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.     

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。     

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。     

无卤阻燃EPDM胶料的性能预测

用回归分析法对EPDM胶料的阻燃性和物理机械性能进行研究并预测胶料的性能．结果表明：无卤阻燃EPDM胶料的阻燃性随氢氧化铝用量和硼酸锌用量增加而提高，物理机械性能则有所下降，建立的数学模型能较好地拟合胶料各项性能与氢氧化铝用量和硼酸锌用量之间的关系，能够准确地预测含不同氯氧化铝用量和硼酸锌用量胶料的性能。     

填充母料及其他常用塑料母料

塑料母料对推动塑料工业的迅猛发展起了很大作用，其主要特点是：可以简化生产工艺过程：原料混合方便，混炼质量均匀，提高生产效率及制品性能指标；减少粉尘飞扬及对设备的磨损；降低制品在换色时清洗螺杆的用料量；延长原料储存的保质期等。     

两步法合成大粒径丁苯胶乳

研究了聚甲基丙烯酸甲酯（MMA）对丁苯（BS）胶乳的附聚作用以增大丁苯胶乳粒径；探讨了无机盐、体系pH值及体系中MMA胶乳与BS胶乳的固含量比（R值）对附聚后丁苯胶乳的粒径及稳定性的影响。结果表明：适当添加无机盐可以稳定胶乳体系，最佳用量（质量分数）为0．1％～0．2％；适当增大体系pH值，可增大胶乳粒径；在附聚过程中，当R值在两个附聚峰附近时，可增大丁苯胶乳的粒径。     

Variability of light absorption properties in optically complex inland waters of Lake Chaohu,China

Absorption coefficients of phytoplankton, colored detrital matter (CDM), non-algal particles (NAP), colored dissolved organic matter (CDOM), and their relative contributions to total non-water absorption (a_t ? w) are essential variables for bio-optical and radiative transfer models. Light absorption properties showed large range and variability sampled at 194 stations throughout Lake Chaohu between May 2013 and April 2015. The a_t ? w was dominated by phytoplankton absorption (a_ph) and NAP absorption (a_d). The contribution of CDOM absorption to a_t ? w was lower than 30%. Phytoplankton and NAP were the primary sources of spatial and vertical variability in absorption properties. Light absorption by CDOM, though significant in magnitude, was relatively constant. CDM absorption (a_dg) was dominated by NAP. The spatial variation of the absorption coefficients from each of the optically active constituents were driven by several main inflow rivers in the western and middle part of Lake Chaohu. Algal blooms and bottom resuspension contributed to vertical variability as observed by phytoplankton and NAP profiles. Specific absorption of phytoplankton had significant spatial and seasonal variations without vertical variation. The spectral slope of absorption showed no significant spatial variability (p > 0.05). Variations of absorption affected different ranges of remote sensing reflectance (R_rs) spectrum, thereby increasing the difficulty of applying the remote sensing algorithm in optically complex waters. Parameters and relationships presented in this study provide useful information for bio-optical models and remote sensing of lakes similar to Lake Chaohu in terms of optical properties.