Adhesive hydrogel has drawn great attention for wide applications in wound healing owing to its excellent biocompatibility and lasting adhesiveness.However,traditional adhesive hydrogels only keep the wound moist to p...Adhesive hydrogel has drawn great attention for wide applications in wound healing owing to its excellent biocompatibility and lasting adhesiveness.However,traditional adhesive hydrogels only keep the wound moist to promote wound healing.It is still imperative to fabricate adhesive hydrogels that exhibit efficient antibacterial ability,active driving dynamic wound closure,and reactive oxygen species(ROS)scavenging together with excellent mechanical properties.Here,a novel hydrogel based on poly(N-isopropyl acrylamide)(PNIPAAm),a thermoresponsive polymer,and tannic acid(TA)-Ag nanoparticles(TA-Ag NPs)exhibiting active contraction,tissue adhesion,anti-inflammatory and antibacterial functions was developed.TA-Ag dispersed in the hydrogel not only functioned as the catalyst to polymerize the reaction but also provided additional anti-inflammatory and antibacterial properties.Besides,tannic acid containing catechol groups endowed the hydrogel with adhesive ability.More interestingly,the obtained hydrogel exhibited the thermoresponsive shrinkage ability,which could mechanically drive wound closure due to the presence of PNIPAAm network.In vivo mouse full-thickness skin defect model demonstrated that this actively contractible and antibacterial hydrogel is a promising dressing to improve wound healing process by accelerating tissue regeneration and preventing bacterial infection.Therefore,this multi-functional adhesive hydrogel developed here may provide a new possibility for wound healing.展开更多
Let ■ be the linear space of all C<sup>1</sup> vector fields X on a compact n-dimensionalC<sup>∞</sup> Riemann manifold（n≥2）,endowed with the C<sup>1</sup> norm ‖X‖<sub>...Let ■ be the linear space of all C<sup>1</sup> vector fields X on a compact n-dimensionalC<sup>∞</sup> Riemann manifold（n≥2）,endowed with the C<sup>1</sup> norm ‖X‖<sub>1</sub>.Write θ（X）for the numberof contractible periodic orbits of X∈（?）,which may be finite or infinite.Let （?）<sup>*</sup> be the set ofall X∈（?） possessing the property that X has a neighbourhood （?） such that every Y∈（?） hasonly a finite number of singularities and at most a countable number of periodic orbits.Inthis paper,it is shown that any given S∈（?） has a neighbourhood （?） in （?） together with anumber λ=λ（?）】0 such that θ（X）≤λfor all X∈（?）.展开更多
With the urgent need to resolve complex behaviors in nonlinear evolution equations,this study makes a contribution by establishing the local existence of solutions for Cauchy problems associated with equations of mixe...With the urgent need to resolve complex behaviors in nonlinear evolution equations,this study makes a contribution by establishing the local existence of solutions for Cauchy problems associated with equations of mixed types.Our primary contribution is the establishment of solution existence,illuminating the dynamics of these complex equations.To tackle this challenging problem,we construct an approximate solution sequence and apply the contraction mapping principle to rigorously prove local solution existence.Our results significantly advance the understanding of nonlinear evolution equations of mixed types.Furthermore,they provide a versatile,powerful approach for tackling analogous challenges across physics,engineering,and applied mathematics,making this work a valuable reference for researchers in these fields.展开更多
Flowfield inverse design can obtain the desired flow and contour with high design efficiency,short design cycle,and small modification need.In this study,the Euler equations are formulated in the stream-function coord...Flowfield inverse design can obtain the desired flow and contour with high design efficiency,short design cycle,and small modification need.In this study,the Euler equations are formulated in the stream-function coordinates and combined with the given boundary conditions to derive a gridless space-marching method for the inverse design of subsonic,transonic,and supersonic flowfields.Designers can prescribe the flow parameters along the reference streamline to design flowfields and aerodynamic contours.The method is validated by the theoretical transonic solution,computational fluid dynamics,and experimental data,respectively.The method supports the fabrication of a Mach 2.0 single expansion tunnel.The calibration data agree well with the prescribed pressure distribution.The method is successfully applied to inverse design of contractions,nozzles,and asymmetric channels.Compared to classical analytic contractions,the contractions designed by the space-marching method provide a more accurate transonic flow.Compared to the classical Sivells’nozzle,the nozzle designed by the space-marching method provides a smaller workload,a more flexible velocity distribution,a 20%reduction in length,and an equally uniform flow.Additionally,the space-marching method is applied to design the asymmetric channels under various Mach numbers.These asymmetric channels perfectly eliminate Mach waves,achieving the shock-free flow turning and high flow uniformity.These results validate the feasibility of the space-marching method,making it a good candidate for the inverse design of subsonic,transonic,and supersonic internal flowfields and aerodynamic contours.展开更多
Blockchain Technology(BT)has emerged as a transformative solution for improving the efficacy,security,and transparency of supply chain intelligence.Traditional Supply Chain Management(SCM)systems frequently have probl...Blockchain Technology(BT)has emerged as a transformative solution for improving the efficacy,security,and transparency of supply chain intelligence.Traditional Supply Chain Management(SCM)systems frequently have problems such as data silos,a lack of visibility in real time,fraudulent activities,and inefficiencies in tracking and traceability.Blockchain’s decentralized and irreversible ledger offers a solid foundation for dealing with these issues;it facilitates trust,security,and the sharing of data in real-time among all parties involved.Through an examination of critical technologies,methodology,and applications,this paper delves deeply into computer modeling based-blockchain framework within supply chain intelligence.The effect of BT on SCM is evaluated by reviewing current research and practical applications in the field.As part of the process,we delved through the research on blockchain-based supply chain models,smart contracts,Decentralized Applications(DApps),and how they connect to other cutting-edge innovations like Artificial Intelligence(AI)and the Internet of Things(IoT).To quantify blockchain’s performance,the study introduces analytical models for efficiency improvement,security enhancement,and scalability,enabling computational assessment and simulation of supply chain scenarios.These models provide a structured approach to predicting system performance under varying parameters.According to the results,BT increases efficiency by automating transactions using smart contracts,increases security by using cryptographic techniques,and improves transparency in the supply chain by providing immutable records.Regulatory concerns,challenges with interoperability,and scalability all work against broad adoption.To fully automate and intelligently integrate blockchain with AI and the IoT,additional research is needed to address blockchain’s current limitations and realize its potential for supply chain intelligence.展开更多
Changes in food production,often driven by distant demand,have a significant influence on sustainable man agement and use of land and water,and are in turn a driving factor of biodiversity change.While the connection ...Changes in food production,often driven by distant demand,have a significant influence on sustainable man agement and use of land and water,and are in turn a driving factor of biodiversity change.While the connection between land use and demand through value chains is increasingly understood,there is no comprehensive concep tualisation of this relationship.To address this gap,we propose a conceptual framework and use it as a basis for a systematic review to characterise value-chain connection and explore its influence on land-use and-cover change.Our search in June 2022 onWeb of Science and Scopus yielded 198 documents,describing studies completed after the year 2000 that provide information on both value-chain connection and land-use or-cover change.In total,we used 531 distinct cases to assess how frequently particular types of land-use or-cover change and value-chain connections co-occurred,and synthesized findings on their relations.Our findings confirm that 1)market inte gration is associated with intensification;2)land managers with environmental standards more frequently adopt environmentally friendly practices;3)physical and value-chain distances to consumers play a crucial role,with shorter distances associated with environmentally friendly practices and global chains linked to intensification and expansion.Incorporating these characteristics in existing theories of land-system change,would significantly advance understanding of land managers’decision-making,ultimately guiding more environmentally responsible production systems and contributing to global sustainability goals.展开更多
Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attack...Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic losses.Since it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are deployed.However,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain contracts.Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution.But the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of detection.This paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert knowledge.In this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode.First,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model training.Finally,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model.In addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.展开更多
Smart contracts on the Ethereum blockchain continue to revolutionize decentralized applications (dApps) by allowing for self-executing agreements. However, bad actors have continuously found ways to exploit smart cont...Smart contracts on the Ethereum blockchain continue to revolutionize decentralized applications (dApps) by allowing for self-executing agreements. However, bad actors have continuously found ways to exploit smart contracts for personal financial gain, which undermines the integrity of the Ethereum blockchain. This paper proposes a computer program called SADA (Static and Dynamic Analyzer), a novel approach to smart contract vulnerability detection using multiple Large Language Model (LLM) agents to analyze and flag suspicious Solidity code for Ethereum smart contracts. SADA not only improves upon existing vulnerability detection methods but also paves the way for more secure smart contract development practices in the rapidly evolving blockchain ecosystem.展开更多
Theoretically,a twinning dislocation must stay on the twinning plane which is the first invariant plane of a twinning mode,because the glide of twinning dislocation linearly transforms the parent lattice to the twin l...Theoretically,a twinning dislocation must stay on the twinning plane which is the first invariant plane of a twinning mode,because the glide of twinning dislocation linearly transforms the parent lattice to the twin lattice.However,recent experimental observations showed that a{1011}{1012}twin variant could cross another variant during twin-twin interaction.It is well known that{1011}twinning is mediated by zonal twinning dislocations.Thus,how the zonal twinning dislocations transmute during twin-twin interaction is of great interest but not well understood.In this work,atomistic simulation is performed to investigate interaction between{1011}twin variants.Our results show that when an incoming twin variant impinges on the other which acts as a barrier,surprisingly,the barrier twin can grow at the expense of the incoming twin.Eventually one variant consumes the other.Structural analysis shows that the twinning dislocations of the barrier variant are able to penetrate the zone of twin-twin intersection,by plowing through the lattice of one variant and transform its lattice into the lattice of the other.Careful lattice correspondence analysis reveals that,the lattice transformation from one variant to the other is close to{1012}{1011}twinning,but the orientation relationship deviates by a minor lattice rotation.This deviation presents a significant energy barrier to the lattice transformation,and thus it is expected such a twin-twin interaction will increase the stress for twin growth.展开更多
As the information sensing and processing capabilities of IoT devices increase,a large amount of data is being generated at the edge of Industrial IoT(IIoT),which has become a strong foundation for distributed Artific...As the information sensing and processing capabilities of IoT devices increase,a large amount of data is being generated at the edge of Industrial IoT(IIoT),which has become a strong foundation for distributed Artificial Intelligence(AI)applications.However,most users are reluctant to disclose their data due to network bandwidth limitations,device energy consumption,and privacy requirements.To address this issue,this paper introduces an Edge-assisted Federated Learning(EFL)framework,along with an incentive mechanism for lightweight industrial data sharing.In order to reduce the information asymmetry between data owners and users,an EFL model-sharing incentive mechanism based on contract theory is designed.In addition,a weight dispersion evaluation scheme based on Wasserstein distance is proposed.This study models an optimization problem of node selection and sharing incentives to maximize the EFL model consumers'profit and ensure the quality of training services.An incentive-based EFL algorithm with individual rationality and incentive compatibility constraints is proposed.Finally,the experimental results verify the effectiveness of the proposed scheme in terms of positive incentives for contract design and performance analysis of EFL systems.展开更多
Faust,as a classic image in Western literature and philosophy,has been endowed with profound philosophical connotations in Johann Wolfgang von Goethe’s epic reconstruction.This article starts from Faust’s dual ident...Faust,as a classic image in Western literature and philosophy,has been endowed with profound philosophical connotations in Johann Wolfgang von Goethe’s epic reconstruction.This article starts from Faust’s dual identity as a“seeker”and a“paradoxical person”,revealing the dialectical unity of his philosophical meaning:In the dynamic process of“infinite pursuit”and“self denial”,Faust not only embodies humanity’s eternal questioning of the essence of existence,but also reflects the struggle and transcendence of human spirit in the dilemma of modernity.Through in-depth analysis of the five stages of Faust’s soul conflict and his contractual relationship with the devil,this article clarifies that Faust is not only a spiritual symbol of the Renaissance to the Age of Enlightenment,but also a mirror of modern society under the guidance of technological rationality.展开更多
Blockchain technology,as a revolutionary tool,is profoundly changing the way the financial field works.Its application has expanded from digital currency to many fields,such as smart contracts,cross-border payments,tr...Blockchain technology,as a revolutionary tool,is profoundly changing the way the financial field works.Its application has expanded from digital currency to many fields,such as smart contracts,cross-border payments,trade finance,and digital identity management,providing important support for simplifying financial service processes,reducing costs,and improving efficiency.However,the widespread application of blockchain technology still faces challenges such as scalability,regulatory compliance,and cybersecurity,limiting its full integration in the financial industry.This study systematically reviews the status quo,development history,and future trends of blockchain technology application in the financial sector,analyzes its key role in capital markets,decentralized finance(DeFi),and other fields,and explores the potential of emerging solutions such as hybrid blockchain and dynamic regulatory frameworks.展开更多
The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses,which involve the calculation of complex functions,such as matrix operations.However,com...The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses,which involve the calculation of complex functions,such as matrix operations.However,complex functions such as matrix operations are difficult to implement on Ethereum Virtual Machine(EVM)-based smart contract platforms due to their distributed security environment limitations.Existing off-chain methods often result in a significant reduction in contract execution efficiency,thus a platform software development kit interface implementation method has become a feasible way to reduce overheads,but this method cannot verify operation correctness and may leak sensitive user data.To solve the above problems,we propose a verifiable EVM-based smart contract cross-language implementation scheme for complex operations,especially matrix operations,which can guarantee operation correctness and user privacy while ensuring computational efficiency.In this scheme,a verifiable interaction process is designed to verify the computation process and results,and a matrix blinding technology is introduced to protect sensitive user data in the calculation process.The security analysis and performance tests show that the proposed scheme can satisfy the correctness and privacy of the cross-language implementation of smart contracts at a small additional efficiency cost.展开更多
Smart contracts are self-executing programs on blockchains that manage complex business logic with transparency and integrity.However,their immutability after deployment makes programming errors particularly critical,...Smart contracts are self-executing programs on blockchains that manage complex business logic with transparency and integrity.However,their immutability after deployment makes programming errors particularly critical,as such errors can be exploited to compromise blockchain security.Existing vulnerability detection methods often rely on fixed rules or target specific vulnerabilities,limiting their scalability and adaptability to diverse smart contract scenarios.Furthermore,natural language processing approaches for source code analysis frequently fail to capture program flow,which is essential for identifying structural vulnerabilities.To address these limitations,we propose a novel model that integrates textual and structural information for smart contract vulnerability detection.Our approach employs the CodeBERT NLP model for textual analysis,augmented with structural insights derived from control flow graphs created using the abstract syntax tree and opcode of smart contracts.Each graph node is embedded using Sent2Vec,and centrality analysis is applied to highlight critical paths and nodes within the code.The extracted features are normalized and combined into a prompt for a large language model to detect vulnerabilities effectivel.Experimental results demonstrate the superiority of our model,achieving an accuracy of 86.70%,a recall of 84.87%,a precision of 85.24%,and an F1-score of 84.46%.These outcomes surpass existing methods,including CodeBERT alone(accuracy:81.26%,F1-score:79.84%)and CodeBERT combined with abstract syntax tree analysis(accuracy:83.48%,F1-score:79.65%).The findings underscore the effectiveness of incorporating graph structural information alongside text-based analysis,offering improved scalability and performance in detecting diverse vulnerabilities.展开更多
Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a part...Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.展开更多
The explosive expansion of the Internet of Things(IoT)systems has increased the imperative to have strong and robust solutions to cyber Security,especially to curtail Distributed Denial of Service(DDoS)attacks,which c...The explosive expansion of the Internet of Things(IoT)systems has increased the imperative to have strong and robust solutions to cyber Security,especially to curtail Distributed Denial of Service(DDoS)attacks,which can cripple critical infrastructure.The proposed framework presented in the current paper is a new hybrid scheme that induces deep learning-based traffic classification and blockchain-enabledmitigation tomake intelligent,decentralized,and real-time DDoS countermeasures in an IoT network.The proposed model fuses the extracted deep features with statistical features and trains them by using traditional machine-learning algorithms,which makes them more accurate in detection than statistical features alone,based on the Convolutional Neural Network(CNN)architecture,which can extract deep features.A permissioned blockchain will be included to record the threat cases immutably and automatically execute mitigation measures through smart contracts to provide transparency and resilience.When tested on two test sets,BoT-IoT and IoT-23,the framework obtains a maximum F1-score at 97.5 percent and only a 1.8 percent false positive rate,which compares favorably to other solutions regarding effectiveness and the amount of time required to respond.Our findings support the feasibility of our method as an extensible and secure paradigm of nextgeneration IoT security,which has constrictive utility in mission-critical or resource-constrained settings.The work is a substantial milestone in autonomous and trustful mitigation against DDoS attacks through intelligent learning and decentralized enforcement.展开更多
The proliferation of deep learning(DL)has amplified the demand for processing large and complex datasets for tasks such as modeling,classification,and identification.However,traditional DL methods compromise client pr...The proliferation of deep learning(DL)has amplified the demand for processing large and complex datasets for tasks such as modeling,classification,and identification.However,traditional DL methods compromise client privacy by collecting sensitive data,underscoring the necessity for privacy-preserving solutions like Federated Learning(FL).FL effectively addresses escalating privacy concerns by facilitating collaborative model training without necessitating the sharing of raw data.Given that FL clients autonomously manage training data,encouraging client engagement is pivotal for successful model training.To overcome challenges like unreliable communication and budget constraints,we present ENTIRE,a contract-based dynamic participation incentive mechanism for FL.ENTIRE ensures impartial model training by tailoring participation levels and payments to accommodate diverse client preferences.Our approach involves several key steps.Initially,we examine how random client participation impacts FL convergence in non-convex scenarios,establishing the correlation between client participation levels and model performance.Subsequently,we reframe model performance optimization as an optimal contract design challenge to guide the distribution of rewards among clients with varying participation costs.By balancing budget considerations with model effectiveness,we craft optimal contracts for different budgetary constraints,prompting clients to disclose their participation preferences and select suitable contracts for contributing to model training.Finally,we conduct a comprehensive experimental evaluation of ENTIRE using three real datasets.The results demonstrate a significant 12.9%enhancement in model performance,validating its adherence to anticipated economic properties.展开更多
With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, i...With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anonymity has provided new ways for Ponzi schemes to commit fraud, posing significant risks to investors. Current research still has some limitations, for example, Ponzi schemes are difficult to detect in the early stages of smart contract deployment, and data imbalance is not considered. In addition, there is room for improving the detection accuracy. To address the above issues, this paper proposes LT-SPSD (LSTM-Transformer smart Ponzi schemes detection), which is a Ponzi scheme detection method that combines Long Short-Term Memory (LSTM) and Transformer considering the time-series transaction information of smart contracts as well as the global information. Based on the verified smart contract addresses, account features, and code features are extracted to construct a feature dataset, and the SMOTE-Tomek algorithm is used to deal with the imbalanced data classification problem. By comparing our method with the other four typical detection methods in the experiment, the LT-SPSD method shows significant performance improvement in precision, recall, and F1-score. The results of the experiment confirm the efficacy of the model, which has some application value in Ethereum Ponzi scheme smart contract detection.展开更多
Attribute-Based Signature(ABS)is a powerful cryptographic primitive that enables fine-grained access control in distributed systems.However,its high computational cost makes it unsuitable for resource-constrained envi...Attribute-Based Signature(ABS)is a powerful cryptographic primitive that enables fine-grained access control in distributed systems.However,its high computational cost makes it unsuitable for resource-constrained environments,and traditional monotonic access structures are inadequate for handling increasingly complex access policies.In this paper,we propose a novel smart contract-assisted ABS(SC-ABS)algorithm that supports nonmonotonic access structures,aiming to reduce client computing overhead while providingmore expressive and flexible access control.The SC-ABS scheme extends the monotonic access structure by introducing the concept of negative attributes,allowing for more complex and dynamic access policies.By utilizing smart contracts,the algorithmsupports distributed trusted assisted computation,and the computation code is transparent and auditable.Importantly,this design allows information about user attributes to be deployed on smart contracts for computation,both reducing the risk of privacy abuse by semi-honest servers and preventing malicious users from attribute concealment to forge signatures.We prove that SC-ABS satisfies unforgeability and anonymity under a random oracle model,and test the scheme’s cost.Comparedwith existing schemes,this scheme has higher efficiency in client signature and authentication.This scheme reduces the computing burden of users,and the design of smart contracts improves the security of aided computing further,solves the problem of attribute concealment,and expresses a more flexible access structure.The solution enables permission control applications in resource-constrained distributed scenarios,such as the Internet of Things(IoT)and distributed version control systems,where data security and flexible access control are critical.展开更多
文摘The annulus and disk complex is defined and researched. Especially, we prove that this complex is contractible and quasi-convex in the curve complex.
基金supported by the National Research Programs of China(Nos.2020YFA0211100,and 2022YFA1206500)the National Natural Science Foundation of China(Nos.52250002,and 52325106)Suzhou Key Laboratory of Nanotechnology and Biomedicine,Collaborative Innovation Center of Suzhou Nano Science and Technology,and the 111 Program from the Ministry of Education of China.
文摘Adhesive hydrogel has drawn great attention for wide applications in wound healing owing to its excellent biocompatibility and lasting adhesiveness.However,traditional adhesive hydrogels only keep the wound moist to promote wound healing.It is still imperative to fabricate adhesive hydrogels that exhibit efficient antibacterial ability,active driving dynamic wound closure,and reactive oxygen species(ROS)scavenging together with excellent mechanical properties.Here,a novel hydrogel based on poly(N-isopropyl acrylamide)(PNIPAAm),a thermoresponsive polymer,and tannic acid(TA)-Ag nanoparticles(TA-Ag NPs)exhibiting active contraction,tissue adhesion,anti-inflammatory and antibacterial functions was developed.TA-Ag dispersed in the hydrogel not only functioned as the catalyst to polymerize the reaction but also provided additional anti-inflammatory and antibacterial properties.Besides,tannic acid containing catechol groups endowed the hydrogel with adhesive ability.More interestingly,the obtained hydrogel exhibited the thermoresponsive shrinkage ability,which could mechanically drive wound closure due to the presence of PNIPAAm network.In vivo mouse full-thickness skin defect model demonstrated that this actively contractible and antibacterial hydrogel is a promising dressing to improve wound healing process by accelerating tissue regeneration and preventing bacterial infection.Therefore,this multi-functional adhesive hydrogel developed here may provide a new possibility for wound healing.
基金Supported by National Natural Science Foundation of China
文摘Let ■ be the linear space of all C<sup>1</sup> vector fields X on a compact n-dimensionalC<sup>∞</sup> Riemann manifold（n≥2）,endowed with the C<sup>1</sup> norm ‖X‖<sub>1</sub>.Write θ（X）for the numberof contractible periodic orbits of X∈（?）,which may be finite or infinite.Let （?）<sup>*</sup> be the set ofall X∈（?） possessing the property that X has a neighbourhood （?） such that every Y∈（?） hasonly a finite number of singularities and at most a countable number of periodic orbits.Inthis paper,it is shown that any given S∈（?） has a neighbourhood （?） in （?） together with anumber λ=λ（?）】0 such that θ（X）≤λfor all X∈（?）.
基金Supported by the National Natural Science Foundation of China(12201368,62376252)Key Project of Natural Science Foundation of Zhejiang Province(LZ22F030003)Zhejiang Province Leading Geese Plan(2024C02G1123882,2024C01SA100795).
文摘With the urgent need to resolve complex behaviors in nonlinear evolution equations,this study makes a contribution by establishing the local existence of solutions for Cauchy problems associated with equations of mixed types.Our primary contribution is the establishment of solution existence,illuminating the dynamics of these complex equations.To tackle this challenging problem,we construct an approximate solution sequence and apply the contraction mapping principle to rigorously prove local solution existence.Our results significantly advance the understanding of nonlinear evolution equations of mixed types.Furthermore,they provide a versatile,powerful approach for tackling analogous challenges across physics,engineering,and applied mathematics,making this work a valuable reference for researchers in these fields.
基金supported by the National Key Research and Development Program of China(No.2019YFA0405300)the National Natural Science Foundation of China(No.12272405).
文摘Flowfield inverse design can obtain the desired flow and contour with high design efficiency,short design cycle,and small modification need.In this study,the Euler equations are formulated in the stream-function coordinates and combined with the given boundary conditions to derive a gridless space-marching method for the inverse design of subsonic,transonic,and supersonic flowfields.Designers can prescribe the flow parameters along the reference streamline to design flowfields and aerodynamic contours.The method is validated by the theoretical transonic solution,computational fluid dynamics,and experimental data,respectively.The method supports the fabrication of a Mach 2.0 single expansion tunnel.The calibration data agree well with the prescribed pressure distribution.The method is successfully applied to inverse design of contractions,nozzles,and asymmetric channels.Compared to classical analytic contractions,the contractions designed by the space-marching method provide a more accurate transonic flow.Compared to the classical Sivells’nozzle,the nozzle designed by the space-marching method provides a smaller workload,a more flexible velocity distribution,a 20%reduction in length,and an equally uniform flow.Additionally,the space-marching method is applied to design the asymmetric channels under various Mach numbers.These asymmetric channels perfectly eliminate Mach waves,achieving the shock-free flow turning and high flow uniformity.These results validate the feasibility of the space-marching method,making it a good candidate for the inverse design of subsonic,transonic,and supersonic internal flowfields and aerodynamic contours.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Blockchain Technology(BT)has emerged as a transformative solution for improving the efficacy,security,and transparency of supply chain intelligence.Traditional Supply Chain Management(SCM)systems frequently have problems such as data silos,a lack of visibility in real time,fraudulent activities,and inefficiencies in tracking and traceability.Blockchain’s decentralized and irreversible ledger offers a solid foundation for dealing with these issues;it facilitates trust,security,and the sharing of data in real-time among all parties involved.Through an examination of critical technologies,methodology,and applications,this paper delves deeply into computer modeling based-blockchain framework within supply chain intelligence.The effect of BT on SCM is evaluated by reviewing current research and practical applications in the field.As part of the process,we delved through the research on blockchain-based supply chain models,smart contracts,Decentralized Applications(DApps),and how they connect to other cutting-edge innovations like Artificial Intelligence(AI)and the Internet of Things(IoT).To quantify blockchain’s performance,the study introduces analytical models for efficiency improvement,security enhancement,and scalability,enabling computational assessment and simulation of supply chain scenarios.These models provide a structured approach to predicting system performance under varying parameters.According to the results,BT increases efficiency by automating transactions using smart contracts,increases security by using cryptographic techniques,and improves transparency in the supply chain by providing immutable records.Regulatory concerns,challenges with interoperability,and scalability all work against broad adoption.To fully automate and intelligently integrate blockchain with AI and the IoT,additional research is needed to address blockchain’s current limitations and realize its potential for supply chain intelligence.
文摘Changes in food production,often driven by distant demand,have a significant influence on sustainable man agement and use of land and water,and are in turn a driving factor of biodiversity change.While the connection between land use and demand through value chains is increasingly understood,there is no comprehensive concep tualisation of this relationship.To address this gap,we propose a conceptual framework and use it as a basis for a systematic review to characterise value-chain connection and explore its influence on land-use and-cover change.Our search in June 2022 onWeb of Science and Scopus yielded 198 documents,describing studies completed after the year 2000 that provide information on both value-chain connection and land-use or-cover change.In total,we used 531 distinct cases to assess how frequently particular types of land-use or-cover change and value-chain connections co-occurred,and synthesized findings on their relations.Our findings confirm that 1)market inte gration is associated with intensification;2)land managers with environmental standards more frequently adopt environmentally friendly practices;3)physical and value-chain distances to consumers play a crucial role,with shorter distances associated with environmentally friendly practices and global chains linked to intensification and expansion.Incorporating these characteristics in existing theories of land-system change,would significantly advance understanding of land managers’decision-making,ultimately guiding more environmentally responsible production systems and contributing to global sustainability goals.
基金partially supported by the National Natural Science Foundation (62272248)the Open Project Fund of State Key Laboratory of Computer Architecture,Institute of Computing Technology,Chinese Academy of Sciences (CARCHA202108,CARCH201905)+1 种基金the Natural Science Foundation of Tianjin (20JCZDJC00610)Sponsored by Zhejiang Lab (2021KF0AB04)。
文摘Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic losses.Since it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are deployed.However,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain contracts.Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution.But the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of detection.This paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert knowledge.In this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode.First,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model training.Finally,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model.In addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.
文摘Smart contracts on the Ethereum blockchain continue to revolutionize decentralized applications (dApps) by allowing for self-executing agreements. However, bad actors have continuously found ways to exploit smart contracts for personal financial gain, which undermines the integrity of the Ethereum blockchain. This paper proposes a computer program called SADA (Static and Dynamic Analyzer), a novel approach to smart contract vulnerability detection using multiple Large Language Model (LLM) agents to analyze and flag suspicious Solidity code for Ethereum smart contracts. SADA not only improves upon existing vulnerability detection methods but also paves the way for more secure smart contract development practices in the rapidly evolving blockchain ecosystem.
基金support from U.S.National Science Foundation(NSF)(CMMI-2016263,2032483).
文摘Theoretically,a twinning dislocation must stay on the twinning plane which is the first invariant plane of a twinning mode,because the glide of twinning dislocation linearly transforms the parent lattice to the twin lattice.However,recent experimental observations showed that a{1011}{1012}twin variant could cross another variant during twin-twin interaction.It is well known that{1011}twinning is mediated by zonal twinning dislocations.Thus,how the zonal twinning dislocations transmute during twin-twin interaction is of great interest but not well understood.In this work,atomistic simulation is performed to investigate interaction between{1011}twin variants.Our results show that when an incoming twin variant impinges on the other which acts as a barrier,surprisingly,the barrier twin can grow at the expense of the incoming twin.Eventually one variant consumes the other.Structural analysis shows that the twinning dislocations of the barrier variant are able to penetrate the zone of twin-twin intersection,by plowing through the lattice of one variant and transform its lattice into the lattice of the other.Careful lattice correspondence analysis reveals that,the lattice transformation from one variant to the other is close to{1012}{1011}twinning,but the orientation relationship deviates by a minor lattice rotation.This deviation presents a significant energy barrier to the lattice transformation,and thus it is expected such a twin-twin interaction will increase the stress for twin growth.
基金supported by the National Natural Science Foundation of China (No.62071070)Major science and technology special project of Science and Technology Department of Yunnan Province (202002AB080001-8)BUPT innovation&entrepreneurship support program (2023-YC-T031)。
文摘As the information sensing and processing capabilities of IoT devices increase,a large amount of data is being generated at the edge of Industrial IoT(IIoT),which has become a strong foundation for distributed Artificial Intelligence(AI)applications.However,most users are reluctant to disclose their data due to network bandwidth limitations,device energy consumption,and privacy requirements.To address this issue,this paper introduces an Edge-assisted Federated Learning(EFL)framework,along with an incentive mechanism for lightweight industrial data sharing.In order to reduce the information asymmetry between data owners and users,an EFL model-sharing incentive mechanism based on contract theory is designed.In addition,a weight dispersion evaluation scheme based on Wasserstein distance is proposed.This study models an optimization problem of node selection and sharing incentives to maximize the EFL model consumers'profit and ensure the quality of training services.An incentive-based EFL algorithm with individual rationality and incentive compatibility constraints is proposed.Finally,the experimental results verify the effectiveness of the proposed scheme in terms of positive incentives for contract design and performance analysis of EFL systems.
文摘Faust,as a classic image in Western literature and philosophy,has been endowed with profound philosophical connotations in Johann Wolfgang von Goethe’s epic reconstruction.This article starts from Faust’s dual identity as a“seeker”and a“paradoxical person”,revealing the dialectical unity of his philosophical meaning:In the dynamic process of“infinite pursuit”and“self denial”,Faust not only embodies humanity’s eternal questioning of the essence of existence,but also reflects the struggle and transcendence of human spirit in the dilemma of modernity.Through in-depth analysis of the five stages of Faust’s soul conflict and his contractual relationship with the devil,this article clarifies that Faust is not only a spiritual symbol of the Renaissance to the Age of Enlightenment,but also a mirror of modern society under the guidance of technological rationality.
基金Exploration and Practice of the Application of Blockchain Technology to the Cultivation of Compound Talents under the Background of Free Trade Port(HKJG2023-18)。
文摘Blockchain technology,as a revolutionary tool,is profoundly changing the way the financial field works.Its application has expanded from digital currency to many fields,such as smart contracts,cross-border payments,trade finance,and digital identity management,providing important support for simplifying financial service processes,reducing costs,and improving efficiency.However,the widespread application of blockchain technology still faces challenges such as scalability,regulatory compliance,and cybersecurity,limiting its full integration in the financial industry.This study systematically reviews the status quo,development history,and future trends of blockchain technology application in the financial sector,analyzes its key role in capital markets,decentralized finance(DeFi),and other fields,and explores the potential of emerging solutions such as hybrid blockchain and dynamic regulatory frameworks.
基金supported in part by the National Natural Science Foundation of China under Grant 62272007,U23B2002in part by the Excellent Young Talents Project of the Beijing Municipal University Teacher Team Construction Support Plan under Grant BPHR202203031+1 种基金in part by the Yunnan Key Laboratory of Blockchain Application Technology under Grant 2021105AG070005(YNB202102)in part by the Open Topics of Key Laboratory of Blockchain Technology and Data Security,The Ministry of Industry and Information Technology of the People’s Republic of China under Grant 20243222。
文摘The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses,which involve the calculation of complex functions,such as matrix operations.However,complex functions such as matrix operations are difficult to implement on Ethereum Virtual Machine(EVM)-based smart contract platforms due to their distributed security environment limitations.Existing off-chain methods often result in a significant reduction in contract execution efficiency,thus a platform software development kit interface implementation method has become a feasible way to reduce overheads,but this method cannot verify operation correctness and may leak sensitive user data.To solve the above problems,we propose a verifiable EVM-based smart contract cross-language implementation scheme for complex operations,especially matrix operations,which can guarantee operation correctness and user privacy while ensuring computational efficiency.In this scheme,a verifiable interaction process is designed to verify the computation process and results,and a matrix blinding technology is introduced to protect sensitive user data in the calculation process.The security analysis and performance tests show that the proposed scheme can satisfy the correctness and privacy of the cross-language implementation of smart contracts at a small additional efficiency cost.
基金supported by the Seoul Business Agency(SBA),funded by the Seoul Metropolitan Government,through the Seoul R&BD Program(FB240022)by the Korea Institute for Advancement of Technology(KIAT),funded by the Korea Government(MOTIE)(RS-2024-00406796)+1 种基金through the HRD Program for Industrial Innovationby the Excellent Researcher Support Project of Kwangwoon University in 2024.
文摘Smart contracts are self-executing programs on blockchains that manage complex business logic with transparency and integrity.However,their immutability after deployment makes programming errors particularly critical,as such errors can be exploited to compromise blockchain security.Existing vulnerability detection methods often rely on fixed rules or target specific vulnerabilities,limiting their scalability and adaptability to diverse smart contract scenarios.Furthermore,natural language processing approaches for source code analysis frequently fail to capture program flow,which is essential for identifying structural vulnerabilities.To address these limitations,we propose a novel model that integrates textual and structural information for smart contract vulnerability detection.Our approach employs the CodeBERT NLP model for textual analysis,augmented with structural insights derived from control flow graphs created using the abstract syntax tree and opcode of smart contracts.Each graph node is embedded using Sent2Vec,and centrality analysis is applied to highlight critical paths and nodes within the code.The extracted features are normalized and combined into a prompt for a large language model to detect vulnerabilities effectivel.Experimental results demonstrate the superiority of our model,achieving an accuracy of 86.70%,a recall of 84.87%,a precision of 85.24%,and an F1-score of 84.46%.These outcomes surpass existing methods,including CodeBERT alone(accuracy:81.26%,F1-score:79.84%)and CodeBERT combined with abstract syntax tree analysis(accuracy:83.48%,F1-score:79.65%).The findings underscore the effectiveness of incorporating graph structural information alongside text-based analysis,offering improved scalability and performance in detecting diverse vulnerabilities.
文摘Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.
文摘The explosive expansion of the Internet of Things(IoT)systems has increased the imperative to have strong and robust solutions to cyber Security,especially to curtail Distributed Denial of Service(DDoS)attacks,which can cripple critical infrastructure.The proposed framework presented in the current paper is a new hybrid scheme that induces deep learning-based traffic classification and blockchain-enabledmitigation tomake intelligent,decentralized,and real-time DDoS countermeasures in an IoT network.The proposed model fuses the extracted deep features with statistical features and trains them by using traditional machine-learning algorithms,which makes them more accurate in detection than statistical features alone,based on the Convolutional Neural Network(CNN)architecture,which can extract deep features.A permissioned blockchain will be included to record the threat cases immutably and automatically execute mitigation measures through smart contracts to provide transparency and resilience.When tested on two test sets,BoT-IoT and IoT-23,the framework obtains a maximum F1-score at 97.5 percent and only a 1.8 percent false positive rate,which compares favorably to other solutions regarding effectiveness and the amount of time required to respond.Our findings support the feasibility of our method as an extensible and secure paradigm of nextgeneration IoT security,which has constrictive utility in mission-critical or resource-constrained settings.The work is a substantial milestone in autonomous and trustful mitigation against DDoS attacks through intelligent learning and decentralized enforcement.
基金supported by the National Natural Science Foundation of China(Nos.62072411,62372343,62402352,62403500)the Key Research and Development Program of Hubei Province(No.2023BEB024)the Open Fund of Key Laboratory of Social Computing and Cognitive Intelligence(Dalian University of Technology),Ministry of Education(No.SCCI2024TB02).
文摘The proliferation of deep learning(DL)has amplified the demand for processing large and complex datasets for tasks such as modeling,classification,and identification.However,traditional DL methods compromise client privacy by collecting sensitive data,underscoring the necessity for privacy-preserving solutions like Federated Learning(FL).FL effectively addresses escalating privacy concerns by facilitating collaborative model training without necessitating the sharing of raw data.Given that FL clients autonomously manage training data,encouraging client engagement is pivotal for successful model training.To overcome challenges like unreliable communication and budget constraints,we present ENTIRE,a contract-based dynamic participation incentive mechanism for FL.ENTIRE ensures impartial model training by tailoring participation levels and payments to accommodate diverse client preferences.Our approach involves several key steps.Initially,we examine how random client participation impacts FL convergence in non-convex scenarios,establishing the correlation between client participation levels and model performance.Subsequently,we reframe model performance optimization as an optimal contract design challenge to guide the distribution of rewards among clients with varying participation costs.By balancing budget considerations with model effectiveness,we craft optimal contracts for different budgetary constraints,prompting clients to disclose their participation preferences and select suitable contracts for contributing to model training.Finally,we conduct a comprehensive experimental evaluation of ENTIRE using three real datasets.The results demonstrate a significant 12.9%enhancement in model performance,validating its adherence to anticipated economic properties.
基金This work was granted by Qin Xin Talents Cultivation Program(No.QXTCP C202115)Beijing Information Science and Technology University+1 种基金the Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Fund(No.GJJ-23)National Social Science Foundation,China(No.21BTQ079).
文摘With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anonymity has provided new ways for Ponzi schemes to commit fraud, posing significant risks to investors. Current research still has some limitations, for example, Ponzi schemes are difficult to detect in the early stages of smart contract deployment, and data imbalance is not considered. In addition, there is room for improving the detection accuracy. To address the above issues, this paper proposes LT-SPSD (LSTM-Transformer smart Ponzi schemes detection), which is a Ponzi scheme detection method that combines Long Short-Term Memory (LSTM) and Transformer considering the time-series transaction information of smart contracts as well as the global information. Based on the verified smart contract addresses, account features, and code features are extracted to construct a feature dataset, and the SMOTE-Tomek algorithm is used to deal with the imbalanced data classification problem. By comparing our method with the other four typical detection methods in the experiment, the LT-SPSD method shows significant performance improvement in precision, recall, and F1-score. The results of the experiment confirm the efficacy of the model, which has some application value in Ethereum Ponzi scheme smart contract detection.
基金supported by National Natural Science Foundation of China.
文摘Attribute-Based Signature(ABS)is a powerful cryptographic primitive that enables fine-grained access control in distributed systems.However,its high computational cost makes it unsuitable for resource-constrained environments,and traditional monotonic access structures are inadequate for handling increasingly complex access policies.In this paper,we propose a novel smart contract-assisted ABS(SC-ABS)algorithm that supports nonmonotonic access structures,aiming to reduce client computing overhead while providingmore expressive and flexible access control.The SC-ABS scheme extends the monotonic access structure by introducing the concept of negative attributes,allowing for more complex and dynamic access policies.By utilizing smart contracts,the algorithmsupports distributed trusted assisted computation,and the computation code is transparent and auditable.Importantly,this design allows information about user attributes to be deployed on smart contracts for computation,both reducing the risk of privacy abuse by semi-honest servers and preventing malicious users from attribute concealment to forge signatures.We prove that SC-ABS satisfies unforgeability and anonymity under a random oracle model,and test the scheme’s cost.Comparedwith existing schemes,this scheme has higher efficiency in client signature and authentication.This scheme reduces the computing burden of users,and the design of smart contracts improves the security of aided computing further,solves the problem of attribute concealment,and expresses a more flexible access structure.The solution enables permission control applications in resource-constrained distributed scenarios,such as the Internet of Things(IoT)and distributed version control systems,where data security and flexible access control are critical.
