Blockchain Papers

Zexing Lu

The rapid development of cryptocurrencies, stablecoins, and central bank digital currencies (CBDCs) has transformed the global monetary landscape and accelerated the transition toward a cashless society. While critics argue that digital currencies threaten financial stability due to volatility, disintermediation, energy consumption, and regulatory concerns, this paper contends that the increasing competition among digital and fiat currencies can generate significant economic benefits. By examining the evolution of cryptocurrencies, the emergence of stablecoins, the global adoption of CBDCs, and the case of Zimbabwe's hyperinflation, this study argues that currency competition encourages governments to pursue more disciplined fiscal and monetary policies, strengthens policy credibility, and helps anchor inflation expectations. Greater monetary credibility also expands policymakers' ability to respond effectively to future economic downturns. Although digital currencies present important risks, many of these challenges can be mitigated through technological innovation, appropriate regulation, and institutional development. Overall, this paper concludes that a wellmanaged transition toward a cashless society can promote competition, innovation, and long-term economic resilience rather than undermine financial stability.

Giovanni De Luca, Angelo Montanino

Although stablecoins occupy a segment of digital-asset markets in which price stability is central by design, their temporary departures from reference values may reveal important information about latent risk and market stress. In this paper, we examine whether bubble and crash signals extracted from traditional cryptocurrencies and stablecoins improve volatility, Value-at-Risk, and Expected Shortfall forecasting and, in connection with these forecasting gains, contribute to the assessment of cross-asset contagions. The analysis applies the Bubble Crash–GARCH models, in which extreme price phases are identified through the Phillips, Shi, and Yu real-time monitoring procedure and incorporated into the conditional mean of returns through event-based dummy variables. For stablecoins, extreme episodes are not inferred from price dynamics in isolation but from deviations between the observed price and the asset-specific reference value. The empirical investigation focuses on Bitcoin, Ethereum, Tether’s USD-pegged (USDT), and Tether Gold and evaluates asset-specific bubble–crash effects and bidirectional contagion channels between traditional cryptocurrencies and stablecoins, using Bitcoin and Tether as the leading representatives of the two market segments. The findings indicate that accounting for bubble and crash episodes leads to more accurate volatility forecasts than standard GARCH benchmarks. For Value-at-Risk and Expected Shortfall, the bubble–crash specifications can improve tail risk forecasting at several tail probability levels through more accurate coverage, lower quantile loss, and stronger ESR backtesting performance. The results also reveal different degrees of price exuberance across the two asset categories: while extreme price dynamics are more evident among traditional cryptocurrencies, deviations from fundamentals are rare for stablecoins. Among stablecoins, USDT exhibits limited but detectable exuberance, whereas Tether Gold does not display extreme price episodes. However, when such deviations occur, as in the case of USDT, they generate significant contagion effects on major cryptocurrencies. Notably, extreme episodes originating in USDT have a stronger impacts on Bitcoin and Ethereum than the reverse spillovers from traditional cryptocurrencies to USDT. Overall, the evidence suggests that stablecoins are not merely passive instruments within the digital-asset ecosystem. Even temporary deviations from their reference values contain valuable information for risk forecasting and contagion monitoring.

Aurelio Tamarit Blay

The Prop Trust Verified Standard (PTVS) v1.0 Reference Architecture establishes the definitive technical specification, capability matrix, and implementation guidelines for the physical verification of tokenized Real-World Assets (RWAs) within the European regulatory framework. This document resolves the "Physical Oracle Gap" — the structural inability of Distributed Ledger Technology (DLT) systems to attest to the physical existence, structural integrity, and legal encumbrances of off-chain assets backing tokenized securities — through a deterministic four-pillar architecture: Pillar I — eIDAS 2.0 Qualified Forensic Audits: On-site inspections conducted by sworn judicial experts under Qualified Electronic Signatures (QES) per Regulation (EU) 2024/1183. Pillar II — SHA-256 Cryptographic Lineage: Canonical JSON serialization with deterministic hashing anchored in permanent registries. Pillar III — Smart Contract Circuit Breakers: The open-source PTVSClaimInjector.sol contract (MIT License) enforces automated protective actions based on PTVS Score. Pillar IV — PTCE Network: Decentralized network of Prop Trust Certified Experts with 85/15 revenue split. Institutional validation: Formal submissions to ESMA (FOI/ESMA/2026-001), EBA (FOI/EBA/2026-002), EIOPA (FOI/EIOPA/2026-003, confirmed & registered), and ECB/SSM (FOI/ECB-SSM/2026-004, ADITO portal) Application to INATBA RWA Working Group (FOI/INATBA/2026-005) Permanent registration at CERN/Zenodo, HAL/CNRS (hal-05713062v1), OSF (DOI: 10.17605/OSF.IO/7D2SJ), and U.S. Copyright Office (Cases 1-15210573311 & 1-15234961091) Open governance via the PTVS Technical Board (17 seats, W3C/ISO-inspired) Document scope: 17 pages covering architecture overview, PTVS Score methodology (0-100), Verifiable Claims lifecycle, ERC-3643/T-REX integration, regulatory alignment matrix (MiCA, Solvency II, Eurosystem, eIDAS 2.0), governance model, 20-capability prior art inventory, and comparative analysis vs. Chainlink, Proof of Reserve, IoT sensors, Big Four audits, and registry oracles. Lead Researcher: Aurelio Tamarit Blay, Certified Judicial Expert (Exp. No. 0161, Spain), ORCID: 0009-0007-5824-3602, Wikidata: Q140774713. Institutional motto: Veritas in Re · Certitudo in Code Canonical source: https://forensics-oracle.org/reference-architecture/

L. L. Ma

The digital transformation of agricultural supply chains requires efficient coordination among heterogeneous stakeholders and reliable information exchange across distributed logistics networks. As a key component linking agricultural production and downstream distribution, collaboration between agricultural product distribution and textile packaging enterprises has become increasingly dependent on intelligent communication and data-sharing infrastructures. This study systematically investigates the strategic management mechanisms and implementation pathways for collaborative development by integrating transaction cost economics, complex adaptive systems theory, and network effects theory. A four-dimensional management framework encompassing technological support, organizational coordination, benefit distribution, and risk prevention is established, in which entropy weight–TOPSIS is employed for strategic objective alignment, blockchain-based architectures enable trusted information sharing, Shapley value optimization supports dynamic benefit allocation, and Value-at-Risk (VaR) models facilitate quantitative risk control. The proposed framework further incorporates smart contracts and permission-controlled data interaction to improve collaboration efficiency while preserving data security. The resulting management architecture provides a quantitative and scalable solution for digital supply chain coordination and demonstrates practical value for intelligent logistics systems. Moreover, its distributed information-sharing mechanisms and network-oriented optimization strategies offer methodological references for communication-enabled industrial ecosystems, wireless sensing infrastructures, and electromagnetic information transmission environments requiring reliable multi-node coordination and secure data exchange.

Halid Syahrani

Through this independent concept, the study introduces a fresh new perspective to the world of modern forensic accounting via a theory called “The Decentralized Fraud Matrix” (DFM). This conceptual research was developed specifically as an analytical tool to dissect the modus operandi of financial crimes in the digital-cyber era—including Web3 environments, blockchain architecture, DeFi protocols, and autonomous DAO systems. The focus of the DFM theory completely breaks away from the basic assumptions of the conventional fraud triangle, which has long been overly preoccupied with measuring human emotions. Mechanically, the originality of this theory rests on the testing of three interlocking cyber indicators in the field. These three indicators include the level of opacity in an actor’s digital identity concealment; technological engineering designed to break the audit trail of fund flows; and the exploitation of loopholes in physical national sovereignty boundaries, as well as cyber “jurisdictional evasion” tactics aimed at neutralizing the enforcement power of on-ground regulations, thereby rendering perpetrators immune to formal legal prosecution

Y. Su

With the rapid advancement of industrial Internet technologies and intelligent wireless sensing infrastructures, efficient data acquisition and information transmission have become fundamental to modern textile supply chain management. The integration of electromagnetic-enabled Internet of Things (IoT) devices, RFID technologies, and intelligent communication networks provides essential support for real-time financial monitoring and digital taxation services. Against this background, this paper investigates the application of intelligent finance and taxation in textile industry supply chains by proposing an integrated framework based on artificial intelligence, blockchain, cloud computing, and IoT technologies. The framework enables transparent financial management, automated tax compliance, dynamic supply chain finance, and end-to-end traceability through seamless integration of operational, financial, and logistics data. Key applications, including blockchain-based material provenance verification, AI-driven credit assessment, automated customs and tax processing, and intelligent risk management, are systematically analyzed. The proposed architecture improves supply chain transparency, operational efficiency, sustainability, and resilience while facilitating data-driven decision-making across textile production and distribution processes. Furthermore, the study demonstrates that intelligent finance and taxation can establish a unified digital ecosystem for financial governance and supply chain collaboration, providing valuable technical references for wireless industrial information acquisition, smart sensing, and communication-assisted digital management in future intelligent manufacturing environments.

C. O. Enuma, Matthias D., V.I.E. Anireh, Bennett E.O.

Abstract The increasing adoption of cloud computing and blockchain-based smart contracts has transformed digital service delivery through decentralized automation, transparency, and trusted transaction execution. However, existing smart contract frameworks continue to face challenges related to privacy preservation, secure computation, intelligent access control, execution integrity, and auditability. Most existing solutions rely on isolated privacy-preserving mechanisms, exposing sensitive information during computation and limiting scalability and overall system performance. This study developed a Model for Privacy-Preserving Smart Contract in Cloud Computing by integrating Zero-Knowledge Proofs (ZKP), Secure Multi-Party Computation (SMPC), Trusted Execution Environments (TEE), Federated Learning (FL), Differential Privacy (DP), Autoencoder-based anomaly detection, GraphSAGE Graph Neural Networks (GNN), Proximal Policy Optimization (PPO), and Blockchain Smart Contracts within a unified architecture. The study adopted the Design Science Research Methodology (DSRM), while Object-Oriented Analysis and Design (OOAD) guided system implementation. The proposed model was evaluated using the CICIDS2017 cybersecurity benchmark dataset across privacy, security, execution integrity, auditability, scalability, computational performance, and cost efficiency. Experimental results achieved 96% privacy preservation, 94% security strength, 99% execution integrity, 98% auditability, and 90% scalability, while the Artificial Intelligence Privacy Engine attained 98.91% validation accuracy, 0.9962 ROC-AUC, 0.9490 Macro F1-score, and 0.9718 Matthews Correlation Coefficient (MCC). Comparative analysis against RBAC, ABAC, and blockchain-based frameworks demonstrated superior performance in privacy preservation, secure computation, intelligent authorization, and auditability. The proposed model provides a practical, scalable, and intelligent solution for secure smart contract execution in privacy-sensitive cloud computing environments. Keywords: Privacy-Preserving Smart Contracts, Cloud Computing, Blockchain, Zero-Knowledge Proofs, Secure Multi-Party Computation, Trusted Execution Environments, Federated Learning, Differential Privacy, Graph Neural Networks, Artificial Intelligence.

C. O. Enuma, Matthias D., V.I.E. Anireh, Bennett E.O.

Abstract Cloud computing has become the preferred platform for deploying blockchain-enabled smart contracts because of its scalability and flexibility. However, existing access control mechanisms such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and conventional blockchain authentication expose sensitive user information during authentication, rely on static authorization policies, and lack intelligent mechanisms for detecting evolving cyber threats. This study proposes an Intelligent Privacy-Preserving Access Control Framework for Cloud-Based Smart Contracts that integrates Modified Groth16 Zero-Knowledge Proofs (ZKP), Secure Multi-Party Computation (SMPC), Trusted Execution Environments (TEE), Federated Learning, Differential Privacy, GraphSAGE Graph Neural Networks, Autoencoder-based anomaly detection, Proximal Policy Optimization (PPO), and Blockchain Smart Contracts. The framework enables credential-free authentication, confidential collaborative computation, adaptive authorization, intelligent threat detection, and immutable blockchain auditing without compromising user privacy. The proposed framework was implemented and evaluated using the CICIDS2017 cybersecurity dataset. Experimental results achieved 96.4% privacy preservation, 94.1% security strength, 99.0% execution integrity, 98.7% auditability, 90.3% scalability, 88.6% computational performance, 86.9% cost efficiency, 98.91% validation accuracy, 99.62% ROC-AUC, 94.90% Macro F1-Score, and an overall system fitness of 94.23%. Comparative evaluation against Hawk, Zether, Ekiden, and a Federated Learning-only IDS demonstrated superior performance across all evaluation metrics. The proposed framework therefore provides an intelligent, scalable, and privacy-preserving access control solution suitable for next-generation cloud-based smart contract systems. Keywords: Privacy-Preserving Access Control; Smart Contracts; Cloud Computing; Zero-Knowledge Proof; Secure Multi-Party Computation; Trusted Execution Environment; Federated Learning; Blockchain.

Narendra Kumar Ch, Dinesh Kumar, Amit Prakash, Dipankar Rajwar · 5 authors

Abstract In the current digital era, the storage of electronic health records on centralized platforms presents significant integrity, privacy and security challenges. Further, access to this stored healthcare data should be quick and efficient, especially during emergencies. Blockchain and edge computing brought a great revolution in managing healthcare data by ensuring security, immutability, and decentralized data sharing with reduced latency. But, the integration of edge computing with the blockchain networks is still a gap to achieve ideal healthcare goals of data security with real-time data processing. The contribution of this work is two-fold. First, a novel deep reinforcement learning based medical data offloading scheme is proposed for offloading healthcare data to the nearby edge servers from the end users. The learning policy uses the proximal policy optimization algorithm for making the optimal offloading decision and minimizes the overall delay and energy consumption of healthcare devices and edge servers. Second, we proposed a secure, scalable, and consent-based data sharing scheme among multiple stakeholders such as patients, hospitals, doctors, healthcare research institutes etc. The EHR sharing scheme uses the AES and RSA algorithms for encryption, which ensures only authorized and consent-based access to the sensitive data stored in IPFS. The performance of the proposed offloading scheme is evaluated in terms of delay and energy consumption whereas data sharing scheme is evaluated in terms of latency and throughput using Hyperledger Besu and Hyperledger Caliper platforms. The experimental study exhibits that the proposed approach is both feasible and scalable, making it suitable for integration into the e-healthcare systems.

Lin Wang, Siew Imm Ng, Norazlyn Kamal Basha

This study investigates the moderating role of blockchain traceability adoption in enhancing consumer engagement and purchase intention within live-streaming agricultural e-commerce platforms in China. Drawing upon the Stimulus-Organism-Response (S-O-R) framework operationalized at the aggregate market level and information asymmetry theory, this research employs longitudinal market-level time-series data spanning 2019 to 2024, utilizing hierarchical regression analysis with Hayes's conditional process framework to examine main effects, mediation mechanisms, and moderation relationships. The empirical findings reveal that platform development and information transparency exert significant positive effects on market purchase behavior, with consumer engagement serving as a partial mediating mechanism transmitting these effects. The moderation analysis demonstrates that blockchain traceability adoption significantly strengthens the relationships between platform stimuli and consumer engagement, with the information transparency pathway exhibiting substantially stronger moderation effects than the platform development pathway, demonstrating that blockchain technology functions as a selective trust-enhancing mechanism that validates quality signals rather than operating as a general platform enhancer—a distinction representing the central empirical contribution of this study. These findings extend the traditional S-O-R framework by incorporating technological infrastructure as a boundary condition shaping stimulus effectiveness at the market level, while providing practical guidance for platform operators and policymakers to prioritize blockchain traceability infrastructure investment in conjunction with transparency enhancement initiatives for promoting high-quality development of agricultural live streaming e-commerce.

Venice Lei De Luna Lucto

No abstract is available for this record.

X. L. Li, D. H. Chen, Y. F. Liu

In blockchain-enabled supply chain finance, traditional credit risk assessment models suffer from conflicts between data sharing and privacy protection, reliance on static evaluation methods, and limited data credibility. To overcome these challenges, this paper proposes a blockchain-based dynamic credit risk assessment model that integrates privacy computing and intelligent risk monitoring. First, blockchain’s immutability and traceability ensure the authenticity and transparency of supply chain transaction data, effectively mitigating information asymmetry and data tampering. Second, privacy-preserving technologies, including homomorphic encryption based on the Paillier algorithm and zk-SNARKs, enable secure data sharing and validity verification without exposing sensitive enterprise information, thereby improving assessment reliability. Third, a dynamic risk monitoring framework is constructed by combining smart contracts, long short-term memory (LSTM) networks, and an improved dynamic graph neural network (DGNN). LSTM models temporal risk evolution in transaction data, while DGNN captures risk propagation among upstream and downstream enterprises. Smart contracts synchronize transaction states in real time, allowing continuous updates of credit risk levels. The proposed secure information processing and dynamic graph modeling strategy also provides a valuable reference for trustworthy data interaction and intelligent decision-making in distributed electromagnetic sensing and communication networks, where reliable information propagation and adaptive resource management are essential. Experimental results based on a textile supply chain dataset show that the proposed model achieves approximately 94% credit assessment accuracy, outperforming traditional static models by 15%–20%, while maintaining excellent response speed and throughput for dynamic financial decision-making. The proposed framework provides a practical and secure solution for blockchain-based credit risk management and offers methodological insights for data-driven engineering systems requiring secure information fusion and dynamic network analysis.

Dušan Mitrović, Ivan Milenković, Miroslav Minović

The growing use of blockchain in e-commerce has produced hybrid environments in which private enterprise ledgers and public blockchain networks operate side by side. Consequently, efficient and secure interoperability between these networks has become increasingly important. This study presents a cross-chain interoperability framework that links a permissioned Hyperledger Fabric network with a public Ethereum network. The framework provides attestations of selected business events rather than moving assets. An interoperability smart contract on Fabric emits cross-chain events; an off-chain validator enforces uniqueness and replay protection; and a public verification contract on Ethereum records an immutable, publicly verifiable attestation of each event. The framework uses a two-of-three validator threshold to attest events, so safety holds as long as no more than one of the three validators is compromised. The prototype was evaluated by processing 21,000 events across sequential, concurrent, and peak-load workloads. On the local network, message validation averaged approximately 12 ms per event, and the interoperability layer added less than 200 ms of overhead per attestation. Sustained throughput ranged from 13.2 to 14.2 attestations per second, while the validator used approximately 16% mean CPU and less than 194 MiB of memory, with no sustained memory growth during the full experiment. On the Ethereum Sepolia public testnet, 55 transactions were confirmed with a 100% success rate and a mean confirmation time of 10,676.62 ms. Gas consumption stayed stable at about 51,743 gas per verification on the local network and about 189,092 gas on Sepolia, and the mean public testnet transaction cost was 0.000692 Sepolia ETH. Five adversarial tests were conducted, covering replay, forgery, malicious relayers, concurrent replay, and denial-of-service attacks. All five tests passed, including the rejection of 500 concurrent replay attempts with zero double registrations. The results show that the framework provides efficient, verifiable, and replay-resistant cross-chain interoperability suited to hybrid e-commerce ledgers.

De-Graft Johnson Dei, Karim Awudu

The rapid expansion of institutional repositories (IRs) has heightened concerns about digital rights management (DRM), copyright protection, content authenticity, and long-term digital preservation, particularly in developing countries where institutional and technological capacities remain constrained. This study examines the feasibility of adopting blockchain technology as a DRM solution for Ghanaian institutional repositories and evaluates whether its application is transformative or largely aspirational. Guided by the Technology–Organization–Environment (TOE) framework and Diffusion of Innovations (DOI) theory, the study employed a sequential explanatory mixed-methods design that integrated quantitative survey data with qualitative interviews with ICT directors, repository managers, academic librarians, systems librarians, and faculty members from eight Ghanaian universities. The findings reveal low DRM maturity across institutional repositories. 40% of participating institutions lacked formal DRM mechanisms. Although awareness of blockchain technology was moderately high among respondents, substantial disparities existed across stakeholder groups, with ICT personnel demonstrating higher levels of understanding than faculty members and academic librarians. Institutional readiness for blockchain adoption remained generally poor, constrained by inadequate infrastructure, funding limitations, insufficient technical expertise, weak policy frameworks, and low organizational preparedness. Despite these limitations, stakeholders expressed strong support for blockchain’s potential to strengthen tamper-proof authorship verification, enhance content authenticity and integrity, improve transparency through immutable audit trails, and automate copyright management through smart contracts. The study further suggests that capacity building, phased implementation strategies, open-source platforms, interdisciplinary collaboration, and institutional policy alignment are critical pathways for integrating blockchain into institutional repositories. The study concludes that blockchain-enabled DRM in Ghanaian IRs is a promising, emerging innovation and that its successful implementation depends on sustained investment in digital infrastructure, institutional reforms, technical training, and supportive regulatory frameworks.

Jaume Martin Bosch, Marco Combetto, Luca Tangi, A. Paula Rodriguez Müller

Introduction Blockchain technology (BCT) has been widely discussed as a potentially valuable technology for advancing sustainable development in the public sector. Its core features, including transparency, immutability and decentralisation, may contribute to more accountable, efficient and inclusive public services. However, limited empirical evidence exists on how BCT-based public sector initiatives align with the United Nations Sustainable Development Goals (SDGs). Methods This study examines 306 public sector BCT-based use cases across the EU, compiled by the Public Sector Tech Watch observatory. We apply a GPT-4o-based AI text classification pipeline to assess the degree of alignment between project descriptions and the 17 SDGs. The pipeline combines refined SDG descriptors, structured prompting and documented model parameters. Its outputs are benchmarked against a human-coded subset to assess validity. Results The results show strong alignment with SDG 9 (Industry, Innovation and Infrastructure) and SDG 17 (Partnerships for the Goals), followed by more moderate alignment with SDG 8 (Decent Work and Economic Growth). By contrast, goals such as SDG 2, SDG 6 and SDG 14 remain weakly represented. These findings provide an empirical overview of how BCT applications in EU public administrations are framed in relation to the SDGs. Discussion By highlighting patterns of alignment between BCT adoption and the SDGs, this study offers evidence to inform policymakers, practitioners and future research on sustainability-oriented public sector innovation. It also demonstrates the value of AI-assisted classification for mapping large corpora of digital government initiatives, while recognising that the results capture stated or perceived alignment rather than verified sustainability impacts.

Sathy Akter*

Currently, the most significant threat to the validity of academic credentials in the United States is the advanced forgery of transcripts along with diploma mills. This research study addresses the potential of blockchain technology as a decentralized means to protect academic credentials. By integrating recent academic research and technical frameworks, this study analyzes the shift from centralized databases to immutable, distributed ledgers. The integration of various perspectives, including advanced zero-knowledge proof architectures as well as legal frameworks for transnational data circulation, is a major innovation of this study. Using a systematic literature review and a case study approach, the research indicates that though blockchain's potential to enhance security and automate processes through smart contracts is indeed great, a number of legal, compliance, and technical barriers have to be removed for it to be a viable option. This study proposes that the combination of artificial intelligence (AI), along with blockchain technology, provides the most secure option for U.S. higher education institutions.

Goldy Soni

This chapter proposes an integrated Blockchain–IoT–AI framework for secure and intelligent quality traceability, particularly in agricultural and rice supply chains. It explains how IoT sensors can continuously collect physical and environmental information, AI models can analyze images and sensor data for quality assessment, and blockchain can securely record important quality events and processing information. The framework supports unique digital identities for rice batches, quality monitoring, defect detection, moisture estimation, quality scoring, and QR-based access to traceability information. The chapter examines applications in rice quality certification, smart rice mills, food safety, warehouses, export-quality monitoring, consumer verification, and government procurement. Challenges related to data quality, sensor reliability, interoperability, stakeholder participation, scalability, and regulatory coordination are also addressed.

Rashon Rahming

The programmable economy lacks a universal computational layer capable of interpreting, translating, verifying, and simulating the mathematical and cryptographic operations that underpin digital assets. Existing tools are fragmented: wallet software provides only rudimentary transaction signing, portfolio trackers offer aggregated views without evidence, and specialized calculators address isolated problems. No general-purpose, cryptographically verifiable, language-native computational environment exists for digital value. KHOTOR is designed to fill this gap. It is a universal, deterministic runtime that interprets the anti-entropic linguistic protocol Kryptophon, transforms plain-language queries into executable computational expressions, and performs multi-domain financial mathematics across asset conversion, transaction analysis, decentralized finance, tokenomics simulation, cryptographic proof generation, and risk assessment. Every output carries an epistemic classification — verified, observed, inferred, simulated, or uncertain — and can be exported as a Gamma-Proof: a cryptographically signed, independently verifiable artifact. This paper presents the complete KHOTOR architecture: a ten-layer computational engine, a formal abstract machine for Kryptophon evaluation, a tiered adoption model that makes the programmable economy accessible to non-technical users while creating a new domain of expertise for professionals, and a product family spanning a public cloud API, a web platform, a handheld consumer device, and integration with dedicated hardware instruments. All components are designed around a single governing principle: every calculation shows its work, every output carries a truth label, and no inference is ever presented as fact.

Beacon Kit

Beacon Kit: Ecosystem epoch heartbeat @ the world game (s). Block-time arbitrage tokenized commodity index, adaptive procedural template @ system of federated DeFi cryptocurrency quantum - AI systems consensus

G. Weerasinghe, M. M. S. A. Karunarathna

The rapid growth of cryptocurrencies and increasing instability in traditional financial systems have significantly transformed global investment behaviour in recent years. In developing countries experiencing economic crises and currency depreciation, investors increasingly seek alternative financial assets that can preserve value and generate higher returns. Sri Lanka has recently experienced severe economic instability characterised by inflation, foreign-exchange shortages, sovereign debt problems, and rapid depreciation of the Sri Lankan rupee. Under these conditions, interest in cryptocurrency investment has increased, particularly among younger and technologically aware investors. Therefore, this study examines whether fiat currency devaluation shifts investment from the stock market to the cryptocurrency market among university students in Sri Lanka. The study adopts a quantitative research approach and uses primary data collected through a structured questionnaire from 150 final-year undergraduate students at the University of Sri Jayewardenepura. Stratified random sampling was used to select respondents from the Faculty of Humanities and Social Sciences, the Faculty of Management Studies and Commerce, and the Faculty of Applied Sciences. Descriptive statistics, chi-square analysis, and binary logistic regression were employed to analyse the relationship between rupee depreciation and cryptocurrency investment behaviour. The findings reveal that depreciation of the Sri Lankan rupee significantly influences investment decisions among university students. Most respondents perceived cryptocurrency investment as more profitable than stock-market investment during periods of economic uncertainty. The chi-square analysis identified significant relationships between cryptocurrency investment behaviour and age, income, stock-market investment, and perceptions of rupee depreciation. Furthermore, the binary logistic regression results confirmed that rupee depreciation positively and significantly affects cryptocurrency investment, whereas stock-market investment had a negative relationship with cryptocurrency investment behaviour. The study concludes that economic instability, declining confidence in fiat currency, and increasing awareness of digital financial systems encourage university students in Sri Lanka to shift their investment preferences from the traditional stock market to cryptocurrency.

Kiryl Minkin, Dariusz Drążkowski

This systematic review synthesises empirical research on individual-level cryptocurrency adoption, distinguishing adoption intention, actual adoption and use, and continuance intention and use. We searched Scopus and Web of Science for English-language empirical studies published between 2019 and 2025 and synthesised findings using a structured narrative approach. Eighty-five studies were included, with reported sample sizes summing to 56,054 participants. No formal study-level risk-of-bias assessment was conducted. The literature was dominated by cross-sectional quantitative studies and technology-adoption frameworks, particularly UTAUT, TAM, TPB, and DOI. Evidence was strongly concentrated on adoption intention (n = 75), whereas actual adoption and use (n = 16) and continuance intention and use (n = 8) were examined much less frequently. Across studies, adoption was associated with psychological, technological, social, economic, knowledge-related, institutional, and individual factors, with no single determinant consistently dominating across outcomes. The synthesis further distinguished direct predictors, mediating mechanisms, moderators, drivers, and barriers. The evidence base is limited by its reliance on self-reported, cross-sectional designs and uneven coverage of realised and continued engagement. Future research should more clearly specify adoption outcomes and use longitudinal, behavioural, and post-adoption designs.

Joel Humphries

While much has been written about the volatility of digital assets, academic scholarship has largely overlooked how blockchain technologies have been adopted and reimagined by LGBTQ+ communities. This article addresses that gap through a digital ethnography of queer NFT communities active during the crypto craze of 2022, combining online participant observation with semi-structured interviews. Drawing on José Esteban Muñoz’s concept of queer futurity, it examines how queer users imagined blockchain as a speculative platform for alternative economic and social possibility—despite the financial risks embedded in the technology’s libertarian and capitalist structures. The article interrogates the utopian rhetoric of inclusion, decentralisation, and wealth redistribution that was deployed within these communities to justify their interest in and holdings of non-fungible tokens (NFTs) and cryptocurrency. Queer leaders leveraged the blockchain to foster inclusive digital communities and promote wealth circulation amongst LGBTQ+ individuals, while community members embraced the technology as a risky opportunity for queer economic mobility. The article positions blockchain as a contested site where competing futurities collide—offering the illusion of liberation and the reproduction of existing inequalities. It argues that while queer users sought to make the blockchain ‘queer from the start,’ their efforts were ultimately constrained by the capitalist logics that underpin the technology.

Muhammad Farooq Shaikh, S. Hamza Hassan, Jawwad Shamsi, Alessia Maccaro · 5 authors

Background and objective The integration of blockchain and digital twin (DT) technologies is increasingly recognised as a promising approach for improving healthcare data integrity, interoperability, privacy, and clinical decision support. While digital twins enable dynamic patient modelling and predictive healthcare applications, blockchain provides secure data governance through decentralised trust, auditability, and access control. However, existing research remains fragmented, with limited synthesis of the architectural integration, regulatory readiness, ethical governance, and interoperability of blockchain-enabled healthcare digital twin systems. This systematic scoping review addresses these gaps by providing a comprehensive architectural and compliance-oriented analysis of the current evidence. Methods A systematic scoping review was conducted following PRISMA 2020 guidelines using Scopus, PubMed, and Web of Science. From 148 identified records, 55 eligible studies published between 2020 and 2025 were included after duplicate removal and eligibility screening. Data were extracted on digital twin functionality, blockchain architecture, healthcare application domains, consensus mechanisms, privacy-preserving strategies, and regulatory and ethical alignment. Structured Python-based visual mapping and comparative analyses were performed to identify architectural, governance, and compliance patterns across the literature. Results The findings demonstrate that blockchain is predominantly employed to provide access control, audit logging, data integrity, consent management, and secure data provenance within healthcare digital twin ecosystems. Patient-level and EHR-centred digital twins represented the most mature application areas, whereas cross-domain and infrastructure-level frameworks dominated early architectural exploration. The review identifies recurring compliance-oriented architectural patterns while revealing substantial gaps in clinically validated deployments, interoperability with established healthcare standards, decentralised governance models, and formal implementation of GDPR- and HIPAA-compliant engineering practices. Comparative heatmap analyses further highlight the uneven maturity of ethical governance and regulatory integration across blockchain functionalities. Conclusion This review provides the first comprehensive compliance-oriented architectural synthesis of blockchain-enabled healthcare digital twin systems by integrating technical architecture, regulatory readiness, ethical governance, and privacy-preserving design patterns within a unified analytical framework. The proposed architectural mapping identifies critical research gaps in interoperability, governance engineering, consensus optimisation, and real-world clinical validation, providing a foundation for the development of trustworthy, GDPR/HIPAA-aligned, FHIR-compatible, and clinically interoperable healthcare digital twin ecosystems.

Tan Liu, Yimin Yu

No abstract is available for this record.