Emergence of complex networks from diffusion on fractal lattices. A special case of the Sierpinski gasket and tetrahedron

A new approach to the assemblage of complex networks displaying the scale-free architecture is proposed. While the growth and the preferential attachment of incoming nodes assure an emergence of such networks according to the Barabási–Albert model, it is argued here that the preferential linking condition needs not to be a principal rule. To assert this statement a simple computer model based on random walks on fractal lattices is introduced. It is shown that the model successfully reproduces the degree distributions, the ultra-small-worldness and the high clustering arising from the topology of scale-free networks.     

Complex force network in marginally and deeply jammed solids

This paper studies the force network properties of marginally and deeply jammed packings of frictionless soft particlesfrom the perspective of complex network theory. We generate zero-temperature granular packings at different pressures by minimizing the inter-particle potential energy. The force networks are constructed as nodes representing particles and links representing normal forces between the particles. Deeply jammed solids show remarkably different behavior from marginally jammed solids in their degree distribution, strength distribution, degree correlation, and clustering coefficient. Bimodal and multi-modal distributions emerge when the system enters the deep jamming region. The results also show that small and large particles can show different correlation behavior in this simple system.     

Electrodeposition of alginate with PEDOT/PSS coated MWCNTs to make an interpenetrating conducting hydrogel for neural interface

Neural interface is a critical component for the communication between the central nervous system and outside devices. But neural tissues withstand constant mechanical activity with outside devices which aggravates inflammation response. The investigation about soft interfacial modification materials for neural implants is a promising way to reduce the mechanical mismatch of neural tissues and implants. In this study, soft alginic acid gel and poly(3, 4-ethylenedioxythiophene)/poly(styrene sulfate)(PEDOT/PSS) coated multi-walled carbon nanotubes(MWCNT) was co-deposited on a microwire neural electrode, then PEDOT/PSS was electrochemically grown through the coating material to form a kind of soft interpenetrating networks (IPNs) for the improvement of neural interface. Compared to unmodified electrodes, the modified electrodes possess higher charge storage capacity (CSC) and lower electrochemical impedance, which would benefit the performance of stimulating and recording respectively. The IPNs are very soft after reswelling in brain tissue, and the cell experiment indicate its fantastic biocompatibility. Furthermore, acute neural recording experiments revealed that the IPNs reduced the noise floor. All these characteristics are greatly desired for the neural interface. Overall, we developed a soft interpenetrating conducting hydrogel coating material on the neural interface with excellent electrical and biological performance.     

Modelling the soundscape quality of urban waterfronts by artificial neural networks

The renewal of the urban waterfronts has become a major focus of attention for politicians and decision makers in the city’s management programs. The recognition of the patterns that define the waterfronts’ identity is essential to select new strategies of intervention for the environmental recovery. In order to create adequate environments for everyday life within a sustainable development, new links between human senses, human perception and design need to be created. Within this wide approach, the landscape and the soundscape play a significant role and can become a key driving force in the implementation of the changes. New techniques have to be tested to identify the sonic and visual parameters capable to explain the specificity of a waterfront. With this purpose, an artificial neural network (ANN) was developed, and the relative importance of the input variables was evaluated. The collected database was also analysed by multiple linear regression (MLR) to compare the outcomes of both models. The urban waterfront of Naples (Italy) was chosen as case study. The results obtained show that the performance of the neural network is better than the one of the linear regression (rANN = 0.949, rMLR = 0.639). The interpretation of the relative importance method is also quite satisfactory in the ANN.     

Microstructure,Rheology, and Potential Oil Absorbency of Poly(Butyl Methacrylate) and Poly(Hydroxyethyl Methacrylate) Blends

To prepare oil-absorptive polymers with moderate cross-linking structure, poly(butyl methacrylate) (PBMA) was synthesized as a linear hydrophobic polymer by suspension polymerization. In addition, hydroxyethyl methacrylate (HEMA), as a monomer, which could construct a network structure among the macromolecules via hydrogen bond interactions, was solution polymerized in dimethylacetamide (DMAc) with PBMA, yielding a polymer blend of PBMA and PHEMA. The solution of the polymer blend was investigated by rotational viscometry and extended rheometry. The results showed that the viscosity varied greatly with the temperature and shear rate for three different compositions. Fourier transform infrared (FTIR) spectra indicated that an entanglement or interlocking cross-linking structure of molecular chains was constructed by hydrogen bonds. The results from nuclear magnetic resonance (NMR) spectra exhibited a downfield movement of the proton peak as influenced by end groups or hydroxyls in the polymer chains. The rheological measurements demonstrated that the cross-linking structure greatly affected the rheological behavior of the blend solution. In addition, the cross-linking structure was also evaluated by oil absorbency of films.     

Effects of delayed recovery and nonuniform transmission on the spreading of diseases in complex networks

We investigate the effects of delaying the time to recovery (delayed recovery) and of nonuniform transmission on the propagation of diseases on structured populations. Through a mean-field approximation and large-scale numerical simulations, we find that postponing the transition from the infectious to the recovered states can largely reduce the epidemic threshold, therefore promoting the outbreak of epidemics. On the other hand, if we consider nonuniform transmission among individuals, the epidemic threshold increases, thus inhibiting the spreading process. When both mechanisms are at work, the latter might prevail, hence resulting in an increase of the epidemic threshold with respect to the standard case, in which both ingredients are absent. Our findings are of interest for a better understanding of how diseases propagate on structured populations and to a further design of efficient immunization strategies.     

应力盘智能控制盘面面形的表征方法及检测技术的研究

分析了CMAC神经网络应力盘智能控制中面形表征及检测的特点,提出了以Zernike多项式系数用于表征应力盘盘面面形的方法,以微位移阵列传感器检测得到的原始数据经插值拟合重构出盘面面型数据,再经Gram-Schimdt正交化后拟合出的Zernike多项式的系数,以此作为CMAC神经网络的输入样本来完成CMAC神经网络的训练。并在实验中初步验证了此方法的可行性。     

A high-precision study of the hopfield model in the phase of broken replica symmetry

Using a multi-spin coding algorithm, the Hopfield model is studied for network sizes up toN=33,968. Thermodynamically stable states are found in a region where the replica-symmetric solution predicts none should exist, but where a one-step replica symmetry-breaking calculation predicts some should exist. Furthermore, the order parameter in this region is found to take on two distinct values, one of which is not predicted by any theory.     

A numerical model of multimode fiber PON frequency response

Frequency response of passive optical network (PON) based on multimode fibers is investigated. The network comprises fibers, connectors and splitters/couplers. It is shown that due to mode filtering at splitters, the frequency response is different for different network nodes in otherwise symmetrical network.     

Network evolution by nonlinear preferential rewiring of edges

The mathematical framework for small-world networks proposed in a seminal paper by Watts and Strogatz sparked a widespread interest in modeling complex networks in the past decade. However, most of research contributing to static models is in contrast to real-world dynamic networks, such as social and biological networks, which are characterized by rearrangements of connections among agents. In this paper, we study dynamic networks evolved by nonlinear preferential rewiring of edges. The total numbers of vertices and edges of the network are conserved, but edges are continuously rewired according to the nonlinear preference. Assuming power-law kernels with exponents α and β, the network structures in stationary states display a distinct behavior, depending only on β. For β>1, the network is highly heterogeneous with the emergence of starlike structures. For β<1, the network is widely homogeneous with a typical connectivity. At β=1, the network is scale free with an exponential cutoff.