Pulsar slow glitches in a solid quark star model

A series of five unusual slow glitches of the radio pulsar B1822-09 (PSR J1825-0935) were observed over the 1995-2005 interval. This phenomenon is understood in a solid quark star model, where the reasonable parameters for slow glitches are presented in the paper. It is proposed that, because of increasing shear stress as a pulsar spins down, a slow glitch may occur, beginning with a collapse of a superficial layer of the quark star. This layer of material turns equivalently to viscous fluid at first, the viscosity of which helps deplete the energy released from both the accumulated elastic energy and the gravitation potential. This performs then a process of slow glitch. Numerical calculations show that the observed slow glitches could be reproduced if the effective coefficient of viscosity is ~10^2 cm^{2}/s and the initial velocity of the superficial layer is order of 10^{-10} cm/s in the coordinate rotating frame of the star.Comment: 5 pages, 5 figures, accepted for publication in MNRAS (Main Journal

Design and Finite Element Analysis of Mixed Aerofoil Wind Turbine Blades

Wind turbine technology is one of the rapid growth sectors of renewable energy all over the world. As a core component of a wind turbine, it is a common view that the design and manufacturing of rotor blades represent about 20% of the total investment of the wind turbine [1]. Moreover, the performance of a wind turbine is highly dependent on the design of the rotor [2]. As well as rotor aerodynamic performance, the structure strength, stiffness and fatigue of the blade are also critical to the wind turbine system service life. This paper presents the design and Finite Element Analysis (FEA) of a 10KW fixed-pitch variable-speed wind turbine blade with five different thickness of aerofoil shape along the span of the blade. The main parameters of the wind turbine rotor and the blade aerodynamic geometry shape are determined based on the principles of the blade element momentum (BEM) theory. Based on the FE method, deflections and strain distributions of the blade under extreme wind conditions are numerically predicted. The results indicate that the tip clearance is sufficient to prevent collision with the tower, and the blade material is linear and safe

Deconfinement phase transition in hybrid neutron stars from the Brueckner theory with three-body forces and a quark model with chiral mass scaling

We study the properties of strange quark matter in equilibrium with normal nuclear matter. Instead of using the conventional bag model in quark sector, we achieve the confinement by a density-dependent quark mass derived from in-medium chiral condensates. In nuclear matter, we adopt the equation of state from the Brueckner-Bethe-Goldstone approach with three-body forces. It is found that the mixed phase can occur, for a reasonable confinement parameter, near the normal nuclear saturation density, and goes over into pure quark matter at about 5 times the saturation. The onset of mixed and quark phases is compatible with the observed class of low-mass neutron stars, but it hinders the occurrence of kaon condensation

Proximity and anomalous field-effect characteristics in double-wall carbon nanotubes

Proximity effect on field-effect characteristic (FEC) in double-wall carbon nanotubes (DWCNTs) is investigated. In a semiconductor-metal (S-M) DWCNT, the penetration of electron wavefunctions in the metallic shell to the semiconducting shell turns the original semiconducting tube into a metal with a non-zero local density of states at the Fermi level. By using a two-band tight-binding model on a ladder of two legs, it is demonstrated that anomalous FEC observed in so-called S-M type DWCNTs can be fully understood by the proximity effect of metallic phases.Comment: 4 pages, 4 figure

Strain Modulated Electronic Properties of Ge Nanowires - A First Principles Study

We used density-functional theory based first principles simulations to study the effects of uniaxial strain and quantum confinement on the electronic properties of germanium nanowires along the [110] direction, such as the energy gap and the effective masses of the electron and hole. The diameters of the nanowires being studied are up to 50 {\AA}. As shown in our calculations, the Ge [110] nanowires possess a direct band gap, in contrast to the nature of an indirect band gap in bulk. We discovered that the band gap and the effective masses of charge carries can be modulated by applying uniaxial strain to the nanowires. These strain modulations are size-dependent. For a smaller wire (~ 12 {\AA}), the band gap is almost a linear function of strain; compressive strain increases the gap while tensile strain reduces the gap. For a larger wire (20 {\AA} - 50 {\AA}), the variation of the band gap with respect to strain shows nearly parabolic behavior: compressive strain beyond -1% also reduces the gap. In addition, our studies showed that strain affects effective masses of the electron and hole very differently. The effective mass of the hole increases with a tensile strain while the effective mass of the electron increases with a compressive strain. Our results suggested both strain and size can be used to tune the band structures of nanowires, which may help in design of future nano-electronic devices. We also discussed our results by applying the tight-binding model.Comment: 1 table, 8 figure

Deep feature fusion model for sentence semantic matching

© 2019 Tech Science Press. All rights reserved. Sentence semantic matching (SSM) is a fundamental research in solving natural language processing tasks such as question answering and machine translation. The latest SSM research benefits from deep learning techniques by incorporating attention mechanism to semantically match given sentences. However, how to fully capture the semantic context without losing significant features for sentence encoding is still a challenge. To address this challenge, we propose a deep feature fusion model and integrate it into the most popular deep learning architecture for sentence matching task. The integrated architecture mainly consists of embedding layer, deep feature fusion layer, matching layer and prediction layer. In addition, we also compare the commonly used loss function, and propose a novel hybrid loss function integrating MSE and cross entropy together, considering confidence interval and threshold setting to preserve the indistinguishable instances in training process. To evaluate our model performance, we experiment on two real world public data sets: LCQMC and Quora. The experiment results demonstrate that our model outperforms the most existing advanced deep learning models for sentence matching, benefited from our enhanced loss function and deep feature fusion model for capturing semantic context