Background:COVID-19 has been impacting on the whole world critically and constantly since late December 2019.Rapidly increasing infections has raised intense worldwide attention.How to model the evolution of COVID-19 ...Background:COVID-19 has been impacting on the whole world critically and constantly since late December 2019.Rapidly increasing infections has raised intense worldwide attention.How to model the evolution of COVID-19 effectively and efficiently is of great significance for prevention and control.Methods:We propose the multi-chain Fudan-CCDC model based on the original single-chain model in[Shao et al.2020]to describe the evolution of COVID-19 in Singapore.Multi-chains can be considered as the superposition of several single chains with different characteristics.We identify the parameters of models by minimizing the penalty function.Results:The numerical simulation results exhibit the multi-chain model performs well on data fitting.Though unsteady the increments are,they could still fall within the range of±30%fluctuation from simulation results.Conclusion:The multi-chain Fudan-CCDC model provides an effective way to early detect the appearance of imported infectors and super spreaders and forecast a second outbreak.It can also explain the data from those countries where the single-chain model shows deviation from the data.展开更多
As cross-chain technologies enable interactions among different blockchains(hereinafter“chains”),multi-chain consensus is becoming increasingly important in blockchain networks.However,more attention has been paid t...As cross-chain technologies enable interactions among different blockchains(hereinafter“chains”),multi-chain consensus is becoming increasingly important in blockchain networks.However,more attention has been paid to single-chain consensus schemes.Multi-chain consensus schemes with trusted miner participation have not been considered,thus offering opportunities for malicious users to launch diverse miner behavior(DMB)attacks on different chains.DMB attackers can be friendly in the consensus process on some chains,called mask chains,to enhance their trust value,while on others,called kill chains,they engage in destructive behaviors on the network.In this paper,we propose a multi-chain consensus scheme named Proof-of-DiscTrust(PoDT)to defend against DMB attacks.The idea of distinctive trust(DiscTrust)is introduced to evaluate the trust value of each user across different chains.The trustworthiness of a user is split into local and global trust values.A dynamic behavior prediction scheme is designed to enforce DiscTrust to prevent an intensive DMB attacker from maintaining strong trust by alternately creating true or false blocks on the kill chain.Three trusted miner selection algorithms for multi-chain environments can be implemented to select network miners,chain miners,and chain miner leaders,separately.Simulation results show that PoDT is secure against DMB attacks and more effective than traditional consensus schemes in multi-chain environments.展开更多
Direct Simulation Monte Carlo(DSMC)solves the Boltzmann equation with large Knudsen number.The Boltzmann equation generally consists of three terms:the force term,the diffusion term and the collision term.While the fi...Direct Simulation Monte Carlo(DSMC)solves the Boltzmann equation with large Knudsen number.The Boltzmann equation generally consists of three terms:the force term,the diffusion term and the collision term.While the first two terms of the Boltzmann equation can be discretized by numerical methods such as the finite volume method,the third term can be approximated by DSMC,and DSMC simulates the physical behaviors of gas molecules.However,because of the low sampling efficiency of Monte Carlo Simulation in DSMC,this part usually occupies large portion of computational costs to solve the Boltzmann equation.In this paper,by Markov Chain Monte Carlo(MCMC)and multicore programming,we develop Direct Simulation Multi-Chain Markov Chain Monte Carlo(DSMC3):a fast solver to calculate the numerical solution for the Boltzmann equation.Computational results show that DSMC3 is significantly faster than the conventional method DSMC.展开更多
The safe storage and sharing of medical data have promoted the development of the public medical field.At the same time,blockchain technology guarantees the safe storage and sharing of medical data.However,the consens...The safe storage and sharing of medical data have promoted the development of the public medical field.At the same time,blockchain technology guarantees the safe storage and sharing of medical data.However,the consensus algorithm in the current medical blockchain cannot meet the requirements of low delay and high throughput in the large-scale network,and the identity of the primary node is exposed and vulnerable to attack.Therefore,this paper proposes an efficient consensus algorithm for medical data storage and sharing based on a master–slave multi-chain of alliance chain(ECA_MDSS).Firstly,institutional nodes in the healthcare alliance chain are clustered according to geographical location and medical system structure to form a multi-zones network.The system adopts master–slave multi-chain architecture to ensure security,and each zone processes transactions in parallel to improve consensus efficiency.Secondly,the aggregation signature is used to improve the practical Byzantine fault-tolerant(PBFT)consensus to reduce the communication interaction of consensus in each zone.Finally,an efficient ring signature is used to ensure the anonymity and privacy of the primary node in each zone and to prevent adaptive attacks.Meanwhile,a trust model is introduced to evaluate the trust degree of the node to reduce the evil done by malicious nodes.The experimental results show that ECA_MDSS can effectively reduce communication overhead and consensus delay,improve transaction throughput,and enhance system scalability.展开更多
As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UA...As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UAVs. Existing weapon-target assignment methods primarily focus on macro cluster constraints while neglecting individual strategy updates. This paper proposes a novel weapon-target assignment method for UAVs based on the multi-strategy threshold public goods game(PGG). By analyzing the concept mapping between weapon-target assignment for UAVs and multi-strategy threshold PGG, a weapon-target assignment model for UAVs based on the multi-strategy threshold PGG is established, which is adaptively complemented by the diverse cooperation-defection strategy library and the utility function based on the threshold mechanism. Additionally, a multi-chain Markov is formulated to quantitatively describe the stochastic evolutionary dynamics, whose evolutionary stable distribution is theoretically derived through the development of a strategy update rule based on preference-based aspiration dynamic. Numerical simulation results validate the feasibility and effectiveness of the proposed method, and the impacts of selection intensity, preference degree and threshold on the evolutionary stable distribution are analyzed. Comparative simulations show that the proposed method outperforms GWO, DE, and NSGA-II, achieving 17.18% higher expected utility than NSGA-II and reducing evolutionary stable times by 25% in large-scale scenario.展开更多
基金the National Natural Science Foundation of China(No.11671098)partially supported by Shanghai Science and technology research program(No.19JC1420101)+1 种基金J.C.is supported in part by the National Natural Science Foundation of China(No.11971121)Y.Y.is supported by Shanghai Sailing Program(No.20YF1412400).
文摘Background:COVID-19 has been impacting on the whole world critically and constantly since late December 2019.Rapidly increasing infections has raised intense worldwide attention.How to model the evolution of COVID-19 effectively and efficiently is of great significance for prevention and control.Methods:We propose the multi-chain Fudan-CCDC model based on the original single-chain model in[Shao et al.2020]to describe the evolution of COVID-19 in Singapore.Multi-chains can be considered as the superposition of several single chains with different characteristics.We identify the parameters of models by minimizing the penalty function.Results:The numerical simulation results exhibit the multi-chain model performs well on data fitting.Though unsteady the increments are,they could still fall within the range of±30%fluctuation from simulation results.Conclusion:The multi-chain Fudan-CCDC model provides an effective way to early detect the appearance of imported infectors and super spreaders and forecast a second outbreak.It can also explain the data from those countries where the single-chain model shows deviation from the data.
基金supported by the Natural Science Basic Research Program of Shaanxi Province,China(No.2023-JC-YB-561)。
文摘As cross-chain technologies enable interactions among different blockchains(hereinafter“chains”),multi-chain consensus is becoming increasingly important in blockchain networks.However,more attention has been paid to single-chain consensus schemes.Multi-chain consensus schemes with trusted miner participation have not been considered,thus offering opportunities for malicious users to launch diverse miner behavior(DMB)attacks on different chains.DMB attackers can be friendly in the consensus process on some chains,called mask chains,to enhance their trust value,while on others,called kill chains,they engage in destructive behaviors on the network.In this paper,we propose a multi-chain consensus scheme named Proof-of-DiscTrust(PoDT)to defend against DMB attacks.The idea of distinctive trust(DiscTrust)is introduced to evaluate the trust value of each user across different chains.The trustworthiness of a user is split into local and global trust values.A dynamic behavior prediction scheme is designed to enforce DiscTrust to prevent an intensive DMB attacker from maintaining strong trust by alternately creating true or false blocks on the kill chain.Three trusted miner selection algorithms for multi-chain environments can be implemented to select network miners,chain miners,and chain miner leaders,separately.Simulation results show that PoDT is secure against DMB attacks and more effective than traditional consensus schemes in multi-chain environments.
文摘Direct Simulation Monte Carlo(DSMC)solves the Boltzmann equation with large Knudsen number.The Boltzmann equation generally consists of three terms:the force term,the diffusion term and the collision term.While the first two terms of the Boltzmann equation can be discretized by numerical methods such as the finite volume method,the third term can be approximated by DSMC,and DSMC simulates the physical behaviors of gas molecules.However,because of the low sampling efficiency of Monte Carlo Simulation in DSMC,this part usually occupies large portion of computational costs to solve the Boltzmann equation.In this paper,by Markov Chain Monte Carlo(MCMC)and multicore programming,we develop Direct Simulation Multi-Chain Markov Chain Monte Carlo(DSMC3):a fast solver to calculate the numerical solution for the Boltzmann equation.Computational results show that DSMC3 is significantly faster than the conventional method DSMC.
基金supported in part by the National Natural Science Foundation of China(61871466).
文摘The safe storage and sharing of medical data have promoted the development of the public medical field.At the same time,blockchain technology guarantees the safe storage and sharing of medical data.However,the consensus algorithm in the current medical blockchain cannot meet the requirements of low delay and high throughput in the large-scale network,and the identity of the primary node is exposed and vulnerable to attack.Therefore,this paper proposes an efficient consensus algorithm for medical data storage and sharing based on a master–slave multi-chain of alliance chain(ECA_MDSS).Firstly,institutional nodes in the healthcare alliance chain are clustered according to geographical location and medical system structure to form a multi-zones network.The system adopts master–slave multi-chain architecture to ensure security,and each zone processes transactions in parallel to improve consensus efficiency.Secondly,the aggregation signature is used to improve the practical Byzantine fault-tolerant(PBFT)consensus to reduce the communication interaction of consensus in each zone.Finally,an efficient ring signature is used to ensure the anonymity and privacy of the primary node in each zone and to prevent adaptive attacks.Meanwhile,a trust model is introduced to evaluate the trust degree of the node to reduce the evil done by malicious nodes.The experimental results show that ECA_MDSS can effectively reduce communication overhead and consensus delay,improve transaction throughput,and enhance system scalability.
基金supported by the National Natural Science Foundation of China (No. 62073267)。
文摘As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UAVs. Existing weapon-target assignment methods primarily focus on macro cluster constraints while neglecting individual strategy updates. This paper proposes a novel weapon-target assignment method for UAVs based on the multi-strategy threshold public goods game(PGG). By analyzing the concept mapping between weapon-target assignment for UAVs and multi-strategy threshold PGG, a weapon-target assignment model for UAVs based on the multi-strategy threshold PGG is established, which is adaptively complemented by the diverse cooperation-defection strategy library and the utility function based on the threshold mechanism. Additionally, a multi-chain Markov is formulated to quantitatively describe the stochastic evolutionary dynamics, whose evolutionary stable distribution is theoretically derived through the development of a strategy update rule based on preference-based aspiration dynamic. Numerical simulation results validate the feasibility and effectiveness of the proposed method, and the impacts of selection intensity, preference degree and threshold on the evolutionary stable distribution are analyzed. Comparative simulations show that the proposed method outperforms GWO, DE, and NSGA-II, achieving 17.18% higher expected utility than NSGA-II and reducing evolutionary stable times by 25% in large-scale scenario.
