Microstructures and mechanical properties of B4C-TiB2-SiC composites fabricated by ball milling and hot pressing

B₄C-TiB₂-SiC composites were fabricated via hot pressing using ball milled B₄C, TiB₂, and SiC powder mixtures as the starting materials. The impact of ball milling on the densification behaviors, mechanical properties, and microstructures of the ceramic composites were investigated. The results showed that the refinement of the powder mixtures and the removal of the oxide impurities played an important role in the improvement of densification and properties. Moreover, the formation of the liquid phases during the sintering was deemed beneficial for densification. The typical values of relative density, hardness, bending strength, and fracture toughness of the composites reached 99.20%, 32.84?GPa, 858?MPa and 8.21?MPa?m^1/2, respectively. Crack deflection, crack bridging, crack branching, and microcracking were considered to be the potential toughening mechanisms in the composites. Furthermore, numerous nano-sized intergranular/intragranular phases and twin structures were observed in the B₄C-TiB₂-SiC composite.     

Nb和时效时间对热轧中锰TRIP钢中P偏聚的影响

热轧态中锰TRIP钢首先经650 ℃退火2 h,随后在550 ℃进行等温时效热处理,采用场发射扫描电镜(FE-SEM)研究该钢中P的偏聚和时效析出行为的变化情况。结果表明,中锰TRIP钢中P在晶界的偏聚是一种非平衡偏聚现象,临界时间约为50 h,与理论计算结果48 h较为吻合。在局部偏聚区域内,C与P存在共偏聚的关系,即P偏聚量高的地方,C含量也高。而合金元素Nb具有抑制P偏聚的效果,在20~70 h时效时间内,可以相对降低6.57%~19.5%的最大P偏聚量。根据电子背散射衍射(EBSD)菊池线分析,P偏聚量低于2.28at%时,P为固溶状态,高于2.28at.%时,P为析出状态。     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

Genes Identification,Molecular Docking and Dynamics Simulation Analysis of Laccases from Amylostereum areolatum Provides Molecular Basis of Laccase Bound to Lignin

An obligate mutualistic relationship exists between the fungus Amylostereum areolatum and woodwasp Sirex noctilio. The fungus digests lignin in the host pine, providing essential nutrients for the growing woodwasp larvae. However, the functional properties of this symbiosis are poorly described. In this study, we identified, cloned, and characterized 14 laccase genes from A. areolatum. These genes encoded proteins of 508 to 529 amino acids and contained three typical copper-oxidase domains, necessary to confer laccase activity. Besides, we performed molecular docking and dynamics simulation of the laccase proteins in complex with lignin compounds (monomers, dimers, trimers, and tetramers). AaLac2, AaLac3, AaLac6, AaLac8, and AaLac10 were found that had low binding energies with all lignin model compounds tested and three of them could maintain stability when binding to these compounds. Among these complexes, amino acid residues ALA, GLN, LEU, PHE, PRO, and SER were commonly present. Our study reveals the molecular basis of A. areolatum laccases interacting with lignin, which is essential for understanding how the fungus provides nutrients to S. noctilio. These findings might also provide guidance for the control of S. noctilio by informing the design of enzyme mutants that could reduce the efficiency of lignin degradation.