Thermodynamic properties and shear viscosity over entropy density ratio of nuclear fireball in a quantum-molecular dynamics model

Thermodynamic and transport properties of nuclear fireball created in the central region of heavy-ion collisions below 400 MeV/nucleon are investigated within the isospin-dependent quantum molecular dynamic (IQMD) model. These properties including the density, temperature, chemical potential, entropy density ($s$) and shear viscosity ($\eta$), are calculated by a generalized hot Thomas Fermi formulism and a parameterized function, which was developed by Danielewicz. As the collision goes on, a transient minimal $\eta/s=5/4\pi-10/4\pi$ occurs in the largest compression stage. Besides, the relationship of $\eta/s$ to temperature ($T$) in the freeze-out stage displays a local minimum which is about 9-20 times $1/4\pi$ around $T$ = 8-12 MeV, which can be argued as indicative of a liquid gas phase transition. In addition, the influences of nucleon-nucleon (NN) cross section ($\sigma_{NN}$) and symmetry energy coefficient ($C_{s}$) are also discussed, and it is found that the results are sensitive to $\sigma_{NN}$ but not to $C_{s}$.Comment: 10 pages, 13 figures; Phys. Rev. C (in press) (x-axis of Fig.1 is corrected

A Comparison of Quintessence and Nonlinear Born-Infeld Scalar Field Using Gold Supernova data

We study the Non-Linear Born-Infeld(NLBI) scalar field model and quintessence model with two different potentials($V(\phi)=-s\phi$ and ${1/2}m^2\phi^2$). We investigate the differences between those two models. We explore the equation of state parameter w and the evolution of scale factor $a(t)$ in both NLBI scalar field and quintessence model. The present age of universe and the transition redshift are also obtained. We use the Gold dataset of 157 SN-Ia to constrain the parameters of the two models. All the results show that NLBI model is slightly superior to quintessence model.Comment: 17 pages, 10 figures, some references adde

Isoscaling in the Lattice Gas Model

The isoscaling behavior is investigated using the isotopic/isobaric yields from the equilibrated thermal source which is prepared by the lattice gas model for lighter systems with A = 36. The isoscaling parameters $\alpha$ and -$\beta$ are observed to drop with temperature. The difference of neutron and proton chemical potential shows a turning point around 5 MeV where the liquid gas phase transition occurs in the model. The relative free neutron or proton density shows a nearly linear relation with the N/Z (neutron to proton ratio) of system and the isospin fractionation is observed.Comment: 5 figures, 5 pages; the final version to appear in Phys Rev

Approximate truth discovery via problem scale reduction

Many real-world applications rely on multiple data sources to provide information on their interested items. Due to the noises and uncertainty in data, given a specific item, the information from different sources may conflict. To make reliable decisions based on these data, it is important to identify the trustworthy information by resolving these conflicts, i.e., the truth discovery problem. Current solutions to this problem detect the veracity of each value jointly with the reliability of each source for every data item. In this way, the efficiency of truth discovery is strictly confined by the problem scale, which in turn limits truth discovery algorithms from being applicable on a large scale. To address this issue, we propose an approximate truth discovery approach, which divides sources and values into groups according to a userspecified approximation criterion. The groups are then used for efficient inter-value influence computation to improve the accuracy. Our approach is applicable to most existing truth discovery algorithms. Experiments on real-world datasets show that our approach improves the efficiency compared to existing algorithms while achieving similar or even better accuracy. The scalability is further demonstrated by experiments on large synthetic datasets.Xianzhi Wang, Quan Z. Sheng, Xiu Susie Fang, Xue Li, Xiaofei Xu, and Lina Ya