Guoyao Wu, Fan Pan, Minyu Luo, Zhiqiang Lan · 5 authors
Conventional service evaluation systems are increasingly plagued by data opacity, susceptibility to tampering, and delayed feedback loops, which erode stakeholder trust and hinder effective quality governance. To address these critical challenges, this study proposes and empirically validates a blockchain-enabled framework for trusted closed-loop management of the entire service evaluation process. The proposed architecture synergizes distributed ledger technology, autonomous smart contracts, and a dynamic Bayesian trust scoring model to achieve real-time data verification, automated corrective feedback, and adaptive trust computation. We analyzed a comprehensive dataset of 1,200 service interactions across the hospitality, healthcare, and e-commerce sectors, characterized by customer satisfaction scores ranging from 5.1 to 9.8, reliability indices between 0.72 and 0.96, and normalized positive interaction frequencies from 0.42 to 0.89. Empirical results demonstrate that the integration of the blockchain framework significantly elevated mean trust scores from 0.71 (± 0.12) to 0.88 (± 0.09), representing a statistically significant 23.7% improvement. Furthermore, the system reduced the variance in satisfaction ratings by 0.48 and lowered overall service discrepancy rates by up to 15.4%. Sector-specific dynamic weight adjustments yielded optimized outcomes, including a 7.4% increase in reliability for healthcare and a 6.3% improvement in consistency for hospitality. Comparative analysis reveals that while conventional digital evaluation systems typically achieve only 5–12% performance gains, our blockchain-based approach substantially enhances trust, accuracy, and process transparency. Crucially, the closed-loop mechanism facilitated timely interventions, reducing critical service deviations by 17.5% in healthcare and 15.4% in e-commerce. These findings offer robust theoretical validation and practical guidelines for deploying transparent, accountable, and adaptive service evaluation ecosystems in diverse industrial contexts.
Electronic evidence of forensic medical images plays a key role in forensic identification. The existing deposit technology is difficult to cope with the dual challenges of image format change and AI forgery, and the fusion mechanism of digital watermarking and blockchain has the problems of robustness and traceability accuracy imbalance. This article proposes a dynamic trusted certificate storage system that integrates deep learning perceptual hash and alliance chain. A semantic hash generation network based on multi-scale frequency domain features is designed, and a lightweight intelligent contract architecture optimized by SM2/SM3 algorithm of state secrets is established. The full link traceability is realized by combining adaptive frequency domain and time domain nested watermarking algorithms. Experiments show that under the attacks of Gaussian noise, JPEG compression and geometric deformation, the Hamming distance of the hash is stable within 3 bits, which is better than the mutation of more than 30 bits in the traditional cryptographic hash. When the rotation is 10, the false recognition rate is less than 1%, and the sample collision probability is maintained at a very low order of magnitude; When the watermark embedding strength increases, the PSNR remains above 45 dB, and the normalized cross-correlation coefficient is higher than 0.92 under the condition of JPEG compression quality of 70. In the alliance chain scenario, the consensus delay of 30 nodes is 180 ms, and the delay rises to 320ms after the expansion of 100 nodes, and the system throughput is not significantly attenuated. In this study, the synergy between robustness, transparency and traceability efficiency is optimized, which can provide a reference technical scheme for judicial acceptance of forensic electronic evidence chain.
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.
R. Priyadharsini, Ravikanth Reddy Vadamala, R. Raajalakshmi, K. Raghav Prasad · 5 authors
The rapid transformation of global business environments driven by digitalization, technological advancement, changing consumer expectations, and competitive market dynamics has significantly altered traditional marketing practices and strategic business operations. Organizations operating in highly dynamic economic ecosystems are increasingly recognizing that conventional marketing frameworks alone are insufficient to sustain long-term growth, customer engagement, and market relevance. In this context, innovation-driven marketing models have emerged as a critical strategic approach that integrates creativity, data intelligence, technological innovation, customer-centric design, and adaptive business strategies to enhance organizational competitiveness and sustainable value creation. This research examines the growing significance of innovation-driven marketing models and their influence on consumer behavior, brand positioning, digital engagement, operational efficiency, and business sustainability across modern industries. The study explores how emerging technologies such as artificial intelligence, machine learning, big data analytics, blockchain, cloud computing, augmented reality, and social media ecosystems are transforming traditional marketing processes into highly personalized, predictive, and experience-oriented systems capable of responding to rapidly evolving market demands. The research further investigates how innovation-oriented marketing strategies support product differentiation, dynamic pricing, omnichannel communication, customer relationship management, and real-time market responsiveness in both online and offline commercial environments. Particular emphasis is placed on the role of innovation in enhancing customer engagement through interactive digital platforms, data-driven personalization, automated communication systems, influencer-based branding strategies, and experiential marketing campaigns. The study also evaluates how organizations leverage innovative business models to improve customer retention, market expansion, and strategic decision-making while simultaneously addressing challenges related to market uncertainty, consumer trust, technological adaptation, and ethical data utilization. A comparative assessment of traditional marketing approaches and innovation-driven marketing frameworks demonstrates that organizations adopting innovation-centric strategies experience stronger consumer loyalty, improved operational agility, enhanced brand visibility, and higher adaptability to changing economic conditions. Additionally, the research highlights the growing importance of sustainability-oriented marketing innovation, where businesses integrate environmental responsibility, social value creation, and ethical consumer engagement into their branding and communication practices. The findings indicate that innovation-driven marketing models not only contribute to commercial profitability but also strengthen organizational resilience and long-term strategic sustainability in highly competitive global markets. The study concludes that future business success increasingly depends on the ability of organizations to continuously innovate their marketing structures, technological capabilities, and customer engagement mechanisms in alignment with digital transformation and evolving consumer expectations. Therefore, innovation-driven marketing represents a transformative strategic paradigm capable of reshaping modern business ecosystems through intelligent, adaptive, and customer-focused value creation models.
Under the dual carbon targets, China's energy companies are speeding up their green transformation, but they usually encounter some common obstacles including lack of capital, weak technical assistance and an incomplete risk control system. The combination of digital technology and financial services provides new approaches to solve these problems. According to the specific characteristics of the transformation of energy enterprises, this research examines the mechanisms of digital finance from two aspects – financing enhancement and technological enhancement. It is found that methods such as digital green loans, bonds and equity financing can efficiently relieve the financial pressure of enterprises, while technologies like big data, blockchain and artificial intelligence can greatly improve the accuracy of emission reduction and the efficiency of energy operation. Furthermore, the enhancing effects have regional differences and threshold characteristics. Thus, countermeasures are put forward from four fields: improving service provision, deepening technological integration, setting up a risk management system and improving policy regulation, which offer guidance for the actual transformation of energy enterprises and the development of relevant policies.
The high aggregation of user relationship and behavioral data in social networks continues to aggravate privacy leaks. How to strike a balance between privacy protection and data availability has become a research hotspot. To collaboratively optimize user information security and community structure identification, this study proposes a social network privacy protection model that integrates differential privacy technology and community discovery algorithms. First, a differential privacy noise injection mechanism is constructed to perturb node data and combine it with blockchain storage to ensure that the data cannot be tampered with. Then, a community division strategy based on information entropy and mutual information is introduced to achieve high-precision community identification through modularity optimization. The accuracy of the proposed model reached 98.1% when the data set size was 800, which was about 3.4% and 9% higher than that of other models, respectively. The root mean square error was 8.2, which was about 20% lower than that of the traditional model. The convergence speed was increased to 380 iterations, which was about 15% faster than that of the comparison algorithm. The privacy protection strength and scalability scores reached 9.3 and 9.5, respectively. The simulation test results showed that, under different data types, the accuracy of the model grew from 0.87 to 0.98, and the F1 value grew from 0.84 to 0.95. The integration of differential privacy and community discovery effectively improves the privacy protection strength and structural analysis accuracy of social networks, providing a highly feasible solution for multi-scenario social data security analysis.
Ensuring transparency and traceability in horticultural supply chains is difficult due to complex logistics, seasonal variability, and multiple intermediaries.We propose a framework that couples b-local irregular vertex coloring (b-LIVC) with a blockchain architecture to enable end-to-end verification of production and trade.On the Jember Regency subdistrict graph, we compute the b-local irregular chromatic number and obtain χ b-lis (J) = 6, yielding six planting color classes that schedule sowing and harvests to distribute output across the year.The local irregularity induces distinct neighborhood weights, which we use as cryptographic features for unique, verifiable batch identifiers.We implement the pipeline on a public blockchain: harvest lots are tokenized as video NFTs with QR links to a verification page and on-chain records.The integration of discrete mathematics and distributed ledgers provides auditable provenance and transaction history, practical scheduling that reduces harvest clustering, and a low-overhead mechanism for farmer-level transparency.
The digital transformation of higher education creates new opportunities to enhance the effectiveness, inclusiveness, and sustainability of dual education systems. However, empirical evidence on the integration of emerging technologies into dual education remains limited in developing and post-Soviet countries. This study investigates stakeholder perceptions of digital transformation in dual higher education in Uzbekistan and explores the potential of Artificial Intelligence (AI), Virtual Reality (VR), and blockchain technologies to support inclusive and sustainable learning environments. A convergent mixed-methods design was used. Quantitative data were collected from 312 students and 80 industry representatives through structured surveys, while qualitative data were obtained from semi-structured interviews with 24 academic staff members involved in dual education programmes. Descriptive statistics, correlation analysis, and thematic analysis were used to examine stakeholder readiness, implementation barriers, and future development priorities. The findings indicate strong support for digital transformation by stakeholders. Most students perceived dual education as more effective than traditional instruction (81%), and 74% expressed interest in AI- and VR-supported learning environments. Employers demonstrated a high readiness to adopt digital assessment tools (85%) and blockchain-based credential verification systems (80%). However, major challenges were identified, including insufficient digital infrastructure, limited funding, inadequate professional development opportunities, and regulatory uncertainty. Only 31% of students considered the existing digital infrastructure sufficient for advanced technology integration.Based on these findings, this study proposes an integrated framework that combines AI-driven personalized learning, VR-based experiential training, and blockchain-enabled credential verification within the principles of Universal Design for Learning (UDL) and Sustainable Development Goal 4 (SDG 4). The framework aims to enhance educational accessibility, strengthen industry–university collaboration, and support equitable participation in dual higher education. This study contributes empirical evidence from a developing country context and offers practical recommendations for policymakers and higher education institutions seeking to implement inclusive and sustainable digital transformation strategies in dual education systems.
Against the backdrop of rapid technological innovation and the growing use of alternative investment instruments, this study examines the dynamic connectedness among decentralized finance assets, AI-based stocks, Islamic stocks and commodities. Covering the period from December 2019 to June 2022, we use the time-varying parameter vector autoregression (TVP-VAR) model to measure the magnitude, direction and evolution of return spillovers across Chainlink, Maker, Basic Attention Token, NVIDIA, Amazon, Google, Microsoft, DJIM World, DJIM EM, gold, crude oil and Global X Lithium and Battery Tech. The connectedness literature has examined spillovers across different asset classes during crisis periods. However, much of this literature focuses mainly on pairwise relationships among traditional asset classes, with limited attention to how emerging, alternative and technology-driven assets interact within a single network. We further assess the role of investor sentiment and network topology in identifying systemic transmitters and receivers. The results show strong interconnectedness, with an average total connectedness index (TCI) of 68.81%. Notably, AI-based stocks, especially Microsoft and NVIDIA, consistently emerge as net transmitters of return shocks, while commodities like gold and crude oil serve as absorbers of shocks. The portfolio results show that network centrality improves risk-adjusted performance by reducing volatility and downside risk. These insights have practical implications for policymakers and market participants, offering guidance for developing effective regulatory frameworks, investment strategies and risk management approaches in an increasingly interconnected financial landscape.
Abstract A secure platform for exchanging and storing agricultural data is provided via a blockchain-powered framework. By integrating edge computing, blockchain technology, and the Internet of Things (IoT) the production of crops can be boosted while using fewer natural resources. In the sector of agriculture, sensors and equipment gather various data about the landscape, which can subsequently be delivered to a server in a cloud environment. Due to its extreme fragility, these data must be securely stored and guarded from unwanted access. The core aim of this work is to propose a hybrid Reconditioned Random value-based Wombat Optimization with Adaptive Multi-scale Vision Transformer-based EfficientNet (RRWO-AMViT-ENet) model integrated with Ethereum smart contracts for secure pest detection and smart irrigation in IoT environments. The gathered agricultural images are initially stored and managed using the Ethereum blockchain. Then, node authentication is performed using the Smart Contract-based Adaptive Deep Support Vector Machine (SC-ADSVM). A Reconditioned Random value-based Wombat Optimization (RRWO) is utilized to optimize the variables of the developed SC-ADSVM. In order to perform pest detection and smart irrigation, the Adaptive Multi-scale Vision Transformer-based EfficientNet (AMViT-ENet) is used. The proposed model is implemented on the IP102-Dataset, where it obtained an accuracy of 96.39% in the pest detection operation. Thus, the proposed model provides effective results for pest detection and the smart irrigation process. From the attained results, it is concluded that the recommended strategy can provide intelligent service to the farmer.
The rapid transformation of financial systems requires secure and intelligent architectures capable of supporting predictive analysis, decentralized operations, and collaborative knowledge exchange. Traditional banking infrastructures often depend on centralized data management, creating challenges related to privacy risks, limited interoperability, and restricted cross-entity collaboration. This research proposes a Decentralized Banking Network (DBN) designed to integrate blockchain-based distributed systems, predictive assessment mechanisms, and secure knowledge-sharing capabilities. The proposed framework enables financial institutions to collaboratively analyse data while maintaining ownership and confidentiality of sensitive information. The architecture combines distributed ledger technology, intelligent prediction models, and decentralized governance mechanisms to improve financial decision-making. Blockchain concepts provide transparency and trust among participating entities, while predictive assessment techniques support risk evaluation, fraud detection, and strategic planning. The theoretical foundation of this research is derived from distributed ledger systems, decentralized control, and federated financial intelligence. Distributed ledger technology provides mechanisms for secure and transparent transactions across independent participants (Sunyaev and Sunyaev, 2020). Recent developments in federated financial ecosystems demonstrate the potential of decentralized analytics for improving risk assessment while maintaining data sovereignty (Arifin Shawn et al., 2025). The proposed network highlights how decentralized banking models can improve security, collaboration, and predictive accuracy. However, challenges related to scalability, regulatory compliance, computational complexity, and governance remain significant considerations. This research provides a conceptual framework for future banking ecosystems where institutions can achieve secure knowledge sharing without compromising confidential financial information.
Nuryadin Ali Mustofa, Agus Pahrudin, Ahmad Fauzan, Laila Maharani
This study examined global research trends in the digital transformation of educational finance and school management through a bibliometric analysis of publications indexed in the Scopus database from 2016 to 2025. Data were collected from the Scopus database on April 14, 2026, using a structured TITLE-ABS-KEY search query. The search initially identified 1,059 documents, which were refined to 162 relevant publications through PRISMA-based inclusion and exclusion criteria. Bibliometric mapping and performance analyses were conducted using Biblioshiny (R) and VOSviewer, supported by data cleaning and standardization through OpenRefine and a thesaurus file. The results showed a significant increase in global research output on digital transformation in educational finance and school management, particularly after 2020. This trend reflects growing scholarly and institutional attention to the role of digital technologies in educational governance and financial administration. Conference proceedings remained the dominant publication source, while journal publications continued to grow. China emerged as the leading contributor, supported by strong institutional productivity and collaboration networks. Thematic analysis identified major research clusters in financial management, information systems, data-driven decision-making, artificial intelligence, e-learning, and educational technology. Recent studies also emphasized sustainability, economic analysis, blockchain, and decentralized finance. This study contributes to the literature by providing a comprehensive global research map and offering policy insights to advance technology-driven, sustainable educational management practices.
This research discusses the incorporation of IoT with blockchain technique to enhance the efficiency of smart farming systems, particularly focusing on plant disease classification, pest detection, and smart irrigation. The study aims to develop a secure and effective IoT-based smart farming framework using the Ethereum blockchain to store and transmit data, and a Hybrid Convolution Adaptive Recurrent MobileNet (HC-ARMNet) model for predictive analytics, optimized by the Improved Secretary Bird Optimization (ISBO) algorithm. The research employs IoT sensors to acquire real-time data, which is then stored in the Ethereum blockchain to ensure security. The HC-ARMNet model, combining 1D/2D convolutions with recurrent connections, processes this data for pest detection and irrigation management. The ISBO algorithm is leveraged to fine-tune the technique's parameters. Datasets used: The proposed system utilizes three standard datasets for evaluation. The PlantifyDr Dataset is used for classifying plant disease, and the Pest Detection Dataset is used for recognizing pests. Also, for the smart irrigation process, the significant field images are collected manually. The accuracy, precision, and FNR rates of the ISBO-HC-ARMNet-aided plant disease classification are 94.16%, 94.2% and 5.87%. At the same time, the ISBO-HC-ARMNet-based pest detection process's accuracy, sensitivity, and specificity are 93.78%, 93.79% and 93.76%, respectively. In addition, the ISBO-HC-ARMNet-based smart irrigation task's MSE is 3.21, SMAPE is 0.03, and MASE is 30.23. Thus, the designed system showcases promising performance over classical approaches in terms of accuracy and error rates for plant disease classification, pest detection, and smart irrigation. The research concludes that the IoT-aided smart farming framework with blockchain and the HC-ARMNet model provides a robust solution for secure and efficient agricultural management. The system's predictive capabilities provide accurate and timely data analysis, facilitating to the improvement of precision agriculture. Future work will focus on improving the system with advanced feature extraction strategies to reduce processing time.
With the rapid development of digital finance, the mode of enterprise credit risk assessment has changed, and now also requires methods that can handle large-scale, diverse data and smart computation. The old system of credit rating has been based on the results of past financial reports and is no longer suitable for evaluating the changes and risks in modern corporate finance. This paper proposes a multi-dimensional model for mining credit reports of mining enterprises and combines structured financial data, transaction information, operating indicators, and other unstructured auxiliary data such as social media presence, online communication, supply chain dynamics, etc. By building a relatively detailed credit report, the bank can gain some information on the risk of a company's credit and its repayment ability for a loan. Algorithms that use machine learning, deep learning, ensemble models and predictive analysis are also known as intelligent default risk assessment algorithms that enhance the accuracy and flexibility of credit assessment. The following are ways to discover abnormal or complex patterns in a large amount of data early on for risk early warning, online credit assessment and dynamic portfolio management. Interoperability of digital finance platforms can support lifelong learning, automation and scalable high-frequency financial data, and maintain security, privacy and regulatory compliance. Although the above have been achieved, there are still deficiencies in the quality of data, interpretability of models, adherence to regulations, and sufficient computational resources, especially for small and medium-sized enterprises and new market institutions. Future research directions include building explainable AI systems, continuous learning, integrating multiple types of data (multimodality), and decentralized finance (DeFi) based on blockchains. At this point, the above technologies are expected to help enterprises strengthen credit risk management in the age of digital finance and provide more accurate and timely credit evaluations.
C. Rupa, K. Vijaya Bhaskar Reddy, Srinivas Jagirdar, Srinivas Rao Pulluri · 6 authors
Land registration and record management systems worldwide continue to face significant challenges, including document fraud, long processing times, and inefficient maintenance procedures. Traditional methods involve several technical limitations that reduce reliability and transparency. To address these issues, the proposed system leverages blockchain technology to improve process efficiency, data integrity, and security in land registration workflows. In the proposed framework, users upload property details and supporting land documents while initiating a sale. However, fraudulent document uploads remain a common issue, enabling sellers to receive payments using forged records without the buyer’s knowledge. To mitigate such risks, a Convolutional Neural Network (CNN) is integrated to authenticate and validate uploaded land documents before further processing. Only documents verified as authentic are stored on the blockchain. The government authority converts these validated documents into Non-Fungible Tokens (NFTs) and mints them on the Ethereum blockchain. A unique hash is generated for each document, enabling secure verification and traceability through platforms such as Etherscan. Once the documents are confirmed to be valid, the property is approved for sale, and the ownership transfer between the seller and buyer is securely executed through the blockchain enabled system. We evaluated the performance of proposed framework by considering both blockchain performance metrics and CNN evaluation metrics.
Ahmad Yani, Septiana Sihombing, Yogi Cahyo Ginanjar
Bitcoin has emerged as a prominent digital asset that blends financial innovation, technological advancement, and speculative behavior. However, its growing adoption raises sustainability concerns due to energy-intensive mining and environmental impacts. This study investigates the determinants of Bitcoin prices within the framework of sustainable digital finance by integrating blockchain fundamentals, technical indicators, and macroeconomic variables. Using daily data from 24 November 2021 to 21 November 2024 (753 observations), the analysis conducted with Stata 16—examines miners’ revenue, hashrate, transactions per block, unique addresses, mining difficulty, and trade volume as internal factors, along with gold prices, WTI crude oil, and the S&P 500 index as external factors. Results show that miners’ revenue, hashrate, and transactions per block have positive and significant effects on Bitcoin prices, emphasizing the importance of mining performance and network activity. Trade volume and unique addresses also display positive but less consistent influences, while mining difficulty remains statistically insignificant. Among external factors, WTI crude oil significantly affects Bitcoin prices. Overall, findings suggest that Bitcoin operates as both a financial asset and a technology-driven ecosystem shaped by blockchain dynamics and macroeconomic conditions. The study highlights the need for sustainable mining practices and transparent regulatory frameworks to enhance environmental efficiency.
Modern company activities depend greatly on inventory management, which covers demand forecasting and inventory optimization to guarantee operational effectiveness and customer happiness. This paper presents a new method fusing blockchain technology with cutting-edge deep learning to overcome these restrictions for better inventory management. Initially, the data are preprocessed using Zmin–max normalization (ZMM), and then feature extraction follows. To extract the spatiotemporal features and capture long-term temporal dependencies in demand data, a hybrid deep learning architecture is presented, built on a Deep Convolutional Koopman Network (CKN) integrated with a Coordinate Attention-Based Gated Recurrent Unit (CKN-CGRU).Genetic Secretary Bird Optimization (GSBO) is used to further tune the model automatically. While the CKN captures complex spatial temporal correlations, the GRU effectively models sequential dependencies. Blockchain architecture with smart contracts and improved Proof-of-Stake consensus is integrated to guarantee data integrity and transparency in stock transactions. This makes it possible to securely, automatically, and in a tamper-proof way record inventory projections, orders, and stock updates. The suggested system improves the stakeholder trust in decentralized inventory management by ensuring complete traceability and real-time auditability throughout the process. Experimental outcomes show the efficiency of the proposed model strategy, with an accuracy of 99.94% and precision of 99.93%.
This study rigorously formulates the complex fund-scheduling problem as a Markov decision process (MDP). It constructs a state space that integrates real-time and forecast information, an atomic action space that conforms to business logic, and a reward function that balances long-term returns against immediate risk. To address the curse of dimensionality and the credit-assignment problem in coordinated scheduling among multiple fund units, a multi-agent deep deterministic policy gradient (MADDPG) algorithm is adopted. Under a centralized-training and decentralized-execution framework, the algorithm reconciles global optimization with decentralized decision-making. In addition, a difference-reward mechanism and Kalman filtering are used to accurately measure each agent’s individual contribution and reduce the impact of environmental noise on reward signals. The results show that, compared with a static rule engine and a conventional linear programming method, the proposed deep reinforcement learning strategy reduces average daily funding costs by 50.4%, lowers the payment failure rate to 0.002%, and maintains a high liquidity buffer adequacy ratio. The strategy also demonstrates clear advantages in decision timeliness, collaborative handling of complex instructions, and self-adaptation potential, thereby providing an innovative pathway for finance-company fund scheduling to progress from intelligentization to automation.
The advent of blockchain technology has introduced new alternatives to traditional banking systems, providing a decentralized, secure, and transparent framework. However, its adoption is still complex and uneven for many reasons. This study provides a comprehensive mapping of the intellectual trajectory, thematic structure, and development of blockchain technology research in the banking sector. Using a hybrid literature review methodology that combines bibliometric analysis and systematic content review, the study analyzes 389 peer-reviewed publications retrieved from Scopus (2015–May 2025). VOSviewer was employed to conduct performance analysis and science mapping, including co-authorship, co-citation, keyword co-occurrence, and bibliographic coupling analyses. In parallel, qualitative thematic analysis identified six clusters: (1) blockchain in banking and financial intermediation to enhance operational efficiency, (2) decentralized finance and cryptocurrencies, (3) integration of blockchain with other digital innovations, (4) trust-related dimensions, (5) institutional and regulatory aspects, and (6) strategies for modernizing banking business models. The findings reveal a steady rise in research output, regional disparities in collaboration, and thematic evolution from early conceptualization to recent signs of diversification of applied research. By integrating quantitative and qualitative insights, this study highlights key research gaps, offers directions for future work, and provides guidance for academics, practitioners, and policymakers on the transformative potential and challenges of blockchain in banking.
This paper focuses on the application of blockchain technology in the field of supply chain finance, with an emphasis on its supportive role in alleviating the financing difficulties of small and medium-sized enterprises. Through theoretical analysis and case study methods, it systematically elaborates how blockchain technology, leveraging its characteristics such as decentralization, traceability, and immutability, enhances the transparency and credibility of supply chain finance, reduces the risks associated with information asymmetry, and thereby improves the availability and efficiency of financing for small and medium-sized enterprises. Taking "Ant Duo-Chain" as an example, the paper analyzes the application effects of blockchain technology in the financing of small and medium-sized enterprises, concluding that this model not only enhances the efficiency of capital circulation but also provides a sustainable path for the stable development and value enhancement of the overall supply chain ecosystem.
Abstract It is becoming more difficult for global supply chain networks to be governed because of issues such as a lack of transparency, data fragmentation, regulatory divergence, and the likelihood of having more than one supplier. This paper introduces an innovative Blockchain-Enabled Governance Framework (BEGF) that amalgamates distributed ledger technology with an ensemble machine learning (ML) risk-scoring engine and a multiobjective linear programming (LP) optimizer to enable supply chain decisions that are transparent, real-time, and verifiable. We use the framework in five long-term industrial case studies: automotive (Toyota), pharmaceutical (Merck), electronics (Samsung), apparel (Zara), and food and drink. These studies include 1,840 supplier nodes in 37 countries over 36 months. The BEGF can predict disruptions with an average AUC of 0.947. It can also reduce the number of supply disruptions by 38.5% and save each business $13.8 million a year. The Pareto-optimal optimizer lowers costs all along the supply chain.
With the increasing demand for fair trading in the energy sector, the use of blockchain technology as a new model for energy trading has begun to come into the public eye. However, there are problems such as the lack of a third party to endorse, relatively low efficiency, and unreasonable distribution. The innovative integration of the dynamic proof of stake (PoS) mechanism and entropy regulation strategy in the energy trading sharding system was proposed to solve problems such as trust deficiency, low transaction efficiency and uneven energy distribution existing in traditional energy trading. In the optimized triangular model, verifiable random functions were adopted to select validators, and the staking weights were dynamically adjusted in combination with the real-time status of nodes and transaction activity. Quantify the distribution of equity using Shannon entropy and set a threshold to trigger redistribution. The Pareto frontier solution set for the three objectives of throughput, security and decentralization was solved through the NSGA. A mechanism was adopted to dynamically adjust the equity weight based on the real-time status of nodes and the activity level of energy transactions, effectively enhancing the efficiency and fairness of transaction verification. The regulation was introduced for quantification to reduce the uncertainty and chaos of the energy trading system, ensuring the rational allocation and efficient utilization of energy resources. The experimental results show that optimized system throughput and attack cost have been significantly improved. Meanwhile, the degree of decentralization has risen to 89%, and the overall performance has increased by 12.8 times. This scheme has advantages such as good transaction efficiency, security and energy conservation and consumption reduction, which is conducive to reducing energy transaction costs, enhancing the transparency and stability of the energy transaction market.
Naim Ayadi, Syed Arshad Hussain, Arif R. Deen, Asadullah Ullah · 9 authors
There is diminished transparency, fragmented information exchange, and lack of trust among geographically dispersed stakeholders, which increasingly challenge global supply chains. The classic centralized systems of supply chain management are not always capable of being able to offer real-time traceability and data integrity which is dependable and effective in contract enforcement. The proposed study is a blockchain-based smart contract design that is focused on ensuring increased transparency, traceability and trust in global supply chain management. The suggested framework will combine automated smart contracts, cryptographic provenance tracking, permissioned blockchain consensus, and a decentralized trust score evaluation mechanism to overcome some of the major operation and governance challenges. A simulated assessment with a multi-tier global supply chain setting of 15 blockchain nodes and 12,000 transactions was performed through experimentation. The findings show that the proposed system attained an average transaction delay of 210 ms, which is very low compared to centralized systems (520 ms), with throughput being raised to 120 transactions per minute. End-to-end traceability performance also improved significantly, with a reduction in trace-back time to 8 s compared with 95s this represents a 100% tampering detection rate. The consensus mechanism ensured that the ledger integrity failed only at a rate of less than 1.1%, even when more than 30% of nodes were faulty. Risk-wise, the trust evaluation algorithm dynamically enhanced reliable supplier scores up to 12%, which facilitated the selection of reliable partners. On the whole, the results prove that smart contracts based on blockchains can drastically enhance the efficiency of operations, data integrity, and confidence in global supply chains, with the platform capable of providing a resilient and scalable backbone for the future supply chain management model.
Abstract Dynamics of financial contagion rapidly and drastically transformed by diversifying the investment preferences. Eventually increased diversification in the investment environment coupled with successive global events induced more complex and non-linear connections between the traditional and emerging markets. In this respect, this research explores the dynamic, asymmetric, and non-linear volatility transmissions among the Decentralized Finance (DeFi), Commodity, Energy, Technology, and Clean Energy Markets by incorporating Long Short Term Memory (LSTM) into the Time Domain of Time Varying Parameters Vector Auto Regression (TD-TVPVAR) model to eliminate the shortcomings of the former studies. Results compare the outputs of the Frequency Extension of TVPVAR (FC-TVPVAR) and LSTM-TVPVAR methods and verify the achievements of the new methodology. Consequently, new approach identify Bitcoin (BTC), gold, and oil markets as the primary sources of volatility, since clean energy market is determined to be the only significant destination of risk. Finally, prediction accuracy and the reliability of the incorporated model are validated by performance metrics and the achievements of the new approach are verified by bootstrapping test results.