Simulating the Evolution of Market Shares: The Effects of Customer Learning and Local Network Externalities

This paper develops a simulation model to investigate competitive technology diffusion, and focuses on examining the influence of customer learning on the evolution of market share when both technologies exhibit local network externalities. Results show that: (1) the equilibrium market share of the new technology is determined by two key factors: the characteristics of customer learning behavior and the strength of local network externalities; (2) moderate network externalities can be beneficial for new technology to dominate the entire market when customers adopt belief-based learning rule; (3) moderate learning rate would facilitate the diffusion of new technology when customers make their decisions based on reinforcement learning; (4) the decay of customer learning and the proportions of imitators in market would help the old technology establish advantage by maintaining demand inertia of customer. The joint effects of psychology of customer behavior and local interactions of customers offer a new mechanism to explain the diffusion of technology in a competitive market with network externalities.     

Incorporating mutation scheme into krill herd algorithm for global numerical optimization

Recently, Gandomi and Alavi proposed a robust meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization. To improve the performance of the KH algorithm, harmony search (HS) is applied to mutate between krill during the process of krill updating instead of physical diffusion used in KH. A novel hybrid meta-heuristic optimization approach HS/KH is proposed to solve global numerical optimization problem. HS/KH combines the exploration of harmony search (HS) with the exploitation of KH effectively, and hence, it can generate the promising candidate solutions. The detailed implementation procedure for this improved meta-heuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most cases, the performance of this hybrid meta-heuristic method (HS/KH) is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, HS, KH, PSO, and SGA. The effect of the HS/FA parameters is also analyzed.     

3D image retrieval based on differential geometry and co-occurrence matrix

3D image retrieval approach is a challenging problem in the research of content-based image retrieval. In this paper, a novel retrieval approach combined differential geometry and co-occurrence matrix is presented. Firstly, Gaussian curvature and mean curvature are utilized to represent the inherent characteristic of spatial surface, and then we use co-occurrence matrix to store the shape information of 3D images. Secondly, normalization process is applied to the co-occurrence matrix and the invariants independence of the translation, scaling, and rotation transforms are proved. In comparison with the recent methods, experiments indicate a lower computation complexity and a better retrieval rate to 3D images with slight different shape characteristic.     

Memristor-based chaotic neural networks for associative memory

In chaotic neural networks, the rich dynamic behaviors are generated from the contributions of spatio-temporal summation, continuous output function, and refractoriness. However, a large number of spatio-temporal summations in turn make the physical implementation of a chaotic neural network impractical. This paper proposes and investigates a memristor-based chaotic neural network model, which adequately utilizes the memristor with unique memory ability to realize the spatio-temporal summations in a simple way. Furthermore, the associative memory capabilities of the proposed memristor-based chaotic neural network have been demonstrated by conventional methods, including separation of superimposed pattern, many-to-many associations, and successive learning. Thanks to the nanometer scale size and automatic memory ability of the memristors, the proposed scheme is expected to greatly simplify the structure of chaotic neural network and promote the hardware implementation of chaotic neural networks.     

新型偶联剂对橡胶力学性能的影响

本文讨论了新型偶联剂用量对陶土填充橡胶与炭黑填充橡胶动态性能与力学性能的影响．并且用红外光谱对偶联剂改性陶土表面进行了表征。结果表明：该偶联剂明显提高硫化胶的动态性能、撕裂强度及陶土填充橡胶的拉伸强度，其用量超过１．５重量份对硫化胶的力学性能有不良影响。     

生物质与煤共燃研究(Ⅱ)燃烧性质分析

对低温热解生物质和煤共燃的燃烧性质进行了研究。在热解温度300℃，热解时间30min下对三种生物质（锯屑、谷壳和花生壳）的热解产品、长焰煤、无烟煤、热解锯屑和长焰煤混样（1：10）、热解锯屑和无烟煤混样（1：10）七个样品的燃烧热重分析发现：热解生物质的燃烧性能相近，组成结构相似，主要分为挥发分释放燃烧阶段和焦炭燃烧阶段，分别位于30℃－400℃和400℃－500℃之间，其出现分别较煤的温度低；长焰煤与热解锯屑混燃可以有效地降低着火温度，而热解锯屑与无烟煤混燃时，由于燃烧性质差异较大，是分别燃烧，不产生协同效果；热解锯屑与长焰煤、无烟煤共燃能够有效地提高煤的着火性能，在总体燃烧性能上，虽然热解锯屑明显好于长焰煤和无烟煤，但混合后对其改变不大。     

新型可见光固化口腔正畸粘合剂研制

介绍了一种新型可见光固化正畸粘合剂的组成、性能及正畸粘接的工艺。此粘合剂由一种单组分液体底胶和单组分糊剂组成。与化学固化粘合剂相比,其操作使用方便,固化时间短。这种新型粘合剂的粘接强度完全满足了正畸粘合剂的要求。     

二氧六环在CA低压反渗透膜中的作用机理

在低压醋酸纤维素一丙酮－甲酰胺反渗船舶这膜铸膜液体体系中，加入二氧六环使膜具有较小的初始平均孔径和较窄的孔径分布，二氧六环起到了调节高分子网络尺寸变小和均一化的作用。在１．０ＭＰａ下进料液浓度为１０００ｍｇ／Ｌ时，膜对Ｎａ２ＳＯ４、ＭＧＳＯ４分离率〉９６％，对ＮａＣｌ为８０％，Ｃ（ＮＯ２）２为８０％，ＭｇＣｌ２为７９％。平均透水率〉１．２５ｍｌ／ｃｍ＾２．ｈ适用于中低盐度水的脱盐软化。     

低挥发分煤在新型干法窑生产中的应用

对劣质煤、低挥发分煤的燃烧特性分析,工艺设备分析,提出预分解窑烧劣质煤、低挥发分煤的可能性、必要性,为我公司烧劣质煤、无烟煤打下了基础。