Blockchain Papers

Deepnil Ray

On May 7, 2024, R. J. Mathar contributed to OEIS A186185 a conjectured order-6 Precursive recurrence, with a matching asymptotic conjectured the same day by V. Kotesovec. We give a short proof, deriving an explicit cubic algebraic equation for the generating function from its defining functional equation and extracting the recurrence via the Bostan-Chyzak-Salvy algebraic-to-holonomic correspondence.

Rachel S. Arnow-Richman

This chapter explores how regulatory contract theory might inform and reshape American employment law, in particular its distinct employment at-will doctrine that permits termination without cause. That principle, and its freedom-of-contract ethos, has long constrained and distorted efforts to regulate employment contracts. Substantive statutory intervention has been piecemeal and reactive. Labor laws that might have supported a collective contract model of regulation have been too weak to sustain a culture of union affiliation or successful bargaining. And individual employment contract law has developed in ways that enhance managerial power and stymie application of traditional policing doctrines. Accepting the futility of efforts to defeat employment-at-will, the chapter imagines how regulatory contract theory might blunt its edges. It advances a common law reform agenda that preserves the core of employment-at-will – employers' substantive discretion to terminate – but imposes non-waivable procedural and relational obligations, including reasonable notice of termination and good-faith limitations on changes to employment terms. The chapter concludes that a reorientation of employment-at-will doctrine, grounded in regulatory contract theory, can enhance workplace fairness while still preserving the basic structure of American employment law.

Yosef Bonaparte

This paper proposes a polar-coordinate framework for Bitcoin prices around the halving cycle, where each revolution is one halving epoch and radial distance represents log price. This alignment reveals recurring patterns conventional time-series charts obscure. We develop five statistics, spanning seasonality amplitude, phase concentration, intercycle repeatability, radial growth, and bull-bear asymmetry, each paired with an explicit null hypothesis and, given the small number of completed cycles, block-bootstrap or randomization inference. To test whether the structure is specific to Bitcoin's supply schedule, we apply the identical clock to Ethereum and the S&P 500 as placebo assets. Bitcoin shows significant halving-phase seasonality (p

Harguneet Singh, Simranpreet Kaur

The implementation of the Goods and Services Tax (GST) on July 1, 2017, represented a watershed moment in India's fiscal history, consolidating a fragmented indirect tax regime into a single unified framework. Nevertheless, for the micro, small and medium enterprises (MSMEs) constituting the industrial sinews of Ludhiana-often heralded as the 'Manchester of North India' and the global epicenter of bicycle manufacturing-this structural transition has proven to be less an emancipation from bureaucratic entanglement and more an intricate maze of procedural obligations. The present investigation critically scrutinizes the tax-related adversities encountered by indigenous MSMEs in Ludhiana, encompassing escalating compliance expenditures, blocked input tax credit (ITC) refunds, inverted duty structures, digital literacy deficits, and acute working capital strangulation. Drawing upon primary data from 150 MSME units (stratified as 50 micro, 50 small, and 50 medium enterprises) and supplemented by semistructured interviews with 30 proprietors operating across the hosiery, bicycle parts, and metal fabrication clusters, the empirical findings reveal that GST compliance costs are markedly regressive, disproportionately eroding the already slender profit margins of micro-enterprises. Furthermore, procedural complexities surrounding the reconciliation and refund of accumulated ITC have precipitated severe liquidity constraints, compelling numerous firms to rely on highinterest informal credit channels to sustain operations. Notwithstanding these operational

Faisal Orakzai

Blockchain is a revolutionary technology transforming the digital economy. This research paper provides a comprehensive primer on blockchain technology, covering its architecture, core principles, and applications. We explore the transition from centralized to decentralized systems, the cryptographic foundations of security (SHA-256, Merkle Trees), and the evolution of smart contracts. Additionally, we analyze the impact of blockchain across various industries and its role in the emerging Web3 infrastructure.

Rohit Dash

Blockchain has evolved beyond speculative cryptocurrency use cases into a technology supporting real world enterprise infrastructure. This report analyzes 50 enterprise blockchain deployments across finance, supply chains, healthcare, energy, and government to identify the factors that influence successful implementation. The analysis finds that narrowly defined use cases with clear operational benefits consistently outperform broad industry transformation initiatives. Governance and coordination among multiple stakeholders remain the primary barriers to large scale adoption, while institutional investment in tokenized real world assets demonstrates sustained momentum. The findings suggest that blockchain adoption is increasingly driven by measurable business value, interoperability, and regulatory alignment rather than technological novelty. This report provides practical insights for organizations evaluating blockchain as enterprise infrastructure.

Alberto Rocha

No abstract is available for this record.

Aleaddin Özer, Murat Aydos

Blockchain technology has revolutionized numerous industries by providing decentralized, transparent, and immutable ledgers. However, its adoption is hindered by persistent security challenges, including arbitrage attacks, liquidity exploits, and noncompliance with antimoney laundering (AML) regulations. This paper proposes an enhanced framework to address these issues, combining dynamic pricing mechanisms, AI‐based anomaly detection, and regulatory compliance checks within a multilayered architecture. The framework is composed of five interconnected layers: the input layer for data collection and validation, the data warehouse layer for structured data classification, the processing layer for anomaly detection and pricing adjustments, and the decision layer for transaction validation, execution, and reporting. The integration of these layers ensures robust security and compliance mechanisms, reducing system vulnerabilities while optimizing efficiency. To validate the proposed framework, we conducted simulations using real‐world blockchain scenarios, including decentralized finance (DeFi) platforms and cryptocurrency exchanges. Results demonstrate significant reductions in arbitrage opportunities and liquidity risks, with improved accuracy in anomaly detection and compliance adherence. For instance, the dynamic pricing mechanism mitigated 87% of arbitrage attack attempts, while the AI‐based anomaly detection achieved an 89% accuracy rate in identifying high‐risk transactions. This study provides actionable insights and a scalable solution for enhancing blockchain security and trust. Future work will focus on integrating cross‐chain interoperability, real‐time threat intelligence, and privacy‐preserving techniques to further expand the framework’s applicability. By addressing critical vulnerabilities, this research contributes to the development of secure, transparent, and compliant blockchain ecosystems, paving the way for wider adoption across industries. Unlike previous blockchain security models, our framework introduces a real‐time, AI‐enhanced risk assessment mechanism that dynamically updates transaction risk scores, mitigating financial threats in decentralized environments. This holistic approach provides a scalable, explainable, and adaptive security system that not only protects decentralized financial infrastructures but also aligns with emerging regulatory requirements, ensuring long‐term applicability.

Rahma Almheiri

No abstract is available for this record.

Steven Paul Nohr

Oracles have become a foundational component of decentralized finance and cross-chain systems by enabling smart contracts to consume external data such as price feeds, timestamps, and event confirmations. However, oracles are fundamentally limited to validating facts and cannot determine whether a transaction or state transition is legally, contractually, or behaviorally permitted. As regulatory regimes such as the European Union’s Markets in Crypto-Assets Regulation (MiCA) impose enforceable obligations on token issuers, service providers, and infrastructure operators, this limitation creates a critical enforcement gap. This paper introduces a Logic Validation Layer (LVL), implemented via the Crystal Validator™, which enforces jurisdictional, behavioral, and policy-based constraints directly within token execution paths. By separating factual data delivery, cross-chain transport, and logic enforcement into distinct architectural layers, the proposed model enables deterministic compliance without sacrificing interoperability, composability, or performance. The paper argues that while oracles remain necessary, logic-level validation has become unavoidable for regulated cross-chain finance.

Silvia Fernanda Corzo Florez

Este ensayo jurídico aborda el análisis de la aplicación de los contratos inteligentes (Smart Contracts) al contrato de seguro en el marco jurídico colombiano. Parte de una explicación sobre qué son los contratos inteligentes y cómo funcionan mediante la tecnología Blockchain, destacando sus ventajas en términos de seguridad, automatización y transparencia. Posteriormente, se centra en el contrato de seguro como una tipología contractual particularmente apta para esta implementación, debido a sus características propias. Este trabajo busca determinar si estos contratos digitales son válidos y útiles en el ámbito asegurador, analizando sus efectos jurídicos desde una perspectiva hermenéutica y a la luz de la teoría de la justicia de John Rawls, con el fin de evaluar si esta innovación tecnológica puede mejorar la relación entre asegurador y asegurado, garantizando una ejecución más eficiente y justa del contrato.

Miguel Valido, António Ravara

Smart contracts are high-stakes software: their immutable, publicly accessible, code may govern assets worth millions, meaning that even minor defects can have severe consequences. The most used techniques to ensure smart contract correctness are testing and formal verification. Testing is almost always employed but is often restricted to unit tests (which often miss edge cases) and has limited coverage, while formal verification can provide strong guarantees but is often costly and complex to apply, demanding substantial time and expertise. Property-based testing bridges this gap by exploring large input spaces and shrinking failures to minimal counterexamples, helping uncover defects early in development. Formal verification can be left to critical features once testing has filtered out common issues. To add to the challenges smart contract developers face, most languages used were not designed with safety and security guarantees built-in. Daml is a smart contract language designed with correctness in mind, featuring a strong static type system, functional programming paradigms, and built-in abstractions for common smart contract patterns. However, Daml currently lacks support for property-based testing, limiting developers' ability to systematically explore input spaces and verify contract properties. This paper introduces Hypothesis2Daml, an open-source library that brings property-based testing to the Daml ecosystem by connecting the Hypothesis testing framework with the Daml JSON API. Hypothesis2Daml enables developers to specify invariants, preconditions, and stateful workflows over realistic ledger interactions, while providing automatic input generation, shrinking, and isolation of ledger state between test cases. The approach is evaluated using a benchmark consisting of eight contracts, three Daml templates, and twenty-eight property-based tests covering happy paths, negative cases, and alternative interaction orders. The results show that property-based testing is feasible for Daml smart contracts, can systematically expose violated properties with minimal counterexamples, and supports effective debugging of realistic, stateful workflows.

Goshgar Ismayilov

Zero-knowledge proof is a special cryptographic technique that allows a prover to convince a verifier about the correctness of a claim without explicitly disclosing the claim itself. With the advancements of blockchain technologies, zero-knowledge proof has been successfully integrated into many decentralized applications over the years. ZoKrates, with its ease-of-use and direct integration to blockchain platforms, has emerged as a leading framework for developing, generating and verifying zero-knowledge proofs. This survey compiles a corpus of 347 documents that cite the original research work of ZoKrates by considering the period ranging from 2018 to 2025. Out of this corpus, this survey systematically selects and analyzes a total of 87 different documents including only peer-reviewed publications and excluding the gray literature. To the best of our knowledge, this is the first survey in the literature to follow a systematic approach to analyze the privacy- preserving applications in blockchain from the perspective of ZoKrates. This survey presents three different classifications over the documents with respect to (i) the applications they develop, (ii) the challenges they frequently encounter and (iii) the metrics they often use to measure performance of their techniques. Based on the challenges identified, this survey finally discusses numerous future research directions to promote potential advancements in the field and attract the attention of scientific and industrial communities. Feedback from readers regarding any inaccuracies or misinformation in this survey is welcome.

Carvalho Tiago, Rijo Gon鏰lo, Gon鏰lves Lio, Amorim Vasco · 5 authors

Traditional electronic Kanban (eKanban) systems depend on manual scans and offer only discrete material visibility, limiting responsiveness and automation in lean manufacturing environments. These operational bottlenecks are magnified in high-mix contexts, where delayed replenishment signals degrade flow stability, increase work-in-progress, and hinder sustainable material handling. Furthermore, vendor-specific systems lack interoperability for scalable automation, constraining the development of intelligent manufacturing solutions. This work investigates whether zone-based replenishment automation can be enabled through real-time locating systems (RTLS) using open interoperability standards, addressing a gap in empirical validation of such approaches. A middleware architecture was developed that integrates ultra-wideband (UWB) positioning, an Omlox-compliant location middleware (DeepHub), and a cloud-based eKanban system to replace manual triggers with geofence-driven order creation. The novelty of this study lies in demonstrating a fully automated Kanban signaling loop built on the open Omlox standard, providing vendor-independent RTLS interoperability and eliminating human intervention in replenishment signaling. This contributes new knowledge on how continuous location data can be converted into actionable replenishment events in a standards-based, modular manner, enabling more intelligent and autonomous material-flow control. A controlled proof-of-concept experiment simulating shop-floor conditions showed that the system achieved a 100% detection success rate, zero duplicate orders, and an average trigger-to-action latency of 2.7 s, while automatically recovering from authentication and WebSocket failures. These results provide the first empirical evidence that Omlox-compliant RTLS middleware can reliably support zone-based eKanban automation. The findings have direct implications for intelligent and sustainable manufacturing by demonstrating a scalable pathway toward interoperable, real-time material-flow systems that reduce manual intervention, avoid unnecessary handling, and lower work-in-progress. More broadly, the work addresses the current lack of empirical validation of open-standard RTLS integration within lean and sustainable production environments.

Piyoosh Rai

No abstract is available for this record.

Christopher K. Odinet, Andrea Tosato

No abstract is available for this record.

Hilary J. Allen

No abstract is available for this record.

Idowu Adetona Ayoade, Omowunmi Mary Longe

Electromobility requires transactive coordination that respects distribution-network limits while preserving auditability and privacy. This study presents a reproducible peer-to-peer energy trading system that integrates a network-constrained market with permissioned blockchain settlement. The market solves a convex welfare program with linearized power-flow limits and recovers nodal prices from dual variables to match bids and offers and determine clear quantities. Settlement uses Hyperledger Fabric via the Gateway API, including proposal endorsement, ordering, validation, and commit notifications. Meter evidence is hashed and, when necessary, stored with private data collections. A co-simulation harness links MATLAB/Simulink and MATPOWER for feeder dynamics and price formation with chaincode and client logic for settlement. Three case studies are evaluated: an urban microgrid, a suburban microgrid, and a mobile electric-vehicle swarm. An Ethereum testnet serves as a public-chain baseline. In the testbed, a tuned Fabric configuration sustained approximately 1.6 to 1.7 thousand transactions per second with 99th-percentile submit-to-commit latency near one second and full deadline compliance at a one-second clearing cadence. Energy delivery accuracy remained tight, Multi-Version Concurrency Control conflicts were low, and dynamic nodal prices reduced EV charging cost relative to a flat tariff while signaling congestion through predictable rent patterns. The contribution is a deployable blueprint that connects network economics to verifiable settlement, with an open repository, benchmarking artefacts, and practical targets for endorsement width, block size, and timeouts, and clear pathways to field trials, stochastic and robust clearing, zero-knowledge meter proofs, and city-scale deployment.

Maria Smith

[Depreciated and replaced by V3] This pre-V3 paper is replaced by the corresponding V3 clean-room reconstruction: There Is No Nothing: A Premise-Free Operational Foundation and an Open Verification Platform for Smithian Fold Theory. The V3 source platform is https://github.com/MettaMazza/ernos-labs-sft-platform. The original DOI, concept DOI, version number and files are preserved for transparent historical provenance; this record must not be presented or cited as current V3 work. Opaque predictive reliability is valuable evidence of performance; it is not by itself a derivation, causal explanation or proof. This paper establishes the Smithian Fold Theory standard: one machine-checked self-proven theorem, zero axioms, zero fitted parameters, exact trace to the One, independent certificates, public evidence and a halt when forcing breaks. The synchronized corpus executes 326 suites and 2,002 exact checks with zero failures, with all 326 generated-C certificates identical to source. Its computational proofs carry the same method into sealed blind protein structure, exact and competitive Chess, exact and competitive Go, native zero-trained-parameter UnisonAI and measurement of fold law inside trained weights. The paper protects authorship and empirical method: agents do not declare Maria Smith's findings, convert their auxiliary failures into her results or impose incumbent theoretical walls. Benchmark victories remain explicit objectives; development evidence directs construction; every positive result is investigated and retained. Scientific author and publication authority: Maria Smith, Ernos Labs. Open source: Smithian Fold Theory of Everything.

Seema C K, Sharan V Talwar, subhash Chandra K R Patel, S. E. R. Sacha Emile R.

The Internet of Things (IoT) presents critical security challenges including device identity spoofing, replay attacks, and data tampering across billions of deployed endpoints. This work presents ZK-IoTChain, a blockchain-enabled security framework that integrates zk-SNARK-based device authentication with Merkle-anchored data integrity in a unified architecture. The proposed system employs a three-layer design consisting of device-side proof generation, on-chain Groth16 verification, and IPFS-based off-chain storage. This architecture ensures privacy-preserving authentication while maintaining scalability and cost efficiency. Experimental evaluation on the Ethereum Sepolia testnet demonstrates a mean proof generation latency of 3.21 seconds and on-chain verification latency of approximately 125 milliseconds, with an average gas cost of 278,400 per authentication. The framework achieves a 99.98% reduction in storage cost compared to full on-chain approaches. Security analysis under the Dolev–Yao adversary model confirms effective mitigation of identity spoofing, replay attacks, data tampering, and man-in-the-middle (MITM) attacks. The results highlight ZK-IoTChain as a practical and efficient solution for secure, scalable, and privacy-preserving IoT ecosystems.

Gboraloo A. W., Eke B., Onuodu F. E.

Decentralized exchanges (DEXs) have emerged as a foundational component of blockchain-based financial systems, enabling trustless asset trading without centralized intermediaries. However, the transparency of public mempools introduces significant vulnerabilities, including front-running, sandwich attacks, transaction reordering, and broader information asymmetry. In response, Cryptographic mechanisms such as Zero Knowledge (ZK) based architectures and commit reveal schemes are increasingly proposed as a solution to these vulnerabilities. This research systematically reviews the structural transparency paradox and cryptographic architectures in Decentralized Exchange based Automated Market Makers (DEX-AMM), evaluate their effectiveness in mitigating Maximal Extractable Values (MEVs), analyze computational complexity trade-offs including proof generation/verification costs, gas overhead, latency, and throughput, and identify why commit-reveal may offer superior practical viability despite zk-proofs' stronger theoretical privacy guarantees. A comprehensive search was conducted across arXiv, IEEE Xplore, ACM Digital Library, Scopus, Web of Science, Google Scholar including grey literatures for studies published between 2021 to 2026. Findings indicate that ZK-based approaches provide strong cryptographic privacy guarantees but often incur computational overhead and integration complexity, zk-rollups provide strong validity guarantees through cryptographic proofs, but their practical security depends heavily on the sequencer layer used by ( zkSync, StarkEx, and Loopring) which is responsible for transaction ordering, which can censor, delay, reorder transactions or cause failure of execution, while Commit-reveal schemes may be superior for real-world DEXs due to their constant time hash-based complexity (O(1)), lower gas costs, sub-second latency, and simpler implementation, despite requiring two-transaction UX friction, which can be mitigated through wallet automation. The computational efficiency advantage of commit-reveal becomes critical as DEX transaction complexity increases, where zk-circuit depth grows exponentially. Future research should prioritize optimizing zk-circuit efficiency, developing zk-commit-reveal hybrids system that balance cryptographic strength with computational practicality, and advancing hash-based commit-reveal schemes with UX improvements. DEX developers should prioritize commit-reveal for latency-sensitive applications and zk-proofs only when strongest cryptographic privacy is mandatory.

М.К. Чижова

No abstract is available for this record.

Bruno Casella, Matthias Jakobs, Marco Aldinucci, Sebastian Buschjäger

No abstract is available for this record.

Miss Sheetal R. Billore

The rapid expansion of Artificial Intelligence (AI) across critical sectors has intensified concerns regarding transparency, accountability, fairness, and ethical compliance. Conventional governance mechanisms are primarily centralized, limiting auditability and increasing risks of bias, manipulation, and data misuse. This research proposes a decentralized governance framework integrating blockchain technology to strengthen ethical oversight in AI systems. Blockchain ensures immutability, transparency, and distributed verification of AI operations, including data usage, model updates, and decision records. Smart contracts are incorporated to automate enforcement of ethical principles such as informed consent, bias monitoring, and regulatory compliance. The framework enhances trust among stakeholders by enabling traceability across the AI lifecycle while protecting data integrity. The proposed model is applicable to domains like healthcare, finance, public administration, and autonomous systems where ethical reliability is critical. The study concludes that blockchainenabled governance provides a robust foundation for responsible and sustainable AI deployment.