Optimization of neural network for ionic conductivity of nanocomposite solid polymer electrolyte system (PEO-LiPF6-EC-CNT)

In this study, the ionic conductivity of a nanocomposite polymer electrolyte system (PEO-LiPF₆-EC-CNT), which has been produced using solution cast technique, is obtained using artificial neural networks approach. Several results have been recorded from experiments in preparation for the training and testing of the network. In the experiments, polyethylene oxide (PEO), lithium hexafluorophosphate (LiPF₆), ethylene carbonate (EC) and carbon nanotubes (CNT) are mixed at various ratios to obtain the highest ionic conductivity. The effects of chemical composition and temperature on the ionic conductivity of the polymer electrolyte system are investigated. Electrical tests reveal that the ionic conductivity of the polymer electrolyte system varies with different chemical compositions and temperatures. In neural networks training, different chemical compositions and temperatures are used as inputs and the ionic conductivities of the resultant polymer electrolytes are used as outputs. The experimental data is used to check the system’s accuracy following the training process. The neural network is found to be successful for the prediction of ionic conductivity of nanocomposite polymer electrolyte system.     

The cut condition for robust network design

We consider the Robust Network Design problem, i.e. the problem of dimensioning the capacities on the edges of a graph to serve point-to-point traffic demands subject to uncertainty. The scope of the paper is to investigate when the cut condition is sufficient to guarantee the feasibility for such problem.     

Vulnerability analysis of interdependent infrastructure systems: A methodological framework

Infrastructure systems such as power and water supplies make up the cornerstone of modern society which is essential for the functioning of a society and its economy. They become more and more interconnected and interdependent with the development of scientific technology and social economy. Risk and vulnerability analysis of interdependent infrastructures for security considerations has become an important subject, and some achievements have been made in this area. Since different infrastructure systems have different structural and functional properties, there is no universal all-encompassing ‘silver bullet solution’ to the problem of analyzing the vulnerability associated with interdependent infrastructure systems. So a framework of analysis is required. This paper takes the power and water systems of a major city in China as an example and develops a framework for the analysis of the vulnerability of interdependent infrastructure systems. Four interface design strategies based on distance, betweenness, degree, and clustering coefficient are constructed. Then two types of vulnerability (long-term vulnerability and focused vulnerability) are illustrated and analyzed. Finally, a method for ranking critical components in interdependent infrastructures is given for protection purposes. It is concluded that the framework proposed here is useful for vulnerability analysis of interdependent systems and it will be helpful for the system owners to make better decisions on infrastructure design and protection.     

Mathematical Analysis of a PDE System for Biological Network Formation

Motivated by recent physics papers describing rules for natural network formation, we study an elliptic-parabolic system of partial differential equations proposed by Hu and Cai [13 Hu , D. (2013). Optimization, Adaptation, and Initialization of Biological Transport Networks. Workshop on multiscale problems from physics, biology and material sciences. May 28–31st, 2014, Shanghai. Available at: http://math.sjtu.edu.cn/conference/multiscale2014/ (accessed 6 January 2015). [Google Scholar], 15 Hu , D. , Cai , D. ( 2013 ). Adaptation and optimization of biological transport networks . Phys. Rev. Lett. 111 : 138701 .[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]]. The model describes the pressure field thanks to Darcy's type equation and the dynamics of the conductance network under pressure force effects with a diffusion rate D ≥ 0 representing randomness in the material structure. We prove the existence of global weak solutions and of local mild solutions and study their long term behavior. It turns out that, by energy dissipation, steady states play a central role to understand the network formation capacity of the system. We show that for a large diffusion coefficient D, the zero steady state is stable, while network formation occurs for small values of D due to the instability of the zero steady state, and the borderline case D = 0 exhibits a large class of dynamically stable (in the linearized sense) steady states.     

Swelling and Network Parameters of 1-Hexadecene-Co-Trimethylolpropane Distearate Monoacrylate Sorbers

Linear and crosslinked copolymers with different compositions of 1-hexadecene and trimethylolpropane distearate monoacrylate monomers were synthesized and evaluated for oil-absorbency application. Different concentrations of ethylene glycol diacrylate (EGDA) and ethylene glycol dimethacrylate (EGDMA) crosslinkers were used. The concentration of both crosslinkers was varied from 0.5% to 2%. Copolymer compositions were determined from ¹H NMR spectroscopy. Monomer reactivity ratios were calculated using Fineman-Ross and Kelen-Tudos techniques at low conversions. The oil absorbency and swelling rate constant were measured and influenced mainly by the degree of crosslinking and the hydrophobicity of copolymer units. The final equilibrium oil content, volume fraction of polymer and swelling capacity were determined at 298 K. The effective crosslinking density V_e, theoretical crosslink density V_t, the average molecular weight between the crosslinks M_c and the polymer-toluene interaction parameter χ were determined from swelling measurements. The efficiencies of EGDA and EGDMA crosslinking agents toward copolymers were determined.     

Polymer networks by molecular dynamics simulation: Formation, thermal, structural and mechanical properties

A molecular dynamics simulation method is presented and used in the study of the formation of polymer networks. We study the formation of networks representing the methylene repeating units as united atoms. The network formation is accomplished by cross-linking polymer chains with dedicated functional end groups. The simulations reveal that during the cross-linking process, initially branched molecules are formed before the gel point; approaching the gel point, larger branched entities are formed through integration of smaller branched molecules, and at the gel point a network spanning the simulation box is obtained; beyond the gel point the network continues to grow through the addition of the remaining molecules of the sol phase onto the gel (the network); the final completion of the reaction occurs by intra-network connection of dangling ends onto unsaturated cross-linkers. The conformational properties of the strands in the undeformed network are found to be very similar with the conformational properties of the chains before cross-linking. The uniaxial deformation of the formed networks is investigated and the modulus determined from the stress-strain curves shows reciprocal scaling with the precursor chain length for networks formed from sufficiently large precursor chains (N ≥ 20).     

基于层次分析法的企业网络舆情危机应对评价研究

网络舆情危机应对评价是企业在新的舆论背景下研究危机应对的重点.采用定量和定性分析有机结合的层次分析法确定指标体系及权重,给出了企业网络舆情危机应对评价的步骤,并进行了评价验证.该评价体系为相关单位网络舆情危机应对评价提供一定的参考和指导.     

On a problem of Hornik

In 1991,Hornik proved that the collection of single hidden layer feedforward neural networks(SLFNs)with continuous,bounded,and non-constant activation functionσis dense in C(K)where K is a compact set in R~s(see Neural Networks,4(2),251-257(1991)).Meanwhile,he pointed out"Whether or not the continuity assumption can entirely be dropped is still an open quite challenging problem".This paper replies in the affirmative to the problem and proves that for bounded and continuous almost everywhere(a.e.)activation functionσon R,the collection of SLFNs is dense in C(K)if and only ifσis un-constant a.e..     

基于布谷鸟算法与BP神经网络的煤灰变形温度预测

以120种煤样为数据基础,采用布谷鸟算法(CS)优化BP(Back Propagation)神经网络,建立了CSBP模型对单煤、煤掺添加剂和配煤等3类样本的煤灰变形温度(DT)样本进行预测。模型以煤灰化学成分及其组合参数等13个变量作为输入量,以变形温度(DT)作为输出量。CSBP模型预测结果与BP神经网络模型预测结果进行对比发现,无论是单煤、煤掺添加剂还是配煤,CSBP模型较BP模型对煤灰变形温度(DT)的预测都更加精准,平均相对误差分别达到了3.11%、4.08%和4.22%。另外,对比3类样本预测结果发现,无论是CSBP模型还是BP模型,相比单煤预测而言,煤掺添加剂及配煤的预测误差都有明显的增加。