Cellular Network Design Site Selection and Frequency Planning

Deployment of cellular networks involves substantial capital investment. Competition motivates service providers to minimize these costs while maintaining service quality. We develop a cost-minimizing planning model that simultaneously determines three important variables – base station location, power level and frequency group assignments for the antennas at each selected base station. It can represent different antenna configurations and frequency groups commonly used in practice. The integer-programming problem is solved using a Lagrangian relaxation based heuristic and computational tests indicate good solution quality for reasonably large problems. We also note the appropriate role of such optimization models in the overall planning process.     

高分子熔体和浓溶液流变性能的研究

基于多重缠结网络结构模型和高分子链上缠结点在流动中可进行动态解缠和再缠结的多重蠕动机理,用统计力学和动力学相结合的方法,分别计算出了缠结链组的末端距分布函数;处于缠结状态下高分子链构象统计分布函数;受力下聚合物熔体粘弹性形变自由能和解除外力下高分子挤出体可回复性粘弹性形变自由能,提出了高分子挤出体可回复形变的粘弹性分子理论。推导出的高分子熔体的回忆函数、简单剪切流下的本构方程和物料函数,并采用一种新的方法测定出物料的四种参数： η0、 GN0、 n′和 a。对于高分子挤出体,可回复性粘弹性形变由快速弹性形变和慢速粘弹性形变两者组成,当把两种形变量的复合结构参数－分子链的反式构象分数引入两种形变自由能表达式后,就从理论上得到了可回复形变量同挤出胀大比间的定量表达式,从而建立起一个具有分子链结构参数的新的挤出胀大比方程,可回复形变量同挤出条件（温度、挤出速率和量以及口模长径比不同的挤出机）和树脂结构特征（分子量及分布）的关系式以及在特殊情形下的简化表达式,并用几种高分子熔融体系的挤出胀大比和可回复性形变量的实验数据对理论进行验证,理论方程同实验数据较好的符合。     

辨识药物定量构效关系的模糊神经网络方法研究

提出一种基于遗传算法的新型模糊神经网络方法,用于计算Benzodiazepines(BZs)类药物的定量构效关系.这类模糊神经网络综合了神经网络、遗传算法与模糊逻辑的各自优势,具有优良的定量构效关系辨识能力,其学习速度较快,不易陷入局部最小区域;网络知识以模糊语言变量的形式加以表达,不仅易于理解,而且能有效地利用已有的专家经验.一旦通过学习获得规律后,不仅能很好地预测化合物的活性,还能对后续的药物分子设计提供有益的理论指导.     

Indicator space configuration for early warning of violent political conflicts by genetic algorithms

Recognition of preconflict situations has a powerful potential for early warning of violent political conflicts. This paper focuses on the design and application of artificial neural networks as classifiers of preconflict situations. Achieving a desired level of performance of the neural network relies on the appropriate construction of recognition space (selection of indicators) and the choice of network architecture. A fast and effective method for the design of reliable neural recognition systems is described. It is based on genetic algorithm techniques and optimizes both the configuration of input space and the network parameters. The implementation of the methodology provides for increased performance of the classifier in terms of accuracy, generalization capacity, computational and data requirements. This revised version was published online in June 2006 with corrections to the Cover Date.     

Bandwidth allocation for guaranteed versus best effort service categories

Modern communication networks evolve towards integration of guaranteed-performance and best-effort service types. The coexistence of these two service types offers substantial benefits, such as resource sharing between service classes, and the ability of the user to select an appropriate service class according to its individual requirements and preferences. Notwithstanding, such interaction gives rise to more complicated system behavior and related performance issues, which need to be explored and understood in order to allow efficient network operation. In this paper we examine potential congestion phenomena, which arise due to the combined effect of bandwidth sharing and user migration between service classes. We propose a simplified fluid model for session flow, consisting of two coupled queues with state-dependent flows, which captures the essential ingredients of service-class interaction. Our analysis shows that the system might exhibit bistable behavior, in the sense that transient congestion may stir the system from a stable and efficient operating point to an inefficient and congested one. We identify conditions which give rise to bistability, and propose a call admission control scheme which prevents the system from getting trapped in a congested-type equilibrium, while not interfering with normal system operation. This revised version was published online in June 2006 with corrections to the Cover Date.     

Electrostatic‐Assembly of Carbon Nanotubes (CNTs) and Polymer Particles in Water: a Facile Approach to Improve the Dispersion of CNTs in Thermoplastics

Summary: A facile and organic‐solvent‐free method for preparing thermoprocessable multiwalled carbon nanotube (MWCNT)‐filled thermoplastics is presented. MWCNTs are oxidized, neutralized, and then assembled with cationic soap‐free poly(methyl methacrylate) (PMMA) particles directly in water. The spontaneous electrostatic coupling between the negatively charged MWCNTs and positively charged PMMA particles, and the viscoelastic and thermomechanical behavior of the nanocomposites, are investigated. The electrostatic coupling interactions improve the dispersion of nanotubes and facilitate the formation of filler networks in the polymer matrix.

Preparation of nanocomposites of oxidized MWCNTs and positively charged PMMA particles through electrostatic assembly.     

3D Nanometer‐Scale Study of Coexisting Bicontinuous Morphologies in a Block Copolymer/Homopolymer Blend

Summary: Coexisting bicontinuous morphologies, one ordered and one disordered, are investigated in a macrophase‐separated poly(styrene‐block‐isoprene) diblock copolymer/homopolystyrene (SI/hS) blend. Two‐phase behavior is attributed to the relatively high hS/S mass ratio (0.92). According to its crystallographic signature and channel coordination as discerned from three‐dimensional (3D) images generated by transmission electron microtomography (TEMT), the ordered morphology is classified as gyroid. The 3D local and global topological characteristics of both bicontinuous morphologies as measured directly from TEMT images are reported. The disordered morphology is further compared with molecular‐field simulations to ascertain the spatial distribution of the constituent species within the blend, thereby demonstrating the utility of high‐resolution 3D imaging coupled with molecular‐level simulations.

     

Properties of asymmetrically evolved community networks

This paper studies a simple asymmetrically evolved community network with a combination of preferential attachment and random properties. An important issue about community networks is to discover the different utility increments of two nodes, where the utility is introduced to investigate the asymmetrical effect of connecting two nodes. On the other hand, the connection of two nodes in community networks can be classified as two nodes belonging to the same or to different communities. The simulation results show that the model can reproduce a power-law utility distribution P(u)～u^-σ, σ = 2 + 1/p, which can be obtained by using mean-field approximation methods. Furthermore, the model exhibits exponential behaviour with respect to small values of a parameter denoting the random effect in our model at the low-utility region and a power-law feature with respect to big values of this parameter at the high-utility region, which is in good agreement with theoretical analysis. This kind of community network can reproduce a unique utility distribution by theoretical and numerical analysis.     

Robust stability analysis for Markovian jumping stochastic neural networks with mode-dependent time-varying interval delay and multiplicative noise

This paper is concerned with the problem of robust stability for a class of Markovian jumping stochastic neural networks (MJSNNs) subject to mode-dependent time-varying interval delay and state-multiplicative noise. Based on the Lyapunov--Krasovskii functional and a stochastic analysis approach, some new delay-dependent sufficient conditions are obtained in the linear matrix inequality (LMI) format such that delayed MJSNNs are globally asymptotically stable in the mean-square sense for all admissible uncertainties. An important feature of the results is that the stability criteria are dependent on not only the lower bound and upper bound of delay for all modes but also the covariance matrix consisting of the correlation coefficient. Numerical examples are given to illustrate the effectiveness.     

Nanocomposite Gels: New Advanced Functional Soft Materials

Summary: Nanocomposite gels (NC gels) consisting of poly(N-alkylacrylamides) and exfoliated inorganic clay were prepared by in-situ, free-radical polymerization at high yield under mild conditions (near ambient temperature, without stirring). Various shapes and different surface forms of NC gels were readily obtained using corresponding vessels and templates, since NC gels were always mechanically tough. The view that polymer/clay networks are formed in NC gels was supported by dynamic viscoelastic and swelling measurements. The entirely different mechanical properties of NC gels, compared with conventional, chemically-crosslinked hydrogels (OR gels), are discussed on the basis of their network structure. In addition to functions previously reported, NC gels exhibit further interesting characteristics, such as inherent incombustibility, good thermal conductivity, large heat capacity, they can be readily colored and fabricated as foams. NC gels can be utilized as environmentally-friendly, soft materials from the viewpoints of resources and waste, as their primary component is water.