Two-Dimensional Inversion Asymmetric Topological Insulators in Functionalized III-Bi Bilayers

The search for inversion asymmetric topological insulators (IATIs) persists as an effect for realizing new topological phenomena. However, so for only a few IATIs have been discovered and there is no IATI exhibiting a large band gap exceeding 0.6 eV. Using first-principles calculations, we predict a series of new IATIs in saturated Group III-Bi bilayers. We show that all these IATIs preserve extraordinary large bulk band gaps which are well above room-temperature, allowing for viable applications in room-temperature spintronic devices. More importantly, most of these systems display large bulk band gaps that far exceed 0.6 eV and, part of them even are up to ~1 eV, which are larger than any IATIs ever reported. The nontrivial topological situation in these systems is confirmed by the identified band inversion of the band structures and an explicit demonstration of the topological edge states. Interestingly, the nontrivial band order characteristics are intrinsic to most of these materials and are not subject to spin-orbit coupling. Owning to their asymmetric structures, remarkable Rashba spin splitting is produced in both the valence and conduction bands of these systems. These predictions strongly revive these new systems as excellent candidates for IATI-based novel applications.Comment: 17 pages,5figure

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

Ultramicroscopic observation of recombinant adenoassociated virus type 2 on the surface of formvarcarbon coated copper grids under different relative humidity and incubation time using negative stain transmission electron microscopy

The purpose of this investigation was to compare the effects of different relative humidity (RH) on the microcosmic conformation of the recombinant AAV-2 virion at 22°C. rAAV-2 virions prepared on copper grid were placed in a high, middle or low RH cabinet and incubated for 72, 48 and 24 h, respectively. The rAAV-2 virions were observed by transmission electron microscope and the values of major axis length, minor axis length and ellipticity of the rAAV-2 virions were obtained using the IMS cell imageanalysis system. After incubation for 48 and 72 h, the major axis length and minor axis length of the rAAV-2 virion started to rapidly decrease in high RH. Conversely, the axis lengths rapidly increased in low RH. Then, the ellipticity of the rAAV-2 virion would almost tend to approach the identical value of0.9 for 48 and 72 h incubations in high RH. The results suggest that the rAAV-2 virion tended to favor a smaller, round, more stable conformation in high RH compared to low RH which implied that the rAAV-2 virion was probably prone to living in high relative humidity conditions

Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection

© 2017 Yanping Xu et al. Android malware detection is a complex and crucial issue. In this paper, we propose a malware detection model using a support vector machine (SVM) method based on feature weights that are computed by information gain (IG) and particle swarm optimization (PSO) algorithms. The IG weights are evaluated based on the relevance between features and class labels, and the PSO weights are adaptively calculated to result in the best fitness (the performance of the SVM classification model). Moreover, to overcome the defects of basic PSO, we propose a new adaptive inertia weight method called fitness-based and chaotic adaptive inertia weight-PSO (FCAIW-PSO) that improves on basic PSO and is based on the fitness and a chaotic term. The goal is to assign suitable weights to the features to ensure the best Android malware detection performance. The results of experiments indicate that the IG weights and PSO weights both improve the performance of SVM and that the performance of the PSO weights is better than that of the IG weights