself a cryptographic control. Validation_Scientific_Provenance_v1.6.4.pdf in the files below is an earlier certificate edition, retained as evidence. It is superseded by the record cited above. Correspondence & AffiliationPrimary Author: Tokino, Michiru (時乃 満)Affiliation: Rincoin Core Research Academic Inquiries: edu@aevust.org Community Governance: @aevustus (Discord) / @aevust (X/Telegram) Keywords: Rincoin, Proof of Rinne (PoR), regenerative crypto-economics, non-equilibrium thermodynamics, non-equilibrium steady state (NESS), stochastic differential equations (SDE), Fokker-Planck equation, recirculation incentive mechanism, macroeconomic homeostasis, Nash equilibrium, cryptographic vault, zero-knowledge proofs (ZKP), modular blockchain architecture, account abstraction, blockchain tetra-lemma, MEV mitigation, sandwich attack resistance, sensitivity analysis, statistical decoupling threshold, phased migration protocol
Public blockchains impose an inherent tension between regulatory compliance and user privacy. Existing on-chain identity solutions require centralized KYC attestors, specialized hardware, or Decentralized Identifier (DID) frameworks needing entirely new credential infrastructure. Meanwhile, over four billion active X.509 certificates constitute a globally deployed, government-grade trust infrastructure largely unexploited for decentralized identity. This paper presents zk-X509, a privacy-preserving identity system bridging legacy Public Key Infrastructure (PKI) with public ledgers via a RISC-V zero-knowledge virtual machine (zkVM). Users prove ownership of standard X.509 certificates without revealing private keys or personal identifiers. Crucially, the private key never enters the ZK circuit; ownership is proven via OS keychain signature delegation (macOS Security.framework, Windows CNG). The circuit verifies certificate chain validity, temporal validity, key ownership, trustless CRL revocation, blockchain address binding, and Sybil-resistant nullifier generation. It commits 13 public values, including a Certificate Authority (CA) Merkle root hiding the issuing CA, and four selective disclosure hashes. We formalize eight security properties under a Dolev-Yao adversary with game-based definitions and reductions to sEUF-CMA, SHA-256 collision resistance, and ZK soundness. Evaluated on the SP1 zkVM, the system achieves 11.8M cycles for ECDSA P-256 (17.4M for RSA-2048), with on-chain Groth16 verification costing ~300K gas. By leveraging certificates deployed at scale across jurisdictions, zk-X509 enables adoption without new trust establishment, complementing emerging DID-based systems.
Framed by research as part of the ongoing project ‘Archiving Community: Social Infrastructure and Small-Scale, Online Radio Stations’ (University of Glasgow, National Library of Scotland and University of Westminster), this 15 minute lecture-performance adopted a creative sound-essay format, spoken live over a soundscape developed collaboratively with artist Excel DJ. Using the name of the record label GLARC (Greater Lanarkshire Auricular Research Council) as a starting point, this talk considered ‘institutional’ constructs and the imaginaries surrounding permanence, location and the infrastructure surrounding music labels and libraries. Spanning texts on community archiving, the digital commons and considering changing ideas around participation, access and listening (particularly around web2/web3), it brought together diverse and sometimes contradictory ideas on time, space and the archive, exploring how institutions are formed, and the peculiar feedback-loops involved with copying, collecting and circulating iterative DIY ecosystems.
Semaria Eva Elita Girsang, Shaumiwaty, Muhammad Noval Aryansah, Mario Putra Sanjaya · 5 authors
The development of blockchain technology has driven the emergence of Non Fungible Tokens (NFTs) as unique digital assets traded through specialized marketplaces, forming a new digital economic ecosystem. Despite the rapid growth of the NFTs market, issues such as price volatility, the dominance of speculative activities, and uncertainty regarding long-term economic value remain insufficiently understood in academic studies. This research aims to analyze the role of NFTs marketplaces in shaping the economic value of digital assets, identify the factors influencing NFTs price dynamics, and evaluate the economic implications of the NFTs market for creators, investors, and marketplace platforms. This study employs an empirical quantitative approach by utilizing NFTs transaction data obtained from the OpenSea API, NonFungible.com, and CryptoSlam. The variables analyzed include NFTs prices, trading volume, liquidity, creator reputation, rarity score, and asset category. Data analysis is conducted using statistical and econometric methods to identify price determinants and market dynamics. The results indicate that NFTs values are significantly influenced by scarcity levels, creator reputation, asset utility, and the visibility provided by marketplaces. Marketplaces play a crucial role in shaping liquidity and market expectations, but they also contribute to increased volatility and speculative tendencies. This study concludes that the NFTs market has the potential to generate real economic value, yet it continues to face risks related to speculation and instability. These findings contribute theoretically to the digital economics literature and provide practical implications for the development of a more sustainable NFTs ecosystem.
Storage scalability is paramount in the era of big data blockchain. A storage-scalable blockchain can effectively scale out state storage to an arbitrary number of nodes and reduce the storage pressure on each, similar to distributed databases. Prior research has extensively utilized sharding techniques to attain storage scalability; however, these approaches invariably compromise safety and liveness guarantees. In this work, we propose a novel state-execution decoupled architecture, and Supermassive Blockchain, a novel storage-scalable Byzantine fault tolerance (BFT) protocol that can sustain the deterministic security properties of conventional BFT protocols. The state management system employs erasure coding to ensure state availability with scalable storage consumption, while the global consensus and execution layers maintain robust security characteristics. Our evaluation indicates that Supermassive Blockchain achieves better storage scalability compared to prior approaches while incurring low network overhead.
Solana has experienced rapid growth due to its high performance and low transaction costs, but the extremely low barrier to token issuance has also enabled widespread Rug Pulls. Unlike Ethereum-based Rug Pulls, which often rely on malicious smart-contract logic, Solana's unified SPL Token program shifts fraudulent execution toward on-chain behavioral manipulation. However, existing research has not systematically examined these Solana-specific Rug Pull patterns, and no public Solana Rug Pull dataset is available for empirical research. To bridge this gap, we present a large-scale measurement study of Rug Pulls on Solana. We manually verify 68 community-reported incidents and curate a benchmark of 117 confirmed Rug Pull tokens, from which we distill three representative on-chain behavioral patterns: Freeze Authority Abuse, Liquidity Withdrawal, and Pump-and-Dump. Guided by these patterns, we design a behavior-guided candidate identification and human-validation pipeline. We apply this pipeline to 100,063 tokens newly issued on Orca, Raydium, and Meteora during the first half of 2025, identifying 76,469 Rug Pull tokens. A random manual audit of 382 samples estimates a labeling false-positive rate of 0.26\%, supporting the reliability of the dataset. We release the resulting dataset and use it to characterize the Solana Rug Pull ecosystem. Our analysis shows that Rug Pulls on Solana exhibit extremely short lifecycles, strong price-driven dynamics, severe economic losses, and highly organized group behaviors. These findings provide new insights into the Solana Rug Pull landscape and support the development of effective on-chain defense mechanisms.
Financial systems have a growing reliance on computer-based and distributed systems, making FinTech systems vulnerable to advanced and quickly emerging cyber-criminal threats. Traditional security systems and fixed machine learning systems cannot identify more intricate fraud schemes whilst also addressing real-time performance and trust demands. This paper presented an Adaptive Neuro-Fuzzy Blockchain-AI Framework (ANFB-AI) to achieve security in FinTech transactions by detecting threats using intelligent and decentralized algorithms. The framework combines both an immutable, transparent and tamper resistant layer of a permissioned blockchain to maintain the immutability, transparency and resistance to tampering of transactions, and an adaptive neuro-fuzzy learning model to learn the presence of uncertainty and behavioural drift in fraud activities. An explicit mathematical model is created to explain the transaction integrity, adaptive threat classification, and unified risk based decision-making. The proposed framework uses Proof-of-Authority consensus to overcome low-latency validation of transactions and scalable real-time financial services. Massive simulations are performed in normal, moderate, and high-fraud conditions with the use of realistic financial and cryptocurrency transactions. The experimental evidence proves that ANFB-AI is always more accurate and precise than recent state-of-the-art algorithms and costs much less in terms of transaction confirmation time, propagation delay of blocks and end-to end latency. ANFB-AI performance supports the appropriateness of adaptive neuro-fuzzy intelligence to blockchain-based FinTech security.
Document authentication remains a pressing challenge in various domains, including financial services, academic credentialing, healthcare, and supply chain management. Existing centralized verification systems are vulnerable to manipulation, inefficiency, and limited transparency. Blockchain technology, with its immutability and tamper-resistant capabilities, offers a strong decentralized alternative; however, many current implementations lack structured, issuer-bound relationships for documents. This paper proposes a blockchain-based model that leverages a hierarchical token structure to authenticate and trace the provenance of high-value digital documents, with a focus on financial records. The model introduces the concept of an issuer-bound parent token and document-linked child tokens, enforcing a structured trust relationship between a legitimate institution and the documents it issues. By combining on-chain cryptographic hashing with off-chain file references, the approach is designed to balance verifiability with scalability. We implement a proof-of-concept using Ethereum-compatible smart contracts on a permissioned blockchain and evaluate it in a consortium-style financial setting. Our functional analyses demonstrate the model’s ability to ensure document integrity, provenance, and resistance to document fraud. This work offers a practical and extensible foundation for secure digital document authentication and verification in financial and other trust-sensitive settings.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
We introduce the Theory of Epistemic Abductive Geometry (TEAG), a framework for non-Bayesian inference grounded in admissible-support contraction under possibility theory. The central object is the TEAG quintuple \( \mathcal{E} = (H, \pi, \{H_\alpha\}_{\alpha\in(0,1]}, C, A) \), where evidence acts by contracting the geometry of admissible hypotheses rather than redistributing probabilistic belief mass. The falsification boundary is a tropical variety — exactly. Under the log-admissibility transformation \( \Phi(h) = -\log\pi(h) \), the canonical TEAG conjunctive update becomes tropical addition in the max-plus semiring: \( \Phi^+(h) = \Phi^-(h) \oplus \psi(h) = \max\!\bigl(\Phi^-(h),\,\psi(h)\bigr), \) where \( \psi(h) = -\log\kappa(y\mid h) \) is the surprisal of hypothesis h under observation y. The falsification boundary is the tropical variety of this polynomial: \( \mathcal{F} = \bigl\{h \in H : \Phi^-(h) = \psi(h)\bigr\}. \) This is the exact locus dividing surviving from falsified hypotheses: h is falsified if and only if \( \psi(h) > \Phi^-(h) \); it survives if and only if \( \Phi^-(h) \geq \psi(h) \). Within the class of possibility-theoretic recursive inference systems, this is, to the best of our knowledge, the first exact algebraic expression of Popper's falsification criterion: the boundary is the zero set of a tropical polynomial, determined entirely by the geometry of the prior impossibility and current surprisal fields. Main results. 1. Epistemic Contraction Theorem. Contraction is tropical addition: \( \Phi^+ = \Phi^- \oplus \psi \). Posterior α-cuts satisfy \( H_\alpha^+ = H_\alpha^- \cap E_\alpha(y) \): geometric intersection, not belief redistribution. The falsification boundary is the tropical variety \( \mathcal{F} \). 2. Possibilistic Cramér–Rao Bound (PCRB} For any filter in the class \( \mathcal{F} \) of epistemically admissible, contraction-based recursive estimators satisfying Axioms 2.1–2.5: \( \mathcal{E}_{\pi,k|k} \geq \mathcal{E}_{\pi,k|k-1} + \tfrac{n}{2}\log(1-I_k) \), where \( I_k \) is the Choquet integral of per-hypothesis surprisal against the prior possibility capacity. Within this class, the ESPF [28] is the unique filter achieving this bound with equality, and is therefore the unique minimax-entropy-optimal set-based recursive estimator under bounded epistemic uncertainty. 3. Tropical Hamilton–Jacobi structure (summary). The TEAG update is structurally consistent with a tropical Lagrangian \( L = T - V \), Legendre transform to a tropical Hamiltonian equal to the surprisal field, and a Hamilton–Jacobi equation whose solution is the tropical addition rule. The Euler–Lagrange equations on the epistemic manifold yield geodesic motion with explicit Levi–Civita connection and Christoffel symbols. This structure is interpretive and consistent with the axioms; full derivations are in the companion paper [31]. Taken together, this structure admits a precise interpretation: the TEAG update rule is a max-plus dynamical system whose governing equations have the same algebraic form as the Hamilton–Jacobi equations of classical mechanics, instantiated on hypothesis space rather than physical space. 4. Gaussian collapse. Probability theory is the collapse limit of TEAG as epistemic width \( W \to 0 \): Choquet converges to Lebesgue, the ESPF recovers the Kalman filter, and \( \mathcal{E}_\pi \to \tfrac{1}{2}\log\det\Sigma + \mathrm{const}(n) \). Probability is earned by evidence, not assumed. Epistemic neutrality and knowledge-system synthesis. Because TEAG's axioms require only a hypothesis space, a possibility field, and a contraction operator — not a probability measure, a likelihood function, or a frequentist grounding — heterogeneous knowledge systems can each instantiate the TEAG quintuple independently. Their joint admissible support intersection is the locus of coherence: the set of hypotheses neither system has falsified. No transformation of one system into the other's representational primitives is required. The composition theory (Section 6) formalizes the coupling architecture. Four instantiations provide the unifying structure: the ESPF [28] for recursive state estimation; the Geometry of Knowing [29] for measure-theoretic collapse; the minimax-entropy optimality proof [30]; and the Possibilistic Language Model (PLM, forthcoming [32]).
The agriculture sector plays a pivotal role in global economies, and optimizing its perishable food supply chain (PFSC) is vital to ensuring food security and transparency. The purpose of the study is to develop a blockchain-based smart contract to secure and provide transparency about perishable goods in the PFSC while delivering the goods between the stakeholders, such as farmers, mandis, and wholesalers. The study enhances collaboration between stakeholders by implementing smart contracts. The delivery status and the transactions have been safely recorded and verified by the stakeholder in the PFSC to ensure data integrity all the way through. The blockchain application has reduced fraud and streamlined the flow of goods and information. Moreover, this study emphasizes providing farmers with a straightforward route to the market to empower them. The benefits for the stakeholders are optimizing inventory control and developing appropriate decision-making skills. A three-echelon PFSC can become more resilient and is able to meet changing market demands by implementing blockchain-based smart contracts. Finally, the study employs blockchain technology to establish a decentralized and efficient PFSC, confirming a tamper-resistant system and enhancing stakeholder trust and collaboration.
This study examines the theoretical contributions of Masayasu Narumi, who served as a policy advisor to Mayor Ichio Asukata of Yokohama. He formulated influential ideas on local autonomy and local public finance. Despite his central role in shaping the theory of reformist local governments,Narumi’s theoretical contributions have not been sufficiently clarified. His work is characterized by its practical orientation, grounded in municipal reform and citizen participation, as well as its historical consciousness in addressing challenges and prospects for local governance. In the context of the 1980s “era of local autonomy,” when the essence of progressive local governments was under critical scrutiny, Narumi advanced a new paradigm such as a “policy-making government” or “citizens’ government.” In his theory of local public finance, Narumi conceptualized local autonomy and local public finance as integrated activities. He argued that the realization of reform and policy within a“ citizens’ government” required a decentralized administrative and fiscal structure between the central and local governments. His formulation of a“ decentralization-oriented fiscal theory” anticipated later debates on local choice and fiscal responsibility. As contemporary Japan again confronts the necessity of municipal reform, Narumi’s pioneering insights into participation, decentralization, and autonomy warrant renewed scholarly attention.
The landscape of e-commerce has witnessed a transformative shift in consumer behavior, driven by the rise of digital technologies and online platforms. As online purchases increase at an alarming rate, fraudulent activity has become a major concern for retailers and consumers alike. The objective of this research is to investigate methods for detecting fraudulent online transactions using machine learning algorithms. This paper proposes a Hybrid Agentic AI Architecture (HSAA) for edge-enabled e-commerce that incorporates intelligent agents and cryptographic security to enable real-time, trustworthy transaction processing. The architecture uses world-model distillation to enable efficient inference on edge devices. HSAA was tested on several large data sets such as a balanced credit card fraud set containing 2,952 transactions. The system scored 96.6% in detecting fraud, indicating very low false positives and high specificity. Negotiation exercises on 400 independent interactions were successful in 59%, with an average discount of 14.2%, using 1,142 zero-knowledge proofs that were verified with 100% validity. Some of the operational performance highlights include a throughput of 585 transactions per second, an average latency of 1.56 milliseconds, and a 81.9% reduction in bandwidth through selective state transfer. The findings support the argument that HSAA is a strong, secure, and high-performance edge-based e-commerce architecture, combining accuracy, efficiency, and reliability. Within HSAA, fraud detection functions as one of the core decision agents, while negotiation and secure execution mechanisms provide the broader operational context for trustworthy edge commerce. The architecture provides a solid basis for future studies in adaptive and autonomous AI-driven commercial systems.
Abstract Rural health systems are networks, which are geographically disseminated and resource limited, in which inefficient inter-hospital coordination has a strong influence on patient outcomes, operational stability and surgical resilience. Regardless of the development of smart hospital technologies, such as 5G-enabled communication opportunities, the integration of digital coordination centers, and telemedicine, the current frameworks are more focused on streamlining intra-hospital processes instead of the inter-hospital distribution of resources. This structural disintegration leads to slow shifts, poor use of bed space, inaccessibility of specialists, and poor responsiveness to surges. This paper suggests Smart Inter-Hospital Representation Network (SIHCN) to be a rural hospital ecosystem distributed systems architecture. The framework combines a granted blockchain based resource registry, real-time capacity monitoring strategies, specialist allocation registries, and adaptive routing logic into a coordination infrastructure. The proposed architecture will be able to guarantee decentralized system control against centralized command models, fault tolerance, and scalable interoperability among autonomous hospital nodes. The paper introduces a conceptual systems model that specifies the network topology, operational data flow, distributed resource synchronization and performance evaluation metrics. The simulation modeling is based on a scenario simulation that assesses the system performance when under routine and emergency surge conditions, showing that the transfer latency, resource balancing, and coordination efficiency is improved. The results make distributed ledger-based coordination a potential engineering technique in enhancing the resilience of rural health networks. This study also addresses the Healthcare Systems Engineering field by re-conceptualizing rural hospital coordination as a distributed resource optimization problem and suggesting an architecture-layer solution that can be applied to low-density, high-variability healthcare settings.
Smart contracts are autonomous systems that execute agreements using code. Their efficiency generated attention from a range of industries. The basis of traditional vulnerability detection techniques, opcode analysis, has limitations in detecting complex vulnerabilities. Our research aims to address these difficulties by developing an automated framework for vulnerability detection, mitigation, and patch deployment. Initially, smart contract data will be collected, followed by a preprocessing step to remove any unnecessary information using lexical analysis and Bidirectional Encoder Representations from Transformers (BERT). Then, the preprocessed data is used to identify the features that are relevant are selected. Following the features being selected, an intellectual engine is used to identify flaws. The intellectual engine that integrates the convolutional neural networks (CNN) and long short-term memory (LSTM) analyzes a subset of preprocessed data for vulnerabilities, with explainable artificial intelligence (XAI) evaluating the importance of each feature to predictions. Our method produces exceptional outcomes with a 99.25% precision, 99.76% accuracy, 99.60% F1-score, and 99.36% recall. Smart contract vulnerability identification, mitigation, and patch generation are improved by the proposed Beluga Crayfish Optimization Algorithm (BCOA) and Crayfish Secretary Bird Optimization Algorithm (CSBOA) together with graph neural networks (GNN). In addition to producing the required fixes, this method offers efficient mitigation techniques. Therefore, it greatly enhances smart contract security and efficiency. In the end, smart contract programs that use this integrated approach are more secure.
Abstract: Remote electronic voting systems require tamper-evident records of ballot submissions, yet the ledger integrity problem - ensuring that the record cannot be silently altered after the fact - has received less formal treatment than ballot-phase cryptography. Existing approaches rely on distributed blockchain consensus, digital signatures on bulletin boards, or external hash-tree timestamping services, each introducing dependencies on specialized infrastructure, continuously trusted parties, or computationally intensive proof systems. This paper provides a formal security analysis of a hash-chained append-only ledger instantiated in a standard relational database with quorum replication, establishing that equivalent tamper-evidence guarantees are achievable under the sole assumption of collision resistance of the instantiated hash function. We define five security properties in the Bellare-Rogaway game-based framework. Tamper-evidence (Proposition 1) bounds any PPT adversary's advantage at 2q(λ) · Adv^CR_H, with a tight reduction to collision resistance. Fork-resistance (Proposition 2) establishes that an adversary corrupting fewer than a quorum threshold of replicas cannot cause divergent chain histories at honest replicas. Retrospective modification resistance (Proposition 3) establishes that post-close modifications are detectable by any auditor holding a real-time replica copy. Cross-ledger binding security (Proposition 4) extends these results to the dual-ledger construction of the Arcaunt architecture, in which a public ballot ledger and a temporal credential ledger are cryptographically bound at insert time, making modifications to either detectable through the other with advantage bounded by 4q(λ) · Adv^CR_H. Selector integrity (Proposition 5) establishes that the last-valid-vote rule - operating on insertion sequence rather than timestamps, making it immune to clock manipulation - is integrity-secure conditional on credential security, formally delineating the boundary between ledger and credential security domains. We apply an eight-metric comparative framework to seven e-voting integrity architectures - hash-chaining, bulletin boards, homomorphic tallying, mixnet-based systems, blockchain, KSI timestamping, and VVPAT hybrids - establishing three findings: tamper-evidence basis is universal but mechanism-specific; fork-resistance is architecturally necessary specifically for revoting-based systems; and auditability complexity is inversely correlated with cryptographic sophistication. The hash-chained relational ledger achieves collision-resistance-based tamper-evidence with O(n) verification accessible to any SQL-capable auditor - a design point unoccupied by existing systems under the same combination of properties. Prototype validation on a Firebird 5.0 implementation confirms that each proposition is instantiated by a specific database trigger mechanism, with 6ms mean ballot submission latency and O(n) verification complexity empirically confirmed.
Nepal?s federal transition has shifted major responsibilities for basic and secondary education to local governments, including rural municipalities. This paper examines how they are using this mandate and what it implies for education equity. Using qualitative analysis of constitutional and legal texts, national sector plans, and municipal education policies and budgets, it focuses on three domains: governance capacity, fiscal capacity, and the alignment of policy and practice. Rural Municipalities (RMs) are beginning to institutionalize their role through education sections, local acts and annual plans, and, in some cases, substantial budget allocations to education. However, legal ambiguities, reliance on earmarked federal grants, limited administrative capacity and politicized teacher management restrict their room for maneuver. These constraints create a gap between rights-based commitments and everyday schooling, especially for disadvantaged children in remote areas, and highlight the need for clearer roles, stronger local capacity and more equitable, flexible financing and accountability.
We demonstrate that the binary payload of the "A Sign In Space" signal (data17square.bin, 8192 bytes) contains a self-referential algebraic structure — a mathematical quine. Through a systematic reverse-engineering and cryptanalytic approach, starting from the raw file as the sole axiom, we derive a chain of algebraic objects over the finite field GF(625): 48 field elements, a 42-amino-acid protein sequence, an elliptic curve, and amino acid coordinate values. The curve parameters recovered from the protein are identical to those derived from the field's primitive element, closing a self-referential loop. The cryptanalysis combines finite field arithmetic, Berlekamp–Massey LFSR analysis, elliptic curve theory, and Margolus cellular automaton reverse-engineering to recover the hidden algebraic structure without any prior knowledge of the encoding scheme. The derived protein is validated by Boltz-2 (AlphaFold3 architecture) structure prediction at three levels of assembly (monomer, homodimer, homotrimer), cross-validated with ESMFold (RMSD = 1.10 Å), and refined with OpenMM (Amber ff14SB). The monomer forms a single alpha-helix with pLDDT = 92.3 and 100% Ramachandran-favored geometry. The homodimer produces a coiled-coil — the most ancient structural motif in biology. The protein uses exactly the five prebiotic amino acids (A, D, E, L, V) with a perfect 21/21 charged/neutral symmetry. Null hypothesis testing (120 alternative inputs, 0 quines produced) and sensitivity analysis (the quine breaks with any single parameter change: 1/150 polynomials, 1/3 step counts, 98/100 bit flips destroy it) confirm the structure is not an artifact of the analysis pipeline. The conservative probability of chance occurrence is approximately 5 × 10⁻¹⁹; under uniformity assumptions, approximately 10⁻⁷⁶. Companion Python scripts (quine_proof.py, verify_123.py) verify all 123 algebraic properties with zero failures. All code and data are provided for full reproducibility. -- Additional notes : This is a preprint resulting from independent reverse-engineering and cryptanalysis of the "A Sign In Space" signal, a simulated extraterrestrial message transmitted by ESA's ExoMars Trace Gas Orbiter in May 2023. The analysis is fully reproducible: running "python3 quine_proof.py data17square.bin" derives every intermediate value from the raw binary file and verifies 47 core assertions with zero failures. The extended script "verify_123.py" checks all 123 algebraic properties. Structure predictions were performed on an NVIDIA RTX 5090 GPU (32 GB VRAM) using Boltz-2 v2.2.1 (AlphaFold3 architecture, maximum precision: 20 recycling cycles, 500 diffusion steps, 20 samples), ESMFold v1 (cross-validation), and OpenMM 8.5 (Amber ff14SB force field, GBn2 implicit solvent, energy minimization + 10 ns molecular dynamics at 300 K). No prior knowledge of the signal's encoding scheme was assumed. The algebraic structure was discovered through systematic cryptanalytic techniques including finite field enumeration, LFSR analysis, elliptic curve point counting, and exhaustive parameter space exploration. If you use any part of this work (data, code, results, figures, or methods), please cite: Lacoche, E. (2026). "A Self-Referential Algebraic Quine in the A Sign In Space Signal." Zenodo. doi:10.5281/zenodo.19218629
Current AI deployment stacks authenticate agents, workloads, and credentials but do not verify which neural network is computing at inference time. Recent incidents — including the undisclosed use of an open-weight foundation model inside a commercial product, industrial-scale distillation campaigns, and emerging agent identity standards that authenticate software without authenticating models — show that this gap has practical consequences. Post-hoc disclosure resolved these incidents; runtime proof would have made the model identity question answerable at inference time. This paper asks whether runtime model identity is technically feasible at frontier scale. We present three results. First, we enrolled and verified five open-weight transformer models spanning 8 billion to 72.7 billion parameters across three families, with zero false acceptances in all pairwise comparisons and self-verification within the acceptance threshold for all models. A thermodynamic observable predicted by extreme value theory remained within two percent of its predicted value across the full range, with no statistically significant scale-dependent correction detected across more than two orders of magnitude in parameter count. Second, we tested structural separability on three declared-lineage distillation pairs spanning 8 billion to 70 billion parameters — each derivative sharing identical architecture with its base — and measured separations ranging from 2,858 to 4,583 times the acceptance threshold, increasing monotonically with model scale across two base-model families. All derivatives self-verified within the acceptance threshold. Third, we demonstrate a frontier-scale software attestation path — including signed JWT issuance and downstream policy consumption — and situate it within a previously formalized attestation architecture that composes with enterprise identity infrastructure, complementing rather than replacing current agent identity frameworks. These results demonstrate that runtime model identity is measurable and separable across the tested range of open-weight instruct-tuned transformers from 8B to 72.7B, with a frontier-validated software attestation path and an inherited route to stronger hardware-backed and proof-backed assurance. 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).