Center of mass energy and system-size dependence of photon production at forward rapidity at RHIC

We present the multiplicity and pseudorapidity distributions of photons produced in Au + Au and Cu + Cu collisions at root(NN)-N-s = 62.4 and 200 GeV. The photons are measured in the region -3.7 < eta < -2.3 using the photon Multiplicity detector in the STAR experiment at RHIC. The number of photons produced per average number of participating nucleon pairs increases with the beam energy and is independent of (lie collision centrality. For collisions with similar average numbers of participating nucleons the photon multiplicities are observed to be similar for An + Au and Cu + Cu collisions at a given beam energy. The ratios of the number of charged particles to photons in the measured pseudorapidity range are found to be 1.4 +/- 0.1 and 1.2 +/- 0.1 for root(NN)-N-s = 62.4 and 200 GeV, respectively. The energy dependence of this ratio could reflect varying contributions from baryons to charged particles, while mesons are the dominant contributors to photon production in the given kinematic region. The photon pseudorapidity distributions normalized by average number of participating nucleon pairs, when plotted as a function of eta-Y-beam, are found to follow a longitudinal scaling independent of centrality and colliding ion species at both beam energies. (C) 2009 Elsevier B.V. All rights reserved.

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP-HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two-dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An "early stopping" strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.

The history of the Yangtze River entering sea since the last glacial maximum: a review and look forward

This review paper provides a brief review on the development of ideas in the fields of the sea level change of the ECS (East China Sea), the history of the Yangtze River entering the sea and paleochannels in the shelf of the ECS since the Last Glacial Maximum (LGM). The paper summarizes two opposite theories about the Yangtze River entering the sea during the LGM. One theory is that the Yangtze River input a lacustrine in the north of Jiangsu province which was defunct in middle Holocene, and the river was once dry. The other was that the Yangtze River still existed and entered into the Okinawa Trough during the LGM, but scholars share different opinions on which course the river ran across and which place the river input the trough. This paper concludes future work is to study the evolution of the Yangtze River and the paleoclimate and the corresponding events as a whole from the view of regional and even global change, and more attention should be paid to the study on mud sediment, the Yangtze River's response to the changes in climate and sea-level, and the channel metamorphosis.