Live-streaming (LS) e-commerce has become a key sales channel linking manufacturers with end markets, yet live-streaming supply chains (LSS) still suffer from information asymmetry and a lack of credibility in the disclosures. Although AI and blockchain offer potential for real-time, traceable, and interactive information sharing, their impact on disclosure strategies remains underexplored in the field of production operations. This study develops game models for a manufacturer and a live-streaming enterprise (LSE), incorporating rational, risk-averse behaviours under both traditional and AI-blockchain disclosure mechanisms. Through model analysis, optimal strategies from the production stage to the sales stage have been identified. Results reveal three disclosure phases – full disclosure, partial disclosure by LSE only, and partial disclosure by both – while LSE consistently exhibits stronger disclosure willingness. The trust-amplifying effect generated by AI-blockchain technology is non-linear. Adoption of AI-blockchain enhances disclosure and profits when costs are below critical thresholds, whereas traditional strategies offer greater operational robustness under high uncertainty or low willingness. Findings highlight how digital technologies reshape incentives and pricing within SCs, providing practical guidance on information disclosure strategies, technology investment decisions and the improvement of SC operational performance.
The rapid digitization of financial services has created increasingly complex environments in which financial institutions must process large volumes of heterogeneous transaction data while simultaneously protecting customers, detecting fraud, managing financial risks, and complying with regulatory requirements. Traditional centralized and rule-based fraud detection systems face significant challenges associated with data volume, processing latency, evolving fraudulent behaviors, class imbalance, and the increasing sophistication of cyber-enabled financial crimes. This paper proposes a distributed intelligent analytics framework for blockchain-based fraud detection and risk management in financial institutions. The framework integrates distributed big data analytics, artificial intelligence, machine learning, blockchain, graph-based learning, and intelligent decision support into a unified architecture. Distributed computing provides scalable processing of heterogeneous financial datasets, while artificial intelligence identifies anomalous transactions and predicts potential risks. Blockchain provides a complementary integrity, traceability, and verification layer for financial transactions. Graph Neural Networks can further model relationships among customers, accounts, devices, merchants, and transactions, enabling the detection of complex fraud patterns that may not be visible through transaction-level analysis. The framework builds on Ramareddy's work on distributed big data analytics for scalable knowledge discovery in heterogeneous systems and Chhunchha's investigation of blockchain's influence on financial institutions. Recent research also indicates growing interest in machine learning, graph-based models, federated learning, and blockchain for financial fraud detection. The proposed framework addresses important challenges including scalability, privacy, class imbalance, concept drift, explainability, cybersecurity, and regulatory compliance. The paper argues that combining distributed analytics with blockchain and AI can provide financial institutions with a more scalable, transparent, adaptive, and intelligent approach to fraud prevention and financial risk management.
John Alexander Taborda, Cesar Enrique Polo Castro, Alexander Armando Bustamante, Holman Dario Bustos
The transition toward decentralized renewable energy systems creates monitoring problems that current digital infrastructures do not solve: sustainability claims are produced by the same actors they evaluate, environmental evidence is reported periodically rather than observed continuously, and the communities most affected by deployment cannot inspect the data used to represent their territories. Existing integrated platforms combine subsets of the blockchain, Internet of Things (IoT) sensing and life cycle assessment (LCA) at the data layer, but they do not organize that integration through an explicit governance structure. This paper contributes a cybernetic governance framework in which the Viable System Model (VSM) supplies the organizing structure of a blockchain–IoT–LCA monitoring architecture, so that sensing, distributed trust, strategic intelligence and participatory governance are recursively coupled rather than sequentially chained. The framework was developed and evaluated under the Design Science Research paradigm, and instantiated in the IMPACT Energy.CO platform across two technology routes, wind and solar, in La Guajira, Cesar, Atlántico and Magdalena, Colombia. Evaluation against six pre-declared criteria reports 45 executed test cases with a 100% pass rate, 90% unit and 87% integration code coverage, load tests up to 5000 concurrent users with zero errors and sub-second mean response, an operating hash-chained provenance layer issuing verifiable LCA certificates, 14 participatory validation workshops, 199 users trained and 166 technicians certified. We use traceability in a deliberately narrow sense throughout: the property whereby a committed record can be linked to the ingested data series, model version and computation that produced it, and its integrity and ordering checked by a party that does not trust the producer. It is provenance and integrity traceability from the point of ingestion onward, and it is not metrological traceability: the architecture cannot verify that an original sensor measurement corresponds to the physical quantity it purports to represent. We accordingly make explicit what the architecture does not guarantee: a ledger protects records after commitment but cannot certify measurement at the point of capture, and we present a threat model, a set of implemented controls and the residual risk that remains. This study contributes an architecture, a reproducible development and evaluation method, and a calibrated account of what verifiable environmental monitoring can and cannot deliver in contested Global-South territories.
Haitham A. Mahmoud, Ahmed Soliman, Mohammed El-Meligy, Azhar Imran · 5 authors
Abstract Modern digital ecosystems rely mostly on blockchain technology, such as decentralized and immutable ledger systems. This technology avails guarantees of secure transaction and data administration in keeping with the privacy of consumers. Thus, the blockchain systems often suffer in resource-constrained environments to experience considerable computational overhead along with low scalability and issues in handling real-time data. To overcome these restrictions, this research incorporates federated learning, decentralized storage using IPFS, and lightweight cryptographic methods to deliver secure, scalable, and real-time analytics in the IoT system. This research has proposed a novel framework based on blockchain, privacy-preserving techniques, and predictive maintenance models to address some of the security, scalability, and reliability challenges observed in IoT ecosystems. The framework guarantees secure data management, efficient real-time analytics, and robust anomaly detection by using the most advanced technologies such as federated learning, decentralized storage, and lightweight cryptographic methods. The suggested technique exceeds traditional methods by means of accuracy and error reduction with the astonishingly low FPV value of 0.005954% and FNR value of 0.000274% while giving extraordinary performance metrics that reach 99.88% accuracy, 99.89% precision, 99.97% recall, and 99.93% F1-score. This solution establishes secure, scalable, and tamper-proof infrastructure for all the applications from industrial automation, healthcare to vehicular networks, hence enabling smart and sustainable IoT governance for these applications.
This paper presents and evaluates a hybrid blockchain architecture for organizational information systems in which PostgreSQL remains the operational database while Ethereum Sepolia is used as an externally verifiable transaction-recording layer. The implemented proof-of-concept is a university wallet system combining a React and TypeScript frontend, an Express.js backend, PostgreSQL with Drizzle ORM, and an OpenZeppelin-based ERC-20 smart contract deployed on Ethereum Sepolia. The system associates successful application transactions with corresponding Ethereum transaction hashes stored in a dedicated relational table. The experimental evaluation uses sequential workloads of 10, 50, and 100 transactions, comprising 160 measured application transactions in total, together with a separate 30-transaction database-mutation experiment. The performance evaluation measures database insertion time, blockchain transaction time, end-to-end execution time, success rate, and gas consumption. All 160 performance-test transactions completed successfully. Mean database insertion time remained below 32 ms, while blockchain transaction time ranged from approximately 15.26 to 21.93 seconds and dominated end-to-end execution time. Mean gas consumption was approximately 40,324 gas per successful transfer. The database-mutation experiment modified the amount field of 7 of 30 successfully recorded transactions after their blockchain references had been established. All seven modified records retained their corresponding blockchain transaction references. However, the experiment did not perform field-level comparison between the modified database records and blockchain event contents and therefore is not presented as a complete cryptographic tamper-detection validation. The implementation and experimental artifacts are publicly available through the associated project repository. The paper presents the work as a proof-of-concept implementation and empirical evaluation of a hybrid database-to-blockchain transaction architecture.
Smart cities are quickly becoming data-driven environments that are dependent on intelligent technologies to make cities efficient and their citizens happy. In this chapter, the author introduces a comprehensive concept of deep learning and blockchain that will be used to secure and improve multimodal smart city applications. It explores the heterogeneity issues of Internet of Multimedia Things (IoMT) data, such as security, privacy, and trust, and shows how deep learning can facilitate intelligent analysis by means of feature extraction, multimodal fusion, and real-time decision-making. Data integrity and transparency, as well as decentralized governance, are guaranteed by blockchain and secure access control and policy automation through smart contracts. It is also in this chapter that mechanisms of identity and trust management, secure data and model management, and privacy are discussed. Applied benefits are demonstrated by use cases in surveillance, transportation, and energy management, whereas challenges and future research discussions provide a basis for secure, resilient, and intelligent urban ecosystems.
The security, integrity, and trustworthiness of heterogeneous data have become a burning issue in the rapidly changing smart agriculture environment. This chapter discusses how machine learning (ML) and blockchain technologies can be integrated to ensure the security of agriculture-based applications in the Internet of Multimedia Things (IoMT). It explains how multimodal agricultural data, including sensor measurements, satellite pictures, videos taken by drones, and farmer feedback, can be smartly analyzed with the help of ML and deep learning algorithms and safely stored and shared with the help of blockchain systems. The chapter brings to the fore ML-based methods in detecting anomalies, predicting yields, detecting diseases, and decision support and blockchain capabilities of decentralization, immutability, smart contracts, and traceability. The proposals of the architectural models of ML-blockchain-based agricultural systems are introduced with a focus on secure data exchange, access management, and trust management. Practical use cases such as supply chain monitoring, precision farming, and sustainable resource management are also discussed in the chapter and end with the main challenges, limitations, and future research directions.
Blockchain is one important building blocks of the Internet of the future, called Web3. The Blockchain technology supports a wide range of applications, spanning from Smart Cities and automotive industries, from agriculture to energy. The healthcare sector, in particular, has experienced a profound impact from blockchain-based technologies, paving the way for the development of true digital healthcare systems. By enabling secure and immutable data storage, and facilitating the sharing of this information among all nodes possessing a local copy of the distributed ledger, blockchain plays a vital role in the analysis of healthcare data. This paper provides a comprehensive survey of the main blockchain platforms utilized in the digital healthcare, integrated with a comparative analysis. In addition, the implementation of Innovative permissioned Blockchain for eHealth (IBEH) is presented and discussed in detail. IBEH addresses key challenges in digital health data management, including secure and controlled access to sensitive health information, ensuring data integrity and traceability, and secure sharing between different healthcare institutions and organizations. This is made possible by decoupling the application and blockchain layers and by a flexible, customizable, and easily deployable infrastructure. IBEH integrates the application-oriented and embedded layer with that of a blockchain network built with the MultiChain platform, which uses smart contracts with permissions, REST APIs, and RPC calls. The main features and its associated smart contracts within the healthcare domain are discussed. Finally, the analysis of performance is provided.
The rapid expansion of the digital currency market and the growing role of stablecoins as potential intermediaries have brought its interconnectedness with traditional financial markets to the forefront of global financial research. Using daily data from 4 January 2021 to 30 September 2025, this study constructs a variable system with the price indices of USDT and USDC as core digital currency proxies, alongside traditional financial asset indices for stocks, bonds, and gold derived via the entropy weight method. We employ a comprehensive set of econometric techniques, including static correlation analysis, vector autoregression (VAR), impulse response functions, and extreme-event shock tests, to systematically investigate the interdependence structure, risk spillover dynamics, time-varying co-movements, and structural changes between the two markets during extreme risk episodes. The findings reveal an overall weak and asymmetric bidirectional spillover relationship between the cryptocurrency and traditional financial markets. Volatility in the digital currency market is found to be largely endogenous, with a limited capacity to transmit shocks externally. Conversely, traditional financial markets—particularly the equity market—exert a more pronounced influence on the digital currency market. Critically, under the impact of extreme risk events, the cross-market linkages exhibit structural breaks; the direction and intensity of correlation can strengthen significantly or even reverse, demonstrating a clear state-dependency. This research provides empirical evidence for understanding the functional role of digital assets within the macro-financial system, their risk transmission pathways, and their implications for systemic financial stability. The findings offer valuable theoretical and practical insights for financial regulators in designing robust cross-market risk prevention frameworks and for investors seeking to optimize asset allocation strategies.
Dwi Fitrizal Salim, Farida Titik Kristanti, Hosam Alden Riyadh, Mailinda Tri Wahyuni
Type of the article: Research ArticleAbstractThis study evaluates and compares risk measurement models for ten major cryptocurrencies: Bitcoin, Ethereum, Tether, Ripple, Dogecoin, Cardano, Binance Coin, Polkadot, Solana, and USD Coin. Using daily log-return data from January 2017 to October 2024, the analysis applies Modified Cornish-Fisher Value-at-Risk and standard, exponential, threshold, and Markov-switching generalized autoregressive conditional heteroskedasticity models. The main comparison is conducted at the 99% confidence level, while model reliability is assessed through out-of-sample backtesting using 500 observations and the Kupiec unconditional coverage and Christoffersen conditional coverage tests. The results reveal substantial heterogeneity in cryptocurrency risk. Modified Cornish-Fisher Value-at-Risk produces highly sensitive estimates for assets with extreme skewness and kurtosis, particularly Ripple, Cardano, and Dogecoin. However, no single model performs consistently better across all assets. Bitcoin is the only cryptocurrency for which all tested models pass both backtesting procedures. The Markov-switching specification provides acceptable coverage for Bitcoin, Ripple, and Dogecoin but does not consistently outperform conventional volatility models. Standard and asymmetric volatility models provide stronger support for Cardano, Binance Coin, and Polkadot, whereas Ethereum, Solana, and USD Coin remain difficult to model under the examined specifications. These findings demonstrate that cryptocurrency risk measurement requires asset-specific model selection based on both estimated loss magnitude and formal backtesting evidence.
Short Summary - Current Internet protocols move, encrypt, authenticate, delegate, and record data—but they never answer one question: was this specific machine-generated act authorised to become real? This article proposes an execution-finality layer between computation and consequence for AI, cloud, telecom, payments, and critical infrastructure. The internet solved transport, secrecy, identity, delegation, and record-keeping. TCP/IP moves the data. TLS and HTTPS protect the channel and authenticate the endpoint. OAuth delegates access. EMV validates the payment credential. Distributed ledgers order and record the event. Every one of these remains essential. None of them answers the question that now matters most: Was the specific act represented by this data authorised to become externally effective? A packet can be delivered perfectly. A channel can be encrypted flawlessly. An endpoint can be genuine. A token can be valid. A cryptogram can verify. A transaction can be recorded. And still — none of that proves that an AI-generated command, a data export, a telecom transmission, a payment, an infrastructure change, a satellite instruction, a database write, or a physical actuation was ever authorised to cross from computation into consequence. WE BUILT OUR SAFEGUARDS FOR HUMAN TIME. MACHINES NO LONGER RUN ON IT. Earlier digital systems lived inside human reaction time. A suspicious payment could be reviewed. A wrongful disclosure could be investigated. Access could be revoked. A harmful output could be pulled down. AI-native infrastructure does not grant that luxury. A modern AI system can call tools, invoke APIs, export files, initiate payments, rewrite databases, reconfigure networks, drive machines, issue telecom commands, and trigger downstream workflows in milliseconds. By the time a log is read, the data has left the jurisdiction. The payment has settled. The command has executed. The infrastructure state has already changed. So the real problem is no longer detection. The real problem is this: Can the system stop the act from becoming effective before validation is complete? Post-event logging is evidence. Evidence is not prevention. THE LAYER THAT WAS NEVER BUILT The disclosed architecture introduces an execution-finality layer between computation and consequence. It replaces nothing. TCP/IP, TLS, HTTPS, OAuth, EMV, identity systems, policy engines, and ledgers all continue to do exactly what they do today. It adds the one technical condition none of them supply: A computational result does not become externally effective merely because a machine generated, signed, routed, or prepared it. An AI model, telecom function, cloud workload, payment system, satellite controller, application, or autonomous device may generate a proposed operation. The architecture treats that operation as a Candidate Act, held in a non-effective state. A Candidate Act may be an AI output, packet, tensor, API call, payment instruction, file export, storage write, model-memory update, telecom transmission, rendering event, actuator command, or any other consequential operation. Before that act can become real, a protected hardware or cryptographically isolated domain validates the required conditions — which may include authority, purpose, consent, jurisdiction, destination, revocation status, policy epoch, runtime integrity, freshness, quota, protected state, and the identity of the intended effectuation boundary. Only on success is protected evidence committed and a narrowly scoped, non-bearer capability released — bound to that particular act, scope, protected state, evidence, destination, and applicable Finality Sink. THE FINALITY SINK: WHERE COMPUTATION BECOMES CONSEQUENCE The Finality Sink is the precise point at which an act would first become externally effective — a model-output emitter, API dispatcher, telecom gateway, radio chain, SmartNIC, DPU, payment terminal, ledger bridge, memory controller, storage writer, renderer, satellite-command interface, or physical actuator. The Finality Sink verifies the capability before permitting release. Verification fails → the act remains non-effective. Verification succeeds → the capability is consumed before or atomically with effectuation, reducing replay, substitution, duplicate execution, and cross-sink misuse. WHY THIS IS NOT "BETTER SECURITY" Conventional systems place checks around an execution path. The application, model server, network function, or payment system typically retains the technical ability to complete the act anyway. This architecture removes that ability. The ordinary compute environment may calculate or prepare the act — but it does not independently hold the final authority to make the act effective. Authority is separated from computation, and verified again at the consequence boundary. Stated in one line each: Layer Question it answers TCP/IP How is information transported? TLS / HTTPS Is the channel protected? OAuth Who may delegate access? EMV Is the payment credential valid? Ledgers What happened, and in what order? Execution Finality May this specific act become real? The contribution is not another policy engine, authentication scheme, audit system, or cryptographic token. It is a structural dependency: protected validation becomes a technical precondition of effectuation. ONE GAP. EVERY INDUSTRY. The computation-to-consequence gap is not an AI problem. It is an infrastructure problem that appears wherever machines act faster than institutions can respond. Artificial intelligence — model outputs, tool calls, agent actions, code execution, data exports, memory writes, retrieval operations, autonomous workflows. Telecommunications and 5G/6G — packet forwarding, network slicing, roaming, radio emission, gateway egress, satellite communications, non-terrestrial networks, machine-to-machine commands. Cloud and data-centre infrastructure — CPUs, GPUs, AI accelerators, memory controllers, DMA engines, SmartNICs, DPUs, storage controllers, accelerator-interconnect boundaries. Financial systems — payment finality, account transfers, settlement, digital assets, CBDCs, ledger commitments, trading instructions. And beyond — data sovereignty, cross-border data use, industrial control, robotics, vehicles, healthcare infrastructure, energy systems, digital twins, content publication, cybersecurity response, critical infrastructure. Critically, the architecture supports jurisdictional and enterprise control without blanket data localisation and without duplicating national infrastructure. Computation may remain distributed and interoperable; only the authority to produce an external consequence stays protected. 8,598 PAGES. YOU ONLY NEED THREE STEPS. Readers are not expected to work through the specification sequentially. 1. Start with the short invention summary.It covers the Candidate Act, non-effective state, Protected Enforcement Domain, validation evidence, scoped capability, Finality Sink, the difference from conventional systems, the novelty position, and industrial applicability. 2. Download the navigation file.It explains the common inventive concept and routes you to the industry-specific embodiments relevant to AI, telecom, satellites, payments, cloud infrastructure, or cybersecurity. The industry mapping sits at approximately pages 57–61 of the main disclosure. 3. Download the main specification — and go straight to your embodiment.The length reflects the number of implementation environments, effectuation boundaries, hardware arrangements, failure states, and anti-bypass variants. It is not one example repeated 8,598 times. THE ONE SENTENCE THAT HOLDS THROUGHOUT A machine may compute, prepare, or propose an act — but computation alone does not create the authority to make that act externally effective
Arif Billah Dar, Isha Kumari, Safika Praveen Sheikh
Abstract The financialization of gold is investigated by analyzing the dynamic return spillovers between traditional, equity-based, exchange-traded and blockchain-based gold investment means. The empirical system includes physical gold, the SPDR Gold Shares ETF (GLD), VanEck Gold Miners ETF (GDX), NYSE Arca Gold BUGS Index (HUI), First Trust Gold Miners ETF (FTGM), PAX Gold (PAXG), and Tether Gold (XAUT). The study employs the Time-Varying Parameter Vector Autoregressive connectedness framework with generalized forecast error variance decomposition to estimate the magnitude, direction and evolution of shock transmission. The findings show that the financial economy around gold is quite tightly connected, with an average TCI of 81.13%, meaning that most of the forecast error variance of the system is attributable to cross-market shocks. GLD is the biggest net shock transmitter followed by GDX and XAUT, while the biggest net receivers are physical gold and PAXG. The results indicate that the price discovery and information transmission have been shifting from the underlying physical bullion market to exchange-traded markets and digital gold markets. The dynamic analysis also reveals that connectedness remains high but fluctuates throughout the sample, whereas PAXG gradually evolves from a net receiver to a net transmitter in the later part of the sample. Using the alternate Quantile connectedness methodology, we also find that the results are robust. Overall, it is clear that the results reflect the gradual transformation of gold from a commodity asset to a highly networked financial asset. The results are relevant to portfolio diversification, hedging, market monitoring and regulation of new gold products.
With the increasing development of Solidity contracts on Ethereum , more developers are reusing them on other compatible blockchains. However, developers may overlook the differences between the designs of the blockchain system, such as the Gas Mechanism and Consensus Protocol , leading to the same contracts on different blockchains not being able to achieve consistent execution as on Ethereum . This inconsistency reveals design flaws in reused contracts, exposing code smells that hinder code reusability, and we define this inconsistency as EVM-Inequivalent Code Smells . In this paper, we conducted an empirical study to reveal the causes and characteristics of EVM-Inequivalent Code Smells . To ensure the identified smells reflect real developer concerns, we analyzed 1,379 security audit reports, 823 bug bounty reports, and 326 Stack Overflow posts related to reused contracts on EVM-compatible blockchains, such as Binance Smart Chain (BSC) and Polygon . Using the Open Card Sorting method, we defined nine types of EVM-Inequivalent Code Smells . To enable efficient detection, we developed EquivSage , a tool that leverages the contextual understanding capabilities of large language models (LLMs) to guide slicing and static taint analysis via task-specific prompts. Symbolic execution is integrated to improve detection reliability. An analysis of 1,263,683 contracts across six EVM-compatible blockchains using EquivSage reveals that, on average, 15.45% contain at least one EVM-Inequivalent Code Smell , underscoring its widespread prevalence. Since 2024, code smells in reused contracts on Ethereum , Polygon , and Optimism have increased significantly. While not all instances lead to financial loss, high frequency and asset exposure highlight the risks inherent in contract reuse. Developers are encouraged to avoid copy-and-paste practices and to detect such smells proactively before reuse.
Quantum computers pose a fundamental threat to blockchain systems that rely on elliptic-curve cryptography. This work reviews the quantum vulnerabilities and associated economic risks of major blockchain platforms, with a focus on Bitcoin, Ethereum, and Solana. We distinguish between at-rest, on-spend, and on-setup attacks and identify the blockchain components most exposed to quantum adversaries. We further review practical migration strategies toward post-quantum security, including NIST-standardized digital signatures and emerging solutions for Solana, Algorand, and Ethereum.
Blessing Emmanuel Oladele, Adekunle Olugbenga Ejidokun, Chukwuemeka O. Agwu
Academic credential verification remains difficult for institutions because manual checks are slow, fragmented, and vulnerable to fraud. Blockchain can improve trust by anchoring credential proofs, but repeated verification requests and growing off-chain repositories can still create performance bottlenecks. This study presents an adaptive blockchain-powered academic credential repository that combines off-chain MySQL storage, Solidity-based hash anchoring, Redis verification caching, and metadata-aware sharding. Full academic records are not stored on-chain or in Redis; only credential hashes, verification responses, and related metadata are used for trust validation and performance optimization. A CodeIgniter 4 prototype was evaluated using synthetic academic credential records and controlled workloads of 1,000, 5,000, and 10,000 verification requests under fresh, mixed, and repeated access patterns. The results show that Redis caching substantially reduced repeated blockchain queries, especially under mixed and repeated workloads, while metadata-aware sharding improved repository organization and supported more targeted credential retrieval. Sepolia testnet validation confirmed smart-contract feasibility, including issuance, verification, revocation, gas use, confirmation time, and event evidence, but was treated separately from scalability testing. The findings indicate that combining blockchain trust anchoring with cache-aware verification and metadata-based repository partitioning can improve the scalability of academic credential repositories, provided that cache consistency, revocation handling, and deployment limitations are carefully managed.
Adoption of peer-to-peer (P2P) trading is very challenging, mainly due to numerous issues and limitations such as lack of trust in the concept and awareness of the technical, economic and social benefits. This paper aims to understand how blockchain technology can address the current issues/limitations of P2P distributed solar energy (DSE) trading. A series of semi-structured interviews were conducted with 23 community energy stakeholders to confirm and expand the stakeholder issues identified in the literature review. A case representing community energy projects was selected to (1) develop and implement a blockchain system and (2) evaluate its ability to eliminate (or reduce) stakeholder issues and meet stakeholder expectations. A blockchain-enabled P2P trading platform was developed using an Ethereum backend. The system clearly demonstrated its ability to deliver full or partial solutions to 12 stakeholder issues. Two stakeholder issues are unable to be addressed via the blockchain platform since they uncovered the weaknesses of blockchain technology. The P2P trading platform has also demonstrated its ability to facilitate decentralized trading and data management. The outcome of this study indicates the areas of P2P trading projects that can be improved by the application of blockchain technology.
Automated anti-money laundering (AML) on public blockchains is usually framed as a detection problem. Because the ledger is public and permanent, automated detection is feasible, but that record shows only that value moved, without showing who moved it or why. We review automated blockchain anti-money laundering as a system in which machine models and human analysts share each decision, following it through four stages: detection, attribution, adjudication, and reporting, and asking at each stage what automation does well, what it must leave to human judgment, and what goes wrong when that judgment is misplaced. The evidence shows a consistent asymmetry. Machine learning is strong at pattern-finding over the permanent public record, where models rank suspicion and clustering heuristics scale, but it weakens sharply as the task turns from finding a pattern to assigning meaning, identity, intent, or accountability. Drawing first on emerging AML-specific studies and then, where direct evidence remains insufficient, on human-factors research from adjacent high-stakes domains, we find an uneven evidence base. Automation is best supported for large-scale detection, alert prioritization, and parts of blockchain attribution, while the evidence becomes more limited as decisions require contextual interpretation, evidential judgment, accountability, and reporting. AML-specific studies identify explainability, flexibility, tool integration, supervisory justification, and human review as important operational requirements, but they do not yet establish how frequently analysts over-rely on, reject, or selectively follow automated recommendations. Evidence from aviation, healthcare, and public administration is therefore used to identify plausible failure mechanisms rather than to claim AML-specific effects. The result is an evidence-weighted allocation matrix that records, for each stage, the strength of the case for automation, the function retained by the analyst, the dominant failure mode, and the empirical question that remains unresolved. Support is strongest for detection, direct but context-dependent for attribution, and more provisional for adjudication and reporting.
Sunday Yunisa, A. Ajah Ifeyinwa, Eturpa Salami Emmanuel
Internet of Medical Things (IoMT) devices, due to their resource constraints, require specialized security solutions that can operate efficiently without compromising performance and maintaining data confidentiality and integrity while minimizing computational overhead. This review examines various IoMT-based security frameworks designed to secure healthcare records in the cloud, emphasizing their effectiveness, challenges, and best practices. The study was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. 100 studies were identified published between 2020to 2025 and 50 papersthat were relevant to the study was carefully selected through a structured screening process. The papers were obtained from major academic databases such as PubMed, Scopus, IEEE Xplore, SpringerLink, Wiley, and Google Scholar. A systematic review protocol was developed before the literature search to establish clear criteria for inclusion and exclusion, ensuring transparency and reproducibility. The review showed that despite the progress made in safeguarding IoMT cloud-based health records, numerous prevailing frameworks predominantly emphasize either encryption or blockchain technology in a singular context, thereby neglecting to exploit the synergistic advantages inherent in the integration of both methodologies. Also, the encryption techniques currently employed for the protection of records within IoMT cloud environments frequently fail to achieve the essential equilibrium between security and operational performance which is characterized by limited resources. The study recommends the formulation of a framework that integrates several encryption schemes and blockchain technology to address the prevailing security problems.
Purpose This study investigates to investigate the main determinants of financial service industry adoption blockchain technology and their impact on financial transparency and efficiency. In particular, the paper looks at how technological readiness, organizational control and supporting regulations enable blockchain use among financial institutions working in the banks of Jordanian banking sector. Design/methodology/approach A quantitative research design based on a structured questionnaire was employed, which was distributed throughout Jordanian banks to senior and middle-level management. Responses were collected from senior executives, heads of divisions, IT managers and branch managers who are involved in both the financial and technological decision-making processes. SmartPLS was used to conduct Partial Least Squares Structure Equation Modeling (PLS-SEM) analysis using SmartPLS on 192 valid responses to check the measurement model and test the theorized relationships in proposed research framework. Results Results show that technological readiness, organizational governance and regulatory support play an important role in encouraging blockchain technology uptake by financial services. Furthermore, financial institutions adopting blockchain update general ledger journal records in an interactive way which is positively associated with the degree to which financial transparency and operational efficiency are attained. This underscores the part played by technological capabilities as well as governance frameworks and regulatory contexts in encouraging successful blockchain adoption while helping to improve institutional performance. Originality/value In articulating a new frame of analysis from a TOE perspective that takes into account technological, organizational, and environmental factors together, the paper contributes to an emerging literature on adoption of blockchain by financial services. Moreover, it carries out an empirical examination of the performance effects associated with introducing blockchain technology into the banks of Jordan the country which is focused on enhancing financial transparency and operational efficiency. JEL classification G32; K22; O16; L60.
Accurate carbon footprint accounting is fundamental for urban environmental governance. However, multi-stakeholder transit networks struggle with data manipulation, privacy risks, and labor-intensive manual auditing. To resolve these trust and scalability bottlenecks, this paper introduces a tri-layer hybrid blockchain framework based on an “off-chain storage, on-chain evidence” paradigm. The architecture synergizes a relational database (MySQL) for high-throughput structured data, the InterPlanetary File System (IPFS) for decentralized raw evidence, and Hyperledger Fabric to immutably anchor dual-layer cryptographic hashes. We engineer a smart contract auditing pipeline that autonomously executes deterministic verification of hash consistency, emission thresholds, and physical logic integrity. Empirical evaluations utilizing a large-scale urban transit dataset injected with adversarial mutations demonstrate high robustness, achieving F1-scores of 1.000 across multidimensional anomalies. This replaces manual testing with statistically significant verification. Ultimately, this framework provides environmental regulators and transit authorities with a highly scalable, privacy-preserving, and trust-minimized infrastructure for continuous carbon footprint traceability.
Homomorphic-encryption blockchain frameworks for IoT sensor aggregation generally rely on classical cryptographic hardness assumptions and seldom account for network topology in liveness and performance analysis. This work introduces Phi-PHE-BC, a topology-aware homomorphic blockchain architecture for secure and privacy-preserving IoT sensor data aggregation. The framework combines threshold Paillier decryption with graph-parameterized security and performance analysis, linking protocol behavior to the validator graph. On-chain Paillier ciphertexts support homomorphic aggregation while providing IND-CPA confidentiality under the Decisional Composite Residuosity assumption, and authentication signatures provide EUF-CMA transaction integrity. Threshold partial-decryption shares are protected by a noise-flooding wrapper that provides information-theoretic privacy under the configured statistical-hiding condition. Under partial synchrony and Byzantine fault-tolerance assumptions, liveness requires validator connectivity kappa(Gv) >= f+1. We derive topology-dependent throughput bounds for tree, star, mesh, and scale-free networks, together with a per-block communication-cost model. A game-theoretic analysis shows that honest validator participation is a dominant strategy under the stated utility model, yielding an all-honest Nash equilibrium. Experiments on Hyperledger Fabric 2.5 show lower end-to-end latency than the selected traditional PHE-blockchain baseline while maintaining controllable threshold-decryption overhead. Results across topology scaling, validator sensitivity, threshold decryption, and Byzantine-load experiments indicate that Phi-PHE-BC is a practical architecture for secure, privacy-preserving, and topology-aware IoT sensor aggregation.
We present an empirical performance evaluation of SILM, a national-scale music-royalty administration platform prototyped for LMKN, Indonesia's collective rights management agency, implemented as a chain of ten event-driven Go microservices connected through an in-memory publish/subscribe bus (Apache Kafka in the production blueprint). The study contributes a Dapper-style trace-per-event instrumentation yielding per-stage latency distributions, a five-point throughput sweep from 100 to 10,000 play events used to locate the operating point and the degradation knee, and a money-conservation and correctness suite. At every tested burst scale up to 10,000 events the pipeline delivers 100\% event delivery and exact money conservation, while median end-to-end (E2E) latency grows approximately 17-fold (0.81 s at 100 events to 13.7 s at 10,000 events); per-stage spans attribute 85.0\% of E2E average latency at the largest scale to a single cross-service queueing stage. A 30-second sustained-load soak at approximately 1,042 events/s exposes the single-consumer ceiling: the bounded subscriber queue overflows in its tail, dropping 7,097 of 31,255 submitted plays (22.7\%), the first measured reliability failure of the platform. All findings are compared against recent published results on tail latency, bottleneck attribution, and channel sizing in event-driven architectures.
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security