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.

Structure learning on Bayesian networks by finding the optimal ordering with and without priors

Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.

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.

Vibration suppression for rotating space slender flexible structures based on novel deformation description and NNSMC controller with hyperbolic tangent function

Rotating Space Slender Flexible Structures(RSSFS)are extensively utilized in space operations because of their light weight,mobility,and low energy consumption.To realize the accurate space operation of the RSSFS,it is necessary to establish a precise mechanical model and develop a control algorithm with high precision.However,with the application of traditional control strategies,the RSSFS often suffers from the chattering phenomenon,which will aggravate structure vibration.In this paper,novel deformation description is put forward to balance modeling accuracy and computational efficiency of the RSSFS,which is better appropriate for real-time control.Besides,the Neural Network Sliding Mode Control(NNSMC)strategy modified by the hyperbolic tangent(tanh)function is put forward to compensate for modeling errors and reduce the chattering phenomenon,thereby improving the trajectory tracking accuracy of the RSSFS.Firstly,a mathematical model for the RSSFS is developed according to the novel deformation description and the vibration theory of flexible structure.Comparison of the deformation accuracy between different models proves that the novel modeling method proposed has high modeling accuracy.Next,the universal approximation property of the Radial Basis Function(RBF)neural network is put forward to determine and compensate for modeling errors,which consist of higher-order modes and the uncertainties of external disturbances.In addition,the tanh function is proposed as the reaching law in the conventional NNSMC strategy to suppress driving torque oscillation.The control law of modified NNSMC strategy and the adaptive law of weight coefficients are developed according to the Lyapunov theorem to guarantee the RSSFS stability.Finally,the simulation and physical experimental tests of the RSSFS with different control strategies are conducted.Experimental results show that the control law according to the novel deformation description and the modified NNSMC strategy can obtain accurate tracking of the rotation and reduce the vibration of the RSSFS simultaneously.

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.

Towards dynamic structure of biological complexes at atomic resolution by cryo-EM

Cryo-electron microscopy makes use of transmission electron microscopy to image vitrified biological samples and reconstruct their three-dimensional structures from two-dimensional projections via computational approaches. After over40 years of development, this technique is now reaching its zenith and reforming the research paradigm of modern structural biology. It has been gradually taking over X-ray crystallography as the mainstream method. In this review, we briefly introduce the history of cryo-EM, recent technical development and its potential power to reveal dynamic structures. The technical barriers and possible approaches to tackle the upcoming challenges are discussed.

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.

黄河上游省区旅游经济网络结构时空演变及影响因素——以宁夏回族自治区为例

探索干旱与半干旱区旅游经济发展情况,对指导黄河上游省区宁夏旅游可持续与高质量发展有一定参考价值。以2010年、2015年、2019年为时间截面,基于修正的引力模型结合社会网络分析法分析了宁夏旅游经济网络结构演变的时空特征。结果发现:(1)宁夏呈现出以省域旅游核心城市辐射带动区域周边城市的网络空间格局,形成以银川为中心,以中卫市、吴忠市、石嘴山市紧密连接的斜三角型旅游经济网络空间;(2)宁夏区域旅游发展的联系逐渐从单极化向多级化演变,中介城市日渐增多,核心城市银川不断巩固发展,中介城市从银川市逐渐增多为中卫市、吴忠市;(3)宁夏整体旅游经济联系日渐紧密,各区域之间的联系与合作不断变化,呈现出空间秩序重构的发展态势。最后,从地理环境、交通距离、资源禀赋、政策支持4个方面分析了影响宁夏旅游经济网络空间结构演化的原因。

中国工业高质量发展的空间网络结构研究

为优化我国工业高质量发展空间格局,促进区域工业协同发展,研究采用时空极差熵权法、社会网络分析法揭示我国工业高质量发展的空间网络结构特征与演化规律。结果表明:我国工业高质量发展水平在时间维度上呈波动上升趋势,在空间维度上呈“极化与均衡并存”的格局;技术创新、工业发展基础和开放发展是推动我国工业高质量发展的核心动力;整体网络结构较为稳健、密度较低,西藏、新疆被孤立在空间关联网络之外,但发展趋势向好;江苏、河南等省份是空间网络中的中心点和关键行动者,发挥辐射带动作用,陕西、重庆等省份起重要连通作用;各区域内部关联性较强,但区域间关联有待提高。最后为提升全域工业高质量发展水平及促进省域工业高质量协同发展提出相关政策建议。

电商背景下长江经济带快递物流多尺度空间结构研究——基于“流空间”视角

如何基于同一网络量纲,剖析不同空间尺度下区域“流”的网络结构及其相互作用,进而探索网络的影响因素,是当前城市物流网络领域的研究方向和难题。本文基于菜鸟运输市场平台“流动”联系数据,尝试从部分到整体的多尺度空间视角出发,通过ArcGIS、社会网络分析法和QAP分析法探索长江经济带全域130个城市及其内部城市集群的快递物流网络空间特征及驱动因素。结论表明:①长江经济带快递物流网络密度较高,但联系强度空间差异性显著,“等级扩散”效应突出,形成以核心城市群为主的快递物流廊道;②高集散规模物流输出型城市主要集中在经济带下游城市群,低集散规模物流输入型城市主要集中在上游城市群,中游城市群则较为均衡;③QAP分析结果表明,不同空间尺度下区域快递物流网络成因存在差异,但快递“流”总体倾向于在经济、消费较为发达,电商、互联网水平较高,快递物流基础设施较为完善的城市间流动。

传统家族型聚落社会空间关联性研究——以东莞塘尾村为例

以广东省东莞市传统家族型聚落塘尾村为例,引入空间句法并以户为空间基本单元,定量描述空间的集聚效应、观察空间单元的结构性特征。弥补既有研究对空间的单元、界域、中心界定较模糊等问题。从社会网络和空间结构关联视角,探讨地域文化和宗族架构与空间的关联,族群内权力等级差异、职能调整和角色转变等对社会空间的影响以及外部社会事件和人物个性对空间的作用等。结合相关性分析,总结和推演以塘尾村为例的东莞地区传统家族型聚落社会空间转换模式及其影响机理,为更好地延续和振兴地方传统聚落提供新思路和新方法。

面向无人作业的天空地协同网络:需求、挑战、技术变革机遇

在偏远、危险地区,机器人可感知环境、智能交互,协同完成复杂任务。以卫星和无人机为主体的天空地协同网络能够为这些机器人提供全域泛在、随遇接入的通信服务。首先,分析无人作业发展趋势和机器人通信需求。基于此,面向机器人通信的特点,建议跳出“信息传输管道”的传统视角,在支持信息传输的同时,关注信息的内容和价值。进一步综合考虑机器人通信中信息生成的动作及其反馈,提出潜在技术变革的切入点。具体地,从结构化通信资源配置、机器人行为调控和感-传-算-控闭环模型三个方面阐述维纳控制论启发的通信范式变革途径。

大跨度空间网架安装方案比选与施工技术研究

为了保证大跨度空间钢网架安装的安全性、经济性和高效性,以实际工程为例,通过对钢网架安装方案进行分析与比选,确定最优方案,并结合施工过程进行验算,顺利地完成了施工。

MC-NAS:一种可视化模块贡献神经架构搜索方法

现有的神经架构搜索方法无法直观地将网络模型与候选模块以及模型识别准确率之间的关系展示出来;同时很多NAS方法可扩展性差,无法将其搜索策略扩展至任意搜索空间。针对上述挑战,提出了一种可视化模块贡献神经架构搜索方法。提出了模块贡献这个概念,并通过分析贡献计算过程的窘境给出了任意搜索空间下的统一采样原则,利用统一的贡献度指导原则给出了不同搜索空间的贡献度计算策略。针对特定的约束条件通过动态网络规划算法生成神经网络体系结构。大量的实验结果表明该算法在任意搜索空间中的有效性。使用CIFAR-10、CIFAR-100和ImageNet16-120数据集在NAS-Bench-201基准测试上平均准确率达到了93.33%、71.07%、42.69%。

大都市圈人口空间组织特征研究:基于地理和网络圈层嵌套视角

厘清大都市圈的人口分布特征及问题是推动都市圈高质量一体化的重要前提。文章基于都市圈的圈域结构理论,借助空间分析技术和社会网络分析方法,从地理圈层和网络圈层视角对中国上海和北京两个典型大都市圈的人口空间组织特征进行对比分析。结果表明:(1)上海大都市圈人口分布呈现多“星火”团簇式集聚,北京则单中心人口集聚模式凸显;(2)从地理圈层视角比较人口规模发现,北京、上海两大都市圈核心区相对于东京和纽约大都市圈人口过密,但人口增长率降低,疏解核心区非核心功能有显著成效,中间和外围圈层显示出较高的人口承载空间;(3)建立人口的空间关联探究两大都市圈人口的网络圈层结构发现,上海大都市圈整体人口关联性更强,板块间互动更密;(4)将网络圈层人口流动嵌套于地理圈层发现,人口高频流动板块均位于两大都市圈的核心圈层,但上海大都市圈圈层内部成员和圈层间联系较北京大都市圈更为紧密。

厦门市旅游流网络结构研究

基于携程旅行和去哪儿网站发布的游客旅行游记数据,以厦门市作为研究区域,通过社会网络分析法,揭示厦门市旅游流整体网络结构特征。结果表明:出游厦门市的游客以“中长假期”为主,停留时间较长。从停留时间来看,四日游占比高于其他时间,本地长途游很受青睐;在对厦门市36个旅游节点的研究中,旅游流网络结构呈不均衡分布,核心景点作用明显;厦门大学在旅游流网络中占据的流量比重大,处于核心位置;海湾公园、鹭江道、日光岩等景点则处于边缘地带。因此,应完善厦门市的旅游流网络结构,实现厦门市的旅游优质化发展。

基于注意力卷积长短时记忆模型的城市出租车流量预测

为解决城市交通拥堵和安全问题,提出一种注意力卷积长短时记忆(ConvLSTM)残差(ACLR)模型,该模型通过结合ConvLSTM、注意力机制和残差结构,分别处理出租车流量的时间、空间、和其他特征,挖掘区域兴趣点(POI)数据对出租车流量的影响,有效提升交通时空特征的提取能力。同时,引入专门的学习元件考虑外部因素和POI密度对交通流量的影响,并利用北京市出租车轨迹数据验证。结果表明:ACLR模型在城市交通流预测中的精度高于差分自回归滑动平均(ARIMA)模型、长短时记忆(LSTM)网络、深度时空残差网络(ST-ResNet)、卷积神经网络(CNN)-残差神经单元-LSTM(CRL)循环神经网络、ACFM等模型,在无POI密度和考虑POI密度的情况下,均有助于提升模型的预测性能,ACLA模型的预测值与真实值基本一致,高峰时段也能与真实值较好地吻合,有效提升交通时空特征的提取能力,降低预测误差,使得交通流量预测性能得到优化。

基于社会网络分析法的传统村落公共空间结构优化研究——以怀化高椅村为例

以怀化市高椅村为例,运用社会网络分析法,借助UCINET软件,构建传统村落公共空间结构网络模型,对空间结构特征进行综合量化分析。结果表明:高椅村整体公共空间网络结构松散,系统连通性较差,小世界特征不明显,部分节点具有边缘化特征;从空间节点的关联性看,各个节点之间的关联性不够,个别节点出现了“脱节”的现象;从空间布局均衡性看,各节点中心性差异较大;从空间集群分布看,整体空间聚集群联系较弱。在此基础上,从整体布局、片区互动、单点品质、空间建设4个方面提出村落公共空间优化策略。