创新研究系统隐性知识生成转化反馈环特性仿真分析

隐性知识反馈环结构是创新研究系统的核心结构,运用组织管理系统动力学理论,进行科研创新系统隐性知识生产转化系统仿真研究,以南昌大学系统动力学创新团队研究系统为例,分析确定该系统的隐性知识量、显性知识量、创新投资量、团队人员数和创新成果量五个核心变量,运用新建的逐枝建模和逐树仿真技术建立五棵流率基本入树模型及其等价流图模型.运用枝向量行列式新增反馈环计算法,计算出系统包含创新成果流位作用于隐性知识流入率的第一类反馈环五条,团队成员数作用于隐性知识流入率第二类正反馈环十条.然后,结合模型的仿真曲线和仿真数据,对第一类反馈环进行极性转移仿真分析和主导反馈环转移仿真分析,对第二类正反馈环进行正反馈环的作用分析,以及"原始创新度"低对这十条正反馈环的制约分析,并基于上述十五条反馈环特性仿真分析结果提出四条创新研究系统发展对策.     

Modeling eBay price using stochastic differential equations

Online auctions have become increasingly popular in recent years. There is a growing body of research on this topic, whereas modeling online auction price curves constitutes one of the most interesting problems. Most research treats price curves as deterministic functions, which ignores the random effects of external and internal factors. To account for the randomness, a more realistic model using stochastic differential equations is proposed in this paper. The online auction price is modeled by a stochastic differential equation in which the deterministic part is equivalent to the second‐order differential equation model proposed in Wang et al. (Journal of the American Statistical Association, 2008, 103, 1100–1118). The model also includes a component representing the measurement errors. Explicit expressions for the likelihood function are also obtained, from which statistical inference can be conducted. Forecast accuracy of the proposed model is compared with the ODE (ordinary differential equation) approach. Simulation results show that the proposed model performs better.     

医药洁净室的计算流体动力学模拟及实测对比

采用计算流体动力学(CFD)模拟,研究3个医药洁净室的气流流型及颗粒物浓度.采用标准k-ε双方程模型,通过模拟颗粒物在洁净室的分布,分析合理的污染物散发位置及散发比例.对比参考发尘量下的结果与实测数据的差异,根据实测数据确定污染散发量,通过试验不同的单位容积发尘量,计算对应的洁净度,得到与实测数据吻合最好的单位容积发尘量.在单位容积发尘量为1 000pc·(m3·min)-1计算条件下,各房间的模拟结果与实测数据数值比较吻合,且经CFD预测的3个房间的洁净度变化趋势与实测一致.     

工业用PET及PA纤维模量-应变曲线的分析

通过拉伸试验数据得到应力—应变曲线和模量-应变曲线可以更全面地反映纤维的力学性能。PET及PA的模量—应变曲线都是明显的双峰曲线。结合拉伸过程中PET纤维内部结构的变化解释PET纤维的特征模量—应变曲线，并讨论如何提高持续使用应变范围的模量问题。PET模量—应变曲线上的第一个模量峰为初始模量，标志着无定形区缠结网的破坏；无定形区蜷曲大分子伸直时模量较低，导致出现模量低谷；由于伸直的缚结大分子模量较高，出现了第二个模量峰。     

用带时序子模块的系统动力学模型预测Brent原油价格

提出在系统动力学模型中加入时序自回归AR(p)子模块，并且自动确定每个AR(p)子模块的阶数p。带时序子模块的系统动力学模型既体现了因素问的横向因果关系，又体现了每个因素的纵向关系。建立欧佩克(OPEC)石油产量、世界GDP、煤炭产量与价格、天然气、需求量、供求差额、消费系数、非欧佩克石油产量、非欧佩克供求差额，石油库存和OPEC组织的期望油价共十一个因素影响下的Brent原油价格预测模型，模型的预测结果表明：在系统动力学模型中加入时序自回归AR(p)子模块的方法是可行的、有效的，并且能提高模型的预测精度。     

带电胶体系统固液相变的分子动力学研究

在修正Yukawa相互作用的基础上，采用常温分子动力学方法数值研究了带电胶体系统的大离子屏蔽效应和多分散性效应对其固液边界的影响．研究结果发现，在屏蔽长度较大时，固液相变曲线出现了明显偏差．初步定性认为这种相边界偏差现象来自于系统多体效应的增强．另外，数值研究了固液相变时键接取向序参数的变化．     

Interaction Dynamics in AEC Global Teamwork

Architecture, engineering, and construction (AEC) global project teamwork is communication intensive and relies heavily on synchronous and asynchronous information and collaboration technologies (ICT). We explore in this paper how an asynchronous ICT, called ThinkTank, reshaped the work practice of design-construction global teams, and how the interaction with this ICT reshaped the purpose and benefits of its use. ThinkTank is a web-based asynchronous collaboration and discussion forum. We introduce the influence diffusion model (IDM) that formalizes the process of identifying the influence of people, messages, and terms mathematically. Discovering who the influence leaders in project teams are can be beneficial and critical from a corporate management perspective, since they can guide or motivate the team towards successful actions and outcomes. We present the ThinkTank-IDM integrated system and its validation with a testbed of 53 AEC global team project archived in ThinkTank over 8 years.     

Modulation of protein biophysical properties by chemical glycosylation: biochemical insights and biomedical implications

Glycosylation constitutes one of the most important posttranslational modifications employed by biological systems to modulate protein biophysical properties. Due to the direct biochemical and biomedical implications of achieving control over protein stability and function by chemical means, there has been great interest in recent years towards the development of chemical strategies for protein glycosylation. Since current knowledge about glycoprotein biophysics has been mainly derived from the study of naturally glycosylated proteins, chemical glycosylation provides novel insights into its mechanistic understanding by affording control over glycosylation parameters. This review presents a survey of the effects that natural and chemical glycosylation have on the fundamental biophysical properties of proteins (structure, dynamics, stability, and function). This is complemented by a mechanistic discussion of how glycans achieve such effects and discussion of the implications of employing chemical glycosylation as a tool to exert control over protein biophysical properties within biochemical and biomedical applications. Received 15 December 2006; received after revision 28 March 2007; accepted 25 April 2007