The outbreak of COVID-19 in 2019 has made people pay more attention to infectious diseases.In order to reduce the risk of infection and prevent the spread of infectious diseases,it is crucial to strengthen individual ...The outbreak of COVID-19 in 2019 has made people pay more attention to infectious diseases.In order to reduce the risk of infection and prevent the spread of infectious diseases,it is crucial to strengthen individual immunization measures and to restrain the diffusion of negative information relevant to vaccines at the opportune moment.This study develops a three-layer coupling model within the framework of hypernetwork evolution,examining the interplay among negative information,immune behavior,and epidemic propagation.Firstly,the dynamic topology evolution process of hypernetwork includes node joining,aging out,hyperedge adding and reconnecting.The three-layer communication model accounts for the multifaceted influences exerted by official media channels,subjective psychological acceptance capabilities,self-identification abilities,and physical fitness levels.Each level of the decision-making process is described using the Heaviside step function.Secondly,the dynamics equations of each state and the prevalence threshold are derived using the microscopic Markov chain approach(MMCA).The results show that the epidemic threshold is affected by three transmission processes.Finally,through the simulation testing,it is possible to enhance the intensity of official clarification,improve individual self-identification ability and physical fitness,and thereby promote the overall physical enhancement of society.This,in turn,is beneficial in controlling false information,heightening vaccination coverage,and controlling the epidemic.展开更多
Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mecha- nisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the h...Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mecha- nisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the hyperedge growth and local-world hyperedge preferential attachment mechanisms. At each time step, a newly added hyperedge encircles a new coming node and a number of nodes from a randomly selected local world. The number of the selected nodes from the local world obeys the uniform distribution and its mean value is m. The analytical and simulation results show that the hyperdegree approximately obeys the power-law form and the exponent of hyperdegree distribution is 7 = 2 ＋ 1/m. Furthermore, we numerically investigate the node degree, hyperedge degree, clustering coefficient, as well as the average distance, and find that the hypemetwork model shares the scale-flee and small-world properties, which shed some light for deeply understanding the evolution mechanism of the real systems.展开更多
As the penetration ratio of wind power in active distribution networks continues to increase,the system exhibits some characteristics such as randomness and volatility.Fast and accurate short-term wind power predictio...As the penetration ratio of wind power in active distribution networks continues to increase,the system exhibits some characteristics such as randomness and volatility.Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control.Based on the spatio-temporal features of Numerical Weather Prediction(NWP)data,it proposes the WVMD_DSN(Whale Optimization Algorithm,Variational Mode Decomposition,Dual Stream Network)model.The model first applies Pearson correlation coefficient(PCC)to choose some NWP features with strong correlation to wind power to form the feature set.Then,it decomposes the feature set using Variational Mode Decomposition(VMD)to eliminate the nonstationarity and obtains Intrinsic Mode Functions(IMFs).Here Whale Optimization Algorithm(WOA)is applied to optimise the key parameters of VMD,namely the number of mode components K and penalty factor a.Finally,incorporating attention mechanism(AM),Squeeze-Excitation Network(SENet),and Bidirectional Gated Recurrent Unit(BiGRU),it constructs the dual-stream network(DSN)for short-term wind power prediction.Comparative experiments demonstrate that the WVMD_DSN model outperforms existing baseline algorithms and exhibits good generalization performance.The relevant code is available at https://github.com/ruanyuyuan/Wind-power-forecast.git(accessed on 20 August 2024).展开更多
文摘The outbreak of COVID-19 in 2019 has made people pay more attention to infectious diseases.In order to reduce the risk of infection and prevent the spread of infectious diseases,it is crucial to strengthen individual immunization measures and to restrain the diffusion of negative information relevant to vaccines at the opportune moment.This study develops a three-layer coupling model within the framework of hypernetwork evolution,examining the interplay among negative information,immune behavior,and epidemic propagation.Firstly,the dynamic topology evolution process of hypernetwork includes node joining,aging out,hyperedge adding and reconnecting.The three-layer communication model accounts for the multifaceted influences exerted by official media channels,subjective psychological acceptance capabilities,self-identification abilities,and physical fitness levels.Each level of the decision-making process is described using the Heaviside step function.Secondly,the dynamics equations of each state and the prevalence threshold are derived using the microscopic Markov chain approach(MMCA).The results show that the epidemic threshold is affected by three transmission processes.Finally,through the simulation testing,it is possible to enhance the intensity of official clarification,improve individual self-identification ability and physical fitness,and thereby promote the overall physical enhancement of society.This,in turn,is beneficial in controlling false information,heightening vaccination coverage,and controlling the epidemic.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.71071098,91024026,and 71171136)supported by the Shanghai Rising-Star Program,China(Grant No.11QA1404500)the Leading Academic Discipline Project of Shanghai City,China(Grant No.XTKX2012)
文摘Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mecha- nisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the hyperedge growth and local-world hyperedge preferential attachment mechanisms. At each time step, a newly added hyperedge encircles a new coming node and a number of nodes from a randomly selected local world. The number of the selected nodes from the local world obeys the uniform distribution and its mean value is m. The analytical and simulation results show that the hyperdegree approximately obeys the power-law form and the exponent of hyperdegree distribution is 7 = 2 ＋ 1/m. Furthermore, we numerically investigate the node degree, hyperedge degree, clustering coefficient, as well as the average distance, and find that the hypemetwork model shares the scale-flee and small-world properties, which shed some light for deeply understanding the evolution mechanism of the real systems.
基金the Science and Technology Project of State Grid Corporation of China under Grant 5400-202117142A-0-0-00the National Natural Science Foundation of China under Grant 62372242.
文摘As the penetration ratio of wind power in active distribution networks continues to increase,the system exhibits some characteristics such as randomness and volatility.Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control.Based on the spatio-temporal features of Numerical Weather Prediction(NWP)data,it proposes the WVMD_DSN(Whale Optimization Algorithm,Variational Mode Decomposition,Dual Stream Network)model.The model first applies Pearson correlation coefficient(PCC)to choose some NWP features with strong correlation to wind power to form the feature set.Then,it decomposes the feature set using Variational Mode Decomposition(VMD)to eliminate the nonstationarity and obtains Intrinsic Mode Functions(IMFs).Here Whale Optimization Algorithm(WOA)is applied to optimise the key parameters of VMD,namely the number of mode components K and penalty factor a.Finally,incorporating attention mechanism(AM),Squeeze-Excitation Network(SENet),and Bidirectional Gated Recurrent Unit(BiGRU),it constructs the dual-stream network(DSN)for short-term wind power prediction.Comparative experiments demonstrate that the WVMD_DSN model outperforms existing baseline algorithms and exhibits good generalization performance.The relevant code is available at https://github.com/ruanyuyuan/Wind-power-forecast.git(accessed on 20 August 2024).
文摘对特定领域的技术机会进行挖掘与分析,可以为企业“从0到1”的原始创新提供新参考和新建议。本文提出了一种基于超链路预测的多元技术机会发现方法。首先,基于技术间多元共现关系构建技术关系超网络,利用IPC(international patent classification)的引用信息和文本信息生成节点特征向量;其次,将超链路预测模型Hyper-SAGNN(a self-attention based graph neural network for hypergraphs)扩展到技术关系超网络中,预测未来多个技术融合形成技术机会的可能性;最后,基于新颖性、中心性、跨领域性等特征构建度量指标,发现潜在的、有价值的多元技术机会。以智能问答技术领域为例,验证了本文方法的科学性和有效性,有效挖掘出高价值的三元技术机会和四元技术机会,为企业的技术战略布局与创新策略提供了决策支持。
