The increasing need for sustainable practices has encouraged listed companies to participate in carbon trading markets. Traditional centralized systems for managing carbon trading data often face challenges such as limited transparency, poor traceability, and security risks, leading to inefficiencies and compliance issues. This research proposes a blockchain-based framework with smart contracts to provide a secure, decentralized mechanism for recording and verifying carbon trading data. The system ensures tamper-proof logs of emission allowances, trading transactions, and verification events, enabling real-time access for regulatory authorities. Data preprocessing uses Z-score normalization to standardize inputs, while Kernel Principal Component Analysis (KPCA) reduces dimensionality and extracts relevant features. To improve decision-making and cost-efficiency, a Dynamic Cuckoo Search-mutated Locust Swarm Optimization (DCSLSO) algorithm is embedded within the smart contracts to optimize carbon credit allocation and trading strategies. The framework is evaluated through simulations under varying energy demands, carbon prices, and multi-fuel scenarios, using synthetic datasets from energy-intensive industries. The DCSLSO model is implemented using Python and TensorFlow. This research demonstrates that blockchain technology, combined with intelligent smart contracts, can modernize carbon trading for listed companies, fostering transparency, accountability, and long-term economic sustainability in emissions management. This research highlights the potential of combining blockchain technology with intelligent optimization to modernize carbon markets, promoting transparency, accountability, and sustainable economic growth in emissions management.
This paper explored how digital transformation and the use of blockchain technology influenced supply chain transparency in pharmaceutical companies operating in emerging Chinese markets. The study incorporated the Technology Acceptance Model (TAM), which facilitated the identification of key aspects such as perceived usefulness, perceived ease of use, attitude, and behavioral intention, along with the mediating variable of self-efficacy. Based on these elements, a conceptual framework was developed, which further aided understanding of the hypothesised relationships examined in the study. Accordingly, a quantitative research design was implemented using a primary data collection method. In the Shanghai pharmaceutical industry, data were collected from a sample of 400 managerial employees. The outcomes of technology integration and transparency were quantitatively examined in relation to one another. The results indicated that blockchain technology and digital transformation enhanced supply chain performance through improved traceability, trust, and efficiency. The study shed further light on the main obstacles to implementation and provided insights for policymakers and industry leaders on improving transparency through advanced digital technologies in China’s expanding pharmaceutical market. The findings confirmed that respondents perceived the synergistic effects of digital transformation and blockchain implementation as having the greatest potential to improve supply chain transparency. Blockchain technology enabled real-time, secure, and distributed immutable ledgers that supported product tracking, counterfeiting prevention, verification of authenticity, and enhanced transparency.
Digital transformation is reshaping healthcare systems worldwide, with nursing practice positioned at the forefront of technology-driven innovation. As nurses increasingly engage in data management, coordination of care, and decision-making across complex health systems, the need for secure, transparent, and trustworthy digital infrastructures has become paramount (Khezr et al., 2019). Among emerging technologies, blockchain, a decentralised and tamper-resistant ledger system, and smart contracts, which enable automated and verifiable transactions, present promising opportunities to enhance trust, accountability, and interoperability within nursing workflows (Khezr et al., 2019; Saeed et al., 2022). Smart contract’s core features: immutability, transparency, and decentralisation, make it particularly suited for addressing long-standing challenges in healthcare data management and nursing administration. In nursing contexts, potential applications include secure sharing of patient records, real-time tracking of clinical documentation, automated credential verification, and protection of consent and privacy (Kuo et al., 2017; Naresh et al., 2025). It may further facilitate process automation in areas such as nursing resource allocation, performance auditing, and continuing education accreditation. Despite its promise, the adoption of blockchain technology in nursing remains in its infancy. Most studies have centered on technical or conceptual models rather than empirical evaluations, and few have examined the direct or indirect impacts on nursing efficiency, patient safety, or care coordination (Hasselgren et al., 2020). Implementation challenges, such as scalability, interoperability with existing hospital information systems, regulatory ambiguity, and user acceptance also persist (Saeed et al., 2022). Moreover, the ethical implications related to data ownership and governance in decentralised environments warrant careful consideration in nursing settings. Given these gaps, a scoping review is needed to synthesise current evidence on the applications and impacts of blockchain and smart contract technologies in nursing practice. This review will explore their roles across nursing service delivery, management, and education, with particular attention to how these technologies influence efficiency, trust, accountability, and data security. By consolidating interdisciplinary insights, this study seeks to guide future research and inform policy and practice frameworks for integrating blockchain and smart contract technologies into the nursing profession.
Prof. M. A. Sayyad, Veerendra Yadav, Dr. Geetika M. Patel, Dr. Prakash Deep · 8 authors
The problem of data privacy, interoperability, cyberattacks, and unauthorized changes of sensitive medical records are becoming critical issues in healthcare information exchange systems. The conventional centralized healthcare designs have single-point failures, inadequate transparency, sluggish data synchronization, and insufficient trust management among dispersed medical organizations. In order to overcome these shortcomings, this paper suggests a Blockchain-Assisted Distributed Artificial Intelligence Framework to Secure Healthcare Information Exchange and Data Integrity. The suggested architecture combines a distributed AI-based healthcare analytics system with blockchain-based immutable ledger systems to provide secure, open, and alteration-free medical data exchange among various healthcare nodes. The automated access control and the secure management of authorization is applied using smart contracts, and intelligent anomaly detection and integrity verification of healthcare transactions are implemented using distributed AI modules. The framework also includes encrypted communication and decentralized consensus systems to promote security and reliability in the context of multi-institutional healthcare settings. Simulated healthcare data based on experimentation shows that the proposed framework has a data integrity verification accuracy of 96.4, anomaly detection accuracy of 92.7 and offers both efficient and secure transaction validation performance at a ratio of 41.3 lower than traditional centralized healthcare systems. The suggested architecture enhances the security of healthcare data and trust management, scalability and interoperability of the next-generation intelligent healthcare ecosystems significantly.
With its decentralized, tamper proof, transparent and traceable characteristics, blockchain technology has shown great potential in fields such as finance, supply chain, and the Internet of Things. However, the public transparency of its ledger poses a serious challenge to user transaction privacy. Traditional privacy protection schemes such as homomorphic encryption and zero knowledge proofs can enhance privacy, but often struggle to balance computational overhead, communication costs, and data availability. This article explores the innovative application of neural networks in blockchain privacy protection and proposes a transaction obfuscation model based on generative adversarial networks. This model utilizes a generator to learn the statistical features of raw transactions and generate difficult to track obfuscated transactions, while ensuring the validity and compliance of obfuscated transactions through discriminators and blockchain verification contracts. The experimental results show that compared with traditional obfuscation methods and differential privacy methods, the proposed model significantly reduces the consumption of privacy budget and computation delay while ensuring high transaction utility (such as reducing address correlation by more than 85%), achieving a better balance between privacy and utility. This study provides new ideas for building efficient and practical blockchain privacy enhancement solutions.
In the process of building materials supply chain management, there are problems such as information opacity, low logistics coordination efficiency, difficulty in material quality traceability, and weak trust mechanism among supply chain entities, which lead to rising costs, low efficiency, and waste of resources. In addition, the construction industry has a large amount of carbon emissions, and the impact of supply chain management on carbon emission reduction cannot be ignored. To this end, this paper introduces blockchain technology to improve supply chain transparency, optimize logistics management, enhance material quality traceability, and explore its role in carbon emission reduction. This paper constructs a blockchain-based building materials supply chain management system, using distributed ledgers to ensure data transparency, smart contracts to automate procurement, acceptance and payment, which is a material traceability system to ensure quality control, the Internet of Things combined with blockchain to optimize logistics management, and establish a carbon emission monitoring and optimization mechanism to achieve real-time data recording and low-carbon scheduling. The system built in this study shows significant advantages in multiple key indicators. The overall carbon emissions of the supply chain in the experimental group are 88 tons of CO2, a 12% decrease compared to 100 tons of CO2 in the control group. The average transportation time in the experimental group is 4.5 hours, while that in the control group is 8.2 hours, a 45.1% decrease. The application of blockchain technology has effectively improved the efficiency and transparency of building materials supply chain management, optimized logistics and material quality control, and played a positive role in carbon emission reduction.
In the domain of agricultural product traceability, while traditional blockchain technologies ensure data immutability, they struggle to verify the authenticity of digital twins generated by generative artificial intelligence (GAI), resulting in a se mantic gap between the physical world and its virtual representation. To address these challenges, this paper proposes Verifiable Twin model driven by Cross-Modal Alignment (VTA-CMAD), which targets three core issues: cross-modal consistency verification between blockchain-stored data and AIGC-generated twins, lightweight zero-knowledge proof framework construction, and incentive-compatible suppression of malicious behaviors. The innovation of this article is reflected in three aspects. Firstly, this article proposes a 3D multimodal alignment algorithm that integrates dynamic time warping. B y integrating physical sensing temporal data, production process images, and cultural semantic descriptions, the optimal transmission mapping of feature space is established. Secondly, design a verifiable circuit zk Vector to transform the inference process of the fine-tuning diffusion model into zero knowledge proof constraints, generating proof files with a size less than 1.2KB. Finally, a dynamic consensus mechanism Proof of Trustworthiness based on Feature Alignment (PoTV) based on feature alignment is constructed to achieve adaptive adjustment of data weights. Experimental results demonstrate that the proposed approach achieves a tamper detection rate of 86.2%, a Gini coefficient of 0.19 for incentive fairness, and reduces multimodal alignment error to 0.11 ± 0.03.
Spa services in wellness tourism often face limitations in transparency, service integration, and customer trust in operational flows. This study develops a blockchain-based smart contract model that integrates five key indicators: reservations, cancellations, customer satisfaction, inventory, scheduling, and finance. A literature review of 113 articles yielded 25 key references, with significant trends such as the occurrence of the keyword “customer reservation” 10,100 times (2020–2024). Linear regression, correlation analysis, and ANOVA methods were used to test the research results. Linear regression predicts the relationship between variables, while correlation measures the strength of the relationship. The calculation results show a Pearson correlation coefficient of 0.93 (α = 0.05), indicating a very strong linear relationship. ANOVA shows significant differences between groups. These findings confirm that blockchain-based smart contracts are effective in digitally automating spa service workflows, strengthening transparency, and improving customer satisfaction
This paper aims to carry out a systematic study on the application of blockchain technology in the field of Accounts Receivable Financing (ARF). The report first peels apart the main pain points of the traditional ARF model (factoring) from the aspect of information asymmetry, transmission of credit and confirmation of rights. Then the report does a thorough analysis on how blockchain technology (especially the characteristics of unchangeable nature, smart contracts, and asset digitization) theoretically solves these pain points, emphasizing the elaboration on the realization path of "penetration of credit". The core of this report is the in-depth study and comparison of four important cases of significance, namely, the "Dual-Chain Connect" of Ant Group, "Yi Enterprise Chain" (YQLink) of Ping An OneConnect, WeBank (based on FISCO BCOS), and "Jing Bao Bei" of JD Technology. Through the comparison of these cases in terms of their business models, technical architecture and risk control mechanism, this report summarizes three mainstream realization mode: "central enterprise-led", "fintech platform-led", and "(digital) bank-led", and reveals their basic difference regarding "source of credit". Finally, the study talks about the common problems confronting the field, such as data silo, interoperability and regulatory uncertainty, and gives its optimistic outlook regarding its future trends of integration into Artificial Intelligence (AI), Internet of Things (IoT), and evolution towards Decentralized Finance (DeFi).
The in-depth application of blockchain technology in the financial sector has made smart contracts the core execution carrier for various decentralized financial businesses. Their security performance is directly related to the safety of financial assets and the stable development of the blockchain financial ecosystem. The immutability of smart contract code makes it difficult to fix vulnerabilities once they occur, which can easily lead to serious risks such as the theft of financial assets and transaction defaults. Moreover, the severity of different vulnerabilities varies significantly. Therefore, accurately defining the risk level of vulnerabilities and predicting the risk level in advance have become the core requirements for the security protection of blockchain applications in the financial field. This paper first explores the distribution patterns and correlation characteristics of the vulnerability features of smart contracts through correlation analysis and violin graph analysis. Then, multiple mainstream machine learning algorithms are introduced to conduct comparative experiments. The results show that the Transformer-LSTM-KELM algorithm proposed in this paper has the best comprehensive performance, with an accuracy rate of 71%. It is 5 percentage points higher than the suboptimal CatBoost and 25 percentage points higher than AdaBoost. With an precision rate of 77%, it is significantly better than all comparison algorithms. Its F1 value of 70% and recall rate of 71% are both at the leading level. This algorithm provides an efficient solution for the precise prevention and control of vulnerability risks in smart contracts in financial scenarios, and has significant practical value in ensuring the safe and compliant operation of blockchain financial business.
With the expansion of international trade scale and the increasing demands for transportation capacity and operational reliability, this paper proposes a port-railway collaborative scheduling system driven by the integration of blockchain and AI. This system adopts the data sharing and anti-tampering mechanism of distributed ledgers, combines machine learning to improve the accuracy of scheduling decisions, thereby achieving self-tuning scheduling plans, intelligent prediction of transportation demands, and maximizing resource utilization. This intelligent scheduling system not only enhances efficiency and security, but also strengthens the synergy between ports and railways, reduces operational costs, and further improves scheduling quality and system reliability. Through practical case verification, it is proved that this system is effective in real scenarios.
Blockchain technology is considered a transformative innovation, offering decentralized, secure, and transparent solutions to various industries, with cryptocurrencies being its most famous application. The volatility and non-linear behavior of cryptocurrency markets pose significant challenges for predicting their prices accurately. Predicting cryptocurrencies prices based on traditional statistical methods often fail to capture the market complex dynamics. Therefore, the recent developments in Artificial Intelligence, especially in deep learning and ensemble-based approaches have presented promising results. This study delivers a comprehensive literature review focusing on applying deep learning and ensemble deep learning algorithms in cryptocurrency time series price prediction. The main deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) are examined with a variety of time intervals and cryptocurrency types. The findings present that deep learning models, especially when used in hybrid or ensemble configurations, have obtained promising results. This review highlights the efficacy and significant potential of ensemble deep learning and its capabilities in cryptocurrencies price trend forecasting offering valuable insights for investors and researchers.
This paper proposes a distributed computing framework that integrates federated learning (FL) and blockchain-enabled smart contracts for automatic flood response management. FL is deployed to process time series sensor data locally for separate sensor devices by training a machine learning (ML) model and then aggregating the trained model parameters obtained from each sensor device to yield final predictions in terms of rainfall levels. The predicted precipitation levels are input into a predefined smart contract to automatically trigger mitigation strategies to be used by frontline safety and maintenance personnel. The results obtained using the proposed framework demonstrate both improved prediction accuracy and data privacy preservation. The validation effort shows that smart contracts can execute context-aware actions, thus enabling fast decision-making for flood response. The developed framework holds the potential to revolutionize decentralized data management, enhance efficient data processing, and ensure data privacy, transparent and secure data communication, and resilience against centralized failures, thereby enabling a more intelligent infrastructure management system to mitigate flood impacts.
With the increasing maturity of blockchain technology, its characteristics such as decentralization, data immutability, and consensus mechanisms can effectively address issues in supply chain finance, including high risk control costs, difficulties in credit endorsement for small and medium-sized enterprises, and cumbersome operational processes. By synthesizing research on the integration of blockchain technology into supply chain financial services and analyzing a case study of JD.com’s application of blockchain technology in supply chain finance ABS business, this paper proposes future development prospects for “blockchain technology + financial services”. The aim is to provide decision-making references for the modern financial services industry to expand operations, improve service performance, and reduce financial risks.
Small-scale enterprises struggle to comply with the Goods and Services Tax (GST) filing process due to limitations in traditional centralized systems. Traditional platforms rely heavily on intermediaries, incur high compliance costs, have sluggish approval times, lack real-time validation, and are prone to manipulation and fraud. The study solves these inefficiencies through the implementation of a blockchain-based GST filing support system based on decentralization, smart contracts, and immutable distributed ledgers. It eliminates human processes and third-party validation services while maintaining a tamper-proof record of GST transactions through automated validation and safe storage. It empirically demonstrates that the proposed system outperforms by increasing efficiency by up to 83.1%, lowering filing error rates to 2.3%, reducing duplications to 1.1%, and providing 88.7% fraud detection accuracy. The result represents a revolutionary strategy for all small-scale industries, improving not only processing time, scalability, and transparency, but also the trust of all stakeholders involved.
The aim of the study is to focus on the marketing communication strategies in the banking and finance sector from past to present, and to detail the concepts of phygital banking and metaverse banking in terms of both usage and the advantages and disadvantages it brings from the perspective of industry professionals. In-depth interviews were conducted with a total of 6 expert bankers from 3 different banks, which constitute the universe of the research while providing sample criteria. The data transcripts created with participant statements were divided into six themes and forty-three sub-codes and presented to expert opinion to ensure the external control of the research. The data were subjected to content analysis using the MAXQDA 2022 qualitative analysis program. Based on the findings, answers were sought to the following questions: (1) What are the definition, scope, and application areas of digital marketing communication in the banking and finance sector? (2) What are the elements of digital marketing communication used in the banking and finance sector? (3) What are the advantages and disadvantages of the digital marketing communication era in the banking and finance sector compared to the traditional marketing communication era shopping experience? According to the data analysis results, participants define digital marketing as a new marketing strategy that enhances consumer experience by combining traditional financial services with digital technologies. In addition, digital applications in the banking and finance sector are concentrated in areas such as application processes, marketing activities, payment systems, and smart voice systems. While the most commonly used digital elements are artificial intelligence (AI) and QR code, augmented reality (AR), virtual reality (VR), and blockchain are following these digital elements. According to the research results, the prominent advantage of digital marketing is experience-orientedness, while it is observed that digital spaces such as metaverse, with their decentralized and anonymous structure, also bring some privacy and security disadvantages. Concepts such as digital and metaverse are important innovative concepts that shape the future understanding of marketing communication. In the study, focusing on the digital marketing strategies used in the banking and finance sector, their characteristic features and technological components were evaluated from the perspective of industry professionals, and recommendations were made to the banking and finance sector based on the findings.
Blockchain technology, combined with smart contracts, serves as a revolutionary force in financial operations by enabling automated, transparent, and tamper-proof transaction execution. The conventional stock market infrastructure faces operational shortcomings due to multiple intermediaries, slow settlement times, and security weaknesses. The integration of deep learning models with blockchain-based smart contracts demonstrates emerging potential to enhance security while delivering precise and efficient financial ecosystem operations. The review investigates how artificial intelligence collaborates with distributed ledger technologies through adaptive deep temporal models as well as consensus optimization and secure contract execution on private Ethereum consortium blockchains. This study examines two advanced financial approaches by evaluating their performance as the Adaptive Deep Temporal Context Network (ADTCN) and the Dynamic Butterfly-Billiards Optimization Algorithm (DB-BOA). Important obstacles related to system expansion, together with system compatibility and model visibility, and on-time deployment, are examined. Future directions for creating intelligent, secure, efficient blockchain-based financial systems are established through the identification of present research gaps.
Mehdi Talaie, Farkhondeh Jabari, Asghar Akbari Foroud
Blockchain technology as a new technology has been able to attract the attention of global communities. The applications of blockchain technology are developing rapidly due to its very desirable features, including decentralization, high data security, visibility, transparency and programmability. Among the applications of blockchain technology, we can mention the optimization of water management systems and sustainable development of water. Therefore, this chapter first examines the concept and structure of blockchain and its function. It also examines the components of blockchain technologies, including consensus protocols, smart contracts, data encryption and distributed ledgers. Then, the history of blockchain technology is presented and the classification of blockchain-based systems is discussed. After that, the strengths, weaknesses, opportunities and threats of blockchain technology are evaluated. Some applications of this technology will be introduced. Also, considering the importance of water and its sustainability, the role of blockchain technology for the water industry and intelligent water management will be investigated.
Simon Fernandez-Vazquez, Omar Alexander León García, Julián Ramírez, Salvatore Cannella
Blockchain is currently a key focus in both academic and industrial domains. The pivotal next step for the sustainable growth of blockchain technology lies in its widespread adoption. This article delineates the research subjects, constraints, gaps, and emerging trends in blockchain within the hydrosystem sector. Our examination, based on 136 publications from the Web of Science (WoS), underscores a thorough exploration of issues, particularly emphasizing security, privacy, and latency. However, the suggested remedies for these issues are still in early stages of development. This study serves as a useful starting point for researchers, offering insights and recommendations for future research areas in the application of the distributed ledger within the hydrosystem sector.