Pharmaceutical Rules of Traditional Chinese Medicine For Stroke Based on Dictionary of Prescriptions and Complex Network Analysis

Objective:Taking the Dictionary of Prescriptions as a clue,we collected effective prescriptions for treating Stroke,and systematically summarized the medication and compatibility rules of the core Chinese medicines for treating Stroke.Methods:A database of prescriptions for stroke based on complex relationship analysis system of traditional Chinese medicine was established.From the point of view of complex network,the hierarchy of drug compatibility rules of compound prescriptions(such as courtiers and envoys)was analyzed,the statistical characteristics of complex networks were analyzed,and an attempt was made to explain the characteristics of complex networks of drugs for stroke.Results:A total of 974 prescriptions were collected,including 1040 Chinese herbs.The core Chinese herbal medicines with nodal degree(>500)for Stroke were divaricate saposhnikovia root,liquorice root,Chinese angelica,tall gastrodia tuber,incised notopterygium rhizome and root.The compatibility relationship of the core Chinese medicines in the treatment of stroke is divaricate saposhnikovia root-liquorice root,ephedra-divaricate saposhnikovia root,divaricate saposhnikovia root-sichuan lovage rhizome,divaricate saposhnikovia root-Chinese angelica in order of frequency.Conclusion:Through the analysis of the common network structure of effective prescriptions for Stroke,the core prescription compatibility structure reflecting the prescription thinking and clinical characteristics was found.

Matrix Measure with Application in Quantized Synchronization Analysis of Complex Networks with Delayed Time via the General Intermittent Control

This paper concerned with the quantized synchronization analysis problem. The scope of state vectors of dynamic systems, based on the matrix measure, is estimated. By using the general intermittent control, some simple yet generic criteria are derived ensuring the exponential stability of dynamic systems. Then, both the general intermittent networked controller and the quantized parameters can be designed, which guarantee that the nodes of the complex network are synchronized. Finally, simulation examples are given to illustrate the effectiveness and feasibility of the proposed method.

Identification of Atrial Fibrillation Using Complex Network Similarity

We investigate the use of complex network similarity for the identification of atrial fibrillation. The similarity of the network is estimated via the joint recurrence plot and Hamming distance. Firstly, we transform multi-electrodes epicardium signals recorded from dogs into the recurrence complex network. Then, we extract features representing its similarity. Finally, epicardium signals are classified utilizing the classification and regression tree with extracted features. The method is validated using 1000 samples including 500 atrial fibrillation cases and 500 normal sinus ones. The sensitivity, specificity and accuracy of the identification are 98.2%, 98.8% and 98.5% respectively. This experiment indicates that our approach may lay a foundation for the prediction of the onset of atrial fibrillation.

Earthquake disaster chain model based on complex networks for urban engineering systems

According to news reports on severe earthquakes since 2008,a total of 51 cases with magnitudes of 6.0 or above were analyzed,and 14 frequently occurring secondary disasters were identified.A disaster chain model was developed using principles from complex network theory.The vulnerability and risk level of each edge in this model were calculated,and high-risk edges and disaster chains were identified.The analysis reveals that the edge“floods→building collapses”has the highest vulnerability.Implementing measures to mitigate this edge is crucial for delaying the spread of secondary disasters.The highest risk is associated with the edge“building collapses→casualties,”and increased risks are also identified for chains such as“earthquake→building collapses→casualties,”“earthquake→landslides and debris flows→dammed lakes,”and“dammed lakes→floods→building collapses.”Following an earthquake,the prompt implementation of measures is crucial to effectively disrupt these chains and minimize the damage from secondary disasters.

Modified Function Projective Synchronization of Complex Networks with Multiple Proportional Delays

This paper deals with the modified function projective synchronization problem for general complex networks with multiple proportional delays. With the existence of multiple proportional delays, an effective hybrid feedback control is designed to attain modified function projective synchronization of networks. Numerical example is provided to show the effectiveness of our result.

Homogeneous and Heterogeneous Traffic of Data Packets on Complex Networks: The Traffic Congestion Phenomenon

We study the congestion phenomenon in a mathematical model of the data packets traffic in transmission networks as a function of the topology and of the load of the network. Two types of traffic are considered: homogeneous and heterogeneous traffic. The congestion phenomenon is studied in stationary conditions through the behaviour of two quantities: the mean travel time of a packet and the mean number of packets that have not reached their destination and are traveling in the network. We define a transformation that maps a network having the small world property (Inet 3037 in our numerical experiments) into a (modified) lattice network that has the same number of nodes. This map changes the capacity of the branches of the graphs representing the networks and can be regarded as an “interpolation” between the two classes of networks. Using this transformation we compare the behaviour of Inet 3037 to the behaviour of a modified rectangular lattice and we study the behaviour of the interpolating networks. This study suggests how to change the network topology and the branch capacities in order to alleviate the congestion phenomenon. In the website: http://www.ceri.uniroma1.it/ceri/zirilli/w6 some auxiliary material including animations and stereo?graphic scenes that helps the understanding of this paper is shown.

Resting-state network complexity and magnitude changes in neonates with severe hypoxic ischemic encephalopathy

Resting-state functional magnetic resonance imaging has revealed disrupted brain network connectivity in adults and teenagers with cerebral palsy. However, the specific brain networks implicated in neonatal cases remain poorly understood. In this study, we recruited 14 termborn infants with mild hypoxic ischemic encephalopathy and 14 term-born infants with severe hypoxic ischemic encephalopathy from Changzhou Children's Hospital, China. Resting-state functional magnetic resonance imaging data showed efficient small-world organization in whole-brain networks in both the mild and severe hypoxic ischemic encephalopathy groups. However, compared with the mild hypoxic ischemic encephalopathy group, the severe hypoxic ischemic encephalopathy group exhibited decreased local efficiency and a low clustering coefficient. The distribution of hub regions in the functional networks had fewer nodes in the severe hypoxic ischemic encephalopathy group compared with the mild hypoxic ischemic encephalopathy group. Moreover, nodal efficiency was reduced in the left rolandic operculum, left supramarginal gyrus, bilateral superior temporal gyrus, and right middle temporal gyrus. These results suggest that the topological structure of the resting state functional network in children with severe hypoxic ischemic encephalopathy is clearly distinct from that in children with mild hypoxic ischemic encephalopathy, and may be associated with impaired language, motion, and cognition. These data indicate that it may be possible to make early predictions regarding brain development in children with severe hypoxic ischemic encephalopathy, enabling early interventions targeting brain function. This study was approved by the Regional Ethics Review Boards of the Changzhou Children's Hospital(approval No. 2013-001) on January 31, 2013. Informed consent was obtained from the family members of the children. The trial was registered with the Chinese Clinical Trial Registry(registration number: ChiCTR1800016409) and the protocol version is 1.0.

Self-Organized Optimization of Transport on Complex Networks

We propose a self-organized optimization mechanism to improve the transport capacity of complex gradient networks. We find that, regardless of network topology, the congestion pressure can be strongly reduced by the self-organized optimization mechanism. Furthermore, the random scale-free topology is more efficient to reduce congestion compared with the random Poisson topology under the optimization mechanism. The reason is that the optimization mechanism introduces the correlations between the gradient field and the local topology of the substrate network. Due to the correlations, the cutoff degree of the gradient network is strongly reduced and the number of the nodes exerting their maximal transport capacity consumedly increases. Our work presents evidence supporting the idea that scale-free networks can efficiently improve their transport capacity by self- organized mechanism under gradient-driven transport mode.

Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

Hierarchical Modeling by Recursive Unsupervised Spectral Clustering and Network Extended Importance Measures to Analyze the Reliability Characteristics of Complex Network Systems

The complexity of large-scale network systems made of a large number of nonlinearly interconnected components is a restrictive facet for their modeling and analysis. In this paper, we propose a framework of hierarchical modeling of a complex network system, based on a recursive unsupervised spectral clustering method. The hierarchical model serves the purpose of facilitating the management of complexity in the analysis of real-world critical infrastructures. We exemplify this by referring to the reliability analysis of the 380 kV Italian Power Transmission Network (IPTN). In this work of analysis, the classical component Importance Measures (IMs) of reliability theory have been extended to render them compatible and applicable to a complex distributed network system. By utilizing these extended IMs, the reliability properties of the IPTN system can be evaluated in the framework of the hierarchical system model, with the aim of providing risk managers with information on the risk/safety significance of system structures and components.

Amplified Signal Response by Neuronal Diversity on Complex Networks

The effect of diversity on dynamics of coupled FitzHugh-Nagumo neurons on complex networks is numerically investigated, where each neuron is subjected to an external subthreshold signal. With the diversity the network is a mixture of excitable and oscillatory neurons, and the diversity is determined by the variance of the system＇s parameter. The complex network is constructed by randomly adding long-range connections （shortcuts） on a nearest-neighbouring coupled one-dimensional chain. Numerical results show that external signals are maximally magnified at an intermediate value of the diversity, as in the case of well-known stochastic resonance, burthermore, the effects of the number of shortcuts and coupled strength on the diversity-induced phenomena are also discussed. These findings exhibit that the diversity may play a constructive role in response to external signal, and highlight the importance of the diversity on such complex networks.

Exploring the characteristics of acupoints in the treatment of stroke with complex network and point mutual information method

Objective:Explore the characteristics of acupoints in the treatment of stroke with complex network and point mutual information method.Methods:The complex network and point wise mutual information system-developed by Chinese academy of Chinese medical sciences wereused to analyze the specific acupoints,compatibility,frequency etc.Results:174 acumoxibustion prescriptions were collected,including 163 acupoints.among them eighteen acupoints were used more than 30 times such as Hegu(LI4),Zusanli(ST36),Quchi(LI11)and Fengshi(GB31).The combinations of 31 acupoints were used more than 15 times,such as the combination of Quchi(LI11)and Zusanli(ST36),the combination of acupoint Quchi(LI11)and Jianyu(LI15),Hegu and Quchi(LI11).The most commonly used treatment method for stroke treatment is to dredge the Yangming meridian and Shaoyang meridian through acupuncture the multiple acupoints located on these two meridians..The commonly used acupoints are mainly distributed in the limbs,head and face.The most commonly used specific acupoint is intersection acupoint.The usage frequency of specific acupoints are higher than that of non-specific acupoints.Conclusion:Dredging the collaterals,dispelling wind-evil and restoring consciousness are the main principle for the treatment of stroke.Specific acupoints in head,face and climbs maybe the main targeted acupoints.Combination of Yang meridians with other meridians is needed to improve the effects.The Yangming meridian and Shaoyang meridian are most used meridians and Hegu(LI4),Quchi(LI11)and Zusanli(ST36)are the most used acupionts.

Nonlinear Relationship and Its Evolutionary Trace between Node Degree and Average Path Length of China Aviation Network Based on Complex Network

In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the node degree and the average path length of China aviation network in 1988,1994,2001,2008 and 2015 was studied.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network.On the basis of the statistical data,the node average path length of China aviation network in 1988,1994,2001,2008 and 2015 was calculated.Through regression analysis,it was found that the node degree had a logarithmic relationship with the average length of node path,and the two parameters of the logarithmic relationship had linear evolutionary trace.Key word:China aviation network,complex network,node degree,average length of node path,logarithmic relationship,evolutionary trace.

Nonlinear Relationship and Its Evolutionary Trace between Average Degree and Average Path Length of Edge Vertices of China Aviation Network Based on Complex Network

In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the average degree and the average path length of edge vertices of China aviation network in 1988,1994,2001,2008 and 2015 was studied.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network.On the basis of the statistical data,the average degree and average path length of edge vertices of China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Through regression analysis,it was found that the average degree had a logarithmic relationship with the average path length of edge vertices and the two parameters of the logarithmic relationship had linear evolutionary trace.

Influence of Topological Properties of Complex Networks on the Effect of Spectral Coarse-Grained Network

Recently, some coarse-graining methods based on network synchronization have been proposed to reduce the network size while preserving the synchronizability of the original network. In this paper, we investigate the effects of the coarse graining process on synchronizability over complex networks under different average path lengths and different degrees of distribution. A large amount of experiments demonstrate a close correlation between the average path length, the heterogeneity of the degree distribution and the ability of spectral coarse-grained scheme in preserving the network synchronizability. We find that synchronizability can be well preserved in spectral coarse-grained networks when the considered networks have a longer average path length or a larger degree of variance.

Identifying influential spreaders in complex networks based on entropy weight method and gravity law

In complex networks,identifying influential spreader is of great significance for improving the reliability of networks and ensuring the safe and effective operation of networks.Nowadays,it is widely used in power networks,aviation networks,computer networks,and social networks,and so on.Traditional centrality methods mainly include degree centrality,closeness centrality,betweenness centrality,eigenvector centrality,k-shell,etc.However,single centrality method is onesided and inaccurate,and sometimes many nodes have the same centrality value,namely the same ranking result,which makes it difficult to distinguish between nodes.According to several classical methods of identifying influential nodes,in this paper we propose a novel method that is more full-scaled and universally applicable.Taken into account in this method are several aspects of node’s properties,including local topological characteristics,central location of nodes,propagation characteristics,and properties of neighbor nodes.In view of the idea of the multi-attribute decision-making,we regard the basic centrality method as node’s attribute and use the entropy weight method to weigh different attributes,and obtain node’s combined centrality.Then,the combined centrality is applied to the gravity law to comprehensively identify influential nodes in networks.Finally,the classical susceptible-infected-recovered(SIR)model is used to simulate the epidemic spreading in six real-society networks.Our proposed method not only considers the four topological properties of nodes,but also emphasizes the influence of neighbor nodes from the aspect of gravity.It is proved that the new method can effectively overcome the disadvantages of single centrality method and increase the accuracy of identifying influential nodes,which is of great significance for monitoring and controlling the complex networks.

Migration Networks Pattern of China’s Floating Population from the Perspective of Complex Network

Since China’s reform and opening-up,the growing disparity between urban and rural areas and regions has led to massive migration.With China’s Rural Revitalization Strategy and the industrial transfer from the eastern coastal areas to the inland,the migration direction and pattern of the floating population have undergone certain changes.Using the 2017 China Migrants Dynamic Survey(CMDS),excluding Hong Kong,Macao,and Taiwan regions of China,organized by China’s National Health Commission,the relationship matrix of the floating population is constructed according to the inflow place of the interviewees and their outflow place(the location of the registered residence)in the questionnaire survey.We then apply the complex network model to analyze the migration direction and network pattern of China’s floating population from the city scale.The migration network shows an obvious hierarchical agglomeration.The first-,second-,third-and fourth-tier distribution cities are municipalities directly under the central government,provincial capital cities,major cities in the central and western regions and ordinary cities in all provinces,respectively.The migration trend is from the central and western regions to the eastern coastal areas.The migration network has‘small world’characteristics,forming nine communities.It shows that most node cities in the same community are closely linked and geographically close,indicating that the migration network of floating population is still affected by geographical proximity.Narrowing the urban-rural and regional differences will promote the rational distribution this population.It is necessary to strengthen the reform of the registered residence system,so that the floating population can enjoy urban public services comparable to other populations,and allow migrants to live and work in peace.

Folding rate prediction using complex network analysis for proteins with two- and three-state folding kinetics

It is a challenging task to investigate the different in- fluence of long-range and short-range interactions on two-state and three-state folding kinetics of protein. The networks of the 30 two-state proteins and 15 three-state proteins were constructed by complex networks analysis at three length scales: Protein Contact Networks, Long-range Interaction Networks and Short-range Interaction Networks. To uncover the relationship between structural properties and folding kinetics of the proteins, the correlations of protein network parameters with protein folding rate and topology parameters contact order were analyzed. The results show that Protein Contact Networks and Short-range Interaction Networks (for both two-state and three-state proteins) exhibit the “small-world” property and Long-range Interaction networks indicate “scale-free” behavior. Our results further indicate that all Protein Contact Networks and Short- range Interaction networks are assortative type. While some of Long-range Interaction Networks are of assortative type, the others are of disassortative type. For two-state proteins, the clustering coefficients of Short-range Interaction Networks show prominent correlation with folding rate and contact order. The assortativity coefficients of Short-range Interaction Networks also show remarkable correlation with folding rate and contact order. Similar correlations exist in Protein Contact Networks of three-state proteins. For two-state proteins, the correlation between contact order and folding rate is determined by the numbers of local contacts. Short- range interactions play a key role in determining the connecting trend among amino acids and they impact the folding rate of two-state proteins directly. For three-state proteins, the folding rate is determined by short-range and long-range interactions among residues together.

Probability Distribution of China Aviation Network Average Degree of Edge Vertices and Its Evolutionary Trace Based on Complex Network

In order to reveal the complex network characteristics and evolution principle of China aviation network, the probability distribution and evolution trace of average degree of edge vertices of China aviation network were studied based on the statistics data of China civil aviation network in 1988, 1994, 2001, 2008 and 2015. According to the theory and method of complex network, the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network. Based on the statistical data, the average degrees of edge vertices in China aviation network in 1988, 1994, 2001, 2008 and 2015 were calculated. Using the probability statistical analysis method and regression analysis approach, it was found that the average degree of edge vertices had the probability distribution of normal function and the position parameters and scale parameters of the probability distribution had linear evolution trace.

Probability Distribution of Ratio of China Aviation Network Edge Vertices Degree and Its Evolutionary Trace Based on Complex Network

In order to reveal the complex network characteristics and evolution principle of China aviation network, the probability distribution and evolution trace of ratio of China aviation network edge vertices degree were studied based on the statistics data of China civil aviation network in 1988, 1994, 2001, 2008 and 2015. According to the theory and method of complex network, the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network. Based on the statistical data, the ratio of edge vertices degree in China aviation network in 1988, 1994, 2001, 2008 and 2015 were calculated. Using the probability statistical analysis method and regression analysis approach, it was found that the ratio of edge vertices degree had linear probability distribution and the two parameters of the probability distribution had linear evolution trace.