Structure of BaGd2（MoO4）4 Crystal

The title compound belongs to monoclinic,space group C2/c with a=5.2694(1),b=12.6659(4),c=19.4108(2) ,β=91.504(2)°,V=1295.06(5) 3,Z=4 and Dc=5.599 g/cm3. The structure of BaGd2(MoO4)4 contains a MoO4 tetrahedron,a distorted GdO8 polyhedron,and Ba2+ ions in a tenfold coordination. The GdO8 polyhedra are linked together through edge-sharing to give a two-dimensional Gd layer. The MoO4 tetrahedra connected to the Gd atoms are capped up and down the Gd layer through common oxygen apices,thus forming a new Gd-Mo layer. Finally,the Gd-Mo layers are held together through bridging BaO10 polyhedra to form a three-dimensional framework. Since the Ba-μ3-O bond has a large average distance of 2.888 ,this structural characteristic will result in a cleavage along the (001) plane.     

长循环寿命结晶@非结晶金属硫化物复合电极材料的制备及其混合电容器性能

通过乙二醇诱导策略成功地设计和构建了结晶@非结晶NiCo₂S₄@MoS₂(v-NCS@MS)纳米结构,利用非结晶MoS₂壳层的柔性保护和带缺陷的内部核 NiCo₂S₄的高比容量,使 v-NCS@MS电极具有高比容量(1 A·g^-1时 1 034 mAh·g^-1)和出色的倍率性能。此外,以v-NCS@MS为正极、活性炭(AC)为负极组装的混合超级电容器在219 W·kg^-1的比功率下可获得111 Wh·kg^-1的高比能量,在不同电流密度下循环15 000次后容量保持率高达80.5%。     

基于人工神经网络研究芳胺喹唑啉衍生物的抗胃癌活性

基于分子电性距离矢量(M1)表征21个芳胺喹唑啉衍生物的分子结构,并与其对人胃癌细胞的抗癌活性(pI)关联.通过最佳变量子集回归方法建立上述化合物抗胃癌活性的三参数(M15、M18、M82)定量构效关系模型.其交叉验证系数(R2cv)、非交叉验证系数(R2)依次为0.386和0.737.通过R、R2cv.、VIF等检验...     

Relative Entropy of Distance Distribution Based Similarity Measure of Nodes in Weighted Graph Data

Many similarity measure algorithms of nodes in weighted graph data have been proposed by employing the degree of nodes in recent years. Despite these algorithms obtaining great results, there may be still some limitations. For instance, the strength of nodes is ignored. Aiming at this issue, the relative entropy of the distance distribution based similarity measure of nodes is proposed in this paper. At first, the structural weights of nodes are given by integrating their degree and strength. Next, the distance between any two nodes is calculated with the help of their structural weights and the Euclidean distance formula to further obtain the distance distribution of each node. After that, the probability distribution of nodes is constructed by normalizing their distance distributions. Thus, the relative entropy can be applied to measure the difference between the probability distributions of the top d important nodes and all nodes in graph data. Finally, the similarity of two nodes can be measured in terms of this above-mentioned difference calculated by relative entropy. Experimental results demonstrate that the algorithm proposed by considering the strength of node in the relative entropy has great advantages in the most similar node mining and link prediction.     

Reeb posets and tree approximations

A well known result in the analysis of finite metric spaces due to Gromov says that given any metric space $(X,d_X)$ there exists a tree metric $t_X$ on $X$ such that ${|d_X-t_X|}_{\infty }$ is bounded above by twice $\mathrm{hyp}\left(X\right)\cdot log\left(2\left|X\right|\right)$. Here $\mathrm{hyp}\left(X\right)$ is the hyperbolicity of $X$, a quantity that measures the treeness of 4-tuples of points in $X$. This bound is known to be asymptotically tight.We improve this bound by restricting ourselves to metric spaces arising from filtered posets. By doing so we are able to replace the cardinality appearing in Gromov’s bound by a certain poset theoretic invariant which can be much smaller thus significantly improving the approximation bound.The setting of metric spaces arising from posets is rich: For example, every finite metric graph can be induced from a filtered poset. Since every finite metric space can be isometrically embedded into a finite metric graph, our ideas are applicable to finite metric spaces as well.At the core of our results lies the adaptation of the Reeb graph and Reeb tree constructions and the concept of hyperbolicity to the setting of posets, which we use to formulate and prove a tree approximation result for any filtered poset.     

On factor-invariant graphs

We classify trivalent vertex-transitive graphs whose edge sets have a partition into a Hamilton cycle and a 1-factor that is invariant under the action of the full automorphism group.     

Hamiltonian cycles in k-partite graphs

Chen et al determined the minimum degree threshold for which a balanced $k$-partite graph has a Hamiltonian cycle. We give an asymptotically tight minimum degree condition for Hamiltonian cycles in arbitrary $k$-partite graphs in that all parts have at most $n/2$ vertices (a necessary condition). To do this, we first prove a general result that both simplifies the process of checking whether a graph $G$ is a robust expander and gives useful structural information in the case when $G$ is not a robust expander. Then we use this result to prove that any $k$-partite graph satisfying the minimum degree condition is either a robust expander or else contains a Hamiltonian cycle directly.