Monolithically Integrated Laser Diode and Electroabsorption Modulator with Dual-Waveguide Spot-Size Converter Output

A 1.60μm laser diode and electroabsorption modulator monolithically integrated with a novel dualwaveguide spot-size converter output for low-loss coupling to a cleaved single-mode optical fiber are demonstrated.The devices emit in a single transverse and quasi single longitudinal mode with an SMSR of 25.6dB. These devices exhibit a 3dB modulation bandwidth of 15. 0GHz, and modulator DC extinction ratios of 16.2dB. The output beam divergence angles of the spot-size converter in the horizontal and vertical directions are as small as 7. 3°× 18. 0°,respectively, resulting in a 3. 0dB coupling loss with a cleaved single-mode optical fiber.

In-plane stray field induced spin-filtering in a two-dimensional electron gas under the modulation of surface ferromagnetic dual-gate

Based upon a hybrid ferromagnet/semiconductor structure consisting of two-dimensional electron gas and a pair of surface ferromagnetic stripes on top, we have theoretically investigated the effect of in-plane stray field omitted frequently in previous studies on the spin-dependent ballistic transport properties in hybrid structure. It is demonstrated here that, in combination with an external-controllable electrostatic modulation, the concerned structure shows a similar function as a lateral spin-polarized resonant tunneling device, where the strong spin-filtering effect occurs and nearly single-mode polarization is anticipated for the proper modulation. More importantly, the spin polarity of transmission electron can be easily transferred from one extreme to the other by switching the magnetization of stripes, showing the promising application as an efficient spin aligner in the developing semiconductor spintronics.

Propulsion characteristics of wing-in-ground effect dual-foil propulsors

Propulsion characteristics of wing-in-ground effect propulsors were investigated using a comparative analysis of thrust and powering characteristics between wing-in-ground (WIG) effect thrusters and traditional screw propellers. WIG thrusters were found to have constant thrust production and efficiency, nearly independent of speed of advance, as contrary to screw propellers, whose optimum efficiency occurs at only one speed point. To produce the same amount of thrust as equivalent screw propellers, WIG thrusters have to work under heavily loaded operating conditions. WIG thrusters were also found to produce a relatively lower but nearly constant efficiency and thrust, independent of speed. Another distinguishing propulsion characteristic revealed for WIG thrusters is that they are capable of operating at much higher speeds, in a range of three to six times that of screw propellers of the same size. While the speed range of screw propellers is mainly limited by their geometric pitch, the speed range of WIG thrusters has no speed limit in ideal fluid. In reality, the speed range is only limited by viscous drag and cavitation, or compressibility, in water or air, respectively. This suggests a potential for WIG thrusters of higher speed application than screw propellers. An experimental investigation and validation of the propulsion system is warranted. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.

A new dual injection system for AMS facility

IEECAS SKLLQG

The mean value concept in mono-linear regression of multi-variables and its application to trace studies in geosciences

IEECAS SKLLQG

Inner structure of Spin(c)(4) gauge potential on 4-dimensional manifolds

The decomposition of Spin(c)(4) gauge potential in terms of the Dirac 4-spinor is investigated, where an important characterizing equation Delta A(mu) = -lambda A(mu) has been discovered. Here, lambda is the vacuum expectation value of the spinor field, lambda = parallel to Phi parallel to(2), and A(mu) the twisting U(1) potential. It is found that when), takes constant values, the characterizing equation becomes an eigenvalue problem of the Laplacian operator. It provides a revenue to determine the modulus of the spinor field by using the Laplacian spectral theory. The above study could be useful in determining the spinor field and twisting potential in the Seiberg-Witten equations. Moreover, topological characteristic numbers of instantons in the self-dual sub-space are also discussed.

多分量对偶超导理论和口袋模型；Multi-component Dual Superconductor Theory and Bag Model

基于联络新参数化方案研究了多分量对偶超导模型。给出了多分量Ginzburg-Landau模型中的自对偶解,并研究了磁通量子数趋于无穷大时的墙涡旋解,以及与口袋模型之间的联系。

Holographic dual of linear dilaton black hole in Einstein-Maxwell-dilaton-axion gravity

Motivated by the recently proposed Kerr/CFT correspondence, we investigate the holographic dual of the extremal and non-extremal rotating linear dilaton black hole in Einstein-Maxwell-Dilaton-Axion Gravity. For the case of extremal black hole, by imposing the appropriate boundary condition at spatial infinity of the near horizon extremal geometry, the Virasoro algebra of conserved charges associated with the asymptotic symmetry group is obtained. It is shown that the microscopic entropy of the dual conformal field given by Cardy formula exactly agrees with Bekenstein-Hawking entropy of extremal black hole. Then, by rewriting the wave equation of massless scalar field with sufficient low energy as the SLL(2, R) x SLR(2, R) Casimir operator, we find the hidden conformal symmetry of the non-extremal linear dilaton black hole, which implies that the non-extremal rotating linear dilaton black hole is holographically dual to a two dimensional conformal field theory with the non-zero left and right temperatures. Furthermore, it is shown that the entropy of non-extremal black hole can be reproduced by using Cardy formula.

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.

Structure and oxygen permeability of a dual-phase membrane

The dual-phase membrane of La0.15Sr0.85Ga0.3Fe0.7O3-delta-Ba0.5Sr0.5Fe0.2Co0.8O3-delta (LSGF-BSCF) was prepared successfully. This membrane was characterized with X-ray diffraction (XRD), scanning electron microscopy (SEM) and electron probe micro-analyzer (EPMA). This membrane has a dense dual-phase structure: LSGF being the dense body of this membrane and BSCF as another phase running along the LSGF body. This structure is favorable for the oxygen permeation through the membrane. The oxygen permeation test shows that the oxygen permeation flux of LSGF-BSCF membrane (Jo(2) = 0.45 ml/min cm(2), at 915 degreesC) is much higher than that of LSGF membrane (Jo(2) = 0.05 ml/min cm(2)). Thickness dependence of oxygen permeation indicates that the oxygen permeation is controlled by the bulk diffusion. Compared to pure BSCF the dual-phase membrane of LSGF-BSCF is stable in reducing atmosphere. (C) 2003 Elsevier B.V. All rights reserved.

Retention modeling and optimization of pH value and solvent composition in HPLC using back-propagation neural networks and uniform design

A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step. seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. 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 both basic and acidic samples.