The study involved a total of 217 respondents. consisting of key stakeholders in higher education, including undergraduate and postgraduate students, academic staff, and administrative personnel. The respondents were selected using a purposive sampling technique to ensure they had relevant experience and understanding of academic data management systems. Among the participants, the majority were students, representing approximately 65%, followed by academic staff at 20%, and administrative personnel at 15%. In terms of gender distribution, 54% were female and 46% were male. Most respondents were aged between 18 and 30 years, reflecting a digitally active population familiar with emerging technologies. Additionally, a significant proportion of respondents reported prior exposure to digital academic systems, while a smaller percentage demonstrated awareness of blockchain technology applications in education. This distribution ensures that the collected data reflects diverse perspectives within the academic ecosystem and supports the reliability of the analysis conducted using Structural Equation Modeling-Partial Least Squares (SEM-PLS).
Blockchain technology offers a distributed, immutable ledger that can improve transparency, traceability and trust among multiple parties in logistics and supply-chain networks. This expanded review synthesises systematic literature from 2018â2025, presents key real-world case studies, quantifies reported benefits, catalogues persistent challenges, and examines the convergence of blockchain with IoT sensors, artificial intelligence and digital twins. Emphasis is placed on food traceability, maritime shipping, pharmaceuticals, sustainability reporting and the socio-technical conditions required for successful adoption. The review also draws on related recent work on supply-chain resilience, digital twins, AIâblockchain integration and emerging quantum approaches to logistics optimization. Illustrative figures and a market-growth curve accompany the analysis. Keywords: Blockchain; Logistics; Supply Chain Management; Traceability; Smart Contracts; Trade Lens; IBM Food Trust; Permissioned Ledger; Interoperability; Digital Twin; IoT Integration; Literature Review; Supply Chain Resilience; Quantum Logistics.
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.
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 CrashâGARCH 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, Tetherâs USD-pegged (USDT), and Tether Gold and evaluates asset-specific bubbleâcrash 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 bubbleâcrash 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.
This paper selects the data of Bitcoin, Gold, and the S&P 500 index from 2018 to 2025, utilizing GARCH(1,1) and DCC-GARCH models to depict the dynamic conditional correlations among assets. By incorporating the Global Geopolitical Risk Index, the 10-year breakeven inflation rate, and the VIX panic index, it constructs daily and monthly cross-frequency regression models to examine their macro-driving mechanisms. The results show that whether at the high-frequency daily level or the smoothed monthly level, macroeconomic variables exhibit extremely significant driving effects on the co-movement of Bitcoin. Under the liquidity squeeze concerns triggered by intensified global panic or high inflation expectations, Bitcoin fails to act as a haven alongside gold. Instead, it exhibits a stronger synchronous crash with the US stock market. This empirical study rejects the hypothesis of Bitcoin as "digital gold," revealing its essence as a "risk amplifier" highly dependent on traditional liquidity, and provides quantitative support for international investors in asset allocation under extreme macroeconomic scenarios.
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" â 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 â through a deterministic four-pillar architecture: Pillar I â 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 â SHA-256 Cryptographic Lineage: Canonical JSON serialization with deterministic hashing anchored in permanent registries. Pillar III â Smart Contract Circuit Breakers: The open-source PTVSClaimInjector.sol contract (MIT License) enforces automated protective actions based on PTVS Score. Pillar IV â 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/
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Blockchain Technology Applications and Security
Digital and Cyber Forensics
Physical Unclonable Functions (PUFs) and Hardware Security
Purpose Rapid technological advancement has accelerated the integration of financial technology (FinTech) into traditional banking systems. Banks have adopted digital payments, artificial intelligence, blockchain solutions and open banking frameworks, thereby increasing competition and prompting regulatory adaptation. This study conducts a theory guided systematic literature review and bibliometric analysis of FinTech banking research (2019â2024) to map the intellectual structure, thematic evolution and research gaps. Design/methodology/approach The review analyses 224 peer reviewed journal articles indexed in the Web of Science Core Collection. Using BibExcel and VOSviewer, the study employs co-citation analysis, keyword co-occurrence mapping, clustering techniques and temporal overlay analysis. The review protocol follows explicit search strings, inclusion criteria and screening procedures to enhance transparency and replicability. Findings Six major thematic domains emerge: competition and risk-taking dynamics, financial inclusion and regulatory boundaries, institutional technology integration, performance and efficiency outcomes, innovation and regulatory economics and digital transformation and adoption behaviour. Temporal analysis reveals a progression from adoption focused inquiry toward governance, competition and systemic stability debates. Despite increasing empirical sophistication, the field remains fragmented across behavioural, institutional and macroprudential levels. Originality/value This study embeds bibliometric mapping within a multi-level theoretical framework integrating diffusion, disruptive innovation and ecosystem perspectives. The research provides a critical synthesis of the evolving FinTech banking literature. The findings identify key research gaps, reveal emerging thematic patterns in FinTech banking research and outline directions for future research while offering implications for banking practitioners and regulators.
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 weightâTOPSIS 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.
Through this independent concept, the study introduces a fresh new perspective to the world of modern forensic accounting via a theory called âThe Decentralized Fraud Matrixâ (DFM). This conceptual research was developed specifically as an analytical tool to dissect the modus operandi of financial crimes in the digital-cyber eraâincluding 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 actorâs 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 âjurisdictional evasionâ tactics aimed at neutralizing the enforcement power of on-ground regulations, thereby rendering perpetrators immune to formal legal prosecution
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.
Decentalized energy systems are a radical departure from the way we have traditionally produced, distributed and consumed electricity â relying upon centralized grids that depend on fossil fuels towards more sustainable, resilient and community-based models. These systems improve grid flexibility, save transmission costs and are fit for renewable power input like solar or wind, since prosumers can generate and distribute energy within the local area. Blockchain technology is going to be a key enabler of this transition, as it offers a reliable, transparent and decentralized platform for energy sharing and grid management. The blockchain technology utilizing the smart contracts and mutual system can provide reliable peer-to-peer power trading, accurate settlement, and less necessity of centralized agency. Its distributed ledger makes the equipment trustable for all participants, and meanwhile it realizes real-time transaction data sharing to optimize grid management like demand response of electricity and certificates tracing of green power production.
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.
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.
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 enhancerâa 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.
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, blockchainâs 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%â20%, 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.
DuĹĄan MitroviÄ, Ivan MilenkoviÄ, Miroslav MinoviÄ
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.