Optimum design of hierarchical stiffened shells for low imperfection sensitivity

A concept of hierarchical stiffened shell is proposed in this study, aiming at reducing the imperfection sen- sitivity without adding additional weight. Hierarchical stiffened shell is composed of major stiffeners and minor stiff- eners, and the minor stiffeners are generally distributed between adjacent major stiffeners. For various types of geo- metric imperfections, e.g., eigenmode-shape imperfections, hierarchical stiffened shell shows significantly low imper- fection sensitivity compared to traditional stiffened shell. Furthermore, a surrogate-based optimization framework is proposed to search for the hierarchical optimum design. Then, two optimum designs based on two different opti- mization objectives （including the critical buckling load and the weighted sum of collapse loads of geometrically imperfect shells with small- and large-amplitude imperfections） are compared and discussed in detail. The illustrative example demonstrates the inherent superiority of hierarchical stiffened shells in resisting imperfections and the effectiveness of the proposed framework. Moreover, the decrease of imperfection sensitivity can finally be converted into a decrease of structural weight, which is particularly important in the development of large-diameter launch vehicles.     

网状多级孔结构氧化铁的制备、合成机理及其光催化性质

以九水合硝酸铁为原料, 利用改进的聚合诱导胶体聚集(PICA)的方法制备出三维网络状多级孔结构氧化铁(HPH). 此结构的制备关键是在合成过程中尿素和甲醛聚合生成脲醛树脂(UF). 脲醛树脂一方面在铁的羟基氧化物生长过程中与之杂化形成杂化产物Fe-UF, 另一方面则进一步聚合, 形成脲醛树脂微球(UFM). 脲醛树脂微球作为模板诱导杂化产物Fe-UF在其表面的聚集. 微球与微球之间则由于表面存在的脲醛树脂间的聚合会相互交联形成网络状结构. 经过煅烧处理后, 脲醛树脂及脲醛树脂微球的分解导致不同尺寸孔结构的生成. 光催化研究结果表明, 产物对罗丹明B的降解效率是商用纳米氧化铁的4倍.     

Analysis of the effects of the global financial crisis on the Turkish economy, using hierarchical methods

We have analyzed the topology of 50 important Turkish companies for the period 2006-2010 using the concept of hierarchical methods (the minimal spanning tree (MST) and hierarchical tree (HT)). We investigated the statistical reliability of links between companies in the MST by using the bootstrap technique. We also used the average linkage cluster analysis (ALCA) technique to observe the cluster structures much better. The MST and HT are known as useful tools to perceive and detect global structure, taxonomy, and hierarchy in financial data. We obtained four clusters of companies according to their proximity. We also observed that the Banks and Holdings cluster always forms in the centre of the MSTs for the periods 2006-2007, 2008, and 2009-2010. The clusters match nicely with their common production activities or their strong interrelationship. The effects of the Automobile sector increased after the global financial crisis due to the temporary incentives provided by the Turkish government. We find that Turkish companies were not very affected by the global financial crisis.     

Detection of community structure in networks based on community coefficients

Determining community structure in networks is fundamental to the analysis of the structural and functional properties of those networks, including social networks, computer networks, and biological networks. Modularity function Q$Q$, which was proposed by Newman and Girvan, was once the most widely used criterion for evaluating the partition of a network into communities. However, modularity Q$Q$ is subject to a serious resolution limit. In this paper, we propose a new function for evaluating the partition of a network into communities. This is called community coefficient C$C$. Using community coefficient C$C$, we can automatically identify the ideal number of communities in the network, without any prior knowledge. We demonstrate that community coefficient C$C$ is superior to the modularity Q$Q$ and does not have a resolution limit. We also compared the two widely used community structure partitioning methods, the hierarchical partitioning algorithm and the normalized cuts (Ncut) spectral partitioning algorithm. We tested these methods on computer-generated networks and real-world networks whose community structures were already known. The Ncut algorithm and community coefficient C$C$ were found to produce better results than hierarchical algorithms. Unlike several other community detection methods, the proposed method effectively partitioned the networks into different community structures and indicated the correct number of communities.     

Hydrothermal synthesis and characterization of ZnS hierarchical microspheres

In the present work, wurtzite ZnS hierarchical microsphere nanostructures composed of nanowires were synthesized through hydrothermal method. The morphologies and microstructures of the as obtained wurtzite ZnS sample were investigated by scanning electron microscopy and transmission electron microscopy. The results show that the diameter of the nanowires is about 10 nm, the length is about 500 nm, growing along the [0 0 1] direction. UV–visible spectroscopy shows that the band gap of the as obtained ZnS hierarchical microspheres is 3.4 eV. Room temperature photoluminescence measurements reveals a strong green emission peak at around 516 nm. The N₂ adsorption–desorption isotherms experiment at 77 K exhibits that the surface area of the ZnS sample is 99.87 m² g⁻¹.     

Effects of imprecise weighting in hierarchical preference programming

Preference programming is a general term for multi-criteria decision analytical approaches allowing incomplete preference information. In the PAIRS method, interval judgments are assigned to weight ratios between attributes to model imprecision in multi-attribute value trees. This paper studies the effects of a hierarchical model structure on the overall imprecision, as the form of the hierarchy also affects the form of imprecision that can be assigned to the model. The aim is to find out good procedural practices for reducing overall imprecision descending inherently from the model structure. The study provides simulation results about the ability of various weighting schemes to identify dominated alternatives, which are discussed with respect to other issues related to the weighting process. According to the results, a hierarchical model is structurally somewhat more unable to identify dominances than a corresponding nonhierarchical model, but its cognitive advantages often cancel out this. The results also suggest paying reasonable attention to the precision of the lower level judgments and to identifying possible correlations between the criteria.     

A binary particle swarm optimization algorithm inspired by multi-level organizational learning behavior

Recently, nature-inspired algorithms have increasingly attracted the attention of researchers. Due to the fact that in BPSO the position vectors consisting of ‘0’ and ‘1’ can be seen as a decision behavior (support or oppose), in this paper, we propose a BPSO with hierarchical structure (BPSO_HS for short), on the basis of multi-level organizational learning behavior. At each iteration of BPSO_HS, particles are divided into two classes, named ‘leaders’ and ‘followers’, and different evolutionary strategies are used in each class. In addition, the mutation strategy is adopted to overcome the premature convergence and slow convergent speed during the later stages of optimization. The algorithm was tested on two discrete optimization problems (Traveling Salesman and Bin Packing) as well as seven real-parameter functions. The experimental results showed that the performance of BPSO_HS was significantly better than several existing algorithms.     

静电纺丝制备多级结构TiO2微纳米纤维

通过对共轴电纺内流体溶液浓度和流速的调控得到4种具有不同内部结构的TiO₂微纳米纤维, 即具有“微孔”、“囊泡”、“竹节”和“管”状结构的TiO₂微纳米纤维, 实现了对纤维内部结构的有效控制, 对于新型仿生中空纤维的研究具有重要意义.     

以天然植物为模板合成高度有序的多级复合孔材料

筛选出两种孔径较大且孔结构规则的植物为大孔模板, 以嵌段共聚物为介孔相模板成功合成了两种具有连续的骨架和贯通的大孔孔道(大孔孔径大于40 μm, 其孔壁为介孔相)的高度有序多级复合孔材料. 对该复合孔材料进行了水热稳定性研究. 用扫描电子显微镜(SEM)、粉末X射线衍射仪(XRD)、高分辨率透射电镜(HRTEM)以及N₂吸附-脱附等测试手段对合成的样品进行了表征. 结果表明, 合成的产物是孔道相互贯通的多级有序复合孔硅材料, 具有较好的水热稳定性. 采用此合成方法可精确地复制大孔植物模板.     

A hierarchical model for rate-dependent polycrystals

A hierarchical model of a polycrystalline aggregate of rigid viscoplastic grains is formulated, and a robust and efficient computational algorithm for its solution is proposed. The polycrystalline aggregate is modeled as a binary tree. The leaves of the binary tree represent grains, and higher tree nodes represent increasingly larger sub-aggregates of grains. The root of the tree represents the entire polycrystalline aggregate. Velocity and traction continuity are enforced across the interface between the children of each non-leaf node in the binary tree. The hierarchical model explicitly models intergranular interactions but is nevertheless comparable in computational effort to the mean field models of polycrystal plasticity. Simulations of tensile, compressive, torsional, and plane strain deformation of copper lead to predictions in good agreement with experiments, and highlight the interconnection between grain deformations and intergranular constraints. It is inferred from the results that a hybrid mean field/hierarchical model represents a computationally efficient methodology to simulate polycrystal deformation while accounting for intergranular interactions.