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
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.
文摘目前,多无人机(Unmanned Aerial Vehicle,UAV)在大规模任务场景下的任务分配问题仍是一个挑战性问题。传统启发式算法可在较低计算复杂度下得到满意的解,但收敛速度慢且难以收敛到全局最优解。为此提出一种基于UAV链、任务链和双阶段修复策略的遗传算法(Genetic Algorithm Based on UAV-chain,Task-chain,and Two-Stage Repair strategy,UTTSRGA)。在编码结构中设计UAV链和任务链来量化任务执行代价,增强了编码中的信息承载能力并显著提升搜索效率。针对交叉操作后出现任务缺失与任务重复问题,设计双阶段修复策略。第一阶段设计随机填充机制,增强对解空间的全局搜索能力;第二阶段设计邻接映射表修复机制,根据任务间的邻接关系提供进化方向,有效引导种群向当前最优解快速收敛。提出动态复合变异策略,融合自适应变异率与基于任务链值的变异点选择,并设计4种功能互补的变异算子,多维度协同优化解的质量。针对大规模场景下的路径交叉问题,引入路径优化策略,从实践角度进一步优化任务分配方案。实验结果表明,UTTSRGA在不同任务规模下,尤其是大规模复杂任务场景中,在解的质量、收敛速度和鲁棒性3个方面均表现出显著优势。
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.
