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The convergence of blockchain technology with artificial intelligence presents a promising paradigm shift in data management and trust within AI ecosystems. Starting from the initial cryptocurrency-oriented versions, the blockchain potential is improved up to the highly scalable and programmable versions available currently. Even though the integration of real-world applications offers a promising future for distributed computing, there are limitations on executing AI models on blockchain due to high external library dependencies, storage, and cost constraints. Addressing this issue, this study explores the transformative potential of integrating blockchain with AI within the paradigm of blockchain 5.0. We propose the next-generation novel blockchain architecture named Doctrina that allows executing AI models directly on blockchain. Compared to the existing approaches, Doctrina allows sharing and using AI services in a fully distributed and privacy-preserved manner. Full article

In this paper, we obtain unique solution and stability results for coupled fractional differential equations with p-Laplacian operator and Riemann–Stieltjes integral conditions that expand and improve the works of some of the literature. In order to obtain the existence and uniqueness of solutions for coupled systems, several fixed point theorems for operators in ordered product spaces are given without requiring the existence conditions of upper–lower solutions or the compactness and continuity of operators. By applying the conclusions of the operator theorem studied, sufficient conditions for the unique solution of coupled fractional integro-differential equations and approximate iterative sequences for uniformly approximating unique solutions were obtained. In addition, the Hyers–Ulam stability of the coupled system is discussed. As applications, the corresponding results obtained are well demonstrated through some concrete examples. Full article

With the deep integration of distributed network technology and intelligent medical care, how to achieve efficient collaboration under the premise of safeguarding data security and system efficiency has become an important challenge for intelligent medical diagnosis systems. The traditional practical Byzantine fault tolerance (PBFT) algorithm has difficulty meeting the demands of large-scale distributed medical scenarios due to high communication overhead and poor scalability. In addition, the existing improvement schemes are still deficient in dynamic node management and complex attack defence. To this end, this paper proposes the VS-PBFT consensus algorithm, which fuses a verifiable random function (VRF) and reputation mechanism, and designs a distributed intelligent medical diagnosis collaboration system based on this algorithm. Firstly, we introduce the VRF technique to achieve random and unpredictable selection of master nodes, which reduces the risk of fixed verification nodes being attacked. Secondly, we construct a dynamic reputation evaluation model to quantitatively score the nodes’ historical behaviors and then adjust their participation priority in the consensus process, thus reducing malicious node interference and redundant communication overhead. In the application of an intelligent medical diagnosis collaboration system, the VS-PBFT algorithm effectively improves the security and efficiency of diagnostic data sharing while safeguarding patient privacy. The experimental results show that in a 40-node network environment, the transaction throughput of VS-PBFT is 21.05% higher than that of PBFT, the delay is reduced by 33.62%, the communication overhead is reduced by 8.63%, and the average number of message copies is reduced by about 7.90%, which demonstrates stronger consensus efficiency and anti-attack capability, providing the smart medical diagnosis collaboration system with the first VS-PBFT algorithm-based technical support. Full article

When creating software, it is important to focus not only on the functionality requested by users but also on ensuring that the software exhibits qualities such as maintainability, testability, comprehensibility, and reusability. To achieve this, developers should consider these qualities from the early stages of development and implementation. In this paper, we propose a novel methodology for the calculation of software metrics to evaluate the aforementioned quality characteristics, as well as their verification on the blockchain. The primary objective is to provide a simple and transparent approach—using smart contracts—for monitoring software metrics throughout the development process. This approach enhances software reliability and facilitates ongoing maintenance. Full article

This systematic review examines the critical intersection of digital transformation, supply chain resilience, and sustainability within manufacturing contexts, with specific implications for Saudi Arabian industries. Through a comprehensive analysis of 124 peer-reviewed articles published between 2018 and 2024, we identify how emerging technologies—including Internet of Things (IoT), artificial intelligence, blockchain, and big data analytics—transform traditional supply chains into dynamic ecosystems capable of withstanding disruptions while advancing sustainability goals. Our findings reveal that digital transformation positively influences both resilience and sustainability outcomes. Still, these relationships are significantly moderated by three key factors: supply chain dynamism, regulatory uncertainty, and integration of innovative technologies. The study demonstrates that while high supply chain dynamism amplifies the positive effects of digital technologies on resilience capabilities, regulatory uncertainty creates implementation barriers that potentially diminish these benefits. Moreover, successfully integrating innovative technologies is a critical mediating mechanism translating digital initiatives into tangible sustainability improvements. The review synthesises these findings into an integrated conceptual framework that captures the complex interrelationships between these domains and provides specific strategic recommendations for Saudi Arabian manufacturing organisations. By addressing the identified research gaps—particularly the lack of industry-specific investigations in emerging economies—this review offers valuable insights for researchers and practitioners seeking to leverage digital transformation for simultaneously efficient, resilient, and sustainable supply chain operations in rapidly evolving business environments. Full article

IoT environments have introduced diverse logistic support services into our lives and communities, in areas such as education, medicine, transportation, and agriculture. However, with new technologies and services, the issue of privacy and data security has become more urgent. Moreover, the rapid changes in IoT and the capabilities of attacks have highlighted the need for an adaptive and reliable framework. In this study, we applied the proposed simulation to the proposed hybrid framework, making use of deep learning to continue monitoring IoT data; we also used the blockchain association in the framework to log, tackle, manage, and document all of the IoT sensor’s data points. Five sensors were run in a SimPy simulation environment to check and examine our framework’s capability in a real-time IoT environment; deep learning (ANN) and the blockchain technique were integrated to enhance the efficiency of detecting certain attacks (benign, part of a horizontal port scan, attack, C&C, Okiru, DDoS, and file download) and to continue logging all of the IoT sensor data, respectively. The comparison of different machine learning (ML) models showed that the DL outperformed all of them. Interestingly, the evaluation results showed a mature and moderate level of accuracy and precision and reached 97%. Moreover, the proposed framework confirmed superior performance under varied conditions like diverse attack types and network sizes comparing to other approaches. It can improve its performance over time and can detect anomalies in real-time IoT environments. Full article

The Internet of Medical Things (IoMT) creates interconnected networks of smart medical devices, utilizing extensive medical data collection to improve patient outcomes, streamline resource management, and guarantee comprehensive life-cycle security. However, the private nature of medical data, coupled with strict compliance requirements, has resulted in the separation of information repositories in the IoMT network, severely hindering protected inter-domain data cooperation. Although current blockchain-based federated learning (BFL) approaches aim to resolve these issues, two persistent security weaknesses remain: privacy leakage and poisoning attacks. This study proposes a privacy-preserving poisoning-resistant blockchain-based federated learning (PPBFL) scheme for secure IoMT data sharing. Specifically, we design an active protection framework that uses a lightweight $(t,n)$-threshold secret sharing scheme to protect devices’ privacy and prevent coordination edge nodes from colluding. Then, we design a privacy-guaranteed cosine similarity verification protocol integrated with secure multi-party computation techniques to identify and neutralize malicious gradients uploaded by malicious devices. Furthermore, we deploy an intelligent aggregation system through blockchain smart contracts, removing centralized coordination dependencies while guaranteeing auditable computational validity. Our formal security analysis confirms the PPBFL scheme’s theoretical robustness. Comprehensive evaluations across multiple datasets validate the framework’s operational efficiency and defensive capabilities. Full article

Counterfeit products cause financial losses for both the manufacturer and the enduser; e.g., fake foods and medicines pose significant risks to the public’s health. Moreover, it is challenging to ensure trust in a product’s supply chain, preventing counterfeit goods from being distributed throughout the network. However, fake product detection methods are expensive and need to be more scalable, whereas a unified traceability system for packaged products is not available. Therefore, this research proposes a product traceability system, named Trusted Traceability Service (TTS), using Blockchain and Self-Sovereign Identity (SSI). The TTS can be incorporated across diverse industries because of its generic and manageable four-layer product packaging strategy. Blockchain-enabled SSI empowers distributed nodes, to verify them without a centralized client–server authorization architecture. Moreover, due to its distributed nature, the proposed TTS framework is scalable and robust, with the use of web3.0 distributed application development. The adoption of Fantom, a public blockchain infrastructure, allows the proposed system to handle thousands of successful transactions more cost-effectively than the Ethereum network. The deployment of the proposed framework in both public and private blockchain networks demonstrated its superiority in execution time and number of successful transactions. Full article

Traditional intellectual property transaction traceability has problems such as information asymmetry, traceability information storage methods relying on centralized databases, and easy tampering of transaction information, etc. A blockchain-based information security mechanism for intellectual property transaction traceability is proposed. Firstly, through the analysis of massive intellectual property transaction case information, the commonality and individuality data are studied, and the structure and scope of data collection requirements for traceability information are established; secondly, the traceability information structure is constructed based on the smart contract and PROV data origin model, the signature verification of traceability information is completed based on the BLS threshold signature of the Dynamic DKG protocol, and the signature process integrates the PROV model and constructs a chained signature structure. The multi-level traceability information verification strategy and process are developed to achieve the security protection of traceability information throughout the entire life cycle of intellectual property transactions. Full article

This systematic literature review investigates the recent applications of artificial intelligence (AI) in supply chain management (SCM), particularly in the domains of resilience, process optimization, sustainability, and implementation challenges. The study is motivated by gaps identified in previous reviews, which often exclude literature published after 2020 and lack an integrated analysis of AI’s contributions across multiple supply chain phases. The review aims to provide an updated synthesis of AI technologies—such as machine learning, deep learning, and generative AI—and their practical implementation between 2021 and 2024. Following the PRISMA framework, a rigorous methodology was applied using the Scopus database, complemented by bibliometric and content analyses. A total of 66 studies were selected based on predefined inclusion criteria and evaluated for methodological quality and thematic relevance. The findings reveal a diverse classification of AI applications across strategic and operational SCM phases and highlight emerging techniques like explainable AI, neurosymbolic systems, and federated learning. The review also identifies persistent barriers such as data governance, ethical concerns, and scalability. Future research should focus on hybrid AI–human collaboration, transparency through explainable models, and integration with technologies such as IoT and blockchain. This review contributes to the literature by offering a structured synthesis of AI’s transformative impact on SCM and by outlining key research directions to guide future investigations and managerial practice. Full article

Unmanned aerial vehicle networks (UAVNs) are widely used to collect various location-related data, with applications ranging from military reconnaissance to the low-altitude economy. Data security and privacy are critical concerns when outsourcing location-related data to a public cloud. To alleviate these concerns, location-related data are encrypted before outsourcing to the public cloud. However, encryption decreases the operability of the outsourced encrypted data; thus, unmanned aerial vehicles cannot operate on the encrypted data directly. Among operations on encrypted location-related data, the forward-secure range query is one of the most fundamental operations. In this paper, we present a forward-secure range query based on spatial division to achieve a highly efficient range query on encrypted location-related data while preserving both data security and privacy. Specifically, various space-filling curves were experimentally investigated for both the range query and the k-nearest-neighbor query. Then, a forward-secure range query (namely, OSFC-FSQ) was constructed on an encrypted dual dictionary. The proposed scheme was evaluated on real-world datasets, and the results show that it outperforms state-of-the-art schemes in terms of accuracy and query time in the cloud. Full article

We propose a comprehensive and practical framework for Chinese corporate fraud prediction which incorporates classifiers, class imbalance, population drift, segmented models, and model evaluation using machine learning algorithms. Based on a three-stage experiment, we first find that the random forest classifier has the best performance in predicting corporate fraud among 17 machine learning models. We then implement the sliding time window approach to handle population drift, and the optimal training window found demonstrates the existence of population drift in fraud detection and the need to address it for improved model performance. Using the best machine learning model and optimal training window, we build general model and segmented models to compare fraud types and industries based on their respective predictive performance via four evaluation metrics and top features using SHAP. The results indicate that segmented models have a better predictive performance than the general model for fraud types with low fraud rates and are as good as the general model for most industries when controlling for training set size. The dissimilarities between the top features set of the general and segmented models suggest that segmented models are useful in providing a better understanding of fraud occurrence. Full article

Blockchain technology has emerged as a transformative solution to address specific aspects of emergency management systems by providing a decentralized and distributed ledger infrastructure that enhances data immutability, transparency, and traceability. This study presents a comprehensive bibliometric analysis of blockchain applications in emergency management covering the period from 2017 to 2024 and based on 248 research articles indexed in the Web of Science Core Collection. The analysis examines collaboration networks, co-citation patterns, citation bursts, and keyword trends to uncover key research clusters and emerging themes. Seven major clusters were identified, with their intellectual core built around influential publications that highlight blockchain’s role in improving transparency, efficiency, and trust in emergency response systems. The findings emphasize the growing impact of blockchain technology in enhancing preparedness and resilience during crises while identifying gaps in global collaboration and interdisciplinary innovation. Full article

Blockchain technology uses a consensus mechanism to create and finalize blocks. The consensus mechanism affects the total performance parameters of the blockchain network, such as throughput. In this paper, we present “Nazfast”, a simplified proof of stake—Byzantine fault tolerance based consensus mechanism to create and finalize blocks. The presented consensus is completed in multiple folds. For block producer and validation committee selection, we used a secure and speeded-up election mechanism, S&Sem, in Nazfast. The consensus is designed for fast block finalization in a malicious environment. The simulation result shows that we approximately achieved three block finalizations in 1 s with almost similar latency. We reduced and fixed the number of validators in the consensus to improve the throughput. We achieved a higher throughput among other consensus of the same family. Because we reduced the number of validators, the safety parameters of the consensus are at risk, so we used Sea Shield to improve the overall consensus safety. This is another blockchain to save nodes’ details when they join/unjoin the network as validators. By using all three parts together, our system is protected from 28-plus different attacks, and we maintain a high decentralization by using S&Sem. Finally, we also enhance the incentive mechanism of consensus to improve the liveness of the network. Full article

In the current context of the wide application of Internet of Things (IoT) technology, cross-domain data sharing based on industrial IoT (IIoT) has become the key to maximizing data value, but it also faces many challenges. In response to the security and privacy issues in cross-domain data sharing, we proposed a cross-domain secure data sharing scheme (CDAS) based on multiple blockchains. The scheme first designs the cross-domain blockchain in layers and assists the device in completing the data sharing on the chain through the blockchain layer close to the edge device. In addition, we combine smart contract design to implement attribute-based access control (ABAC) and anonymous identity registration. This method simplifies device resource access by minimizing middleware confirmation, double-checking device access rights, and preventing redundant requests caused by illegal access attempts. Finally, in terms of data privacy and security, IPFS is used to store confidential data. In terms of ensuring data sharing security, searchable encryption (SE) is applied to the overall data sharing and improved. Users can find the required data by searching the ciphertext links in the blockchain system to ensure the secure transmission of private data. Compared with the traditional ABAC scheme, we have added modules for data privacy protection and anonymous authentication to further protect user data privacy. At the same time, compared with the access control scheme based on attribute encryption, our scheme has certain advantages in the time complexity calculation of key algorithms such as policy matching and encryption algorithm. At the same time, with the assistance of the edge blockchain layer, it can reduce the burden of limited computing resources of the device. This scheme can solve the security and efficiency problems of cross-domain data sharing in the industrial Internet of Things through security and experimental analysis. Full article