Synthesis and Crystal Structure of a Three-dimensional (3D) Complex Mn(H_2O)_2(HNic)_2 (HNic=2-Hydroxynicotinic Acid)

The title complex Mn（H2O）2（HNic）2 （C22H12MnN2O8, Mr = 367.18） crystallizes in monoclinic, space group P21/c with a = 7.5735（8）, b = 12.5295（13）, c = 7.6466（8）A.β = 101.2790（10）°, Z = 2, V= 711.59（13） A^3, D, = 1.714 g/cm^3,μ（MoKa） = 0.974 mm^-1, F（000） = 374, R1 （1255 observed reflections （Ⅰ 〉 2σ（Ⅰ）） = 0.0250） and wR2 = 0.0662 （all data）. In this paper, we report the complexation of Mn（Ⅱ） by the bidentate ligand 2-hydroxynicotinic acid （HNic）. In the crystal the Mn（Ⅱ） ion exhibits a deformed octahedron structure. The title complex Mn（H2O）2（HNic）2 has a three-dimensional （3D） network structure extended by hydrogen bonds, which are formed by two typical eight-membered hydrogen-bonded rings.

Generative deep-learning-embedded asynchronous structured light for three-dimensional imaging

Three-dimensional(3D)imaging with structured light is crucial in diverse scenarios,ranging from intelligent manufacturing and medicine to entertainment.However,current structured light methods rely on projector-camera synchronization,limiting the use of affordable imaging devices and their consumer applications.In this work,we introduce an asynchronous structured light imaging approach based on generative deep neural networks to relax the synchronization constraint,accomplishing the challenges of fringe pattern aliasing,without relying on any a priori constraint of the projection system.To overcome this need,we propose a generative deep neural network with U-Net-like encoder-decoder architecture to learn the underlying fringe features directly by exploring the intrinsic prior principles in the fringe pattern aliasing.We train within an adversarial learning framework and supervise the network training via a statisticsinformed loss function.We demonstrate that by evaluating the performance on fields of intensity,phase,and 3D reconstruction.It is shown that the trained network can separate aliased fringe patterns for producing comparable results with the synchronous one:the absolute error is no greater than 8μm,and the standard deviation does not exceed 3μm.Evaluation results on multiple objects and pattern types show it could be generalized for any asynchronous structured light scene.

Three-dimensionally interconnected Co9S8/MWCNTs composite cathode host for lithium–sulfur batteries

Several challenging issues,such as the poor conductivity of sulfur,shuttle effects,large volume change of cathode,and the dendritic lithium in anode,have led to the low utilization of sulfur and hampered the commercialization of lithium–sulfur batteries.In this study,a novel three-dimensionally interconnected network structure comprising Co9 S8 and multiwalled carbon nanotubes(MWCNTs)was synthesized by a solvothermal route and used as the sulfur host.The assembled batteries delivered a specific capacity of1154 m Ah g-1 at 0.1 C,and the retention was 64%after 400 cycles at 0.5 C.The polar and catalytic Co9 S8 nanoparticles have a strong adsorbent effect for polysulfide,which can effectively reduce the shuttling effect.Meanwhile,the three-dimensionally interconnected CNT networks improve the overall conductivity and increase the contact with the electrolyte,thus enhancing the transport of electrons and Li ions.Polysulfide adsorption is greatly increased with the synergistic effect of polar Co9 S8 and MWCNTs in the three-dimensionally interconnected composites,which contributes to their promising performance for the lithium–sulfur batteries.

Preparation and Crystal Structure of Sarcosine 5-Nitrosalicylic Acid Organic Adduct

The title compound (C10H12N2O7, Mr = 272.22) crystallizes in triclinic, space group P1 with a = 5.532(2), b = 9.760(4), c = 11.731(5) ?, α = 68.107(7), β = 89.179(7), γ = 77.830(7)o, V = 573.1(4) ?3, Z = 2, Dc = 1.578 g/cm3, F(000) = 284 and μ(MoKa) = 0.136 mm-1. The final R = 0.0400 and wR = 0.0951 for 1468 observed reflections with I > 2σ(I). The title compound is a 1:1 adduct of sarcosine and 5-nitrosalicylic acid. The nitrogen atom of sarcosine is protonated, and the proton is from the carboxyl group of sarcosine and 5-nitrosalicylic acid with the probability of 50 percent for each. The 5-nitrosalicylic acid and sarcosine molecule of the title adduct are ABAB arranged along the c axis. There exist a lot of hydrogen bonds in the structure, linking sarcosine and 5-nitrosalicylic acid to form a three-dimensional network.

Hydrothermal Synthesis and Crystal Structure of a New Sandwich-type Molybdenum Phosphate

The crystal structure of the title compound, [enH2][Fe{MoⅤ6O12(OH)3(HPO4)- (H2PO4)3}2]6en6H2O (en = H2NCH2CH2NH2), hydrothermally synthesized from a mixture of Na2MoO42H2O, Fe2(SO4)3, H3PO4, H2N(CH2)2NH2 and water, has been determined by single- crystal X-ray diffraction. The crystal is of triclinic, space group P?with a = 11.9014(1), b = 13.4246(2), c = 13.8719(2) , a = 87.465(1), b = 69.981(1), g = 64.960(1)? V = 1873.46(4) 3, Z = 1, Mr = 2997.89, F(000) = 1466, m = 2.427 mm-1 and Dc = 2.657 g/cm3. The final R = 0.0404 for 5570 observed reflections (I > 2s(I)). The structural analysis reveals that each cluster anion contains two coplanar {Mo6} rings of six edge-sharing Mo(O5OH) octahedra, and the two {Mo6} rings are linked together through one octahedral FeⅡ ion to generate a sandwich-type cluster with rigorous () symmetry. Moreover, these clusters are further linked into a three-dimensional frame- work by hydrogen bonds.

Synthesis and Crystal Structure of O-Ethyl-N- (2,3,4-tri-O-acetyl-β-D-xylopyranosyl)-thiocarbamate

The crystal and molecular structures of O-ethyl-N-(2,3,4-tri-O-acetyl-β-D-xylopyranosyl)-thiocarbamate were determined by X-ray crystallography. It crystallizes in the orthorhombic system with space group P2(1)2(1)2(1), lattice parameters a=0.90636(18) nm, b=0.94716(19) nm, c=2.1855(4) nm, V=1.8762(7) nm 3, and Z=4. All the substituents are in equatorial positions. There are four intramolecular interactions, each forming a five-membered ring. The molecules are linked by interactions to form three-dimensional framework. Atoms O6 and O8 show positional disorder.

圆盘体系在各向同性压缩下的接触结构及晶格化

结合离散元模拟及接触网络的统计分析对双分散圆盘体系在各向同性压缩下的接触结构及其晶格化进行研究。离散元模拟中通过改变大小颗粒的粒径比控制分散程度,使得体系成为单分散或者近似单分散的体系。用接触类型的百分比描述接触结构的统计特征。给出了体系的压强以及各种接触类型的百分比随堆积分数变化的曲线,研究了摩擦与粒径比对这些曲线的影响,并且着重讨论了摩擦和单分散程度对晶格结构的影响。模拟结果表明,随着圆盘数的增多,临界体积分数逐渐增大;随着摩擦系数与粒径比的增大,临界体积分数逐渐减小。摩擦与单分散程度是影响接触结构晶格化的主要因素。对于单分散体系,当摩擦系数较小时,体系在持续的压缩过程中容易形成三角晶格团簇;随着摩擦系数的增大,体系晶格化程度减小;当摩擦系数大于0.8以后,很难形成三角晶格团簇。对于近似单分散的体系,只有单分散程度很高时,压缩过程的持续才会导致明显的晶格化;随着粒径比的增大,晶格结构难以出现,体系通常形成无序堆积。

压缩试验中的土颗粒接触网络分析

传统连续介质力学本质上属于宏观统计的力学理论,它不涉及颗粒介质的细观结构。而对于颗粒介质来说,具有典型的多尺度结构,仅凭宏观唯象描述不足以概括其复杂物理力学性质。使用离散元法,进行散粒体二维压缩试验,将复杂网络理论引入颗粒介质的研究中,通过复杂网络手段分析散粒体接触网络中的最大簇、配位数、网络直径等参数变化,进一步分析研究压缩试验中颗粒内部的力学特性。

The three-dimensional structure and the relationship between external and internal vascularizations in the brain of rat embryos

Background There have been no detailed reports of the three-dimensional structure and the relationship between the external and internal vascularizations observed successively for a long duration in the rat fetus, although many authors have studied the vascular morphology of the developing brain. This study examined the three-dimensional structure of both the external and internal vascularizations of the prenatal rat telencephalon from embryonic days 12 (E12) to 20 (E20).Method A microvascular casting method for scanning electron microscopy (SEM) was used in this study, along with vascular staining using gold-gelatine solution-autometallography (GGS-AMG) after intravascular injection of colloidal gold, as well as hematoxylin-eosin ( HE) staining for paraffin embedded specimens.Results In GGS-AMG stains, E16 fetuses had a few short perforating cortical blood vessels (SPCVs); E17 fetuses had long perforating cortico-medullary vessels (LPCVs). Older fetuses had specific patterns of vascular networks in the cortex and the deeper subcortical part of the telencephalon. In the cortex, fine longitudinal blood vessels were connected by transverse channels. The deep telencephalon had fine blood vessels running in all directions. Using SEM, the external vascularization was already visible in E12 fetuses as arborizations of arterial branches, forming a mesh of fine vascular networks covering the telencephalon. A coralliform fine venous plexus was observed in the external vascularization of E16 fetuses. There were ring-like anastomoses and bud-like protrusions in the network of small blood vessels, most likely the angiogenesis of fetal vessels. From E12 to E16, an immature and incomplete internal vascularization began to appear. There were short blood vessels with ballooned terminals branching from the external vascularization. They penetrated the brain tissue to form networks in the superficial layer, comparable to SPCVs. In E17 to E20 fetuses, tortuous venous branches, straight arterial blood vessels, and a fine network of small blood vessels formed the external vascularization. There were fewer arterial than venous branches connecting to the fine networks of small blood vessels. LPCVs were noted at E17, at the time the white matter emerged. They branched from the external vascularization, and perpendicularly penetrated the brain surface, traversing the cortical plate, and entering into the deep brain. At E17, arterial and venous blood vessels could be clearly distinguished in the external vascularization. At E20, the cortex and white matter contained specific arrangements of networks of fine blood vessels, as seen by GGS-AMG staining.Conclusion These findings show that the development of both the external and internalvascularization follows the development of thetelencephalon. In particular, the emergence ofthe cortical plate and white matter on E16 andE17 influence the development of both theinternal and the external vascularization. Thelaminal arrangement of blood vessels was notobserved corresponding to the respective laminalneuronal layers.

基于多粒度聚类和多元特征统计的低压配电网拓扑识别与监测

针对低压配电网中用户的户变关系及相位信息常存在错误且变动较为频繁的现象,提出了一种基于多粒度聚类和多元特征统计的低压配电网拓扑识别与监测方法,包含拓扑聚类识别和拓扑统计监测2个阶段。在拓扑聚类识别阶段,基于用户电压波动曲线的α-峭度和α-偏度提取数据粗粒度空间特征,采用密度峰值聚类算法识别所有用户的户变关系;在数据细粒度特征空间,通过考虑延迟效应的动态时间弯曲距离算法优化密度计算过程,实现相位关系的精确聚类。在拓扑统计监测阶段,基于多粒度聚类结果,采用邻域保持嵌入算法建立多元统计监测模型,实现新增用户或拓扑有变化的个别用户的拓扑快速识别。实际算例的分析结果验证了所提方法的有效性。

加卸载状态下散粒体力链结构的复杂网络分析

岩土类散粒体在加卸载过程中会呈现出不可逆的力学行为,可从复杂网络参数演化角度分析散粒体加卸载细观机制。采用离散单元法,进行散粒体加载―卸载―再加载过程的三轴数值模拟,并分析了加卸载过程中散粒体的宏观应力-应变演化及其对应的不同强接触体系的复杂网络特征。研究表明:散粒体的应力-应变曲线在加卸载过程中形成“滞回环”,在微观配位数和聚类系数上亦表现出了不可逆行为;采用不同阈值划分不同加卸载状态的强弱接触体系,随着阈值的增大,各应力状态对应的细观结构差异减小;强接触体系的最大簇在特定阈值区间内达到贯穿边界的极限,其几何特征与拓扑特征反映了散粒体加卸载过程的不可逆变化。

土颗粒材料中的复杂网络结构演化分析

复杂网络分析方法可应用于土颗粒材料在加载过程中的结构演化分析以及搭建土颗粒材料的微细观与宏观尺度上力学参数之间的联系。主要叙述了我们近十年来采用复杂网络方法分析土颗粒材料的结构演化以及细观参数与宏观力学特性之间的相互关系方面的研究成果。首先介绍了复杂网络中的基本概念(平均度、聚类系数、平均路径长度、力圈);然后分析了双轴条件下的土颗粒材料的细观结构演化以及土坡失稳启动与滑动过程的接触网络参数变化规律;再者分析了考虑颗粒形状、中主应力系数、循环加载路径复杂加载条件下的土颗粒材料的细观结构演化规律;最后,把接触网络的细观结构参数与宏观力学特性相关联,建立了状态参数与平均度、细观结构参数与动强度、平均度和最短路径长度与修正剑桥模型的屈服函数、平均度与基础垂直压力之间的关系,并基于最大流修改了统一硬化模型中参数的取值。

基于社团结构的多粒度结构洞占据者发现及分析

目前研究者已提出一些基于社团结构的结构洞发现方法,然而不同粒度下社团划分结果使网络呈现层次化结构,影响社团结构中节点跨越结构洞的程度。本文基于网络社团划分思想提出一种分层网络的结构洞发现方法MG_Max D。首先,使用分层递阶社团划分算法(本文使用EAGLE算法),得到不同粒度的社团结构;然后,使用结构洞发现算法MG_Max D得到不同粒度下的结构洞占据者;最后,使用结构洞跨越程度指标分析不同粒度下的社团结构对节点跨越结构洞程度的影响。在公用和真实数据集上的实验结果表明节点跨越结构洞的程度即结构洞节点的优势将随着粒度的变细而增大。

网络化指挥控制的社团结构挖掘方法

对复杂网络的社团结构进行数据挖掘有助于宏观把握复杂系统的主体倾向和骨干联系。在构建网络化指挥控制模型的基础上,从复杂性和动态性特征出发,提出基于局部信息检测的多粒度社团挖掘方法。实验分析结果表明,该方法有效、可行,为多视角观察网络化作战条件下的指挥控制提供了新的研究思路和分析方法。

光纤智能结构的传感网络敏感区域探测

实用的传感网络控制决策以及相应的控制系统保证了智能结构的使用可靠性,而传感网络敏感区域的确定影响到传感网络信号的有效性和传感网络覆盖位置的正确性。本文利用了与光纤智能结构匹配性较好的粗糙集理论粒度计算方法,对光纤智能结构的传感器网络敏感区域进行探测。在研究了机翼盒段光纤智能夹层传感网络后,得出了敏感区域分布和传感网络的覆盖位置,经过分析认为,此法可对光纤智能结构传感网络敏感区域进行探测并可以简省传感网络信号。

二维空间聚类的树ART2模型

ART2网络是一种著名的聚类方法,已实际应用于诸多领域,其作用于二维空间数据,不仅存在模式漂移和向量幅度信息缺失的问题,而且难以适应不规则形态分布的空间数据的聚类。提出了一种树ART2网络模型(TART2),通过长期记忆(LTM)模式的调整和向量幅度信息的学习,使ART2网络保持了带空间距离约束的旧模式记忆;引入树结构优化,降低了警戒参数设置的主观要求,减少了模式交混现象的发生。对比实验结果表明,TART2网络更适用于带状分布的空间数据聚类,具有较高的可塑性和自适应性。

BGrR:基于粒计算的大规模网络路径的加速方法

大规模网络路径问题是社会网络信息处理的基本问题。将粒计算方法引入到大规模网络研究中,结合社会网络分层和社团结构性质建立网络的多粒度层次模型,实现网络的多粒度存储,将大规模网络复杂结构映射到不同粒度空间中。为了降低问题求解的复杂度,将最短路径问题映射到不同粒度空间中,将搜索过程从粗粒度空间向细粒度空间跳转以搜索路径信息,提出基于多粒度空间的最短路径搜索算法(BGrR)来加速大规模网络路径搜索。在实验中,以城市道路交通网络为数据源,通过与A*和ALT方法比较,验证了所提算法的有效性。

基于局部信息检测的多粒度社团挖掘方法

从复杂性和动态性特征出发,给出了复杂网络局部模块度的定义,并提出了基于局部信息检测的社团发现算法,认为局部模块度值最大的节点集合就是最理想的社团结构。在此基础上提出了多粒度社团挖掘方法,为多视图观察复杂网络结构特征提供了新的研究思路。最后的实验分析表明了方法的有效性和可行性。

网络结构分析的粒计算

网络结构分析是人工智能领域基本问题。应用粒计算方法讨论了网络结构信息计算,从粒计算基本问题角度,采用商空间理论研究了网络结构粒化和粒化后不同粒度空间中的问题,特别是基于粒化如何计算不同粒层的粒间距离问题。应用方面,讨论了大规模网络结构最短路径搜索问题。作为大规模网络路径分析的预处理方法,选择社团作为基本粒,将大规模网络粒化到不同的粒度空间,形成不同粒度商空间的分层递阶粒度链。提出了基于分层递阶粒度链的大规模网络的启发式路径搜索方法。与A*和ALT方法进行了比较,验证了粒计算方法的有效性。

分层递阶的网络结构洞占据者挖掘及分析

结构洞是在社会网络信息传播中占据重要位置的一类关键节点。据研究,5%的结构洞控制着50%的信息传播。学者们研究了单一粒度网络下结构洞的挖掘方法及分析,然而很多网络存在分层递阶的多粒度结构特性,对分层递阶网络的结构洞挖掘和分析具有现实意义。因此,该文提出了一种分层递阶网络的多粒度结构洞挖掘方法 HI-SH,并对不同粒度下的结构洞进行了分析。在该方法中,首先对网络进行多粒度社团划分,得到每一粒度下网络的社团;然后,根据两级信息传播理论,使用单一粒度下结构洞挖掘算法,挖掘每一粒度下top-k结构洞。在公用数据Topic16和真实数据上进行了实验,结果表明,网络的结构洞是动态变化的,单一粒度下的结构洞排名不能代表整个网络的结构洞排名。