An overview of research status of soft physics in high energy heavy-ion collision experiments and recent experimental results are presented. The experimental status on fluctuations and correlations has been reviewed and the outlook for research status of soft physics in LHC/ALICE has been introduced in this paper.
We present the performance of the ALICE muon spectrometer for measuring the charm and beauty inclusive p t differential production cross sections via single muons and unlike-sign dimuons in proton-proton collisions at√ s = 14 TeV.
An electrical model for multi-strip resistive plate chamber （RPC） is presented, and a comparison between simulation results and test data is carried on. Based on the model, the influences of the RPC＇s design parameters on the readout are studied with PSpice simulation. Cross-talk （CT） phenomenon is observed and the relative amplitudes of the CT are studied for different design parameters.
The society structure plays an important role in shaping the attitudes,beliefs and public opinion.For studying the role of the society structure in opinion dynamics,we analyze the Sznajd model on small-world network formed by adding shortcuts in a lattice consisting of N nodes arranged in a ring and on two-dimensional(2-D)regular lattice.Through computer simulation,we find that there exists a pseudo-phase transition from the coexistence state forφ<φ_(c) to the consensus state forφ>φ_(c),whereφ_(c) is some threshold for the shortcut densityφ,which is dependent of the complex network topology and the dimensionality of complex networks.Our observations indicate the dependence of the opinion dynamics on the complex system topology.
A first-principles derivation is presented of canonical distributions for a finite thermostat taking into account nonextensive energy. Parameterizing this energy by λ , we derive an explicit form for the distribution functions by regulating λ , and then explore the nontrivial relationship between these functions and energy nonextensivity, as well other system parameters such as system size. A variational entropy function is also derived from these distribution functions.
Spatial distance has a remarkable effect on the attended mode of a network embedded in a certain space. First, we investigate how spatial restriction leads to information-information correlation that is strong, linear and positive in real networks. We then construct a two-dimensional space, define the action radius R for nodes of networks, and propose a class of models that depend on spatial distance. Information correlation of the models is consistent with that of real networks. The spatial distance plays a leading role in generating assortative mixing by degree, while the generation of disassortative mixing relies on both the degree of preferential attachment and spatial restriction.
Varentropy is used as a general measure of probabilistic uncertainty for a complex network, inspired by the first and second laws of thermodynamics, but not limited to the equilibrium system. By exploring the relationship between the varentropy of the scale free distribution and the exponent of power laws as well as network size, we get the optimal design of a scale-free network against random failures. The behaviors of varentropy and the Shannon entropy of double Pareto law degree distribution are analyzed to compare their usefulness. Our conclusion is that varentropy is suitable and reliable.
Motivated by the need to include the different characteristics of individuals and the damping effect in predictions of epidemic spreading, we build a model with variant coefficients and white Gaussian noise based on the traditional SIR model. The analytic and simulation results predicted by the model are presented and discussed. The simulations show that using the variant coefficients results in a higher percentage of susceptible individuals and a lower percentage of removed individuals. When the noise is included in the model, the percentage of infected individuals has a wider peak and more fluctuations than that predicted using the traditional SIR model.
We construct a weighted network of scientific collaboration in computational geometry and study the statistical properties of the network. In addition, we introduce a parameter called the collaboration relationship parameter to measure the collaboration between scientists. The collaboration relationship parameter of two scientists depends not only on the connection weights between the nodes, but also on the network's structure. The stability of the network's structure in terms of different edge removal strategies is also studied. According to the parameter, we find that a community structure exists in this type of network.
