Urban road network evolution mechanism based on the ‘direction preferred connection’ and ‘degree constraint’

The urban road network is a complex system that exhibits the properties of self-organization and emergence. Recent theoretical and empirical studies have mainly focused on the structural properties of the urban road networks. This research concentrates on some important parameters such as degree, average degree, meshedness coefficient, betweeness, etc. These parameters of the real road network exhibit specific statistical properties. Some studies show that perhaps these specific statistical properties are caused by a compromise mechanism of the formation of a minimum spanning tree and the greedy triangulation. Inspired by these results, we propose a principle to construct the network (we call it a MG network in this paper) whose structure is located between the minimum spanning tree and the greedy triangulation at first. The structural properties of the MG network are analyzed. We find the formation mechanism of the MG network cannot explain the urban road network evolution well. Then, based on the formation mechanism of the MG network, we add the ‘direction preferred connection’ and ‘degree constraint’ principles to the urban road network evolution simulation process. The result of the simulation network turns out to be a planar network that is in accordance with reality. Compared with the real road network’s structural properties, we find the simulation results are so consistent with it. It indicates the validation of the model and also demonstrates perhaps the ‘direction preferred connection’ and ‘degree constraint’ principle can explain the urban road network evolution better.     

Overlapping community detection using a generative model for networks

Detecting overlapping communities is a challenging task in analyzing networks, where nodes may belong to more than one community. Many present methods optimize quality functions to extract the communities from a network. In this paper, we present a probabilistic method for detecting overlapping communities using a generative model. The model describes the probability of generating a network with the model parameters, which reflect the communities in the network. The community memberships of each node are determined based on a probabilistic approach using those model parameters, whose values can be obtained by fitting the model to the network. This method has the advantage that the node participation degrees in each community are also computed. The proposed method is compared with some other community detection methods on both synthetic networks and real-world networks. The experiments show that this method is efficient at detecting overlapping communities and can provide better performance on the networks where a majority of nodes belong to more than one community.     

NON-DESTRUCTIVE EVALUATION OF HORIZONTAL CRACK DETECTION IN BEAMS USING TRANSVERSE IMPACT

Numerical and experimental studies for crack detection in beam employing transverse impact are presented. In the numerical study, a beam model of wave propagation is developed to calculate the time history of beam response before, over and after the crack region. It is expected that the resulting wave in the beam will be scattered by the crack and will carry information on the location and geometry of the crack. Experiments using a scanning laser vibrometer on specimens containing simulated crack are then conducted to verify the numerical results. Comparison study between the numerical results and experimental observations are conducted; good correlation between theory and experiment is observed. The beam model of wave propagation and adaptive multilayer perceptron networks (MLP) are then used for inverse identification of crack parameters (i.e., crack location, depth and length) in the beams. Time-domain displacement responses calculated using the present beam model containing predetermined crack parameters are used as training data for the MLP. Once the MLP is trained, the MLP networks are then employed for inverse determination of an unknown crack in a beam using experimental displacement responses measured with a scanning laser vibrometer. Examples show that the procedure performs well for the determination of a wide range of values for the location, depth and length of the crack.     

FitzHugh-Nagumo神经元网络的联想记忆与分割

以广泛讨论的Fitz Hugh-Nagumo神经元节点组成脉动神经元网络,从神经系统空时模式编码理论研究网络的记忆(或模式)存储与时间分割问题.给定一个输入模式,它是几种模式的叠加,网络能够以一部分神经元同步发放的形式一个接一个地分割出每一种模式.如果输入的模式有缺损,系统能够把它们恢复成原型,即神经网络的联想记忆功能.模拟需要调节耦合强度和噪声强度等参数使得网络在特定的参数值和中等强度噪声达到最优的时间分割,与广泛讨论的随机共振现象一致.     

A dynamic model for traffic network flow

Concerning the link properties in traffic networks, we introduce a dynamic equation of road flow into each link, and thereby propose a dynamic model for network flow. Using this model, we investigate the evolutions of inflow, outflow and flow on each link caused by a small perturbation of the network inflow under different route choice rules. Numerical results show that the dynamic model can reasonably capture the basic characteristics of network flow.     

Dynamic optical visualization on the interaction between propagating crack and stationary crack

Dynamic interactions between the propagating crack and the static crack in PMMA material are studied by combining high-speed Schardin camera with optical caustic method. A series of dynamic optical bifocal patterns (the specimen-focused image and the off-focused image) around the propagating crack tip and the static crack tip are recorded for PMMA thin strip which contains two collinear-edge-cracks subjected to tensile loading, the variations of the caustic diameter and the distortion of the caustic shape are revealed due to the influence of local stress singularity at the crack tip. Interactions between the moving crack and the static crack are analyzed by means of the evolution of dynamic fracture parameters. The influence of crack interaction on fracture parameters is discussed based on both a K-dominance assumption and a higher order transient crack-tip expansion. These results will be useful to the evaluation of dynamic properties and the design of structures in the cracked polymer material.     

Automatic ridgelet image enhancement algorithm for road crack image based on fuzzy entropy and fuzzy divergence

True estimation of the boundary of a road crack and its size is a major task for its automatic detection. The improvement of visual effects of a road image is necessary for such a task. Therefore, we propose an automatic ridgelet image enhancement algorithm. A nonlinear function plays an important role in the enhancement algorithm in the ridgelet domain of an image. However, it is difficult to adjust the parameters of the nonlinear function adaptively with the variation of the road crack image input. Based on the fuzzy entropy criterion, we introduce two fuzzy divergences and two supplementary linear combinations between the fuzzy entropy and two fuzzy divergences as new measurements to solve the threshold segmentation problem in the ridgelet domain. According to the distribution histogram of magnitudes of the ridgelet high-frequency coefficients, we obtain the optimal segmentation thresholds that act as the parameters of the nonlinear function by using the maximum or minimum measurements of fuzzy entropy and fuzzy divergence, respectively. The self-adaptive nonlinear function makes it possible to realize the automatic enhancement of a road crack image. Experimental results show that our image enhancement algorithm can effectively enhance the global and local contrastive effects on road crack images.     

有倾向性重连产生的反匹配网络

在具有网络结构的系统中度关联属性对于动力学行为具有重要的影响, 所以产生适当度关联网络的方法对于大量网络系统的研究具有重要的作用. 尽管产生正匹配网络的方法已经得到很好的验证, 但是产生反匹配网络的方法还没有被系统的讨论过. 重新连接网络中的边是产生度关联网络的一个常用方法. 这里我们研究使用重连方法产生反匹配无标度网络的有效性. 我们的研究表明, 有倾向的重连可以增强网络的反匹配属性. 但是有倾向重连不能使皮尔森度相关系数下降到-1, 而是存在一个依赖于网络参数的最小值. 我们研究了网络的主要参数对于网络度相关系数的影响, 包括网络尺寸, 网络的连接密度和网络节点的度差异程度. 研究表明在网络尺寸大的情况下和节点度差异性强的情况下, 重连的效果较差. 我们研究了真实Internet网络, 发现模型产生的网络经过重连不能达到真实网络的度关联系数.     

Developing a Durable Quiet Road Surface

Road traffic noise can have a significant impact on the quality of life for residents close to major road networks. One of the most effective measures for reducing the noise from road traffic, particularly on high-speed roads, is to ensure the use of a low noise road surface. Research on pavement construction and the measurement of its acoustic properties has shown that significant noise reductions can be achieved through the use of certain road surface types. However certain low noise road surfaces do not exhibit the desired durability associated with more traditional pavements, leading to costly and disruptive maintenance regimes.
This article looks at the mechanisms involved in tyre/road noise generation and how these interact with various road surface properties including a brief overview of some common surface types. It then goes on to explain how these concepts informed the development of an asphalt surfacing material with enhanced durability and good acoustic performance without compromising safety. Progress in testing the resulting Premium Asphalt Surfacing System (PASS) is outlined, including the completion of a successful network trial.     

Pattern formation and selection in quasistatic fracture

Fracture in quasistatically driven systems is studied by means of a discrete spring-block model. Developed from close comparison with desiccation experiments, it describes crack formation induced by friction on a substrate. The model produces cellular, hierarchical patterns of cracks, characterized by a mean fragment size linear in the layer thickness, in agreement with experiments. The selection of a stationary fragment size is explained by exploiting the correlations prior to cracking. A scaling behavior associated with the thickness and substrate coupling, derived and confirmed by simulations, suggests why patterns have similar morphology despite their disparity in scales.     

Assortative mixing in networks

A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. Here we measure mixing patterns in a variety of networks and find that social networks are mostly assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortatively mixed network, which we study both analytically and numerically. Within this model we find that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.     

担载ZnTHPP的TiO2杂化材料的制备及结构表征

采用溶胶-凝胶(Sol-gel)法将四(对-羟基)苯基锌卟啉(ZnTHPP)担载于TiO2机网络中,得到了一种新的有机/无机杂化材料.利用热重(TG)、红外光谱(FTIR)、紫外(UV)及固体漫反射光谱(DRS)、扫描电镜(SEM)对杂化材料的结构和形貌进行了表征,结果表明ZnTHPP/TiO2杂化材料具有良好的热稳定性,锌卟啉以化学键的形式与TiO2结合,并且锌卟啉的加入有效地拓宽了TiO2的可见光吸收范围.     

Decentralised control of material or traffic flows in networks using phase-synchronisation

We present a self-organising, decentralised control method for material flows in networks. The concept applies to networks where time sharing mechanisms between conflicting flows in nodes are required and where a coordination of these local switches on a system-wide level can improve the performance. We show that, under certain assumptions, the control of nodes can be mapped to a network of phase-oscillators.By synchronising these oscillators, the desired global coordination is achieved. We illustrate the method in the example of traffic signal control for road networks. The proposed concept is flexible, adaptive, robust and decentralised. It can be transferred to other queuing networks such as production systems. Our control approach makes use of simple synchronisation principles found in various biological systems in order to obtain collective behaviour from local interactions.     

Analyzing neuronal networks using discrete-time dynamics

We develop mathematical techniques for analyzing detailed Hodgkin-Huxley like models for excitatory-inhibitory neuronal networks. Our strategy for studying a given network is to first reduce it to a discrete-time dynamical system. The discrete model is considerably easier to analyze, both mathematically and computationally, and parameters in the discrete model correspond directly to parameters in the original system of differential equations. While these networks arise in many important applications, a primary focus of this paper is to better understand mechanisms that underlie temporally dynamic responses in early processing of olfactory sensory information. The models presented here exhibit several properties that have been described for olfactory codes in an insect’s Antennal Lobe. These include transient patterns of synchronization and decorrelation of sensory inputs. By reducing the model to a discrete system, we are able to systematically study how properties of the dynamics, including the complex structure of the transients and attractors, depend on factors related to connectivity and the intrinsic and synaptic properties of cells within the network.     

Linear and non-linear dielectric properties of a short-pitch ferroelectric liquid crystal stabilized by a polymer network

Linear and non-linear dielectric measurements were carried out on a ferroelectric liquid crystal stabilized by an anisotropic polymer network. The polymerization process was achieved at room temperature. It was performed from an achiral monomer in the ferroelectric chiral smectic C phase, exhibiting a very short helical pitch and a large polarization. The linear and non-linear dielectric spectroscopy were also completed by textural morphology as well as structural and ferroelectric characterizations. All these measurements were carried out on a pure ferroelectric liquid crystal material and on composite films containing two polymer concentrations. The increase of the polymer network density leads to a decrease of the dielectric strength determined in the linear and non-linear dielectric spectroscopy. The complementarity between the linear and non-linear dielectric measurements and their confrontation with a theoretical model allowed the simultaneous determination of some physical parameters such as macroscopic polarization, rotational viscosity and twist elastic energy. We also discuss the effect of the polymer network density on the obtained physical parameters.     

Traffic gridlock on complex networks

Here we study how a traffic jam spreads on complex networks when driven by an increasing flux between certain initial and final points. For that purpose, we developed two new traffic models based on vehicular traffic and applied them on the Apollonian network and the Swiss road network. The first model is an electrical analog, using ohmic and non-ohmic resistors which is a classical approach in Physics while the second one which we call the herding model, is based on human driving behavior. For both models, we study the sequence of clogged roads up to the traffic gridlock and display the fragilities of the network. In the electrical model, by increasing the external potential, resistors burn out, as the voltage drop between the ends increases above a certain threshold. Analyzing both models, we observed some power-law functions that occur only near a traffic gridlock as well as the dependence on topological features of the network and influence on flux and the robustness in Apollonian networks of different generations.     

A model for crack formation during active solid pyrolysis of a char-forming solid: crack patterns; surface area generation; volatile mass efflux

A model is developed for the formation and propagation of cracks in a material sample that is heated at its top surface, pyrolyses, and then thermally degrades to form char. In this work the sample is heated uniformly over its entire top surface by a hypothetical flame (a heat source). The pyrolysis mechanism is described by a one-step overall reaction that is dependent nonlinearly on the temperature (Arrhenius form). Stresses develop in response to the thermal degradation of the material by means of a shrinkage strain caused by local mass loss during pyrolysis. When the principal stress exceeds a prescribed threshold value, the material forms a local crack. Cracks are found to generally originate at the surface in response to heating, but occasionally they form in the bulk, away from ever-changing material boundaries. The resulting cracks evolve and form patterns whose characteristics are described. Quantities examined in detail are: the crack spacing in the pyrolysis zone; the crack length evolution; the formation and nature of crack loops which are defined as individual cracks that have joined to form loops that are disconnected from the remaining material; the formation of enhanced pyrolysis area; and the impact of all of the former quantities on mass flux. It is determined that the mass flux from the sample can be greatly enhanced over its nominal (non-cracking) counterpart. The mass efflux profile qualitatively resembles those observed in Cone Calorimeter tests.     

Sparse coding for layered neural networks

We investigate storage capacity of two types of fully connected layered neural networks with sparse coding when binary patterns are embedded into the networks by a Hebbian learning rule. One of them is a layered network, in which a transfer function of even layers is different from that of odd layers. The other is a layered network with intra-layer connections, in which the transfer function of inter-layer is different from that of intra-layer, and inter-layered neurons and intra-layered neurons are updated alternately. We derive recursion relations for order parameters by means of the signal-to-noise ratio method, and then apply the self-control threshold method proposed by Dominguez and Bollé to both layered networks with monotonic transfer functions. We find that a critical value _C of storage capacity is about 0.11|a ln a|⁻¹ (a1) for both layered networks, where a is a neuronal activity. It turns out that the basin of attraction is larger for both layered networks when the self-control threshold method is applied.     

Self-sustained firing activities of the cortical network with plastic rules in weak AC electrical fields

Both external and endogenous electrical fields widely exist in the environment of cortical neurons. The effects of a weak alternating current (AC) field on a neural network model with synaptic plasticity are studied. It is found that self-sustained rhythmic firing patterns, which are closely correlated with the cognitive functions, are significantly modified due to the self-organizing of the network in the weak AC field. The activities of the neural networks are affected by the synaptic connection strength, the external stimuli, and so on. In the presence of learning rules, the synaptic connections can be modulated by the external stimuli, which will further enhance the sensitivity of the network to the external signal. The properties of the external AC stimuli can serve as control parameters in modulating the evolution of the neural network.     

Optimal Population Coding for Dynamic Input by Nonequilibrium Networks

The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.