The dispersed SiO2 microspheres supported Ru catalyst with enhanced activity for ammonia decomposition

Ru nanoparticles supported on SiO₂ microspheres (Ru/SiO₂-GUS) were prepared by the glucose-urea-metallic salt method and applied in the decomposition of ammonia. In the glucose-urea-metallic salt method, glucose as the carbon template plays a significant role in the formation of diffusion-beneficial structural properties of Ru/SiO₂-GUS, and also induceds the modification of the electronic state of Ru. Ru/SiO₂-GUS exhibited higher catalytic activity compared with the catalyst prepared with the impregnation method. The catalytic performance of Ru/SiO₂-GUS was further enhanced with the addition of either K or Cs——the addition order and amount strongly affecting the catalytic performance. When the ratio of K/Cs to Ru is 2, the alkali metal (KOH/CsOH) solution is added in the homogeneous solution of glucose, urea, RuCl₃ and the colloidal silica, the promotion effect of K/Cs is the strongest, particularly under lower reaction temperatures. However, the promotion effects of K and Cs are different as reveled by the combined results of H₂-TPR, XPS and NH₃-TPSR. More NH₃ can be absorbed on K–Ru/SiO₂-GUS and the electron density of Ru decreased. By contrast, more metallic Ru formed on Cs–Ru/SiO₂-GUS, facilitating N₂ recombination.     

一种使用多跳事实的端到端知识库实体描述生成方法

自动化实体描述生成有助于进一步提升知识图谱的应用价值,而流畅度高是实体描述文本的重要质量指标之一。该文提出使用知识库上多跳的事实来进行实体描述生成,从而贴近人工编撰的实体描述的行文风格,提升实体描述的流畅度。该文使用编码器—解码器框架,提出了一个端到端的神经网络模型,可以编码多跳的事实,并在解码器中使用关注机制对多跳事实进行表示。该文的实验结果表明,与基线模型相比,引入多跳事实后模型的BLEU-2和ROUGE-L等自动化指标分别提升约8.9个百分点和7.3个百分点。     

AP对HATO热分解影响的机制

为详细了解高氯酸铵(AP)对5,5'-联四唑-1,1'-二氧二羟铵(HATO)热分解影响的机制,采用热重-质谱-傅里叶红外光谱(TG-MS-FTIR)联用技术、差示扫描量热法(DSC)、傅里叶红外光谱(FTIR)方法,对HATO和HATO/AP共混物的热分解特性、气体产物以及凝聚相变化进行了研究。结果表明,HATO具有两个连续热分解阶段,HATO/AP共混物则有3个热分解阶段;HATO、AP共混后,HATO使得AP熔融峰消失,AP可使HATO的热分解初始温度提前,热分解时间延长且不影响分解完全性;HATO热分解气体产物有CO_2、N_2O、HCN、NH_3、NO、N_2、H_2O,而HATO/AP共混物热分解产生气体主要有N_2、CO_2、N_2O、HCN、NH_3、H_2O、HCN、NO、HCl、NOCl;另外,采用等转化率法计算HATO和HATO/AP共混物四唑环基团的活化能分别为53.38 kJ·mol~(-1)和60.69 kJ·mol~(-1);通过对比HATO和HATO/AP共混物热分解特性以及凝聚相特征基团的变化,阐释了AP使HATO热分解温度提前的机理很可能是:AP的铵根离子与HATO之间发生了质子转移;推测AP导致HATO热分解时间延长的原因为:HATO/AP共混物产生的NH_3与热分解中间体1,1'-二羟基-5,5-联四唑(BTO)反应生成5,5'-联四唑-1,1'-二氧铵盐(ABTOX)。     

Topological Characterization of Book Graph and Stacked Book Graph

Degree based topological indices are being widely used in computer-aided modeling, structural activity relations, and drug designing to predict the underlying topological properties of networks and graphs. In this work, we compute the certain important degree based topological indices like Randic index, sum connectivity index, ABC index, ABC₄ index, GA index and GA₅ index of Book graph B_n and Stacked book graph B_m,n. The results are analyzed by using edge partition, and the general formulas are derived for the above-mentioned families of graphs.     

松辽盆地伏龙泉断陷边界断层构造反转率

盆地边界断层通常为同沉积断层，同沉积断层活动时间相对短且具有间歇性，地层沉积时间相对较长，这决定了断陷盆地的地层分布格局。通过分析同沉积断层理论模式，认为断陷缓坡带地层厚度与边界断层下盘厚度相当，边界断层下盘通常缺失与上盘对应的地层，可选择断陷盆地缓坡带地层厚度代替边界断层下盘厚度，建立虚拟下盘厚度格架，然后运用位移-距离曲线等方法对边界断层开展定量研究。伏龙泉断陷位于松辽盆地东南部，为东断西超的半地堑盆地，发育4条边界断层，依次控制了4个沉积次洼，4条边界断层的活动强度和反转程度存在差异。伏龙泉断陷经历了断陷期、拗陷期、反转期三个主要的构造期次，分别对应于边界断层强烈活动、停止活动、反转活动3个演化阶段，发生于白垩纪末的反转活动使得边界断层再次活化，形成油气运移通道，深层油气向上运移到浅层圈闭中，形成次生油气藏。通过定量计算伏龙泉断陷各边界断层的反转率，结合油气富集规律认识，认为反转率适中的区域最有利于次生油气藏的形成和保存。     

A dual Newton strategy for tree‐sparse quadratic programs and its implementation in the open‐source software treeQP

This paper presents a dual Newton scheme for tree‐sparse quadratic programs as they may arise in the field of stochastic programming. Previous work suggests to introduce auxiliary variables to decompose the tree into scenarios and use Newton's method to solve a dual problem formulation. Following a different approach, we apply the same principle directly on the tree‐sparse problem, avoiding the increase in dimensionality. In combination with a tailored algorithm for the calculation of the step direction, which is typically the most expensive operation per iteration, the proposed algorithm achieves a linear complexity in the number of nodes and supports parallel processing of the tree branches in a stage‐wise fashion. An open‐source implementation of the presented dual Newton strategy is publicly available in treeQP , a toolbox of open‐source solvers for tree‐sparse quadratic programs.     

How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study

This paper replicates the Diebold and Yilmaz (2012) study on the connectedness of the commodity market and three other financial markets: the stock market, the bond market, and the FX market, based on the Generalized Forecast Error Variance Decomposition, GEFVD. We show that the net spillover indices (of directional connectedness), used to assess the net contribution of one market to overall risk in the system, are sensitive to the normalization scheme applied to the GEFVD. We show that, considering data generating processes characterized by different degrees of persistence and covariance, a scalar-based normalization of the Generalized Forecast Error Variance Decomposition is preferable to the row normalization suggested by Diebold and Yilmaz since it yields net spillovers free of sign and ranking errors.     

基于边图的线性流重叠社区发现算法

重叠网络的社区发现是复杂网络研究中的重要问题。为了提高网络中重叠社区发现的时间效率，提出一种基于边图的线性流重叠社区发现算法LBSA。算法首先对于边图网络中的边进行随机的依次处理，完成节点的初步社区划分，再将其中重叠小社区合并到相似度最大的其他大社区中得到最终的社区。通过以上步骤，算法能够以接近线性的时间复杂度得到网络的重叠结构。从最终的实验结果来看，与其他算法相比，该算法能够在更短的时间有质量地发现网络中的重叠社区。     

A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing

The cell is the smallest unit of living beings, which has structural and functional properties. Almost all cell behaviors are regulated by various intracellular reactions initiated by the signaling. The signaling and the distance between cells influence each other. Thus, cell-location-based modeling and analyzing of histopathological tissues provide important information to the expert. In literature, methods such as distance-based threshold, K-Nearest Neighbor, Voronoi graphs, Delaunay triangulation, and colored graph have been used. However, circular neighborhood relationships of cells have not been considered by any CAD system so far despite of its crucial impact. Thus, we developed the circular neighborhood cell-graph. Histopathological images of liver were classified by using features extracted from T-Distance, K-Nearest Neighbor, Voronoi, Delaunay, and the proposed cell-graph. Then, the classification performances of the methods were compared. Experimental results show that liver tissue images can be classified with accuracy of 95.7% by using the features provided by the proposed cell-graph model.     

多共振分量融合CNN的行星齿轮箱故障诊断

针对行星齿轮箱中各部件所激起的振动成分混叠、早期故障特征经常被较强的各级齿轮谐波成分以及环境噪声所湮没的问题，提出一种多共振分量融合卷积神经网络（multi-resonance component fusion based convolutional neural network，简称MRCF-CNN）的行星齿轮箱故障诊断方法。首先，对振动信号进行共振稀疏分解，得到包含齿轮谐波成分的高共振分量和可能包含轴承故障冲击成分的低共振分量；其次，构建多共振分量融合卷积神经网络，将得到的高、低共振分量和原始振动信号进行自适应的特征级融合，通过有监督的方式训练模型并进行行星齿轮箱故障诊断。对行星齿轮箱实验数据的分析结果表明，该方法能够有效分类行星齿轮箱中滚动轴承和齿轮的故障，成功对行星齿轮箱故障进行诊断，同时能够进一步增强卷积神经网络对振动信号所蕴含的故障信息的辨识能力。