A novel utilized method of tar derived from biomass gasification for fabricating binder-free all-solid-state hybrid supercapacitors

As an industrial pollutant, tar derived from biomass gasification is used as the precursor for fabricating a novel carbon-metal hydroxides composite electrode. A slurry (the mixture of tar, KOH and melamine) is daubed uniformly onto the nickel foam, which is directly carbonized to form NPC@LDH electrode material. This electrode is further coated with NiCo-LDH nanosheets using an electrodeposition method to form NF@NPC@LDH. The newly made NF@NPC@LDH electrode exhibits a high specific capacity of 9.6 F cm⁻² at a current density of 2 mA cm⁻² and good rate performance (55.3% retention). Furthermore, a hybrid NF@NPC@LDH//NF@PC all-solid-state supercapacitor is fabricated, and the device exhibits high energy density of 1.28 mWh cm⁻³ at a power density of 8.04 mW cm⁻³, low resistance and good cycling stability.     

Qualitative representation of spatial knowledge in two-dimensional space

Various relation-based systems, concerned with the qualitative representation and processing of spatial knowledge, have been developed in numerous application domains. In this article, we identify the common concepts underlying qualitative spatial knowledge representation, we compare the representational properties of the different systems, and we outline the computational tasks involved in relation-based spatial information processing. We also describesymbolic spatial indexes, relation-based structures that combine several ideas in spatial knowledge representation. A symbolic spatial index is an array that preserves only a set of spatial relations among distinct objects in an image, called the modeling space; the index array discards information, such as shape and size of objects, and irrelevant spatial relations. The construction of a symbolic spatial index from an input image can be thought of as a transformation that keeps only a set of representative points needed to define the relations of the modeling space. By keeping the relative arrangements of the representative points in symbolic spatial indexes and discarding all other points, we maintain enough information to answer queries regarding the spatial relations of the modeling space without the need to access the initial image or an object database. Symbolic spatial indexes can be used to solve problems involving route planning, composition of spatial relations, and update operations.     

开流形的曲率与拓扑

应用比较几何的方法研究了完备非紧且具有特定曲率条件的黎曼流形，证明了在一定Pinching条件限制下，流形具有有限拓扑型或者微分同胚于R^n。     

Automating the identification and analysis of protein {beta}-barrels

ßBarrels are widespread and well-studied featuresof a great many protein structures. In this paper an unsuper-visedmethod for the detection of P-barrels is developed based ontechniques from graph theory. The hydrogen bonded connectivityof ß-sheets is derived using standard pattern recognitiontechniques and expressed as a graph. Barrels correspond to topologicalrings in these connectivity graphs and can thus be identifiedusing ring perception algorithms. Following from this, the characteristictopological structure of a barrel can be expressed using a novelform of reduced nomenclature that counts sequence separationsbetween successive members of the ring set These techniquesare tested by applying them to the detection of barrels in anon-redundant subset of the Brookhaven database. Results indicatethat topological rings do seem to correspond uniquely to ß-barrelsand that the technique, as implemented, finds the majority ofbarrels present in the dataset.     

A Novel Topological Index F and Its Correlation With Standard Formaton Enthalpies of AB_n(g) Molecules

The topological index F* is defined and obtained by the method of a non-dimensional unit calculation in which three matrices multiply with each other. These matrices represent the connective cases of atoms in a molecule, the structural features of atoms on top and the bonded cases of the adjacent atoms respectively. The standard formation enthalpies of ABn(g) molecules were correlated with F'* (A = C, Al, Si, Ti, Zr, B = F, Cl, Br, I, H, n=1 -4) and these correlation coefficients are all more than 0.96. Some molecules (e.g CH4, SiH4,etc. )can be preferably handled by F* but can not be dealt with by other topological indices. By contrast to traditional hydrogen suppressed graph, the contribution of hydrogen atoms to structures and properties of molecules is considered.     

模拟电路的模块级故障诊断

本文以多端口元件或子网络为基本单元，提出了一种模块级故障诊断方案，这种方案的故障模型更符合电路的实际情况，因而具有更广泛的实际意义，文中首先推导了模块故障诊断议程，并对模块故障可测性问题进行了一般性讨论。然后，给出了模块故障可测的几个拓扑条件，并通过定义广义独立通路使这些拓扑条件适用于一般的非互易网络。     

光学全交叉网络与SW榕树（F＝S＝2）网络拓扑等价的多样性

本文对有关互连网络拓扑等价的分析方法作了简单评述。并采用互连网络拓扑等价的图分析法分析了自由空间光学互连全交叉网络与ＳＷ榕树网络的拓扑等价及其多样性，拓展了全交叉网络在数字光计算、光子交换及多处理机系统的潜在应用。     

Skeleton chain coding for Chinese characters

The compression of Chinese characters is very important for Chinese office automation and desktop publishing. In this paper, various methods are used to compress 13,051 Chinese characters losslessly by coding their skeleton points. The skeleton points are composed of isolated points and curves. To trace every point in a curve once and consecutively is a NP problem. Therefore, we use contour path following and revisiting algorithms to trace skeleton curves with chain codes. The remaining isolated skeleton points are encoded by an Elias code. Our coding method has a better compression rate than that of conventional skeleton coding.     

连续箱梁桥中跨合龙段劲性骨架优化设计

结合某连续箱梁桥悬臂施工实践,对中跨合龙段劲性骨架设置和受力进行了分析,推导了劲性骨架设计的理论计算公式,并分析了劲性骨架刚度大小对桥梁整体结构的影响。     

TopoAct: Visually Exploring the Shape of Activations in Deep Learning

Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e.combinations of neuron firings, at various layers of the network in response to a particular input. With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. In particular, we develop visualizations that show the shape of the activation space, the organizational principle behind neuron activations, and the relationships of these activations within a layer. Applying tools from topological data analysis, we present TopoAct , a visual exploration system to study topological summaries of activation vectors. We present exploration scenarios using TopoAct that provide valuable insights into learned representations of neural networks. We expect TopoAct to give a topological perspective that enriches the current toolbox of neural network analysis, and to provide a basis for network architecture diagnosis and data anomaly detection.