The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is sprea...The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor.The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues.Firstly,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same network.Subsequently,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social networks.Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed set.Under the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance.Experimental results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social networks.Moreover,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.展开更多
Longevity and stay ability are parameters in any breeding programme. </span><span style="font-family:Verdana;">The </span><span style="font-family:Verdana;">Overstaying of a...Longevity and stay ability are parameters in any breeding programme. </span><span style="font-family:Verdana;">The </span><span style="font-family:Verdana;">Overstaying of any animal </span><span style="font-family:Verdana;">o</span><span style="font-family:Verdana;">n a farm has got negative effects </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> the farmer. Short stay also means early exit of good animal</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> that could have been used for a longer time and more productive. The intention of this review paper is to emphasize the importance of models that an upcoming breeding program can use without affecting the quality of the herd. In this paper</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> longevity is taken purely on </span><span style="font-family:Verdana;">the </span><span style="font-family:""><span style="font-family:Verdana;">performance measure. The functional and type traits of longevity should be studied further before all models are put into play. But the case of closed breeding programmes, performance traits are easily measurable because many animals will exit the herd as they have been sold to farmers hence many functional straits will be expressed outside the herd. Genetic evaluation of the Kenyan Sahiwal should account for inbreeding. This study has also </span><span style="font-family:Verdana;">provided genetic and phenotypic parameters to enable </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">inclusion of longevity in the breeding objective for the Sahiwal cattle improvement pro</span><span style="font-family:Verdana;">gramme.展开更多
This research is performed based on the modeling of biological signals. We can produce Heart Rate (HR) and Heart Rate Variability (HRV) signals synthetically using the mathematical relationships which are used as inpu...This research is performed based on the modeling of biological signals. We can produce Heart Rate (HR) and Heart Rate Variability (HRV) signals synthetically using the mathematical relationships which are used as input for the Integral Pulse Frequency Modulation (IPFM) model. Previous researches were proposed same methods such as one model of ECG signal synthetically based on RBF neural network, a model based on IPFM with random threshold, method was based on the estimation of produced signals which are dependent on autonomic nervous system using IPFM model with fixed threshold, a new method based on the theory of vector space that based on time-varying uses of IPMF model (TVTIPMF) and special functions, and two different methods for producing HRV signals with controlled characteristics and structure of time-frequency (TF) for using non-stationary HRV analysis. In this paper, several chaotic maps such as Logistic Map, Henon Map, Lorenz and Tent Map have been used. Also, effects of sympathetic and parasympathetic nervous system and an internal input to the SA node and their effects in HRV signals were evaluated. In the proposed method, output amount of integrator in IPFM model was compared with chaotic threshold level. Then, final output of IPFM model was characterized as the HR and HRV signal. So, from HR and HRV signals obtaining from this model, linear features such as Mean, Median, Variance, Standard Deviation, Maximum Range, Minimum Range, Mode, Amplitude Range and frequency spectrum, and non-linear features such as Lyapunov Exponent, Shanon Entropy, log Entropy, Threshold Entropy, sure Entropy and mode Entropy were extracted from artificial HRV and compared them with characteristics as extracted from natural HRV signal. Also, in this paper two patients that called high sympathetic Balance and Cardiovascular Autonomy Neuropathy (CAN) which is detected and evaluated by HRV signals were simulated. These signals by changing the values of the some coefficients of the normal simulated signal and with extracted frequency feature from these signals were simulated. For final generation of these abnormal signals, frequency features such as energy of low frequency band (EL), energy of high frequency band (HL), ratio of energy in low frequency band to the energy in high frequency band (EL/EH), ratio of energy in low frequency band to the energy in all frequency band (EL/ET) and ratio of energy in high frequency band to the energy in all frequency band (EH/ET) from abnormal signals were extracted and compared with these extracted values from normal signals. The results were closely correlated with the real data which confirm the effectiveness of the proposed model. Various signals derived from the output of this model can be used for final analysis of the HRV signals, such as arrhythmia detection and classification of ECG and HRV signals. One of the applications of the proposed model is the easy evaluation of diagnostic ECG signal processing devices. Such a model can also be used in signal compression and telemedicine application.展开更多
Threshold voltage (V<sub>TH</sub>) is the most evocative aspect of MOSFET operation. It is the crucial device constraint to model on-off transition characteristics. Precise V<sub>TH</sub> value...Threshold voltage (V<sub>TH</sub>) is the most evocative aspect of MOSFET operation. It is the crucial device constraint to model on-off transition characteristics. Precise V<sub>TH</sub> value of the device is extracted and evaluated by several estimation techniques. However, these assessed values of V<sub>TH</sub> diverge from the exact values due to various short channel effects (SCEs) and non-idealities present in the device. Numerous prevalent V<sub>TH</sub> extraction methods are discussed. All the results are verified by extensive 2-D TCAD simulation and confirmed through analytical results at 10-nm technology node. Aim of this research paper is to explore and present a comparative study of largely applied threshold extraction methods for bulk driven nano-MOSFETs especially at 10-nm technology node along with various sub 45-nm technology nodes. Application of the threshold extraction methods to implement noise analysis is briefly presented to infer the most appropriate extraction method at nanometer technology nodes.展开更多
社交网络中影响最大化问题的研究一直是社交网络分析的重点之一,其技术在人们生活的很多领域中具有应用价值.针对现有影响最大化算法存在时间复杂度高、算法精度低和不稳定的问题,文中利用线性阈值模型的能够将影响力累积的特性,提出一...社交网络中影响最大化问题的研究一直是社交网络分析的重点之一,其技术在人们生活的很多领域中具有应用价值.针对现有影响最大化算法存在时间复杂度高、算法精度低和不稳定的问题,文中利用线性阈值模型的能够将影响力累积的特性,提出一种基于度和影响力的混合启发式算法—DIH(Degree and Influence Heuristic)算法.该算法综合考虑网络的传播特性和结构特性,基于线性阈值模型将整个影响最大化计算分为两个启发阶段:首先进行度折启发,在激活节点的同时将节点的影响力积累,然后进行影响力启发,将度折启发期间积累的影响力爆发,从而激活更多的节点.为保证算法的效率,本文在影响力启发阶段设计了一种近似估计节点影响力的计算方法.最后,本文在三个不同的真实网络中验证了算法的有效性.展开更多
基金supported by the National Social Science Fund of China (Grant No.23BGL270)。
文摘The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor.The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues.Firstly,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same network.Subsequently,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social networks.Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed set.Under the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance.Experimental results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social networks.Moreover,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.
文摘Longevity and stay ability are parameters in any breeding programme. </span><span style="font-family:Verdana;">The </span><span style="font-family:Verdana;">Overstaying of any animal </span><span style="font-family:Verdana;">o</span><span style="font-family:Verdana;">n a farm has got negative effects </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> the farmer. Short stay also means early exit of good animal</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> that could have been used for a longer time and more productive. The intention of this review paper is to emphasize the importance of models that an upcoming breeding program can use without affecting the quality of the herd. In this paper</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> longevity is taken purely on </span><span style="font-family:Verdana;">the </span><span style="font-family:""><span style="font-family:Verdana;">performance measure. The functional and type traits of longevity should be studied further before all models are put into play. But the case of closed breeding programmes, performance traits are easily measurable because many animals will exit the herd as they have been sold to farmers hence many functional straits will be expressed outside the herd. Genetic evaluation of the Kenyan Sahiwal should account for inbreeding. This study has also </span><span style="font-family:Verdana;">provided genetic and phenotypic parameters to enable </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">inclusion of longevity in the breeding objective for the Sahiwal cattle improvement pro</span><span style="font-family:Verdana;">gramme.
文摘This research is performed based on the modeling of biological signals. We can produce Heart Rate (HR) and Heart Rate Variability (HRV) signals synthetically using the mathematical relationships which are used as input for the Integral Pulse Frequency Modulation (IPFM) model. Previous researches were proposed same methods such as one model of ECG signal synthetically based on RBF neural network, a model based on IPFM with random threshold, method was based on the estimation of produced signals which are dependent on autonomic nervous system using IPFM model with fixed threshold, a new method based on the theory of vector space that based on time-varying uses of IPMF model (TVTIPMF) and special functions, and two different methods for producing HRV signals with controlled characteristics and structure of time-frequency (TF) for using non-stationary HRV analysis. In this paper, several chaotic maps such as Logistic Map, Henon Map, Lorenz and Tent Map have been used. Also, effects of sympathetic and parasympathetic nervous system and an internal input to the SA node and their effects in HRV signals were evaluated. In the proposed method, output amount of integrator in IPFM model was compared with chaotic threshold level. Then, final output of IPFM model was characterized as the HR and HRV signal. So, from HR and HRV signals obtaining from this model, linear features such as Mean, Median, Variance, Standard Deviation, Maximum Range, Minimum Range, Mode, Amplitude Range and frequency spectrum, and non-linear features such as Lyapunov Exponent, Shanon Entropy, log Entropy, Threshold Entropy, sure Entropy and mode Entropy were extracted from artificial HRV and compared them with characteristics as extracted from natural HRV signal. Also, in this paper two patients that called high sympathetic Balance and Cardiovascular Autonomy Neuropathy (CAN) which is detected and evaluated by HRV signals were simulated. These signals by changing the values of the some coefficients of the normal simulated signal and with extracted frequency feature from these signals were simulated. For final generation of these abnormal signals, frequency features such as energy of low frequency band (EL), energy of high frequency band (HL), ratio of energy in low frequency band to the energy in high frequency band (EL/EH), ratio of energy in low frequency band to the energy in all frequency band (EL/ET) and ratio of energy in high frequency band to the energy in all frequency band (EH/ET) from abnormal signals were extracted and compared with these extracted values from normal signals. The results were closely correlated with the real data which confirm the effectiveness of the proposed model. Various signals derived from the output of this model can be used for final analysis of the HRV signals, such as arrhythmia detection and classification of ECG and HRV signals. One of the applications of the proposed model is the easy evaluation of diagnostic ECG signal processing devices. Such a model can also be used in signal compression and telemedicine application.
文摘Threshold voltage (V<sub>TH</sub>) is the most evocative aspect of MOSFET operation. It is the crucial device constraint to model on-off transition characteristics. Precise V<sub>TH</sub> value of the device is extracted and evaluated by several estimation techniques. However, these assessed values of V<sub>TH</sub> diverge from the exact values due to various short channel effects (SCEs) and non-idealities present in the device. Numerous prevalent V<sub>TH</sub> extraction methods are discussed. All the results are verified by extensive 2-D TCAD simulation and confirmed through analytical results at 10-nm technology node. Aim of this research paper is to explore and present a comparative study of largely applied threshold extraction methods for bulk driven nano-MOSFETs especially at 10-nm technology node along with various sub 45-nm technology nodes. Application of the threshold extraction methods to implement noise analysis is briefly presented to infer the most appropriate extraction method at nanometer technology nodes.
文摘社交网络中影响最大化问题的研究一直是社交网络分析的重点之一,其技术在人们生活的很多领域中具有应用价值.针对现有影响最大化算法存在时间复杂度高、算法精度低和不稳定的问题,文中利用线性阈值模型的能够将影响力累积的特性,提出一种基于度和影响力的混合启发式算法—DIH(Degree and Influence Heuristic)算法.该算法综合考虑网络的传播特性和结构特性,基于线性阈值模型将整个影响最大化计算分为两个启发阶段:首先进行度折启发,在激活节点的同时将节点的影响力积累,然后进行影响力启发,将度折启发期间积累的影响力爆发,从而激活更多的节点.为保证算法的效率,本文在影响力启发阶段设计了一种近似估计节点影响力的计算方法.最后,本文在三个不同的真实网络中验证了算法的有效性.
