In this study the effect of Sheng-ai Injection i.e. Red Ginseng-Ophiopogon Root Injection (one kind of traditional Chinese medicines) on the contractivity of diaphragm was observed. The results confirmed that Sheng-ai...In this study the effect of Sheng-ai Injection i.e. Red Ginseng-Ophiopogon Root Injection (one kind of traditional Chinese medicines) on the contractivity of diaphragm was observed. The results confirmed that Sheng-ai Injection increased Pdi of the fatigued diaphragm in rabbits and reduced the time needed for the recovery of Pdi of fatigued diaphragm to the normal value. These results suggest that Sheng-Mai Injection can increase the contractive force and promote the recovery of the fatigued diaphragm. The effect of Sheng-ai Injection on the contractivity of the isolated diaphragmatic bundle of rats was also observed and the results confirmed that Sheng-ai Injection increased the diaphragmatic contractive force directly. This effect of increasing the contractive force of diaphragm was attenuated by adding calcium channel blocker isoptin and disappeared when there was no calcium in the extracellular fluid. It is deduced, therefore, that the mechanism of the effect of Sheng-mai Injection is related to the increased influx of calcium from extracellular fluid into the cells.展开更多
Let （Xt）t≥0 be a symmetric strong Markov process generated by non-local regular Dirichlet form （D, （D）） as follows：where J（x, y） is a strictly positive and symmetric measurable function on Rd × Rd. We s...Let （Xt）t≥0 be a symmetric strong Markov process generated by non-local regular Dirichlet form （D, （D）） as follows：where J（x, y） is a strictly positive and symmetric measurable function on Rd × Rd. We study the intrinsic hypercontractivity, intrinsic supercontractivity, and intrinsic ultracontractivity for the Feynman-Kac semigroup展开更多
A series of eontractivity and exponential stability results for the solutions to nonlinear neutral functional differential equations （NFDEs） in Banach spaces are obtained, which provide unified theoretical foundatio...A series of eontractivity and exponential stability results for the solutions to nonlinear neutral functional differential equations （NFDEs） in Banach spaces are obtained, which provide unified theoretical foundation for the contractivity analysis of solutions to nonlinear problems in functional differential equations （FDEs）, neutral delay differential equations （NDDEs） and NFDEs of other types which appear in practice.展开更多
Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring ...Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring the security of Industrial Control Production Systems(ICPSs)has become a critical challenge.These systems are highly vulnerable to attacks such as denial-of-service(DoS),eclipse,and Sybil attacks,which can significantly disrupt industrial operations.This work proposes an effective protection strategy using an Artificial Intelligence(AI)-enabled Smart Contract(SC)framework combined with the Heterogeneous Barzilai-Borwein Support Vector(HBBSV)method for industrial-based CPS environments.The approach reduces run time and minimizes the probability of attacks.Initially,secured ICPSs are achieved through a comprehensive exchange of views on production plant strategies for condition monitoring using SC and blockchain(BC)integrated within a BC network.The SC executes the HBBSV strategy to verify the security consensus.The Barzilai-Borwein Support Vectorized algorithm computes abnormal attack occurrence probabilities to ensure that components operate within acceptable production line conditions.When a component remains within these conditions,no security breach occurs.Conversely,if a component does not satisfy the condition boundaries,a security lapse is detected,and those components are isolated.The HBBSV method thus strengthens protection against DoS,eclipse,and Sybil attacks.Experimental results demonstrate that the proposed HBBSV approach significantly improves security by enhancing authentication accuracy while reducing run time and authentication time compared to existing techniques.展开更多
The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats,making intrusion detection a critical aspect of ensuring their secure operation.Tradit...The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats,making intrusion detection a critical aspect of ensuring their secure operation.Traditional intrusion detection systems have limitations in terms of centralized architecture,lack of transparency,and vulnerability to single points of failure.This is where the integration of blockchain technology with signature-based intrusion detection can provide a robust and decentralized solution for securing smart transportation systems.This study tackles the issue of database manipulation attacks in smart transportation networks by proposing a signaturebased intrusion detection system.The introduced signature facilitates accurate detection and systematic classification of attacks,enabling categorization according to their severity levels within the transportation infrastructure.Through comparative analysis,the research demonstrates that the blockchain-based IDS outperforms traditional approaches in terms of security,resilience,and data integrity.展开更多
We thank Power et al.1 for their interest in our review2 and for contributing to this important scientific discussion.We welcome their commentary and acknowledge the merit of continuing to scrutinize and refine interp...We thank Power et al.1 for their interest in our review2 and for contributing to this important scientific discussion.We welcome their commentary and acknowledge the merit of continuing to scrutinize and refine interpretations in this evolving field.Given that much research time and financial investment is being given to the study of the effects of eccentric training in both athletic and clinical contexts,it is incumbent on our field to demonstrate whether eccentric contractions are a key(or the key)stimulus for sarcomerogenesis(increases in serial sarcomere number(SSN)).展开更多
As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security ...As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.展开更多
Since the advent of smart contracts,security vulnerabilities have remained a persistent challenge,compromsing both the reliability of contract execution and the overall stability of the virtual currency market.Consequ...Since the advent of smart contracts,security vulnerabilities have remained a persistent challenge,compromsing both the reliability of contract execution and the overall stability of the virtual currency market.Consequently,the academic community has devoted increasing attention to these security risks.However,conventional approaches to vulnerability detection frequently exhibit limited accuracy.To address this limitation,the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks(GNNs).The proposedmethod first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts.These graphs are subsequently processed using GNNs to efficiently identify contracts with a high likelihood of vulnerabilities.For these high-risk contracts,symbolic execution is employed to perform fine-grained,path-level analysis,thereby improving overall detection precision.Experimental results on a dataset comprising 10,079 contracts demonstrate that the proposed method achieves detection precisions of 93.58% for reentrancy vulnerabilities and 92.73% for timestamp-dependent vulnerabilities.展开更多
Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work pr...Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work proposes Secured-FL,a blockchain-based defensive framework that combines smart contract-based authentication,clustering-driven outlier elimination,and dynamic threshold adjustment to defend against adversarial attacks.The framework was implemented on a private Ethereum network with a Proof-of-Authority consensus algorithm to ensure tamper-resistant and auditable model updates.Large-scale simulation on the Cyber Data dataset,under up to 50%malicious client settings,demonstrates Secured-FL achieves 6%-12%higher accuracy,9%-15%lower latency,and approximately 14%less computational expense compared to the PPSS benchmark framework.Additional tests,including confusion matrices,ROC and Precision-Recall curves,and ablation tests,confirm the interpretability and robustness of the defense.Tests for scalability also show consistent performance up to 500 clients,affirming appropriateness to reasonably large deployments.These results make Secured-FL a feasible,adversarially resilient FL paradigm with promising potential for application in smart cities,medicine,and other mission-critical IoT deployments.展开更多
The creator economy is revolutionizing the way in which individuals can profit from their engagement with online platforms.In this paper,we initiate the formal study of online learning in a creator economy by modeling...The creator economy is revolutionizing the way in which individuals can profit from their engagement with online platforms.In this paper,we initiate the formal study of online learning in a creator economy by modeling it as a three-party game between users,a platform,and content creators.The platform interacts with creators through contracts under a principal-agent framework and with users via a recommender system.We study how the platform can jointly optimize contracts and recommendation policies in an online learning setting.We analyze return-based and feature-based contracts.Under smoothness assumptions,return-based contracts achieve regretΘ(T^(2/3)).For feature-based contracts,we introduce an intrinsic dimension d and prove a regret bound O(T^(d+1)/(d+2)),which is tight for linear families.展开更多
Graph neural networks(GNNs)have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control-and data-flow graphs.Despite their...Graph neural networks(GNNs)have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control-and data-flow graphs.Despite their effectiveness,most GNN-based vulnerability detectors operate as black boxes,making their decisions difficult to interpret and thus less suitable for critical security auditing.The information bottleneck(IB)principle provides a theoretical framework for isolating task-relevant graph components.However,existing IB-based implementations often encounter unstable optimization and limited understanding of code semantics.To address these issues,we introduce ContractGIB,an interpretable graph information bottleneck framework for function-level vulnerability analysis.ContractGIB introduces three main advances.First,ContractGIB introduces an Hilbert–Schmidt Independence Criterion(HSIC)based estimator that provides stable dependence measurement.Second,it incorporates a CodeBERT semantic module to improve node representations.Third,it initializes all nodes with pretrained CodeBERT embeddings,removing the need for hand-crafted features.For each contract function,ContractGIB identifies themost informative nodes forming an instance-specific explanatory subgraph that supports the model’s prediction.Comprehensive experiments on public smart contract datasets,including ESC andVSC,demonstrate thatContractGIB achieves superior performance compared to competitive GNN baselines,while offering clearer,instance-level interpretability.展开更多
A task allocation problem for the heterogeneous unmanned aerial vehicle (UAV) swarm in unknown environments is studied in this paper.Considering that the actual mission environment information may be unknown,the UAV s...A task allocation problem for the heterogeneous unmanned aerial vehicle (UAV) swarm in unknown environments is studied in this paper.Considering that the actual mission environment information may be unknown,the UAV swarm needs to detect the environment first and then attack the detected targets.The heterogeneity of UAVs,multiple types of tasks,and the dynamic nature of task environment lead to uneven load and time sequence problems.This paper proposes an improved contract net protocol (CNP) based task allocation scheme,which effectively balances the load of UAVs and improves the task efficiency.Firstly,two types of task models are established,including regional reconnaissance tasks and target attack tasks.Secondly,for regional reconnaissance tasks,an improved CNP algorithm using the uncertain contract is developed.Through uncertain contracts,the area size of the regional reconnaissance task is determined adaptively after this task assignment,which can improve reconnaissance efficiency and resource utilization.Thirdly,for target attack tasks,an improved CNP algorithm using the fuzzy integrated evaluation and the double-layer negotiation is presented to enhance collaborative attack efficiency through adjusting the assignment sequence adaptively and multi-layer allocation.Finally,the effectiveness and advantages of the improved method are verified through comparison simulations.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
Banks rely on soft information when assessing loan applications,making it crucial to evaluate the trustworthiness of potential borrowers in order to set loan conditions,even in a legal environment where contractual ri...Banks rely on soft information when assessing loan applications,making it crucial to evaluate the trustworthiness of potential borrowers in order to set loan conditions,even in a legal environment where contractual rights are straightforwardly enforced.Given the common belief that certain personality traits—such as trustworthiness,reliability,loyalty,thriftiness,and stinginess—are more often linked to conservatives(Republicans)than to liberals(Democrats),we investigate whether companies with conservative chief executive officers(CEOs)secure more advantageous loan terms compared to others.Our findings indicate that firms with conservative CEOs are able to negotiate bank loans with lower interest spreads and upfront fees.While we do not observe a direct impact of CEO overconfidence on loan pricing,we reveal that the combined influence of CEO conservatism and overconfidence contributes to our primary findings.Additionally,we discovered that conservative CEOs tend to receive more favorable non-price conditions(fewer covenants)and are less inclined to offer collateral.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
文摘In this study the effect of Sheng-ai Injection i.e. Red Ginseng-Ophiopogon Root Injection (one kind of traditional Chinese medicines) on the contractivity of diaphragm was observed. The results confirmed that Sheng-ai Injection increased Pdi of the fatigued diaphragm in rabbits and reduced the time needed for the recovery of Pdi of fatigued diaphragm to the normal value. These results suggest that Sheng-Mai Injection can increase the contractive force and promote the recovery of the fatigued diaphragm. The effect of Sheng-ai Injection on the contractivity of the isolated diaphragmatic bundle of rats was also observed and the results confirmed that Sheng-ai Injection increased the diaphragmatic contractive force directly. This effect of increasing the contractive force of diaphragm was attenuated by adding calcium channel blocker isoptin and disappeared when there was no calcium in the extracellular fluid. It is deduced, therefore, that the mechanism of the effect of Sheng-mai Injection is related to the increased influx of calcium from extracellular fluid into the cells.
基金The authors would like to thank Professor Mu-Fa Chen and Professor Feng-Yu Wang for introducing them the field of functional inequalities when they studied in Beijing Normal University, and for their continuous encouragement and great help in the past few years. The authors are also indebted to the referees for valuable comments on the draft. This work was supported by the National Natural Science Foundation of China （Grant No. 11201073）, Japan Society for the Promotion of Science （No. 26.04021）, the Natural Science Foundation of Fujian Province （No. 2015J01003）, and the Program for Nonlinear Analysis and Its Applications （No. IRTL1206） （for Jian Wang）.
文摘Let （Xt）t≥0 be a symmetric strong Markov process generated by non-local regular Dirichlet form （D, （D）） as follows：where J（x, y） is a strictly positive and symmetric measurable function on Rd × Rd. We study the intrinsic hypercontractivity, intrinsic supercontractivity, and intrinsic ultracontractivity for the Feynman-Kac semigroup
基金Supported by the National Natural Science Foundation of China (No. 11001033)Natural Science Foundation of Hunan Province (No. 10JJ4003)+3 种基金the Open Fund Project of Key Research Institute of Philosophies and Social Sciences in Hunan Universitiesthe Major Foundation of Educational Committee of Hunan Province(No. 09A002 [2009])the Scientific Innovation Foundation for the Electric Power Youth of Chinese Society for Electrical Engineeringthe Science and Technology Planning Project of Hunan Province (No. 2010SK3026)
文摘A series of eontractivity and exponential stability results for the solutions to nonlinear neutral functional differential equations （NFDEs） in Banach spaces are obtained, which provide unified theoretical foundation for the contractivity analysis of solutions to nonlinear problems in functional differential equations （FDEs）, neutral delay differential equations （NDDEs） and NFDEs of other types which appear in practice.
文摘Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring the security of Industrial Control Production Systems(ICPSs)has become a critical challenge.These systems are highly vulnerable to attacks such as denial-of-service(DoS),eclipse,and Sybil attacks,which can significantly disrupt industrial operations.This work proposes an effective protection strategy using an Artificial Intelligence(AI)-enabled Smart Contract(SC)framework combined with the Heterogeneous Barzilai-Borwein Support Vector(HBBSV)method for industrial-based CPS environments.The approach reduces run time and minimizes the probability of attacks.Initially,secured ICPSs are achieved through a comprehensive exchange of views on production plant strategies for condition monitoring using SC and blockchain(BC)integrated within a BC network.The SC executes the HBBSV strategy to verify the security consensus.The Barzilai-Borwein Support Vectorized algorithm computes abnormal attack occurrence probabilities to ensure that components operate within acceptable production line conditions.When a component remains within these conditions,no security breach occurs.Conversely,if a component does not satisfy the condition boundaries,a security lapse is detected,and those components are isolated.The HBBSV method thus strengthens protection against DoS,eclipse,and Sybil attacks.Experimental results demonstrate that the proposed HBBSV approach significantly improves security by enhancing authentication accuracy while reducing run time and authentication time compared to existing techniques.
基金supported by the National Research Foundation(NRF),Republic of Korea,under project BK21 FOUR(4299990213939).
文摘The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats,making intrusion detection a critical aspect of ensuring their secure operation.Traditional intrusion detection systems have limitations in terms of centralized architecture,lack of transparency,and vulnerability to single points of failure.This is where the integration of blockchain technology with signature-based intrusion detection can provide a robust and decentralized solution for securing smart transportation systems.This study tackles the issue of database manipulation attacks in smart transportation networks by proposing a signaturebased intrusion detection system.The introduced signature facilitates accurate detection and systematic classification of attacks,enabling categorization according to their severity levels within the transportation infrastructure.Through comparative analysis,the research demonstrates that the blockchain-based IDS outperforms traditional approaches in terms of security,resilience,and data integrity.
文摘We thank Power et al.1 for their interest in our review2 and for contributing to this important scientific discussion.We welcome their commentary and acknowledge the merit of continuing to scrutinize and refine interpretations in this evolving field.Given that much research time and financial investment is being given to the study of the effects of eccentric training in both athletic and clinical contexts,it is incumbent on our field to demonstrate whether eccentric contractions are a key(or the key)stimulus for sarcomerogenesis(increases in serial sarcomere number(SSN)).
基金supported by the Key Project of Joint Fund of the National Natural Science Foundation of China“Research on Key Technologies and Demonstration Applications for Trusted and Secure Data Circulation and Trading”(U24A20241)the National Natural Science Foundation of China“Research on Trusted Theories and Key Technologies of Data Security Trading Based on Blockchain”(62202118)+4 种基金the Major Scientific and Technological Special Project of Guizhou Province([2024]014)Scientific and Technological Research Projects from the Guizhou Education Department(Qian jiao ji[2023]003)the Hundred-Level Innovative Talent Project of the Guizhou Provincial Science and Technology Department(Qiankehe Platform Talent-GCC[2023]018)the Major Project of Guizhou Province“Research and Application of Key Technologies for Trusted Large Models Oriented to Public Big Data”(Qiankehe Major Project[2024]003)the Guizhou Province Computational Power Network Security Protection Science and Technology Innovation Talent Team(Qiankehe Talent CXTD[2025]029).
文摘As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.
基金supported by the National Key Research and Development Program of China(2020YFB1005704).
文摘Since the advent of smart contracts,security vulnerabilities have remained a persistent challenge,compromsing both the reliability of contract execution and the overall stability of the virtual currency market.Consequently,the academic community has devoted increasing attention to these security risks.However,conventional approaches to vulnerability detection frequently exhibit limited accuracy.To address this limitation,the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks(GNNs).The proposedmethod first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts.These graphs are subsequently processed using GNNs to efficiently identify contracts with a high likelihood of vulnerabilities.For these high-risk contracts,symbolic execution is employed to perform fine-grained,path-level analysis,thereby improving overall detection precision.Experimental results on a dataset comprising 10,079 contracts demonstrate that the proposed method achieves detection precisions of 93.58% for reentrancy vulnerabilities and 92.73% for timestamp-dependent vulnerabilities.
文摘Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work proposes Secured-FL,a blockchain-based defensive framework that combines smart contract-based authentication,clustering-driven outlier elimination,and dynamic threshold adjustment to defend against adversarial attacks.The framework was implemented on a private Ethereum network with a Proof-of-Authority consensus algorithm to ensure tamper-resistant and auditable model updates.Large-scale simulation on the Cyber Data dataset,under up to 50%malicious client settings,demonstrates Secured-FL achieves 6%-12%higher accuracy,9%-15%lower latency,and approximately 14%less computational expense compared to the PPSS benchmark framework.Additional tests,including confusion matrices,ROC and Precision-Recall curves,and ablation tests,confirm the interpretability and robustness of the defense.Tests for scalability also show consistent performance up to 500 clients,affirming appropriateness to reasonably large deployments.These results make Secured-FL a feasible,adversarially resilient FL paradigm with promising potential for application in smart cities,medicine,and other mission-critical IoT deployments.
文摘The creator economy is revolutionizing the way in which individuals can profit from their engagement with online platforms.In this paper,we initiate the formal study of online learning in a creator economy by modeling it as a three-party game between users,a platform,and content creators.The platform interacts with creators through contracts under a principal-agent framework and with users via a recommender system.We study how the platform can jointly optimize contracts and recommendation policies in an online learning setting.We analyze return-based and feature-based contracts.Under smoothness assumptions,return-based contracts achieve regretΘ(T^(2/3)).For feature-based contracts,we introduce an intrinsic dimension d and prove a regret bound O(T^(d+1)/(d+2)),which is tight for linear families.
基金supported by the National Natural Science Foundation of China(Grant Nos.52208424,52208416,52078091,and 52108399)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0102).
文摘Graph neural networks(GNNs)have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control-and data-flow graphs.Despite their effectiveness,most GNN-based vulnerability detectors operate as black boxes,making their decisions difficult to interpret and thus less suitable for critical security auditing.The information bottleneck(IB)principle provides a theoretical framework for isolating task-relevant graph components.However,existing IB-based implementations often encounter unstable optimization and limited understanding of code semantics.To address these issues,we introduce ContractGIB,an interpretable graph information bottleneck framework for function-level vulnerability analysis.ContractGIB introduces three main advances.First,ContractGIB introduces an Hilbert–Schmidt Independence Criterion(HSIC)based estimator that provides stable dependence measurement.Second,it incorporates a CodeBERT semantic module to improve node representations.Third,it initializes all nodes with pretrained CodeBERT embeddings,removing the need for hand-crafted features.For each contract function,ContractGIB identifies themost informative nodes forming an instance-specific explanatory subgraph that supports the model’s prediction.Comprehensive experiments on public smart contract datasets,including ESC andVSC,demonstrate thatContractGIB achieves superior performance compared to competitive GNN baselines,while offering clearer,instance-level interpretability.
基金National Natural Science Foundation of China (12202293)Sichuan Science and Technology Program (2023NSFSC0393,2022NSFSC1952)。
文摘A task allocation problem for the heterogeneous unmanned aerial vehicle (UAV) swarm in unknown environments is studied in this paper.Considering that the actual mission environment information may be unknown,the UAV swarm needs to detect the environment first and then attack the detected targets.The heterogeneity of UAVs,multiple types of tasks,and the dynamic nature of task environment lead to uneven load and time sequence problems.This paper proposes an improved contract net protocol (CNP) based task allocation scheme,which effectively balances the load of UAVs and improves the task efficiency.Firstly,two types of task models are established,including regional reconnaissance tasks and target attack tasks.Secondly,for regional reconnaissance tasks,an improved CNP algorithm using the uncertain contract is developed.Through uncertain contracts,the area size of the regional reconnaissance task is determined adaptively after this task assignment,which can improve reconnaissance efficiency and resource utilization.Thirdly,for target attack tasks,an improved CNP algorithm using the fuzzy integrated evaluation and the double-layer negotiation is presented to enhance collaborative attack efficiency through adjusting the assignment sequence adaptively and multi-layer allocation.Finally,the effectiveness and advantages of the improved method are verified through comparison simulations.
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Banks rely on soft information when assessing loan applications,making it crucial to evaluate the trustworthiness of potential borrowers in order to set loan conditions,even in a legal environment where contractual rights are straightforwardly enforced.Given the common belief that certain personality traits—such as trustworthiness,reliability,loyalty,thriftiness,and stinginess—are more often linked to conservatives(Republicans)than to liberals(Democrats),we investigate whether companies with conservative chief executive officers(CEOs)secure more advantageous loan terms compared to others.Our findings indicate that firms with conservative CEOs are able to negotiate bank loans with lower interest spreads and upfront fees.While we do not observe a direct impact of CEO overconfidence on loan pricing,we reveal that the combined influence of CEO conservatism and overconfidence contributes to our primary findings.Additionally,we discovered that conservative CEOs tend to receive more favorable non-price conditions(fewer covenants)and are less inclined to offer collateral.
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.
