Superconductivity in the vicinity of antiferromagnetic order in CrAs

One of the common features of unconventional, magnetically mediated superconductivity as found in the heavy-fermions, high-transition-temperature (high-Tc) cuprates, and iron pnictides superconductors is that the superconductivity emerges in the vicinity of long-range antiferromagnetically ordered state.[1] In addition to doping charge carriers, the application of external physical pressure has been taken as an effective and clean approach to induce the unconventional superconductivity near a magnetic quantum critical point (QCP).[2,3] Superconductivity has been observed in a majority of 3d transition-metal compounds,[4-9] except for the Cr- and Mn-based compounds in the sense that the low-lying states near Fermi level are dominated by their 3d electrons. Herein, we report on the discovery of superconductivity on the verge of antiferromagnetic order in CrAs via the application of external high pressure. Bulk superconductivity with Tc ~ 2 K emerges at the critical pressure Pc ~ 8 kbar, where the first-order antiferromagnetic transition at TN = 265 K under ambient pressure is completely suppressed. Abnormal normal-state properties associated with a magnetic QCP have been observed nearby Pc. The close proximity of superconductivity to an antiferromagnetic order suggests an unconventional pairing mechanism for the superconducting state of CrAs. The present finding opens a new avenue for searching novel superconductors in the Cr and other transitional-metal based systems

Analysis of an Alanine/Arginine Mixture by Using TLC/FTIR Technique

We applied TLC/FTIR coupled with mapping technique to analyze an alanine/arginine mixture. Narrow band TLC plates prepared by using AgI as a stationary phase were used to separate alanine and arginine. The distribution of alanine and arginine spots was manifested by a 3D chromatogram. Alanine and arginine can be successfully separated by the narrow band TLC plate. In addition, the FTIR spectra of the separated alanine and arginine spots on the narrow band TLC plate are roughly the same as the corresponding reference IR spectra

The development and application of an inviscid inverse method

A two-dimensional inviscid inverse method is developed, verified and applied in this paper. The method solves the Euler equation in absolute reference frame by a cell-centered finite volume method, and the hybrid Runge-Kutta method is used for time integration. Different from the direct method, the inverse method imposes a unique “transpiration” boundary condition on the blade surfaces. The inputs of inverse method are pressure loading and blade tangential thickness distribution along the blade chord. During the time marching process, the blade shape is periodically updated. When the solution is converged, the blade shape will be stabled. In the paper, the principle of the inverse method is described in detail. Then the developed inverse method is verified against a consistence test: recover an axial compressor cascade from a different start. Finally, to demonstrate the powerful capability of the method, it is used to redesign the cascade, and final results give an improved aerodynamic performance

Studies on Immune Clonal Selection Algorithm and Application of Bioinformatics

Abstract: Immune algorithms (IAs) are microscopic view of evolutionary algorithms (EAs) and applied in combinatorial optimization problems. This paper addresses to a clonal selection algorithm (CSA) that is one of the most representative IA and was applied into the protein structure prediction (PSP) on AB off-lattice model, in which the memory B cells of the CSA was innovated by employing different strategies: local search and global search in the phase of the mutation. And the CSA was further improved by adding aging operator to combat the premature convergence. However the pure aging operator didn't achieve effective results and sometimes the optimum solution was eliminated. To resolve this problem, the current best solution was reserved by an antibody and it was not eliminated when its age reached its life span. In our experiments the improved algorithm was compared with the standard CSA and the pure aging CSA, which of the results demonstrated that the improved strategy with the memory B cells and long life aging was very effective to overcome premature convergence and to avoid trapped in the local-best solution, and it was also effective in keeping the diversity of the small size population. On the other hand, one novel hybrid algorithm Quantum Immune(QI), which combines Quantum Algorithm (QA) and Immune Clonal Selection(ICS) Algorithm, has been presented for dealing with multi-extremum and multi-parameter problem based on AB off-lattice model in the predicting 2D protein folding structure. Clonal Selection Algorithm was introduced into the hypermutation operators in the Quantum Algorithm to improve the local search ability, and double chains quantum coded was designed to enlarge the probability of the global optimization solution. It showed that the solution mostly trap into the local optimum, to escape the local best solution the aging operator is introduced to improve the performance of the algorithm. Experimental results showed that the lowest energies and computing-time of the improved Quantum Clonal Selection(QCS) algorithm were better than that of the previous methods, and the QCS was further improved by adding aging operator to combat the premature convergence. Compared with previous approaches, the improved QCS algorithm remarkably enhanced the convergence performance and the search efficiency of the immune optimization algorithm

Noise Attenuation of Seismic Data via Deep Multiscale Fusion Network

Convolutional neural network- (CNN-) based deep learning (DL) architectures have achieved great success in many fields such as remote sensing, medical image processing, and computer vision. Recently, CNN-based models have also been attempted to solve geophysical problems. This paper presents a noise attenuation method of seismic data via a novel deep learning (DL) architecture, namely, deep multiscale fusion network (MSFN). Firstly, we integrate multiscale fusion (MSF) block to adaptively exploit local signal features at different scales from seismic data. And then, a series of stacked MSF blocks are formed into MSFN, which can restore the noisy seismic data effectively and preserve more useful signal information. Furthermore, a comparative study of our method and other leading edge ones is conducted by using synthetic seismic records and the SEG/EAGE salt and overthrust models. The results qualitatively and quantitatively show the capability of our method of achieving higher peak signal-to-noise ratios (PSNRs) while preserving much more useful information, comparing with other methods. Finally, our method is utilized in the real seismic data processing, obtaining satisfactory results