Thermodynamical quantities of lattice full QCD from an efficient method

I extend to QCD an efficient method for lattice gauge theory with dynamical fermions. Once the eigenvalues of the Dirac operator and the density of states of pure gluonic configurations at a set of plaquette energies (proportional to the gauge action) are computed, thermodynamical quantities deriving from the partition function can be obtained for arbitrary flavor number, quark masses and wide range of coupling constants, without additional computational cost. Results for the chiral condensate and gauge action are presented on the $10^4$ lattice at flavor number $N_f=0$, 1, 2, 3, 4 and many quark masses and coupling constants. New results in the chiral limit for the gauge action and its correlation with the chiral condensate, which are useful for analyzing the QCD chiral phase structure, are also provided.Comment: Latex, 11 figures, version accepted for publicatio

A heuristic forecasting model for stock decision

This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural network prediction. Results are compared with a benchmark buy-and-hold strategy. The forecasting model was found capable of consistently outperforming this benchmark strategy

Analysis and modelling of flood risk assessment using information diffusion and artificial neural network

Floods are a serious hazard to life and property. The traditional probability statistical method is acceptable in analysing the flood risk but requires a large sample size of hydrological data. This paper puts forward a composite method based on artificial neural network (ANN) and information diffusion method (IDM) for flood analysis. Information diffusion theory helps to extract as much useful information as possible from the sample and thus improves the accuracy of system recognition. Meanwhile, an artificial neural network model, back-propagation (BP) neural network, is used to map the multi-dimensional space of a disaster situation to a one-dimensional disaster space and to enable resolution of the grade of flood disaster loss. These techniques all contribute to a reasonable prediction of natural disaster risk. As an example, application of the method is verified in a flood risk analysis in China, and the risks of different flood grades are determined. Our model yielded very good results and suggests that the methodology is effective and practical, with the potentiality to be used to forecast flood risk for use in flood risk management. It is also hoped that by conducting such analyses lessons can be learned so that the impact of natural disasters such as floods can be mitigated in the future.Keywords: artificial neural network, information diffusion, flood, risk analysis, assessmen

Improved lattice QCD with quarks: the 2 dimensional case

QCD in two dimensions is investigated using the improved fermionic lattice Hamiltonian proposed by Luo, Chen, Xu, and Jiang. We show that the improved theory leads to a significant reduction of the finite lattice spacing errors. The quark condensate and the mass of lightest quark and anti-quark bound state in the strong coupling phase (different from t'Hooft phase) are computed. We find agreement between our results and the analytical ones in the continuum.Comment: LaTeX file (including text + 10 figures

Temperature dependence of electron-spin relaxation in a single InAs quantum dot at zero applied magnetic field

The temperature-dependent electron spin relaxation of positively charged excitons in a single InAs quantum dot (QD) was measured by time-resolved photoluminescence spectroscopy at zero applied magnetic fields. The experimental results show that the electron-spin relaxation is clearly divided into two different temperature regimes: (i) T < 50 K, spin relaxation depends on the dynamical nuclear spin polarization (DNSP) and is approximately temperature-independent, as predicted by Merkulov et al. (ii) T > about 50 K, spin relaxation speeds up with increasing temperature. A model of two LO phonon scattering process coupled with hyperfine interaction is proposed to account for the accelerated electron spin relaxation at higher temperatures.Comment: 10 pages, 4 figure

Influence of thermal strains on the electrocaloric and dielectric properties of ferroelectric nanoshells

Author name used in this publication: Veng Cheong Lo2009-2010 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe