Clifford algebra method for network expression,computation, and algorithm construction

Clifford algebra is introduced as a theoretical foundation for network topology expression and algorithm construction. Network nodes are coded with basis vectors in a vector space , and the edges and k‐walk routes can be expressed by 2‐blades and k‐blades, respectively, in the Clifford algebra Cl(n,0). The topologies among nodes, edges, and routes of networks can be directly calculated, and the network routes can be extended and traversed with oriented join products. The network algorithm construction processes based on Clifford algebra are instantiated by the single source shortest path algorithm. The experimental results on different scale random networks suggest that Clifford algebra is suited for network expression and relation computation. The Clifford algebra‐based shortest path algorithm is vivid and clear in geometric meaning and has great advantage on temporal and spatial complexity. Copyright © 2013 John Wiley & Sons, Ltd.     

Complex new materials from simple chemistry: Combining an amino‐substituted polysiloxane and carboxylic acids

Changes in rheological, adhesive, and swelling properties of quaternary salts, made by adding one of eight mono‐ or six α,ω‐alkanedioic acids (the latter with two to six or nine carbon atoms) to 6‐7PSil (a polysiloxane with 6%–7% of the monomer units contain a 3‐aminopropyl group) have been correlated with the salt structures. The simple acid‐base chemistry initiates drastic changes in the bulk properties of the materials that depend on the amount and type of the added acid. Thus, the quaternized forms of the 6‐7PSil have significantly enhanced viscoelastic and adhesive properties compared to those of the initial polysiloxane, and they can swell selectively liquids based on their polarity. Also, interpenetrating networks have been made in situ by photopolymerization of salts with vinylic carboxylic acids. DFT calculations on model systems consisting of dimethylammonium α,ω‐alkanedioate salts with two to six carbon atoms provide insights into the interactions responsible for the unexpected dependence of the properties of the 6‐7PSil salts on the chain lengths of the diacids. The potential for applying the methodologies described here to systems with other amino‐substituted polymers and other acid types is noted. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2017 , 55, 3851–3861     

Ultrasonic treatment of water contaminated with various pollutants onto copper nanowires loaded on activated carbon using response surface methodology and artificial intelligent

In this study, the potential application of copper nanowires loaded on activated carbon for simultaneous removal of Disulfine blue (DB), Crystal violet (CV) and Sunset yellow (SY) has been described. The relation between adsorption properties with variables such as solution pH, adsorbent value, contact time and initial dyes concentration was investigated and optimized. A three‐layer artificial neural network (ANN) model was utilized to predict dyes removal (%) by adsorbent following conduction of experiments. The training of network at above mention experimental data confirms its ability to forecast the removal performance with a linear transfer function (purelin) at output layer. The Levenberg–Marquardt algorithm and tangent sigmoid transfer function (tansig) with 16 neurons at the hidden layer was applied. Parameters were optimized by central composite design (CCD) combined with response surface methodology (RSM) and desirability function. The accuracy of ANN was judged according to both MSE and AAD% at optimal conditions and results indicate its superiority to RSM model in term of higher R² and lower AAD% values. This observation was also corroborated by the parity plots between the predicted and experimental values. The ANN model was better in both data fitting and prediction capability in comparison to RSM model.     

Joint impact of nodes-mobility and channel estimation error on the performance of two-way relay systems

This paper presents the performance analysis of a two-way relay system with nodes-mobility (NM) and channel estimation errors (CEE) under time-varying Rayleigh fading. Herein, analog network coding protocol is employed at the relay node. All the participating nodes are half-duplex and equipped with single-antenna devices. Specifically, we utilize first-order Markovian model to characterize the impact of NM in the channel. Along with the NM, we also consider that the CEE may exist in practical scenarios, where nodes require to estimate the channel state information. By incorporating these factors, we first derive the instantaneous end-to-end (e2e) signal-to-noise ratios (SNRs) at two source nodes. Then, we obtain the exact closed-form expressions for distribution and density functions of the two e2e SNRs. After that, we derive the accurate expressions of various performance metrics, viz., sum-bit error rate, overall outage probability, and ergodic sum-rate. Further, to attain more insights into the considered system, we deduce the asymptotic behavior of these performance metrics. Finally, numerical and Monte-Carlo simulation results are provided to validate our theoretical analysis and to illustrate the impact of NM and CEE on the performance measures of the considered system.     

Acrylate Network Formation by Free‐Radical Polymerization Modeled Using Random Graphs

A novel technique is developed to predict the evolving topology of a diacrylate polymer network under photocuring conditions, covering the low‐viscous initial state to full transition into polymer gel. The model is based on a new graph theoretical concept being introduced in the framework of population balance equations (PBEs) for monomer states (mPBEs). A trivariate degree distribution that describes the topology of the network locally is obtained from the mPBE, which serves as an input for a directional random graph model. Thus, access is granted to global properties of the acrylate network which include molecular size distribution, distributions of molecules with a specific number of crosslinks/radicals, gelation time/conversion, and gel/sol weight fraction. Furthermore, an analytic criterion for gelation is derived. This criterion connects weight fractions of converted monomers and the transition into the gel regime. Valid results in both sol and gel regimes are obtained by the new model, which is confirmed by a comparison with a “classical” macromolecular PBE model. The model predicts full transition of polymer into gel at very low vinyl conversion (<2%). Typically, this low‐conversion network is very sparse, as becomes apparent from the predicted crosslink distribution.     

Cleaving Direct‐Laser‐Written Microstructures on Demand

Using an advanced functional photoresist we introduce direct‐laser‐written (DLW) 3D microstructures capable of complete degradation on demand. The networks consist exclusively of reversible bonds, formed by irradiation of a phenacyl sulfide linker, giving disulfide bonds in a radical‐free step‐growth polymerization via a reactive thioaldehyde. The bond formation was verified in solution by ESI‐MS. To induce cleavage, dithiothreitol causes a thiol–disulfide exchange, erasing the written structure. The mild cleavage of the disulfide network is highly orthogonal to other, for example, acrylate‐based DLW structures. To emphasize this aspect, DLW structures were prepared incorporating reversible structural elements into a non‐reversible acrylate‐based standard scaffold, confirming subsequent selective cleavage. The high lateral resolution achievable was verified by the preparation of well‐defined line gratings with line separations of down to 300 nm.     

基于经验小波变换和卷积神经网络的水泵水轮机无叶区流态特征提取及识别研究

监测和识别原型水泵水轮机无叶区的流动状态,对于保证抽水蓄能电站的运行安全性和稳定性有非常重要的意义。本文提出了一种基于经验小波变换、散布熵和卷积神经网络原理的流态特征提取和识别方法,首先使用经验小波变换对压力脉动信号进行分解,然后通过计算各分量的散布熵提取流态相关特征,最后通过利用特征–标签对训练卷积神经网络得到的智能识别模型,实现了无叶区流态识别。利用从国内某水泵水轮机采集到的发电、抽水和空转工况下实测压力脉动信号对该方法进行了测试,测试的平均准确率达到了94.84%,证明了该方法的有效性。

     

Delay-independent exponential stability of stochastic Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion terms

Different from the approaches used in the earlier papers, in this paper, the Halanay inequality technique, in combination with the Lyapunov method, is exploited to establish a delay-independent sufficient condition for the exponential stability of stochastic Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion terms. Moreover, for the deterministic delayed Cohen–Grossberg neural networks, with or without reaction–diffusion terms, sufficient criteria for their global exponential stability are also obtained. The proposed results improve and extend those in the earlier literature and are easier to verify. An example is also given to illustrate the correctness of our results.     

Basic features of plastic strains: From micro-mechanics to incrementally nonlinear models

Most rate-independent constitutive relations for granular materials are based on the existence of a regular flow rule. This assumption states that once the mechanical state of a material point belongs to the yield surface, then the direction of the plastic strains is independent of the loading direction. In this paper, the notion of a regular flow rule is shown to exist only for two-dimensional and axisymmetric loading conditions. By considering our incrementally nonlinear constitutive model, it is established that this notion disappears as soon as more general loading conditions are applied, as also predicted from discrete element simulations. Moreover, a sound micro-mechanical interpretation of the vanishing of a regular flow rule in three-dimensional loading conditions is given from a multi-scale perspective using the micro-directional model. This model highlights the great influence of the loading history on the shape of the plastic Gudehus response-envelope.