The Natural Economic Wealth framework is theoretically complete. Its axioms are established, its instruments are derived, and its adoption mechanism is formalised. But a theory is not yet a practice. This paper addresses the institutional container within which the Qoin economy can be realised: the legal, social, and organisational structures that protect it from absorption, disruption, or destruction by the existing monetary order. The container is built from four interlocking elements: cooperative law, which provides legal personhood, democratic governance, and non-profit distribution; distributed ledger architecture, which provides immutability, resilience, and verifiability; historical prece- dent, which demonstrates that parallel economic systems can survive and thrive along- side FIAT; and community governance, which ensures that the Qoin economy remains accountable to its members. The paper draws on six historical precedents—the Swiss WIR system (1934–present), M-Pesa (2007–present), Bitcoin (2009–present), BerkShares (2006–present), the coopera- tive credit tradition (1844–present), and the Irish banking crisis (1970)—to demonstrate that the Qoin economy is not a theoretical construct seeking legislative permission, but a practical system that can be realised within existing legal frameworks. The paper con- cludes by outlining the path to adoption: from first adopters in communities with large informal sectors, through growing Marketplaces with deepening profile data, to the pro- gressive accumulation of Free Wealth and the eventual maturity of the thermodynamic commons.
Efficient material traceability and lifecycle management are essential for achieving circular utilization in indoor renovation projects. This study proposes a reversible decoration framework based on a Digital Material Cycle Map (DMCM) to support the tracking, recovery, and reuse of construction materials throughout their service lifecycle. The framework integrates Digital Product Passport concepts, RFID- and QR-based identification, distributed ledger technology, and lifecycle data management to establish a unified material information architecture. A modular and detachable construction strategy is further developed using standardized interfaces and non-destructive disassembly mechanisms, enabling efficient component recovery and reuse. In addition, a material traceability workflow is introduced to support condition assessment, lifecycle auditing, and recycling decision-making based on dynamically updated records. The framework promotes information sharing among manufacturers, designers, contractors, and recycling organizations through standardized data interfaces. By combining material identification, information transmission, and lifecycle monitoring, the proposed approach provides an engineering-oriented solution for digital material governance, intelligent sensing, and distributed infrastructure management.
El vertiginoso avance de las tecnologías de la información y la consolidación de la economía digital han conducido a la emergencia de los criptoactivos como instrumentos financieros de uso masivo, generando profundos desafíos para los sistemas tributarios actuales. El presente artículo científico estudia el contacto entre los criptoactivos y el principio de legalidad tributaria en Ecuador, adentrándose en las lagunas de la ley para la determinación, valoración y control de las rentas digitales. A través de un enfoque cualitativo de corte exploratorio y descriptivo se estudia la normativa constitucional y tributaria ecuatoriana realizando síntesis con los estándares que aparecen en la propuesta presentada por la Organización para la Cooperación y el Desarrollo Económicos (OCDE). Los hallazgos indican la ausencia de una reserva de ley que establezca de manera expresa la índole jurídica y la determinación de la cuantía de las operaciones con criptoactivos, lo que genera inseguridad jurídica, quebranta los principios de justicia tributaria y afecta la recaudación tributaria. Se delimitan cuatro áreas que se consideran decisivas en este campo: la indeterminación del hecho imponible, la dificultad en la valoración de los activos volátiles, la evasión del Impuesto a la Salida de Divisas (ISD) a través de operaciones cruzadas, y la falta de herramientas tecnológicas en la administración tributaria para realizar un seguimiento de las operaciones descentralizadas. La investigación ofrece una propuesta de armonización normativa que respete el marco constitucional ecuatoriano sin aniquilar la innovación tecnológica, concluyendo que existe la necesidad de la actualización de la ley tributaria para que incluya de manera expresa las rentas digitales en el hecho imponible, se constituyan mecanismos de valorización confiables y se apliquen tecnologías de auditoría en blockchain para el seguimiento fiscal de esta clase de operaciones.
Open access
Business, Innovation, and Economy
Economic, Educational, Environmental and Organizational Development
In recent years the growth of cloud computing, Internet of Things (IoT), artificial intelligence (AI) and edge intelligence has been increasing, and with it the need for portable, scalable and secure computing infrastructures that can process vast amounts of data that is dispersed, and has very low latency. Traditional cloud infrastructures are typically based on central server deployments which can be costly to deploy, immobile, have potentially greater communication latency, and waste resources in dynamic workload environments. In this paper, we introduced an Intelligent Portable Edge – Cloud Computing Architecture (IPECA) that combines the portable computing hardware, AI-based workload prediction, adaptive resource optimization, container-based virtualization and secure edge-cloud collaboration into a single computing architecture. In conventional architectures, there is no intelligent resource orchestration mechanism, which can provide flexible allocation of computational resources according to the property of workload, thermal status, energy consumption, network availability and so on. The architecture also features an adaptive security layer leveraging multiple layers of authentication, secure communication protocols, blockchain for integrity verification and on-the-fly system health monitoring to enhance cyber resilience. Simulations are conducted with varying workloads to gauge the effectiveness of the proposed architecture, and compared to traditional cloud and edge-cloud architectures with the metrics of latency, throughput, CPU utilization, response time, energy consumption, thermal efficiency, and resource utilization. Experiments demonstrate significant energy savings, scalability, responsiveness of the system and efficiency of computations using secure distributed processing. The suggested architecture is viable for the coming intelligent cloud infrastructures that are essential for smart city, industrial IoT, digital healthcare, education and enterprise computing.
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.
This study investigates the theoretical foundations, practical applications, and optimization strategies of financial big data analysis and network security optimization in support of Sustainable Development Goals (SDGs). A comprehensive framework is developed to integrate sustainable financial management, environmental cost-benefit analysis, socially responsible investment decision-making, and sustainable supply chain management. The study further proposes a network security optimization architecture incorporating multi-level data encryption, access control, real-time threat monitoring, intelligent defense mechanisms, and blockchain-based data protection. The proposed framework is particularly applicable to communication-intensive environments, including wireless communication infrastructures and antenna-supported information transmission networks, where secure and reliable financial data exchange is essential. Experimental analyses demonstrate that the integration of financial big data technologies and network security mechanisms enhances data protection, operational efficiency, and sustainable decision-making capabilities. The results provide a practical reference for secure financial data governance and sustainable development in complex digital and communication-oriented systems.
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
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.
This pamphlet argues that the fiat monetary system is fundamentally incompatible with the deflationary nature of technological progress. It proposes an Energy Standard — a decentralized, blockchain-based currency backed by physically produced kilowatt-hours — as a thermodynamic anchor for money in the age of AI and robotics. Drawing on the Austrian School of Economics (Mises, Hayek), game theory, and thermodynamics, it analyses incentive structures in energy markets and makes the case for a market-driven ecological transition without state coercion.ditigal: petznek.at/pamphlet
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.
A transformação digital tem ampliado a adoção de tecnologias capazes de fortalecer a eficiência, a transparência e a confiabilidade dos processos organizacionais. Nesse contexto, este estudo propõe um modelo conceitual de integração entre a tecnologia blockchain e o Índice de Resiliência Organizacional (IRO), com o objetivo de fortalecer a produtividade, a eficiência empresarial, a governança corporativa e a confiabilidade dos indicadores utilizados na gestão organizacional. A pesquisa possui abordagem qualitativa, exploratória e descritiva, fundamentada em revisão bibliográfica e análise documental sobre blockchain, transformação digital, produtividade, eficiência organizacional, governança corporativa e resiliência organizacional. O modelo proposto incorpora o blockchain como uma camada tecnológica de confiança aplicada ao IRO, possibilitando maior integridade, autenticidade, rastreabilidade, transparência e auditabilidade dos dados utilizados na avaliação da resiliência organizacional. A integração também considera o potencial dos contratos inteligentes para automatizar procedimentos, validar evidências, atualizar indicadores e fortalecer mecanismos de controle interno. A análise demonstra que informações organizacionais mais seguras e verificáveis podem contribuir para decisões baseadas em evidências, redução de vulnerabilidades, melhoria dos processos, fortalecimento da governança e maior capacidade adaptativa das organizações. Como contribuição teórica, o estudo aproxima dois campos ainda pouco integrados na literatura: blockchain e avaliação da resiliência organizacional. Conclui-se que a integração entre blockchain e IRO constitui uma proposta inovadora para ampliar as aplicações da tecnologia blockchain na Administração e apoiar o desenvolvimento de organizações mais produtivas, eficientes, transparentes, resilientes e sustentáveis.
C. N. He, H. D. Chen, H. J. Tian, J. Zhang · 5 authors
This study investigates low-carbon investment strategies in power supply chains under the combined influence of carbon quota mechanisms (CQM) and blockchain technology (BCT). A two-echelon system consisting of a power generator and an electricity retailer is modeled, and four decision scenarios are constructed by considering blockchain adoption under both the grandfathering method (GFM) and benchmarking method (BMM). A Stackelberg game framework is employed to analyze the interactions among low-carbon technology investment, low-carbon electricity promotion, market demand, and enterprise profitability. Results show that the BMM consistently induces higher low-carbon investment levels, stronger market demand, and greater retailer profitability than the GFM, regardless of blockchain adoption. Furthermore, blockchain-enabled information traceability exhibits a significant threshold effect: when implementation costs remain below a critical level, trusted information transmission enhances consumer green trust, stimulates demand for low-carbon electricity, and improves the economic performance of supply-chain participants. Sensitivity analysis further demonstrates that consumer green trust, low-carbon preference, and responsiveness to low-carbon promotion positively influence both emissionreduction efforts and enterprise profitability, whereas excessive blockchain deployment costs weaken these benefits. The proposed framework provides a quantitative methodology for analyzing information-enabled lowcarbon decision making and coordinated investment strategies in modern power systems.
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 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.
Risqy Pradana Putra, Fara Triadi -, Ahmad Rofiq Hakim
Perkembangan teknologi informasi mendorong penerapan sistem yang lebih efisien dan transparan dalam manajemen gudang. Penelitian ini bertujuan merancang sistem manajemen gudang berbasis QR Code dan Blockchain untuk meningkatkan akurasi, keamanan, dan efisiensi pelacakan barang. Sistem mengintegrasikan mikrokontroler ESP32, modul GM65 barcode scanner, printer thermal, dan UPS sebagai sumber daya mandiri. QR Code digunakan untuk identifikasi dan pelacakan barang secara real-time, sedangkan Blockchain memastikan data transaksi tersimpan secara aman, transparan, dan tidak dapat diubah. Penelitian menggunakan metode Waterfall yang meliputi analisis kebutuhan, perancangan, implementasi, dan pengujian sistem. Hasil pengujian menunjukkan bahwa sistem mampu melakukan pencatatan, pemindaian, dan pembaruan data stok secara real-time dengan tingkat akurasi yang tinggi. Sistem ini memberikan solusi yang efektif untuk meningkatkan efisiensi operasional, keamanan data, dan transparansi dalam pengelolaan gudang berbasis Internet of Things (IoT).
Game-based teaching (GBT) has gained widespread adoption in modern education, yet teachers bear heavy burdens in designing GBT activities and interpreting student learning performance, while centralized educational data storage brings prominent security and credibility risks. To tackle the above bottlenecks, this paper proposes SmartTA, an integrated teaching assistant system combining GBT recommendation modules, automated machine learning (AutoML), and blockchain. Specifically, SmartTA supplies customized GBT cases and exam scoring suggestions for teachers, and leverages AutoML to automatically mine student learning behaviors with zero coding requirements. Three groups of experiments are conducted to validate the system: AutoML achieves a maximum prediction accuracy of 93% on six public educational datasets; the Hyperledger Fabric-based blockchain prototype enables data insertion with an average latency of approximately 2.2 seconds and query latency of approximately 150 ms; 20 frontline educational practitioners provide 85% positive user feedback. The experimental results suggest that SmartTA may help reduce teachers’ lesson preparation workload, support improved instructional quality, while enabling tamper-resistant data storage via blockchain. This study realizes the practical fusion of AutoML and blockchain for GBT scenarios, and establishes a novel, secure, data-driven teaching assistance paradigm that is accessible to non-technical educators.
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.
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.
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.
The rapid development of blockchain technology has introduced new forms of digital assets, including Non-Fungible Tokens (NFTs) and metaverse virtual land, creating legal uncertainty regarding their status as inheritable property under Indonesian Islamic Family Law. This study examines the legal status of these digital assets as inheritance objects, analyzes their distribution based on fiqh al-mawārīth and Indonesian positive law, and proposes a legal framework to strengthen legal certainty in digital inheritance. This research employs a normative legal method using statutory, conceptual, and Islamic jurisprudential approaches. Legal materials were analyzed through descriptive and deductive legal reasoning. The findings demonstrate that NFTs and metaverse virtual land satisfy the Islamic legal characteristics of māl because they possess lawful ownership, measurable economic value, legal control, and transferability, thereby qualifying as al-tirkah (inheritance estate). Their distribution should follow the principles of fiqh al-mawārīth while accommodating the technical characteristics of blockchain-based assets, particularly digital wallets and private-key access. The study also identifies a regulatory gap in Indonesian positive law concerning digital asset inheritance. Unlike previous studies that primarily discuss digital assets from commercial or general legal perspectives, this research develops an integrated framework combining Islamic inheritance law, Indonesian positive law, and digital estate planning to strengthen legal certainty, protect heirs' rights, and contribute to the development of Islamic Family Law in the digital era.
Hebat Allah Adel, sayed abdelgaber, Wessam H. El-Behaidy
Ensuring transparency and security in digital recruitment systems remains a critical challenge. This study proposes BC-XAIA, a unified framework that integrates blockchain, smart contracts, explainable artificial intelligence (XAI), and agile methodology to enable consistent, secure, and traceable recruitment decision-making. Smart contracts, implemented in Solidity and deployed using the Remix Ethereum IDE, automate key processes such as identity verification, data access control, and behavior monitoring, reducing reliance on centralized intermediaries. To support intelligent decision-making, multiple machine learning models, including Random Forest, Logistic Regression, and Support Vector Machine (SVM), were trained and evaluated on a recruitment dataset, with Random Forest achieving the highest performance, reaching an accuracy of 93%. To enhance transparency, SHAP and LIME were employed to provide both global and local interpretability of model predictions. Furthermore, agile methodology is embedded to drive continuous adaptation, iterative development, and stakeholder feedback throughout the recruitment lifecycle. Unlike existing recruitment systems that treat blockchain, AI, and explainability separately, BC-XAIA unifies these technologies within an agile and decentralized architecture. Overall, BC-XAIA establishes a secure, transparent, and explainable decentralized recruitment ecosystem that enhances trust, fairness, and intelligent decision-making in next-generation HR systems.
Abstract Machine learning models for audit anomaly detection are commonly evaluated using proprietary or synthetic datasets, with limited validation against official audit outcomes. This study proposes a four-layer ML framework designed to reduce audit detection risk and evaluates its performance on 3,329,189 general ledger transactions from three consecutive fiscal years (FY2023 to FY2025) of a Mongolian public sector energy utility. The dataset comprises 9,909 account rows and a cumulative debit flow of MNT 16.34 trillion. The proposed framework integrates unsupervised ensemble labeling through Isolation Forest, Z-score analysis, and debit-credit ratio screening, followed by supervised classification with Random Forest, Gradient Boosting, and Decision Tree models. An explainable AI layer maps SHAP feature attributions to specific ISA requirements. Against a simulated 20% MUS baseline, Random Forest achieves F1 = 0.966, AUC = 0.999, and Detection Risk = 2.01%, compared to MUS Detection Risk of 38.05% to 52.7%. McNemar’s test confirms statistically significant superiority (χ² = 1,666.63, p