Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital w...Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital world. These networks can be viewed as a collection of nodes and edges, where users and their interactions are represented as nodes and the connections between them as edges. Understanding the factors that contribute to the formation of these edges is important for studying network structure and processes. This knowledge can be applied to various areas such as identifying communities, recommending friends, and targeting online advertisements. Several factors, including node popularity and friends-of-friends relationships, influence edge formation and network growth. This research focuses on the temporal activity of nodes and its impact on edge formation. Specifically, the study examines how the minimum age of friends-of-friends edges and the average age of all edges connected to potential target nodes influence the formation of network edges. Discrete choice analysis is used to analyse the combined effect of these temporal factors and other well-known attributes like node degree (i.e., the number of connections a node has) and network distance between nodes. The findings reveal that temporal properties have a similar impact as network proximity in predicting the creation of links. By incorporating temporal features into the models, the accuracy of link prediction can be further improved.展开更多
In the end of 2007, Air China Limited (hereinafter "Air China") and Shanghai Airlines, formally joined Star Alliance at a ceremony held in the new Terminal Three at Beijing Capital International Airport.... In the end of 2007, Air China Limited (hereinafter "Air China") and Shanghai Airlines, formally joined Star Alliance at a ceremony held in the new Terminal Three at Beijing Capital International Airport. With the addition of the two Chinese airlines, Star Alliance now has 19 member carriers operating 17,000 daily flights to 897 destinations in 160 countries.……展开更多
In vitro neural networks offer a simplified model to study brain nervous system functions and represent a vital platform for investigating cerebral neural activities.Microelectrode array(MEA)chips are commonly used to...In vitro neural networks offer a simplified model to study brain nervous system functions and represent a vital platform for investigating cerebral neural activities.Microelectrode array(MEA)chips are commonly used to construct modular neural networks and enable electrical stimulation and recording for uncovering signal generation and conduction mechanisms.However,conventional two-dimensional(2D)MEA chips face significant limitations,including restricted neuronal growth dimensions and insufficient neuron density.Herein,we present a novel micro-integrated chip featuring a three-dimensional(3D)physical microtube array that facilitates the regulated,confined growth of neurons.The microtube array not only provides a 3D microenvironment for neuronal growth and differentiation but also enhances neuronal network density and structural organization.Furthermore,by integrating the microtube array with a customized MEA,precise electrical stimulation can be applied to modular neural networks.Experimental results demonstrate that electrical stimulation effectively promotes the formation of connection pathways between adjacent 3D neural networks.Variable-parameter electrical stimulation experiments reveal that increasing voltage enhances the Young’s modulus of neurons,highlighting the method’s role in supporting the stable development of neuronal networks.This modular culture platform,combined with precise electrical stimulation,paves the way for constructing high-density 3D neuronal networks and enables synchronous control of modular neural activities.The proposed approach holds significant potential for advancing applications in neuroscience,tissue engineering,and organ-onchip technologies.展开更多
Based on the assumptions of ＂information transfer＂ and ＂information creation＂, this paper educes the multiplied growth mechanism of network information： that the gross quantity of network information （Im） is ab...Based on the assumptions of ＂information transfer＂ and ＂information creation＂, this paper educes the multiplied growth mechanism of network information： that the gross quantity of network information （Im） is about n times as much as the quantity of real network information （Ir）. According to this theoretical model, we give a uniform explanation to all kinds of information growth models in existence, and put forward some proposals, such as ＂forbidding information transfer＂ and ＂building up the central information base＂, to control the repeated information flooding on the network and facilitate the full use of network information.展开更多
To evaluate the trail potential of converged heterogeneous network （CHN） market, the logistic method for adoption modeling of CHN is used. User growth ＆ penetration have been taken as two variants to find saturatio...To evaluate the trail potential of converged heterogeneous network （CHN） market, the logistic method for adoption modeling of CHN is used. User growth ＆ penetration have been taken as two variants to find saturation condition in market. Model is continuous in time but modifications are done for discrete recurrence equation, commonly known as logistic map. Dynamic and static phases are taken into consideration while penetration decay is not covered in this model.展开更多
In this paper, we propose an evolving network model growing fast in units of module, according to the analysis of the evolution characteristics in real complex networks. Each module is a small-world network containing...In this paper, we propose an evolving network model growing fast in units of module, according to the analysis of the evolution characteristics in real complex networks. Each module is a small-world network containing several interconnected nodes and the nodes between the modules are linked by preferential attachment on degree of nodes. We study the modularity measure of the proposed model, which can be adjusted by changing the ratio of the number of inner- module edges and the number of inter-module edges. In view of the mean-field theory, we develop an analytical function of the degree distribution, which is verified by a numerical example and indicates that the degree distribution shows characteristics of the small-world network and the scale-free network distinctly at different segments. The clustering coefficient and the average path length of the network are simulated numerically, indicating that the network shows the small-world property and is affected little by the randomness of the new module.展开更多
Reliability parameter selection is very important in the period of equipment project design and demonstration. In this paper, the problem in selecting the reliability parameters and their number is proposed. In order ...Reliability parameter selection is very important in the period of equipment project design and demonstration. In this paper, the problem in selecting the reliability parameters and their number is proposed. In order to solve this problem, the thought of text mining is used to extract the feature and curtail feature sets from text data firstly, and frequent pattern tree （FPT） of the text data is constructed to reason frequent item-set between the key factors by frequent patter growth （FPC） algorithm. Then on the basis of fuzzy Bayesian network （FBN） and sample distribution, this paper fuzzifies the key attributes, which forms associated relationship in frequent item-sets and their main parameters, eliminates the subjective influence factors and obtains condition mutual information and maximum weight directed tree among all the attribute variables. Furthermore, the hybrid model is established by reason fuzzy prior probability and contingent probability and concluding parameter learning method. Finally, the example indicates the model is believable and effective.展开更多
Dongguan is an important industrial city, located in the Pearl River Delta, South China. Recently, Dongguan city experienced a rapid urban growth with the locational advantage by transforming from traditional agricult...Dongguan is an important industrial city, located in the Pearl River Delta, South China. Recently, Dongguan city experienced a rapid urban growth with the locational advantage by transforming from traditional agricultural region to modern manufacturing metropolis. The urban transformation became the usual change in China under the background of urbanization which belongs to one trend of globalization in the 21st century. This paper tries to analyze urban growth simulation based on remotely sensed data of previous years and the related physical and socio-economic factors and predict future urban growth in 2024. The study examined and compared the land use/cover (LUC) changes over time based on produced maps of 2004, 2009, and 2014. The results showed that water and forest area decreased since the past years. In contrast, the urban land increased from 2004 to 2014, and this increasing trend will continue to the future years through the urbanization process. Having understood the spatiotemporal trends of urban growth, the study simulated the urban growth of Dongguan city for 2024 using neural network simulation technique. Further, the figure of merit (FoM) of simulated map of 2014 map was 8.86%, which can be accepted in the simulation and used in the prediction process. Based on the consideration of water body and forest, the newly growth area is located in the west, northeast, and southeast regions of Dongguan city. The finding can help us to understand which areas are going to be considered in the future urban planning and policy by the local government.展开更多
Due to high cost of fixing failures, safety concerns, and legal liabilities, organizations need to produce software that is highly reliable. Software reliability growth models have been developed by software developer...Due to high cost of fixing failures, safety concerns, and legal liabilities, organizations need to produce software that is highly reliable. Software reliability growth models have been developed by software developers in tracking and measuring the growth of reliability. Most of the Software Reliability Growth Models, which have been proposed, treat the event of software fault detection in the testing and operational phase as a counting process. Moreover, if the size of software system is large, the number of software faults detected during the testing phase becomes large, and the change of the number of faults which are detected and removed through debugging activities becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. Therefore in such a situation, we can model the software fault detection process as a stochastic process with a continuous state space. Recently, Artificial Neural Networks (ANN) have been applied in software reliability growth prediction. In this paper, we propose an ANN based software reliability growth model based on Ito type of stochastic differential equation. The model has been validated, evaluated and compared with other existing NHPP model by applying it on actual failure/fault removal data sets cited from real software development projects. The proposed model integrated with the concept of stochastic differential equation performs comparatively better than the existing NHPP based model.展开更多
In this afticle, three uopt problems on the developlnent and plabong of Beijing metropolis are expounded (1) Having analyzed the process of urbanlzation dunng 45 years, the autihor divides it into three Stages :city e...In this afticle, three uopt problems on the developlnent and plabong of Beijing metropolis are expounded (1) Having analyzed the process of urbanlzation dunng 45 years, the autihor divides it into three Stages :city expansion, qrban sprawl and fonmation of centnpotal urban nngr. Then a rank-number-size law is pLif fotward to predict the development and allocation of satellite towns (2) Having looked back on the change of urban transpoftation nforork, the atlthor points out its merits and demerits.According to the increases of traffic flow, improving measures for the urban road systems are demonstrated. (3) Having reviewed the llistory of commercial distnbution and referred to previous lnodels, the author roprds the spatial structure of business as a combined network from central place and idealized stnucture of lnetropolitan area. The trade and service orgaluzation in n1ain urban area and peripheral zone can be planned with defferent ways. The atlthor predicts a rational network of commerce and service of Beijing with a strong BCD Which is accepted by some trade authorities展开更多
文摘Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital world. These networks can be viewed as a collection of nodes and edges, where users and their interactions are represented as nodes and the connections between them as edges. Understanding the factors that contribute to the formation of these edges is important for studying network structure and processes. This knowledge can be applied to various areas such as identifying communities, recommending friends, and targeting online advertisements. Several factors, including node popularity and friends-of-friends relationships, influence edge formation and network growth. This research focuses on the temporal activity of nodes and its impact on edge formation. Specifically, the study examines how the minimum age of friends-of-friends edges and the average age of all edges connected to potential target nodes influence the formation of network edges. Discrete choice analysis is used to analyse the combined effect of these temporal factors and other well-known attributes like node degree (i.e., the number of connections a node has) and network distance between nodes. The findings reveal that temporal properties have a similar impact as network proximity in predicting the creation of links. By incorporating temporal features into the models, the accuracy of link prediction can be further improved.
文摘 In the end of 2007, Air China Limited (hereinafter "Air China") and Shanghai Airlines, formally joined Star Alliance at a ceremony held in the new Terminal Three at Beijing Capital International Airport. With the addition of the two Chinese airlines, Star Alliance now has 19 member carriers operating 17,000 daily flights to 897 destinations in 160 countries.……
基金supported by the National Key R&D Program of China(Project No.2022YFB4700100)the Innovation Promotion Research Association of the Chinese Academy of Sciences(NO.2022199)+2 种基金the Applied Basic Research Program of Liaoning Province(NO.2023JH2/101600037)the National Natural Science Foundation of China(Grant No.62403454 and 62303446)the Hong Kong Research Grants Council(NO.11216120).
文摘In vitro neural networks offer a simplified model to study brain nervous system functions and represent a vital platform for investigating cerebral neural activities.Microelectrode array(MEA)chips are commonly used to construct modular neural networks and enable electrical stimulation and recording for uncovering signal generation and conduction mechanisms.However,conventional two-dimensional(2D)MEA chips face significant limitations,including restricted neuronal growth dimensions and insufficient neuron density.Herein,we present a novel micro-integrated chip featuring a three-dimensional(3D)physical microtube array that facilitates the regulated,confined growth of neurons.The microtube array not only provides a 3D microenvironment for neuronal growth and differentiation but also enhances neuronal network density and structural organization.Furthermore,by integrating the microtube array with a customized MEA,precise electrical stimulation can be applied to modular neural networks.Experimental results demonstrate that electrical stimulation effectively promotes the formation of connection pathways between adjacent 3D neural networks.Variable-parameter electrical stimulation experiments reveal that increasing voltage enhances the Young’s modulus of neurons,highlighting the method’s role in supporting the stable development of neuronal networks.This modular culture platform,combined with precise electrical stimulation,paves the way for constructing high-density 3D neuronal networks and enables synchronous control of modular neural activities.The proposed approach holds significant potential for advancing applications in neuroscience,tissue engineering,and organ-onchip technologies.
基金This work was supported by the National Natural Science Foundation of China （Grant No. 70273032）.
文摘Based on the assumptions of ＂information transfer＂ and ＂information creation＂, this paper educes the multiplied growth mechanism of network information： that the gross quantity of network information （Im） is about n times as much as the quantity of real network information （Ir）. According to this theoretical model, we give a uniform explanation to all kinds of information growth models in existence, and put forward some proposals, such as ＂forbidding information transfer＂ and ＂building up the central information base＂, to control the repeated information flooding on the network and facilitate the full use of network information.
基金Supported by the National Natural Science Foundation of China(60772066)
文摘To evaluate the trail potential of converged heterogeneous network （CHN） market, the logistic method for adoption modeling of CHN is used. User growth ＆ penetration have been taken as two variants to find saturation condition in market. Model is continuous in time but modifications are done for discrete recurrence equation, commonly known as logistic map. Dynamic and static phases are taken into consideration while penetration decay is not covered in this model.
基金supported by the National Natural Science Foundation of China (Grant No.51078165)the Fundamental Research Funds for Central Universities,China (Grant No.HUST 2010MS030)
文摘In this paper, we propose an evolving network model growing fast in units of module, according to the analysis of the evolution characteristics in real complex networks. Each module is a small-world network containing several interconnected nodes and the nodes between the modules are linked by preferential attachment on degree of nodes. We study the modularity measure of the proposed model, which can be adjusted by changing the ratio of the number of inner- module edges and the number of inter-module edges. In view of the mean-field theory, we develop an analytical function of the degree distribution, which is verified by a numerical example and indicates that the degree distribution shows characteristics of the small-world network and the scale-free network distinctly at different segments. The clustering coefficient and the average path length of the network are simulated numerically, indicating that the network shows the small-world property and is affected little by the randomness of the new module.
基金the Weapon Equipment Beforehand Research Foundation of China(No.9140A19030314JB35275)the Army Technology Element Foundation of China(No.A157167)
文摘Reliability parameter selection is very important in the period of equipment project design and demonstration. In this paper, the problem in selecting the reliability parameters and their number is proposed. In order to solve this problem, the thought of text mining is used to extract the feature and curtail feature sets from text data firstly, and frequent pattern tree （FPT） of the text data is constructed to reason frequent item-set between the key factors by frequent patter growth （FPC） algorithm. Then on the basis of fuzzy Bayesian network （FBN） and sample distribution, this paper fuzzifies the key attributes, which forms associated relationship in frequent item-sets and their main parameters, eliminates the subjective influence factors and obtains condition mutual information and maximum weight directed tree among all the attribute variables. Furthermore, the hybrid model is established by reason fuzzy prior probability and contingent probability and concluding parameter learning method. Finally, the example indicates the model is believable and effective.
文摘Dongguan is an important industrial city, located in the Pearl River Delta, South China. Recently, Dongguan city experienced a rapid urban growth with the locational advantage by transforming from traditional agricultural region to modern manufacturing metropolis. The urban transformation became the usual change in China under the background of urbanization which belongs to one trend of globalization in the 21st century. This paper tries to analyze urban growth simulation based on remotely sensed data of previous years and the related physical and socio-economic factors and predict future urban growth in 2024. The study examined and compared the land use/cover (LUC) changes over time based on produced maps of 2004, 2009, and 2014. The results showed that water and forest area decreased since the past years. In contrast, the urban land increased from 2004 to 2014, and this increasing trend will continue to the future years through the urbanization process. Having understood the spatiotemporal trends of urban growth, the study simulated the urban growth of Dongguan city for 2024 using neural network simulation technique. Further, the figure of merit (FoM) of simulated map of 2014 map was 8.86%, which can be accepted in the simulation and used in the prediction process. Based on the consideration of water body and forest, the newly growth area is located in the west, northeast, and southeast regions of Dongguan city. The finding can help us to understand which areas are going to be considered in the future urban planning and policy by the local government.
文摘Due to high cost of fixing failures, safety concerns, and legal liabilities, organizations need to produce software that is highly reliable. Software reliability growth models have been developed by software developers in tracking and measuring the growth of reliability. Most of the Software Reliability Growth Models, which have been proposed, treat the event of software fault detection in the testing and operational phase as a counting process. Moreover, if the size of software system is large, the number of software faults detected during the testing phase becomes large, and the change of the number of faults which are detected and removed through debugging activities becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. Therefore in such a situation, we can model the software fault detection process as a stochastic process with a continuous state space. Recently, Artificial Neural Networks (ANN) have been applied in software reliability growth prediction. In this paper, we propose an ANN based software reliability growth model based on Ito type of stochastic differential equation. The model has been validated, evaluated and compared with other existing NHPP model by applying it on actual failure/fault removal data sets cited from real software development projects. The proposed model integrated with the concept of stochastic differential equation performs comparatively better than the existing NHPP based model.
文摘In this afticle, three uopt problems on the developlnent and plabong of Beijing metropolis are expounded (1) Having analyzed the process of urbanlzation dunng 45 years, the autihor divides it into three Stages :city expansion, qrban sprawl and fonmation of centnpotal urban nngr. Then a rank-number-size law is pLif fotward to predict the development and allocation of satellite towns (2) Having looked back on the change of urban transpoftation nforork, the atlthor points out its merits and demerits.According to the increases of traffic flow, improving measures for the urban road systems are demonstrated. (3) Having reviewed the llistory of commercial distnbution and referred to previous lnodels, the author roprds the spatial structure of business as a combined network from central place and idealized stnucture of lnetropolitan area. The trade and service orgaluzation in n1ain urban area and peripheral zone can be planned with defferent ways. The atlthor predicts a rational network of commerce and service of Beijing with a strong BCD Which is accepted by some trade authorities
