Intense local plasma heating by stopping of ultrashort ultraintense laser pulse in dense plasma

Self-trapping, stopping, and absorption of an ultrashort ultraintense linearly polarized laser pulse in a finite plasma slab of near-critical density is investigated by particle-in-cell simulation. As in the underdense plasma, an electron cavity is created by the pressure of the transmitted part of the light pulse and it traps the latter. Since the background plasma is at near-critical density, no wake plasma oscillation is created. The propagating self-trapped light rapidly comes to a stop inside the slab. Subsequent ion Coulomb explosion of the stopped cavity leads to explosive expulsion of its ions and formation of an extended channel having extremely low plasma density. The energetic Coulomb-exploded ions form shock layers of high density and temperature at the channel boundary. In contrast to a propagating pulse in a lower density plasma, here the energy of the trapped light is deposited onto a stationary and highly localized region of the plasma. This highly localized energy-deposition process can be relevant to the fast ignition scheme of inertial fusion.

Nuclear stopping and compression in heavy-ion collisions at intermediate energies

The nuclear stopping and the radial flow are investigated with an isospin-dependent quantum molecular dynamics (IQMD) model for Ni + Ni and Pb + Pb from 0.4 to and 1.2 GeV/u. The expansion velocity as well as the degree of nuclear stopping are higher in the heavier system at all energies. The ratio between the flow energy and the total available energy in center of mass of the colliding systems exhibits a positive correlation to the degree of nuclear stopping. The maximum density (rho(max)) achieved in the compression is comparable to the hydrodynamics prediction only if the non-zero collision time effect is taken into account in the later. Due to the partial transparency, the growing of the maximum density achieved in the central region of the fireball with the increase of beam energy becomes gradually flat in the 1 GeV/u energy regime. (C) 2008 Elsevier B.V. All rights reserved.

Application of rough set-based analysis to extract spatial relationship indicator rules: An example of land use in Pearl River Delta

Spatial relations, reflecting the complex association between geographical phenomena and environments, are very important in the solution of geographical issues. Different spatial relations can be expressed by indicators which are useful for the analysis of geographical issues. Urbanization, an important geographical issue, is considered in this paper. The spatial relationship indicators concerning urbanization are expressed with a decision table. Thereafter, the spatial relationship indicator rules are extracted based on the application of rough set theory. The extraction process of spatial relationship indicator rules is illustrated with data from the urban and rural areas of Shenzhen and Hong Kong, located in the Pearl River Delta. Land use vector data of 1995 and 2000 are used. The extracted spatial relationship indicator rules of 1995 are used to identify the urban and rural areas in Zhongshan, Zhuhai and Macao. The identification accuracy is approximately 96.3%. Similar procedures are used to extract the spatial relationship indicator rules of 2000 for the urban and rural areas in Zhongshan, Zhuhai and Macao. An identification accuracy of about 83.6% is obtained.

Uncovering the rules for protein-protein interactions from yeast genomic data

Identifying protein-protein interactions is crucial for understanding cellular functions. Genomic data provides opportunities and challenges in identifying these interactions. We uncover the rules for predicting protein-protein interactions using a frequent pattern tree (FPT) approach modified to generate a minimum set of rules (mFPT), with rule attributes constructed from the interaction features of the yeast genomic data. The mFPT prediction accuracy is benchmarked against other commonly used methods such as Bayesian networks and logistic regressions under various statistical measures. Our study indicates that mFPT outranks other methods in predicting the protein-protein interactions for the database used. We predict a new protein-protein interaction complex whose biological function is related to premRNA splicing and new protein-protein interactions within existing complexes based on the rules generated.