Pub Date : 2025-09-01Epub Date: 2025-01-23DOI: 10.1016/j.bcra.2025.100274
Wittawat Kositwattanarerk
Decentralized exchange (DEX) platforms such as Uniswap and Balancer operate on several pools where each pool contains two or more cryptocurrencies and constitutes direct trading pairs. The drawbacks here are that liquidity provision requires the contribution of tokens in a specific proportion, and trading may require hopping between pools, hence increasing the transaction fee and gas fee. We propose an automated market maker (AMM) protocol where liquidity providers (LPs) can deposit any amount of tokens into the pool. The protocol preserves the proportion of tokens by total value at the time of deposit and can be seen as a personalized self-balancing portfolio manager. In addition, since the invariant function is dynamic, all exchange pairs are executed from a single composite pool. Nevertheless, the scheme is vulnerable to flash loan attacks and must be used in conjunction with preventive measures.
{"title":"Dynamic exponent market maker: personalized portfolio manager and one pool to trade them all","authors":"Wittawat Kositwattanarerk","doi":"10.1016/j.bcra.2025.100274","DOIUrl":"10.1016/j.bcra.2025.100274","url":null,"abstract":"<div><div>Decentralized exchange (DEX) platforms such as Uniswap and Balancer operate on several pools where each pool contains two or more cryptocurrencies and constitutes direct trading pairs. The drawbacks here are that liquidity provision requires the contribution of tokens in a specific proportion, and trading may require hopping between pools, hence increasing the transaction fee and gas fee. We propose an automated market maker (AMM) protocol where liquidity providers (LPs) can deposit any amount of tokens into the pool. The protocol preserves the proportion of tokens by total value at the time of deposit and can be seen as a personalized self-balancing portfolio manager. In addition, since the invariant function is dynamic, all exchange pairs are executed from a single composite pool. Nevertheless, the scheme is vulnerable to flash loan attacks and must be used in conjunction with preventive measures.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100274"},"PeriodicalIF":5.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-03-28DOI: 10.1016/j.bcra.2025.100285
Christian Gang Liu , Peter Bodorik , Dawn Jutla
In our previous research, we addressed the problem of automated transformation of models, represented using the business process model and notation (BPMN) standard, into the methods of a smart contract. The transformation supports BPMN models that contain complex multi-step activities that are supported using our concept of multi-step nested trade transactions, wherein the transactional properties are enforced by a mechanism generated automatically by the transformation process from a BPMN model to a smart contract. In this paper, we present a methodology for repairing a smart contract that cannot be completed due to events that were not anticipated by the developer and thus prevent the completion of the smart contract. The repair process starts with the original BPMN model fragment causing the issue, providing the modeler with the innermost transaction fragment containing the failed activity. The modeler amends the BPMN pattern on the basis of the successful completion of previous activities. If repairs exceed the inner transaction’s scope, they are addressed using the parent transaction’s BPMN model. The amended BPMN model is then transformed into a new smart contract, ensuring consistent data and logic transitions. We previously developed a tool, called TABS+, as a proof of concept (PoC) to transform BPMN models into smart contracts for nested transactions. This paper describes the tool TABS+R, developed by extending the TABS+ tool, to allow the repair of smart contracts.
{"title":"Automated mechanism to support trade transactions in smart contracts with upgrade and repair","authors":"Christian Gang Liu , Peter Bodorik , Dawn Jutla","doi":"10.1016/j.bcra.2025.100285","DOIUrl":"10.1016/j.bcra.2025.100285","url":null,"abstract":"<div><div>In our previous research, we addressed the problem of automated transformation of models, represented using the business process model and notation (BPMN) standard, into the methods of a smart contract. The transformation supports BPMN models that contain complex multi-step activities that are supported using our concept of multi-step nested trade transactions, wherein the transactional properties are enforced by a mechanism generated automatically by the transformation process from a BPMN model to a smart contract. In this paper, we present a methodology for repairing a smart contract that cannot be completed due to events that were not anticipated by the developer and thus prevent the completion of the smart contract. The repair process starts with the original BPMN model fragment causing the issue, providing the modeler with the innermost transaction fragment containing the failed activity. The modeler amends the BPMN pattern on the basis of the successful completion of previous activities. If repairs exceed the inner transaction’s scope, they are addressed using the parent transaction’s BPMN model. The amended BPMN model is then transformed into a new smart contract, ensuring consistent data and logic transitions. We previously developed a tool, called TABS+, as a proof of concept (PoC) to transform BPMN models into smart contracts for nested transactions. This paper describes the tool TABS+<em>R</em>, developed by extending the TABS+ tool, to allow the repair of smart contracts.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100285"},"PeriodicalIF":6.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-08-23DOI: 10.1016/j.jobb.2025.08.003
Sara S.H. Abu Zaid , Omar F. Khabour , Sawsan Abuhammad
Symptoms of coronavirus disease of 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), range from asymptomatic to life-threatening, affecting the respiratory system and many other organs, and involved in SARS-CoV-2 pathogenesis, particularly its viral entry, is the enzyme transmembrane protease serine 2 (TMPRSS2). In response, in this study, polymorphisms in the TMPRSS2 gene (rs2070788 and rs12329760) were examined for their association with COVID-19 susceptibility and severity in Jordanians. The study participants (n = 861) included 427 individuals who had never contracted COVID-19 (control group) and 434 who had experienced the disease (case group). The case group was subsequently divided into two subgroups: severe (n = 166) and non-severe (n = 268) COVID-19. The results show that rs2070788 is associated with disease susceptibility in all inheritance models (P < 0.05), as well as an association between rs12329760 and COVID-19 disease severity in both the dominant and over-dominant models (P < 0.05). Further, a higher body mass index (BMI) and older age were associated with disease severity (p < 0.05), though females were less likely to develop severe COVID-19 and to be hospitalized. In conclusion, polymorphisms in the TMPRSS2 gene may be considered a potential predictor of COVID-19 outcomes in the Jordanian population. The study findings could be used to manage similar viral infections in Jordan in the future.
{"title":"The association between TMPRSS2 gene rs2070788 and rs12329760 variants and COVID-19 susceptibility and severity: A study from Jordan","authors":"Sara S.H. Abu Zaid , Omar F. Khabour , Sawsan Abuhammad","doi":"10.1016/j.jobb.2025.08.003","DOIUrl":"10.1016/j.jobb.2025.08.003","url":null,"abstract":"<div><div>Symptoms of coronavirus disease of 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), range from asymptomatic to life-threatening, affecting the respiratory system and many other organs, and involved in SARS-CoV-2 pathogenesis, particularly its viral entry, is the enzyme transmembrane protease serine 2 (TMPRSS2). In response, in this study, polymorphisms in the <em>TMPRSS2</em> gene (rs2070788 and rs12329760) were examined for their association with COVID-19 susceptibility and severity in Jordanians. The study participants (n = 861) included 427 individuals who had never contracted COVID-19 (control group) and 434 who had experienced the disease (case group). The case group was subsequently divided into two subgroups: severe (n = 166) and non-severe (n = 268) COVID-19. The results show that rs2070788 is associated with disease susceptibility in all inheritance models (P < 0.05), as well as an association between rs12329760 and COVID-19 disease severity in both the dominant and over-dominant models (P < 0.05). Further, a higher body mass index (BMI) and older age were associated with disease severity (p < 0.05), though females were less likely to develop severe COVID-19 and to be hospitalized. In conclusion, polymorphisms in the <em>TMPRSS2</em> gene may be considered a potential predictor of COVID-19 outcomes in the Jordanian population. The study findings could be used to manage similar viral infections in Jordan in the future.</div></div>","PeriodicalId":52875,"journal":{"name":"Journal of Biosafety and Biosecurity","volume":"7 3","pages":"Pages 107-113"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-04-07DOI: 10.1016/j.bcra.2025.100286
Wenqing Li , Zhenguang Liu , Jianhai Chen , Zhe Liu , Qinming He
The rapid expansion of decentralized finance (DeFi) applications has catalyzed the emergence of new blockchain systems at an unprecedented pace. However, these systems are largely evolving in isolation, hindering the development of a cohesive ecosystem where value and data can flow seamlessly across networks. Blockchain interoperability technologies are introduced to break down these communication barriers and facilitate effective interactions between different blockchain systems. In recent years, numerous approaches and solutions to blockchain interoperability have been proposed. While some reviews have attempted to categorize cross-chain solutions based on blockchain standards and architectures, a more in-depth analysis is warranted. In this work, we investigate mainstream cross-chain solutions from the perspective of their principles, applications, protocols, and performance. To clarify the concept of blockchain interoperability, we propose a conceptual model that characterizes both asset interoperability and data interoperability. Furthermore, we introduce a hierarchical architecture to categorize and analyze representative cross-chain solutions, covering both academic research and industrial implementations. To maximize the utility of this review for a wide audience, we also highlight open challenges and identify future directions in the field of blockchain interoperability, expecting to provide a comprehensive overview of cross-chain solutions.
{"title":"Towards blockchain interoperability: a comprehensive survey on cross-chain solutions","authors":"Wenqing Li , Zhenguang Liu , Jianhai Chen , Zhe Liu , Qinming He","doi":"10.1016/j.bcra.2025.100286","DOIUrl":"10.1016/j.bcra.2025.100286","url":null,"abstract":"<div><div>The rapid expansion of decentralized finance (DeFi) applications has catalyzed the emergence of new blockchain systems at an unprecedented pace. However, these systems are largely evolving in isolation, hindering the development of a cohesive ecosystem where value and data can flow seamlessly across networks. Blockchain interoperability technologies are introduced to break down these communication barriers and facilitate effective interactions between different blockchain systems. In recent years, numerous approaches and solutions to blockchain interoperability have been proposed. While some reviews have attempted to categorize cross-chain solutions based on blockchain standards and architectures, a more in-depth analysis is warranted. In this work, we investigate mainstream cross-chain solutions from the perspective of their principles, applications, protocols, and performance. To clarify the concept of blockchain interoperability, we propose a conceptual model that characterizes both asset interoperability and data interoperability. Furthermore, we introduce a hierarchical architecture to categorize and analyze representative cross-chain solutions, covering both academic research and industrial implementations. To maximize the utility of this review for a wide audience, we also highlight open challenges and identify future directions in the field of blockchain interoperability, expecting to provide a comprehensive overview of cross-chain solutions.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100286"},"PeriodicalIF":5.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-04-10DOI: 10.1016/j.bcra.2025.100288
Damiano Di Francesco Maesa , Matteo Loporchio , Frank Tietze
This paper considers the application scenario of Intellectual Property (IP) management, a business process yet to fully embrace digitisation and the advantages it brings. We propose to leverage Distributed Ledger Technology (DLT) to digitise license agreements management by providing automated and trustworthy royalty computation, transaction execution, and payment distribution. This can be achieved by employing smart licenses, i.e., bundles of smart contracts implementing the royalty logic of license agreements. To provide scalability, flexibility, and resilience, we propose to deploy smart licenses on a network of networks model, i.e., a set of heterogeneous networks potentially running different DLT protocols and connected by cross-chain information exchange protocols. A novel advantage of the network of networks approach is that it allows for the use of private values for royalty computation, which is impossible in the traditional model. Of course, supporting private DLT networks requires privacy-preserving cross-chain schemes, a still open problem in the literature. This is why we present two alternative privacy-preserving cross-chain schemes for our considered application scenario of license agreements management, one based on Homomorphic Encryption (HE) and the other on Zero-Knowledge (ZK) proofs. Besides discussing their theoretical advantages and drawbacks, we present an experimental evaluation of a prototype implementation of smart licenses based on both schemes.
{"title":"Privacy-preserving and automated intellectual property license agreements over heterogeneous blockchain networks","authors":"Damiano Di Francesco Maesa , Matteo Loporchio , Frank Tietze","doi":"10.1016/j.bcra.2025.100288","DOIUrl":"10.1016/j.bcra.2025.100288","url":null,"abstract":"<div><div>This paper considers the application scenario of Intellectual Property (IP) management, a business process yet to fully embrace digitisation and the advantages it brings. We propose to leverage Distributed Ledger Technology (DLT) to digitise license agreements management by providing automated and trustworthy royalty computation, transaction execution, and payment distribution. This can be achieved by employing smart licenses, i.e., bundles of smart contracts implementing the royalty logic of license agreements. To provide scalability, flexibility, and resilience, we propose to deploy smart licenses on a network of networks model, i.e., a set of heterogeneous networks potentially running different DLT protocols and connected by cross-chain information exchange protocols. A novel advantage of the network of networks approach is that it allows for the use of private values for royalty computation, which is impossible in the traditional model. Of course, supporting private DLT networks requires privacy-preserving cross-chain schemes, a still open problem in the literature. This is why we present two alternative privacy-preserving cross-chain schemes for our considered application scenario of license agreements management, one based on Homomorphic Encryption (HE) and the other on Zero-Knowledge (ZK) proofs. Besides discussing their theoretical advantages and drawbacks, we present an experimental evaluation of a prototype implementation of smart licenses based on both schemes.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100288"},"PeriodicalIF":5.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-03-21DOI: 10.1016/j.bcra.2025.100277
Chunming Rong , Jungwon Seo , Zihan Zhao , Ferhat Ozgur Catak , Jiahui Geng , Martin Gilje Jaatun
As organizations increasingly seek to build Foundation Models (FMs) using their own proprietary data, many are adopting private and in-house cloud infrastructures (often in addition to public clouds) to address concerns over cost, data privacy, and data sovereignty. However, these isolated private clouds frequently lack interoperability, creating barriers to cross-institutional collaboration, which is vital for training robust Domain-Specific Foundation Models (DSFMs) that rely on large and diverse datasets. Additionally, underutilized resources in private clouds lead to significant global energy inefficiencies. In this paper, we propose the Federated Large Domain Model System (FLDMS), a conceptual framework designed to facilitate collaborative foundation model development across multiple private cloud environments. We review the necessary enabling technologies, including decentralized protocols for data privacy and Large Language Models (LLMs) for automated orchestration, and present a high-level system design demonstrating how these components can be integrated. By enabling secure and efficient cross-organization cooperation, FLDMS provides a blueprint for building DSFMs while addressing the inefficiencies inherent in siloed private cloud systems.
{"title":"Federated Large Domain Model System","authors":"Chunming Rong , Jungwon Seo , Zihan Zhao , Ferhat Ozgur Catak , Jiahui Geng , Martin Gilje Jaatun","doi":"10.1016/j.bcra.2025.100277","DOIUrl":"10.1016/j.bcra.2025.100277","url":null,"abstract":"<div><div>As organizations increasingly seek to build Foundation Models (FMs) using their own proprietary data, many are adopting private and in-house cloud infrastructures (often in addition to public clouds) to address concerns over cost, data privacy, and data sovereignty. However, these isolated private clouds frequently lack interoperability, creating barriers to cross-institutional collaboration, which is vital for training robust Domain-Specific Foundation Models (DSFMs) that rely on large and diverse datasets. Additionally, underutilized resources in private clouds lead to significant global energy inefficiencies. In this paper, we propose the Federated Large Domain Model System (FLDMS), a conceptual framework designed to facilitate collaborative foundation model development across multiple private cloud environments. We review the necessary enabling technologies, including decentralized protocols for data privacy and Large Language Models (LLMs) for automated orchestration, and present a high-level system design demonstrating how these components can be integrated. By enabling secure and efficient cross-organization cooperation, FLDMS provides a blueprint for building DSFMs while addressing the inefficiencies inherent in siloed private cloud systems.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100277"},"PeriodicalIF":5.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-04-10DOI: 10.1016/j.bcra.2025.100287
Chu Chen , Xuan Wang , Bin Yu , Yumo Tian , Xiaoyu Lu , Pinghong Ren , Jin Liu
With the widespread adoption of WebAssembly (Wasm) smart contracts in popular blockchain platforms such as EOSIO, vulnerability attacks on Wasm smart contracts have become a serious problem. To protect the legitimate interests of blockchain users, it is necessary to detect vulnerabilities in Wasm smart contracts. However, detection faces a great challenge in that the source code of Wasm smart contracts is rarely released publicly. Although many approaches have made great progress in vulnerability detection, they still suffer from inefficiently generating function invocation sequences to track inter-function dependencies, ineffectively tracking sensitive information flows, and a considerable number of False Positives (FPs). To address these issues, we present a new concolic fuzzing approach for detecting vulnerabilities in Wasm smart contracts via information flows and function invocation sequences, namely, WASIF. Also, we implement the open-source prototype of the WASIF and conduct extensive experiments to evaluate it. The experimental results show that WASIF effectively and efficiently detects vulnerabilities in Wasm smart contracts and outperforms the state-of-the-art concolic fuzzer WASAI on most metrics.
{"title":"WASIF: In-depth detection of vulnerabilities in Wasm smart contracts via information flows and function invocation sequences","authors":"Chu Chen , Xuan Wang , Bin Yu , Yumo Tian , Xiaoyu Lu , Pinghong Ren , Jin Liu","doi":"10.1016/j.bcra.2025.100287","DOIUrl":"10.1016/j.bcra.2025.100287","url":null,"abstract":"<div><div>With the widespread adoption of WebAssembly (Wasm) smart contracts in popular blockchain platforms such as EOSIO, vulnerability attacks on Wasm smart contracts have become a serious problem. To protect the legitimate interests of blockchain users, it is necessary to detect vulnerabilities in Wasm smart contracts. However, detection faces a great challenge in that the source code of Wasm smart contracts is rarely released publicly. Although many approaches have made great progress in vulnerability detection, they still suffer from inefficiently generating function invocation sequences to track inter-function dependencies, ineffectively tracking sensitive information flows, and a considerable number of False Positives (FPs). To address these issues, we present a new concolic fuzzing approach for detecting vulnerabilities in Wasm smart contracts via information flows and function invocation sequences, namely, WASIF. Also, we implement the open-source prototype of the WASIF and conduct extensive experiments to evaluate it. The experimental results show that WASIF effectively and efficiently detects vulnerabilities in Wasm smart contracts and outperforms the state-of-the-art concolic fuzzer WASAI on most metrics.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100287"},"PeriodicalIF":5.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-04-17DOI: 10.1016/j.bcra.2025.100292
Huijian Han , Mingwei Wang , Feng Yang , Linpeng Jia , Yi Sun , Rui Zhang
Rollup stands out as one of the most effective techniques for blockchain Layer-2 scaling. By processing transactions off-chain, it significantly enhances the throughput. However, the most rollup implementations currently rely on centralized sequencers, exposing the system and users to censorship attacks and risking network paralysis. In contrast, fully decentralized sequencers encounter latency issues and reduced throughput during the consensus phase. We propose a multislot weighted leader election algorithm based on shared sequencers, apply the proposer–builder separation (PBS) model, and use the fuzzy cognitive map (FCM) to analyze and optimize the important influence parameters. With its low trust dependence and high functionality, the probability of selecting malicious nodes is reduced. The sequencing and consensus are separated, so that the transaction can quickly reach soft confirmation. We implement this algorithm in a shared sequencer prototype. The experimental results show that the proposed algorithm parameter settings are in line with the expectations, and the probability of electing malicious nodes is significantly reduced. The transactions per second (TPS) of the network can cope with the throughput requirements of the Layer-2.
Rollup是区块链第2层扩展最有效的技术之一。通过处理链下交易，它显著提高了吞吐量。然而，大多数rollup实现目前依赖于集中式测序器，这将系统和用户暴露于审查攻击之下，并有网络瘫痪的风险。相比之下，完全分散的测序器在共识阶段遇到延迟问题和吞吐量降低。提出了一种基于共享序列的多时隙加权领袖选举算法，采用提议者-构建者分离（PBS）模型，并利用模糊认知图（FCM）对重要影响参数进行分析和优化。该算法具有较低的信任依赖性和较高的功能性，降低了选择恶意节点的概率。排序和共识分离，使交易能够快速达到软确认。我们在一个共享的音序器原型中实现了这个算法。实验结果表明，提出的算法参数设置符合预期，显著降低了恶意节点的当选概率。网络的TPS （transactions per second）可以满足第二层的吞吐量要求。
{"title":"A Layer-2 expansion shared sequencer model for blockchain scalability","authors":"Huijian Han , Mingwei Wang , Feng Yang , Linpeng Jia , Yi Sun , Rui Zhang","doi":"10.1016/j.bcra.2025.100292","DOIUrl":"10.1016/j.bcra.2025.100292","url":null,"abstract":"<div><div>Rollup stands out as one of the most effective techniques for blockchain Layer-2 scaling. By processing transactions off-chain, it significantly enhances the throughput. However, the most rollup implementations currently rely on centralized sequencers, exposing the system and users to censorship attacks and risking network paralysis. In contrast, fully decentralized sequencers encounter latency issues and reduced throughput during the consensus phase. We propose a multislot weighted leader election algorithm based on shared sequencers, apply the proposer–builder separation (PBS) model, and use the fuzzy cognitive map (FCM) to analyze and optimize the important influence parameters. With its low trust dependence and high functionality, the probability of selecting malicious nodes is reduced. The sequencing and consensus are separated, so that the transaction can quickly reach soft confirmation. We implement this algorithm in a shared sequencer prototype. The experimental results show that the proposed algorithm parameter settings are in line with the expectations, and the probability of electing malicious nodes is significantly reduced. The transactions per second (TPS) of the network can cope with the throughput requirements of the Layer-2.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100292"},"PeriodicalIF":5.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid advancement of Large Language Models (LLMs) has created unprecedented opportunities for industrial automation, process optimization, and decision support systems. As industries seek to leverage LLMs for industrial tasks, understanding their architecture, deployment strategies, and fine-tuning methods becomes critical. In this review, we aim to summarize the challenges, key technologies, current status, and future directions of LLM in Prognostics and Health Management(PHM). First, this review introduces deep learning for PHM. We begin by analyzing the architectural considerations and deployment strategies for industrial environments, including acceleration techniques and quantization methods that enable efficient operation on resource-constrained industrial hardware. Second, we investigate Parameter Efficient Fine-Tuning (PEFT) techniques that allow industry-specific adaptation without prohibitive computational costs. Multi-modal capabilities extending LLMs beyond text to process sensor data, images, and time-series information are also discussed. Finally, we explore emerging PHM including anomaly detection systems that identify equipment malfunctions, fault diagnosis frameworks that determine root causes, and specialized question-answering systems that empower workers with instant domain expertise. We conclude by identifying key challenges and future research directions for LLM deployment in PHM. This review provides a timely resource for researchers, engineers, and decision-makers navigating the transformative potential of language models in industry 4.0 environments.
{"title":"Large language models for PHM: a review of optimization techniques and applications","authors":"Tingyi Yu, Junya Tang, Qingyun Yu, Li Li, Ying Liu, Raul Poler","doi":"10.1007/s43684-025-00100-5","DOIUrl":"10.1007/s43684-025-00100-5","url":null,"abstract":"<div><p>The rapid advancement of Large Language Models (LLMs) has created unprecedented opportunities for industrial automation, process optimization, and decision support systems. As industries seek to leverage LLMs for industrial tasks, understanding their architecture, deployment strategies, and fine-tuning methods becomes critical. In this review, we aim to summarize the challenges, key technologies, current status, and future directions of LLM in Prognostics and Health Management(PHM). First, this review introduces deep learning for PHM. We begin by analyzing the architectural considerations and deployment strategies for industrial environments, including acceleration techniques and quantization methods that enable efficient operation on resource-constrained industrial hardware. Second, we investigate Parameter Efficient Fine-Tuning (PEFT) techniques that allow industry-specific adaptation without prohibitive computational costs. Multi-modal capabilities extending LLMs beyond text to process sensor data, images, and time-series information are also discussed. Finally, we explore emerging PHM including anomaly detection systems that identify equipment malfunctions, fault diagnosis frameworks that determine root causes, and specialized question-answering systems that empower workers with instant domain expertise. We conclude by identifying key challenges and future research directions for LLM deployment in PHM. This review provides a timely resource for researchers, engineers, and decision-makers navigating the transformative potential of language models in industry 4.0 environments.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00100-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-15DOI: 10.1007/s43684-025-00104-1
Ryan O’Neil, Abdelhakim Khatab, Claver Diallo
In many industrial settings, fleets of assets are required to operate through alternating missions and breaks. Fleet Selective Maintenance (FSM) is widely used in such contexts to improve the fleet performance. However, existing FSM models assume that upcoming missions are identical and require only a single system configuration for completion. Additionally, these models typically assume that all missions must be completed, overlooking resource constraints that may prevent readying all systems within the available break duration. This makes mission prioritization and assignment a necessary consideration for the decision-maker. This work proposes a novel FSM model that jointly optimizes system to mission assignment, component and maintenance level selection, and repair task allocation. The proposed framework integrates analytical models for standard components and Deep Neural Networks (DNNs) for sensor-monitored ones, enabling a hybrid reliability assessment approach that better reflects real-world multi-component systems. To account for uncertainties in maintenance and break durations, a chance-constrained optimization model is developed to ensure that maintenance is completed within the available break duration with a specified confidence level. The optimization model is reformulated using two well-known techniques: Sample Average Approximation (SAA) and Conditional Value-at-Risk (CVaR) approximation. A case study of military aircraft fleet maintenance is investigated to demonstrate the accuracy and added value of the proposed approach.
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