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The theoretical studies of optical properties of Ga$_{1-x}$In$_x$As self-assembled quantum-wires (QWR's) made of short-period superlattices with strain-induced lateral ordering are presented. Valence-band anisotropy, band mixing, and effects due to local strain distribution at the atomistic level are all taken into account. Using realistic material parameters which are experimentally feasible, we perform simulations of the absorption spectra for both inter-subband and inter-band transitions (including the excitonic effect) of this material.It is shown that the self-assembled QWR's have favorable optical properties for application in infra-red detection with normal incidence. The wavelength of detection ranges from 10 $\mu m$ to 20 $\mu m$ with the length of QWR period varying from 140\AA to 300\AA., Comment: 16 pages, 15 figures

This paper will demonstrate what china was doing, is doing and will do for its economic expansion during and after global crisis. Many thoughts and theories about Chinese economic reforms are analyzed. The road map and goal of Chinese economic growth are examined and outlined in this paper. How to keep Chinese economy fast growth is this paper’s key issue which is thoroughly discussed.

Since Chinese economy takes a fast growth path toward the top of the world economy, it is interesting to examine China’s way of development. This paper will demonstrate what china was doing, is doing and will do for its economic expansion during and after global crisis. Many thoughts and theories about Chinese economic reforms are analyzed. The road map and goal of Chinese economic growth are examined and outlined in this paper. How to keep Chinese economy fast growth is this paper’s key issue which is thoroughly discussed.

Starting from the operator formalism, Ward identities are derived for W3 conformal field theories on a torus. The action of the zero mode of the spin-three field inside correlation functions is discussed.

In order to invesitigate the effectiveness of chinese stock market, we take the real data from the market and calculate the seasonal pattern of the stock prices both for the indices and stock prices.We aslo analyze the size factors in the seasonal pattern. It is found chinese stock market is strong in the seasonal pattern which means it is not a strong effective market.

We analyzed the distribution of the eigenvalues of the empirical cross correlation matrix constructed from the data of Chinese stock market and the distribution of the components of some eigenvectors corresponding to certain eigenvalues. Compared with the analytical random matrix theory results, we found that a small part about 11%of eigenvalues are out of the range of random matrix theory results and that the distribution of the components of the eigenvector corresponding to the largest eigenvalue is evidently different from that of random matrix theory. These results demonstrate that chinese stock market contains 11% non efficient part.

In this paper, we attempt to study quantitatively the effects on macroeconomics due to different shocks. The interactions between different shocks are the major subjects. Furthermore, the dynamics of shocks, i.e, the time evolutions of shocks, and the propagation of shocks are studied in detail by using the quantitative models. The approach of this paper can be applied to any economic shocks, particularly, price shocks, technology shocks, unemployment shocks, and outer capital flow shocks. As an example, we investigate the interaction of price shock and exchange rate shock. At the same time, we also discuss the macroeconomic policy’s effects on economic business cycle.

We calculate the correlation effects for the stock indices as an indicator. The change pattern is demonstrated. It is found that the chinese stock market is toward a less correlation system. It means chinese stock market is getting better.

We study the random matrix technique for the financial data correlations. The usual correlation matrices are known to be noise dressed. We apply a new and alternative method to estimate the true correlations. To be more efficient in getting rid of the error due to the finite observations of the time series data, we use the de-trend data and power mapping correlation matrices approach to analyze the real world financial data and calculate the VaR. We test the time persistence of our results by comparing with the ordinary noise dressed results. It is found that the results are more powerful in predicting.