High temperature plastic deformation constitutive model of Mg-Zn-Zr-Y alloy

In order to accurately predict the flow stress of Mg-Zn-Zr-Y alloy at high temperature, the hot compression test of Mg-Zn-Zr-Y alloy was carried out on Gleeble-1500 thermal / mechanical simulator. The deformation temperature was 523 K, 573 K, 623 K, and the strain rate was 0,01 ~ 1 s-1. By obtaining the true stress-strain curve, the strain compensation factor Z parameter was introduced into the Arrhenius equation to establish a more accurate strain coupling constitutive model. The results show that the theoretical value of the peak stress calculated by the constitutive model is in good agreement with the experimental results, and the average relative error is 5,67 %, which verifies the feasibility of the model

A Low Temperature Synthetic Route to Nanocrystalline TiN

A simple chemical synthetic route has been developed to prepare nanocrystalline titanium nitride (TiN) in an autoclave, by the reaction of metallic Ti with NaNH2 at low temperature of 500–600 °C. The samples were characterized by X-ray powder diffraction, transmission electron microscopy, and X-ray photoelectron spectra. The possible reaction mechanism of this process is also discussed. This method may be extended to the synthesis of other metal nitrides.Keywords: Nanocrystalline, titanium nitride, synthesi

Energy Loss Effect in High Energy Nuclear Drell-Yan Process

The energy loss effect in nuclear matter, which is another nuclear effect apart from the nuclear effect on the parton distribution as in deep inelastic scattering process, can be measured best by the nuclear dependence of the high energy nuclear Drell-Yan process. By means of the nuclear parton distribution studied only with lepton deep inelastic scattering experimental data, measured Drell-Yan production cross sections for 800GeV proton incident on a variety of nuclear targets are analyzed within Glauber framework which takes into account energy loss of the beam proton. It is shown that the theoretical results with considering the energy loss effect are in good agreement with the FNAL E866

Optimization of Texture Rendering of 3D Building Model Based on Vertex Importance

In 3D building models, a large number of texture maps with different sizes increase the number of model data loading and drawing batches, which greatly reduces the drawing efficiency of the model. Therefore, this paper proposes a texture set mapping method based on vertex importance. Firstly, based on the 2D space boxing algorithm, the texture maps are merged and a series of Mipmap texture maps are generated, and then the vertex curvature, texture variability and location information of each vertex are calculated, normalized, and weighted to get the importance of each vertex, and then finally, different Mipmap-level textures are remapped according to the importance of the vertices. The experiment proves that the algorithm in this paper can reduce the amount of texture data on the one hand, and avoid the rendering pressure brought by the still large amount of data after merging on the other hand, so as to improve the rendering efficiency of the model

Peierls distorted chain as a quantum data bus for quantum state transfer

We systematically study the transfer of quantum state of electron spin as the flying qubit along a half-filled Peierls distorted tight-binding chain described by the Su-Schrieffer-Heeger (SSH) model, which behaves as a quantum data bus. This enables a novel physical mechanism for quantum communication with always-on interaction: the effective hopping of the spin carrier between sites $A$ and $B$ connected to two sites in this SSH chain can be induced by the quasi-excitations of the SSH model. As we prove, it is the Peierls energy gap of the SSH quasi-excitations that plays a crucial role to protect the robustness of the quantum state transfer process. Moreover, our observation also indicates that such a scheme can also be employed to explore the intrinsic property of the quantum system.Comment: 10 pages, 6 figure

Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction

Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision. Most prior works adopt a selective attention mechanism over sentences in a bag to denoise from wrongly labeled data, which however could be incompetent when there is only one sentence in a bag. In this paper, we propose a brand-new light-weight neural framework to address the distantly supervised relation extraction problem and alleviate the defects in previous selective attention framework. Specifically, in the proposed framework, 1) we use an entity-aware word embedding method to integrate both relative position information and head/tail entity embeddings, aiming to highlight the essence of entities for this task; 2) we develop a self-attention mechanism to capture the rich contextual dependencies as a complement for local dependencies captured by piecewise CNN; and 3) instead of using selective attention, we design a pooling-equipped gate, which is based on rich contextual representations, as an aggregator to generate bag-level representation for final relation classification. Compared to selective attention, one major advantage of the proposed gating mechanism is that, it performs stably and promisingly even if only one sentence appears in a bag and thus keeps the consistency across all training examples. The experiments on NYT dataset demonstrate that our approach achieves a new state-of-the-art performance in terms of both AUC and top-n precision metrics

Manganese dioxide nanosheet functionalized sulfur@PEDOT core-shell nanospheres for advanced lithium-sulfur batteries

MnO2 nanosheet functionalized S@PEDOT core–shell nanospheres demonstrate highly enhanced electrochemical performance for Li–S batteries, benefitting from effectively trapping polysulfides, minimizing polysulfide dissolution, and improving cathode conductivity and wettability.This is the accepted manuscript. The final version is available at http://pubs.rsc.org/en/content/articlelanding/2016/ta/c6ta03211g#!divAbstract