醛固酮对实验大鼠肝纤维化形成的影响

目的　探讨醛固酮 (ALD)的致肝纤维化作用。方法　SD大鼠 90只 ,随机分成 3组。正常对照组 (8只 ) :正常饮食饮水 ,皮下注射花生油 ;模型组 (42只 ) :复合因素制成肝硬化模型 ;螺内酯干预组 (40只 ) :造模方法同模型组 ,螺内酯每天 10 0mg·kg-1灌胃。分别于第 2、4、6、8周末随机处死 8只大鼠 ,眼球采血并取肝右叶组织测定血清和肝组织ALD水平及胶原面密度。结果　大鼠血浆及肝组织ALD水平在模型组随肝纤维化加重同步升高 ,在螺内酯干预组较同期模型组明显升高 (P <0 .0 5 ) ,但肝内胶原面密度明显减少 (P <0 .0 5 )。结论　ALD对大鼠肝纤维化形成起促进作用 ,螺内酯可能通过拮抗ALD受体对肝纤维化有一定预防作用     

重型卡车主动悬置驾驶室舒适性仿真研究

为了满足长途运输时重型卡车行驶的平顺性要求,利用基于线性矩阵不等式(LMI)的H2控制算法对驾驶室安装了主动悬置的六自由度1/2重型卡车模型进行振动控制,所采用的路面激励为积分白噪声随机路面激励.大量的仿真结果显示,主动控制悬置系统明显降低了驾驶室的俯仰角加速度和俯仰角,采用主动控制悬置系统可有效地改善卡车行驶平顺性和乘坐舒适性.     

Improved Fuzzification Method for Multi-Objective Decision-Making and Its Application in Evaluation of Highway Planning

IntroductionFuzzysettheoryhasbeenstudiedextensivelyoverthepast 30 years,andhasfoundsuccessfulapplicationsandimplementationsinvariousareas .Mostoftheearlyinterestsinfuzzysettheorywerefocusedonrepresentingtheuncertaintyinhumancognitiveprocesses.Nowfuzzysett…     

基于群体理想解的多属性群决策算法

为了求解多属性群决策问题，采用理想点法，对每个单一属性将个体判断集结成群体判断，构造出了群体多属性决策矩阵．从而，将复杂的多属性群决策问题转化成一般的多属性决策问题，并采用理想点法进行了求解．最后，给出了1个群体投资项目选择的算例．     

SMA骨架标准研究

传统观点认为，粗集料的干插捣间隙率VDRC大于压实混合料中粗集料间隙率VMix是SMA骨架结构形成的标准，但是VDRC与VMix在压实方法、测量精度、换算密度指标等方面存在系统误差，导致VDRC在多数情况下都大于VMix，失去了优选级配的作用。试验及结果分析表明，用体积法测量的毛体积密度换算的VMix较合理，用VMix随细集料增加的突变点判定骨架形成是可行的。     

阿霉素对培养心肌细胞膜流动性的影响及能气朗的保护作用

应用荧光探剂（ＤＰＨ）标记完整培养心肌细胞膜脂区，用稳态荧光偏振技术观测阿霉素（ＡＤＲ）对心肌细胞膜流动性的影响及能气朗（ＣｏＱ１０）的保护作用。结果表明，培养液中加入ＡＤＲ能使心肌细胞膜流动性明显低于对照组（Ｐ＜０．０５）；ＣｏＱ１０能有效地对抗ＡＤＲ的损伤作用（Ｐ＜０．０５）。提示ＡＤＲ具有损伤心肌细胞膜的作用，ＣｏＱ１０可保护和维持心肌细胞正常功能。     

一种新型的动态矩阵控制算法

对无约束动态矩阵控制（ＤＭＣ）算法进行了改进，提出了一种新的ＤＭＣ控制算法，即用遗传算法对ＤＭＣ的设计参数进行在线寻优，由优化结果来计算实际控制作用，与一般的定参数ＤＭＣ相比，其闭环性能更好，能有效地处理各种约束。仿真实例表明了该方法的有效。     

模糊逻辑诊断技术在汽油机故障诊断中的应用

根据汽油机故障征兆与故障原因之间并非一一对应的不确定关系特点,有针对性地提出了模糊逻辑诊断原理,给出了利用模糊逻辑诊断技术对汽油机故障进行分析诊断的方法,通过实例说明了模糊逻辑诊断技术在汽油机故障诊断中的应用．     

整周模糊度改进算法研究与实现

介绍了在短基线状态下利用单频单历元双差载波相位定位时模糊度固定的基本理论.结合正则化理论和阻尼矩阵的思想,提出了一种用改进的正则化方程来改善法方程病态性的方法.算例分析表明,应用改进的正则化算法可以改善法矩阵的病态性,得到更加精确的浮动解,利用LAMBAD方法搜索可得到准确的整周模糊度解.     

Designing H∞/GH 2 static-output feedback controller for vehicle suspensions using linear matrix inequalities and genetic algorithms

This paper presents an approach to design the H_∞/GH ₂ static-output feedback controller for vehicle suspensions by using linear matrix inequalities (LMIs) and genetic algorithms (GAs). Three main performance requirements for an advanced vehicle suspension are considered in this paper. Among these requirements, the ride-comfort performance is optimized by minimizing the H_∞ norm of the transfer function from the road disturbance to the sprung mass acceleration, while the road-holding performance and the suspension deflection limitation are guaranteed by constraining the generalized H₂ (GH ₂) norms of the transfer functions from the road disturbance to the dynamic tyre load and the suspension deflection to be less than their hard limits, respectively. At the same time, the controller saturation problem is considered by constraining its peak response output to be less than a given limit using the GH ₂ norm as well. A four-degree-of-freedom half-car model with active suspension system is applied in this paper. Several kinds of H_∞/GH ₂ static-output feedback controllers, which use the available sprung mass velocities or the suspension deflections as feedback signals, are obtained by using the GAs to search for the possible control gain matrices and then resolving the LMIs together with the minimization optimization problem. These designed H_∞/GH ₂ static-output feedback controllers are validated by numerical simulations on both the bump and the random road responses which show that the designed H_∞/GH ₂ static-output feedback controllers can achieve similar or even better active suspension performances compared with the state-feedback control case in spite of their simplicities.