Abstract Heritage buildings are highly vulnerable to structural degradation due to aging materials, environmental exposure, and natural disasters, necessitating intelligent and realâtime monitoring solutions. The current study proposes a dew computingâenabled digital twin framework integrated with Explainable Artificial Intelligence (XAI) for structural risk evaluation and health prediction of heritage infrastructure. The framework combines Internet of Thingsâbased sensing, dewâfogâcloud computing architecture, blockchainâbased data security, and a hybrid deep learning model to enable efficient, low latency, and reliable monitoring. Temporal structural data are processed using a Convolutional Neural NetworkâGated Recurrent Unit (GRU) model for feature extraction and timeâseries prediction of the Structural Health Index, while a Random Forest (RF) classifier categorizes structural risk into safe, degraded, and critical states. Shapley Additive explanationsâbased XAI is incorporated to enhance interpretability and support expert decisionâmaking. Experimental evaluation on a simulated dataset of 45,212 instances demonstrates the effectiveness of the proposed approach. The GRUâbased model achieves high prediction performance with an accuracy of 94.42%, sensitivity of 94.85%, specificity of 97.01%, and F1âscore of 94.43%. Regression analysis shows low prediction errors (Mean Absolute Error: 0.0158, Root Mean Squared Error: 0.0198) and a high coefficient of determination (), indicating strong agreement between predicted and actual structural states. The RF classifier further achieves 94.64% accuracy in structural risk classification. The framework exhibits low latency (~0.000195 s per sample), high reliability under noisy conditions (up to 99%), and strong scalability across increasing dataset sizes. Overall, the proposed system provides a robust, scalable, and interpretable solution for proactive Structural Health Monitoring and riskâaware maintenance of heritage buildings, significantly improving realâtime decisionâmaking and longâterm conservation strategies.
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.
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 â 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 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.
In the rapidly evolving landscape of financial technology (FinTech), the intersection of digital innovation capabilities (DICs) and Islamic social finance presents a fertile ground for enhancing sustainability in financial practices. This study employs a qualitative approach, specifically content analysis of existing literature sourced from journal databases. This theoretical review explores how DICs encompassing digitalization and digital transformation can influence the sustainability of Islamic social finance initiatives. Islamic social finance, rooted in principles of social justice and equitable distribution, aims to address socio-economic challenges while adhering to Shariah compliance. By synthesizing current literature and theoretical frameworks, this review elucidates the potential strategies in optimizing Islamic social finance mechanisms, improving transparency, efficiency, and reach. The analysis highlights key digital innovations, such as blockchain, artificial intelligence (AI), and cloud computing (CC). The review also proposes a conceptual model for integrating DICs with Islamic social finance to foster greater sustainability. This theoretical examination offers insights into how digital advancements can support the long-term goals of Islamic social finance, contributing to both economic development and social welfare.
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 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
Alicja Fandrejewska, Monika Eisenbardt, Tomasz Eisenbardt
The chapter provides a comprehensive overview of the functioning of contemporary consumers and services within the digital landscape, emphasizing the transition from traditional marketing and classical market models to customer-oriented approaches in online environments. It examines the impact of rapid technological development, data-driven processes, and the emergence of the information society on the evolution of digital services. Particular attention is given to key technological trends, including e-commerce, e-banking, e-health, e-learning, cloud computing, the Internet of Things, blockchain, and artificial intelligence, as well as to the opportunities and risks associated with these transformations, such as disinformation and increasing uncertainty. The chapter argues that contemporary consumers operate in a complex, dynamic, and highly digitized environment, in which services are increasingly intangible, personalized, and data-driven. It concludes by linking these considerations to the research approach and presenting selected empirical findings related to the issues discussed.
The convergence of digital technologies and sustainability assessments is changing the dynamics of environmental governance and corporate accountability. This chapter analyzes the way in which artificial intelligence, IoT, blockchain, digital twins, cloud analytics, and robotic process automation technologies contribute to sustainability assessment through their use in measuring, monitoring, and reporting on the environmental and social performance of corporations. Relying on the latest academic research, legislation, and business practice in this field, the chapter considers theoretical background, real-world applications, and governance issues related to digital sustainability assessment. A comprehensive analytical structure is provided, comprising data gathering, analysis, verification, and reporting, alongside comparative tables of relevant technologies, methods, legislation, problems, and solutions.
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 â a decentralized, blockchain-based currency backed by physically produced kilowatt-hours â 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
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.
Fazeel Ahmed Khan, Andi Fitriah Binti Abdul Kadir, Adamu Abubakar Ibrahim, Mohammad Shadab Khan
Abstract The growing volume and complexity of network data necessitate advance solutions for network traffic analysis and security. The Deep Packet Inspection (DPI) offers a granular approach to monitoring, filtering and classifying network traffic to enforce security policies, optimize QoS and detect malicious activities. The proposed study addresses these issues by exploring the emerging but promising integration of blockchain and machine learning techniques to improve DPI. It contributes by providing a comprehensive details on the application domain of DPI with a focus on network security, performance and management. Also, the study proposes a research roadmap to guide the future development on the development of blockchain-enabled intelligent solutions for DPI. Using PRISMA methodology, several existing studies were evaluated addressing the potential application of blockchain and machine learning in DPI. The survey has identified significant challenges towards the integration including real-time IP packet inspection efficiency, QoS performance and the impact of high traffic volume on DPI. It concludes that DPI has wider applications to be integrated with emerging technologies particularly in machine learning and blockchain. The future research should focus on advance machine learning paradigms such as continual and federated learning while blockchain technology should be resolved with scalability challenges to be utilized effectively for next-generation DPI solutions.
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 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.