Analysis of network traffic flow dynamics based on gravitational field theory

For further research on the gravity mechanism of the routing protocol in complex networks,we introduce the concept of routing awareness depth,which is represented by ρ.On this basis,we define the calculation formula of the gravity of the transmission route for the packet,and propose a routing strategy based on the gravitational field of the node and the routing awareness depth.In order to characterize the efficiency of the method,we introduce an order parameter,η,to measure the throughput of the network by the critical value of phase transition from free flow to congestion,and use the node betweenness centrality,B,to test the transmission efficiency of the network and congestion distribution.We simulate the network transmission performance under different values of the routing awareness depth,ρ.Simulation results show that if the value of the routing awareness depth ρ is too small,then the gravity of the route is composed of the attraction of very few nodes on the route,which cannot improve the capacity of the network effectively.If the value of the routing awareness depth ρ is greater than the network's average distance l,then the capacity of the network may be improved greatly and no longer change with the sustainable increment of routing awareness depth ρ,and the routing strategy performance enters into a constant state.Moreover,whatever the value of the routing awareness depth ρ,our algorithm always effectively balances the distribution of the betweenness centrality and realizes equal distribution of the network load.     

Immunization for scale-free networks by random walker

Based on the random walk and the intentional random walk, we propose two types of immunization strategies which require only local connectivity information. On several typical scale-free networks, we demonstrate that these strategies can lead to the eradication of the epidemic by immunizing a small fraction of the nodes in the networks. Particularly, the immunization strategy based on the intentional random walk is extremely efficient for the assortatively mixed networks.     

Prediction of chaotic systems with multidimensional recurrent least squares support vector machines

In this paper, we propose a multidimensional version of recurrent least squares support vector machines (MDRLS-SVM) to solve the problem about the prediction of chaotic system. To acquire better prediction performance, the high-dimensional space, which provides more information on the system than the scalar time series, is first reconstructed utilizing Takens's embedding theorem. Then the MDRLS-SVM instead of traditional RLS-SVM is used in the high-dimensional space, and the prediction performance can be improved from the point of view of reconstructed embedding phase space. In addition, the MDRLS-SVM algorithm is analysed in the context of noise, and we also find that the MDRLS-SVM has lower sensitivity to noise than the RLS-SVM.     

A congestion alleviated scheme in optical burst switching network

An optical burst switching(OBS)network platform is established with a ring topology of three nodes.A congestion alleviated scheme using advanced token protocol and wavelength tunable receivers is demon- strated to optimize the network platform.Experimental results testify that this scheme can resist collision at the level of 0.1% congestion rate.     

Mirror nodes in growing random networks

In this article, we consider mirror nodes, which are widely used to reduce local burden, and present two models that are practical for the Internet and probably some other networks. One model introduces an upper limit to the number of links that a node can have, beyond which the node will share the total links with a newly introduced one. Similarly, in the second model, if the number of links exceeds a limit, a new node will be introduced. Unlike the first model, the new node shares with the old one the chance of receiving new links but not the existing links. These models are analytically treated, and from the degree distribution, we can see that the number of nodes with medium links (half the upper limit) increases at the expense of the loss of highly connected nodes. By reducing the burden, this may improve the robustness of the networks.     

Local Prediction of Chaotic Time Series Based on Support Vector Machine

Based on phase space delay-coordinate reconstruction of a chaotic dynamics system, we propose a local prediction of chaotic time series using a support vector machine （SVM） to overcome the shortcomings of traditional local prediction methods. The simulation results show that the performance of this proposed predictor for making onestep and multi-step prediction is superior to that of the traditional local linear prediction method and global SVM method. In addition, it is significant that its prediction performance is insensitive to the selection of embedding dimension and the number of nearest neighbours, so the satisfying results can be achieved even if we do not know the optimal embedding dimension and how to select the number of nearest neighbours.     

Multi-target pursuit formation of multi-agent systems

The main goal of this paper is to design a team of agents that can accomplish multi-target pursuit formation using a developed leader--follower strategy. It is supposed that every target can accept a certain number of agents. First, each agent can automatically choose its target based on the distance from the agent to the target and the number of agents accepted by the target. In view of the fact that all agents are randomly dispersed in the workplace at the initial time, we present a numbering strategy for them. During the movement of agents, not every agent can always obtain pertinent state information about the targets. So, a developed leader--follower strategy and a pursuit formation algorithm are proposed. Under the proposed method, agents with the same target can maintain a circle formation. Furthermore, it turns out that the pursuit formation algorithm for agents to the desired formation is convergent. Simulation studies are provided to illustrate the effectiveness of the proposed method.     

Multi—Player and Multi—Choice Quantum Game

We investigate a multi-player and multi-choice quantum game.We start from a two-player and two-choice game,and the result is better than its classical version.Then we extend this to N-player and N-choice cases.In the quantum domain,we provide a strategy with which players can always avoid the worst outcome.Also,by changing the value of the parameter of the initial state,the probabilities for players to obtain the best pay-off will be much higher than in its classical version.     

Chaotic Control of Network Traffic

A method of chaotic control on network traffic is presented. By this method, the chaotic network traffic can be controlled to a pre-assigned equilibrium point according to chaotic prediction and the largest Lyapunov exponent of
the traffic on congested link is reduced, thereby the probability of traffic burst and network congestion can be reduced. Numerical examples show that this method is effective.     

Hybrid control of bifurcation and chaos in stroboscopic model of Internet congestion control system

Interaction between transmission control protocol （TCP） and random early detection （RED） gateway in the Internet congestion control system has been modelled as a discrete-time dynamic system which exhibits complex bifurcating and chaotic behaviours. In this paper, a hybrid control strategy using both state feedback and parameter perturbation is employed to control the bifurcation and stabilize the chaotic orbits embedded in this discrete-time dynamic system of TCP/RED. Theoretical analysis and numerical simulations show that the bifurcation is delayed and the chaotic orbits are stabilized to a fixed point, which reliably achieves a stable average queue size in an extended range of parameters and even completely eliminates the chaotic behaviour in a particular range of parameters. Therefore it is possible to decrease the sensitivity of RED to parameters. By using the hybrid strategy, we may improve the stability and performance of TCP/RED congestion control system significantly.     

Robust and efficient quantum private comparison of equality with collective detection over collective-noise channels

We present a protocol for quantum private comparison of equality(QPCE) with the help of a semi-honest third party(TP).Instead of employing the entanglement,we use single photons to achieve the comparison in this protocol.By utilizing collective eavesdropping detection strategy,our protocol has the advantage of higher qubit efficiency and lower cost of implementation.In addition to this protocol,we further introduce three robust versions which can be immune to collective dephasing noise,collective-rotation noise and all types of unitary collective noise,respectively.Finally,we show that our protocols can be secure against the attacks from both the outside eavesdroppers and the inside participants by using the theorems on quantum operation discrimination.     

Investigation of N Deficiency in Cherry Trees Using Visible and Near-Infrared Spectra Part of the Spectrum in Field Condition

This objective of the study was to develop a model for the determination of N deficiency in cherry trees using a combination of visible near infrared methods and spectro-radiometric measurement. In our experimental design, cherry seedlings were grown under various N deficiency conditions in nutrient-controlled containers. The reflectance values of plant leaves were measured using a spectro-radiometer. Plant leaves samples were simultaneously collected. Their nutrient contents were determined in the laboratory. Afterwards, we performed a statistical comparison of the reflectance values. Sample analysis results established the significant wavelengths. Moreover, we received accurate regression models for predicting N deficiency in cherry leaves that were grown in nutrient solutions. Next, we verified the model validity by measuring the reflectance of the leaves collected from cherry orchards at various locations using a spectroradiometer. Nutrient deficiencies were calculated using the developed model, and then, the predicted and measured data were compared to evaluate model validity. From these results, we determined the wavelengths that yielded the most accurate results for N prediction, selected from the blue and green regions of the spectrum. We established that for N prediction in cherry trees, the simplest model can be created using 560 and 570 nm wavelengths. However, the evaluated model can be applicable only under certain conditions. We concluded that in order to develop a prediction method with sufficient application capacity, as well as the ability to assess nutritional and physiological characteristics, the ecology condition of the plant should be properly considered based on the model.     

New physical effects on the decay Bs→γγ in the technicolor model

In this paper we calculate the contributions to the branching ratio of Bs→γγ from charged pseudo-Goldstone bosons appearing in the one generation Technicolor model. We find that the theoretical value of the branching ratio, BR(Bs→γγ), including the contributions of PGBs, P± and P±8, is very different from the standard model (SM) prediction. The new physical effects can provide a one to two order of magnitude enhancement of the SM results. It is shown that the decay Bs→γγ can test new physical signals from the technicolor model.     

Detecting and describing the modular structures of weighted networks

In the functional properties of complex networks, modules play a central role. In this paper, we propose a new method to detect and describe the modular structures of weighted networks. In order to test the proposed method, as an example, we use our method to analyse the structural properties of the Chinese railway network. Here, the stations are regarded as the nodes and the track sections are regarded as the links. Rigorous analysis of the existing data shows that using the proposed algorithm, the nodes of network can be classified naturally. Moreover, there are several core nodes in each module. Remarkably, we introduce the correlation function $G_{rs}$, and use it to distinguish the different modules in weighted networks.     

Improving robustness of complex networks by a new capacity allocation strategy

The robustness of infrastructure networks has attracted great attention in recent years. Scholars have studied the robustness of complex networks against cascading failures from different aspects. In this paper, a new capacity allocation strategy is proposed to reduce cascading failures and improve network robustness without changing the network structure.Compared with the typical strategy proposed in Motter–Lai(ML) model, the new strategy can reduce the scale of cascading failure. The new strategy applied in scale-free network is more efficient. In addition, to reasonably evaluate the two strategies, we introduce contribution rate of unit capacity to network robustness as evaluation index. Results show that our new strategy works well, and it is more advantageous in the rational utilization of capacity in scale-free networks.Furthermore, we were surprised to find that the efficient utilization of capacity costs declined as costs rose above a certain threshold, which indicates that it is not wise to restrain cascading failures by increasing capacity costs indefinitely.     

New physical effects on the decay Bs →γγ in the technicolor model

In this paper we calculate the contributions to the branching ratio of Bs →γγ from charged pseudo-Goldstone bosons appearing in the one generation Technicolor model. We find that the theoretical value of the branching ratio, BR（Bs →γγ）, including the contributions of PGBs, P^± and P8^±, is very different from the standard model （SM） prediction. The new physical effects can provide a one to two order of magnitude enhancement of the SM results. It is shown that the decay Bs →γγ can test new physical signals from the technicolor model.     

Modelling of chaotic systems based on modified weighted recurrent least squares support vector machines

Positive Lyapunov exponents cause the errors in modelling of the chaotic time series to grow exponentially. In this paper, we propose the modified version of the support vector machines (SVM) to deal with this problem. Based on recurrent least squares support vector machines (RLS-SVM), we introduce a weighted term to the cost function tocompensate the prediction errors resulting from the positive global Lyapunov exponents. To demonstrate the effectiveness of our algorithm, we use the power spectrum and dynamic invariants involving the Lyapunov exponents and the correlation dimension as criterions, and then apply our method to the Santa Fe competition time series. The simulation results shows that the proposed method can capture the dynamics of the chaotic time series effectively.     

Duplication： a Mechanism Producing Disassortative Mixing Networks in Biology

Assortative/disassortative mixing is an important topological property of a network. A network is called assortative mixing if the nodes in the network tend to connect to their connectivity peers, or disassortative mixing if nodes with low degrees are more likely to connect with high-degree nodes. We have known that biological networks such as protein-protein interaction networks （PPI）, gene regulatory networks, and metabolic networks tend to be disassortative. On the other hand, in biological evolution, duplication and divergence are two fundamental processes. In order to make the relationship between the property of disassortative mixing and the two basic biological principles clear and to study the cause of the disassortative mixing property in biological networks, we present a random duplication model and an anti-preference duplication model. Our results show that disassortative mixing networks can be obtained by both kinds of models from uncorrelated initial networks. Moreover, with the growth of the network size, the disassortative mixing property becomes more obvious.     

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.     

Quantum metrology with coherent superposition of two different coded channels

We investigate the advantage of coherent superposition of two different coded channels in quantum metrology. In a continuous variable system, we show that the Heisenberg limit 1/N can be beaten by the coherent superposition without the help of indefinite causal order. And in parameter estimation, we demonstrate that the strategy with the coherent superposition can perform better than the strategy with quantum SWITCH which can generate indefinite causal order. We analytically obtain the general form of estimation precision in terms of the quantum Fisher information and further prove that the nonlinear Hamiltonian can improve the estimation precision and make the measurement uncertainty scale as 1/Nm for m ≥ 2. Our results can help to construct a high-precision measurement equipment, which can be applied to the detection of coupling strength and the test of time dilation and the modification of the canonical commutation relation.