Effect of nanosilica addition on bioactivity and in vivo properties of calcium aluminate cement

This study aims at assessing the influence of nanosilica on the bioactivity and mechanical properties of calcium aluminate cement. For this purpose, nanosilica was applied as a replacement for calcium aluminate cement at 0, 2, 5 and 8 wt%. The main components were analyzed by scanning electron microscope coupled with surface imaging and elemental analysis, fourier transform infrared spectroscopy and X-ray diffraction analysis. To estimate the bioactivity of specimens, hydroxyapatite formation on the surface of cement paste was investigated in the simulated body fluid solution. In addition, in vivo evaluation of calcium aluminate cement was performed in subcutaneous tissue of rats. To investigate the mechanical properties, both compressive and flexural strengths were also measured. The results revealed that by increasing nanosilica up to 8 wt%, the strength enhanced. Moreover all cement paste samples with various amounts of nanosilica represented good bioactivity because of formation of bonelike apatite layer on the surface of specimens within 28 days after soaking in simulated body fluid. In vivo experiments indicated that the cement sample was absorbed by the tissue and there was no infection at the implant site. Based on the in vitro and in vivo results, the specimen with 2 wt% nanosilica represented the highest bioactivity.     

核电材料在模拟反应堆环境中应力腐蚀破裂测试技术与性能评价

简要介绍了采用慢应变速率试验，Ｕ型弯曲和Ｃ形环试验等技术，分别对８００合金，３０４和３１６及３１６Ｔｉ不锈钢Ａ５３３Ｂ压力容器用多在模拟核反应堆环境中的应力腐蚀破裂敏感性性进行的试验研究的一些主要结果；并结合电化学测试和表面膜俄歇电子能谱分析结果进行了讨论。     

一种改进的交错脉冲解速度模糊方法

研究了脉冲多普勒气象雷达中用于解速度模糊的交错脉冲方法，理论分析了时域脉冲对处理中速度估计公式的局限性，同时对该公式进行了修正。仿真结果表明，改进的交错脉冲方法具有更好的解速度模糊性能，而且易于实时处理。     

EM procedures using mean field-like approximations for Markov model-based image segmentation

Image segmentation using Markov random fields involves parameter estimation in hidden Markov models for which the EM algorithm is widely used. In practice, difficulties arise due to the dependence structure in the models and approximations are required. Using ideas from the mean field approximation principle, we propose a class of EM-like algorithms in which the computation reduces to dealing with systems of independent variables. Within this class, the simulated field algorithm is a new stochastic algorithm which appears to be the most promising for its good performance and speed, on synthetic and real image experiments.     

面向大规模定制的虚拟设计研究

综述了大规模定制的范围经济性和面向大规模定制的基本设计方法，并提出了一个虚拟设计框架，该框架改变了传统的设计模式，利用先进的设计方法学和仿真支撑技术为产品开发提供了最大柔性和敏捷性。同时，它提供了一个多学科合作的设计环境，用户直接参与进设计循环中。最后，结合新型剑杆织机的开发，展示了一个能够支持新产品快速成功开发的虚拟设计环境。     

1999年三峡大坝混凝土温控防裂研究与实践

1999年，为三峡工程二期施工阶段由开挖为主转入混凝土浇筑高峰期的关键一年，面临混凝土浇筑强度高、进度紧、温控设计要求严、高温季节浇筑基础约束区混凝土和施工设备投产晚的严峻局面。长江委设计院对1999年高温季节浇筑大坝基础约束区混凝土进行了专题研究。通过对大坝温度应力仿真研究和夏季各种温控措施效果分析，提出了夏季浇筑基础约束区混凝土的综合温控防裂措施。通过三峡工程参建各方共同努力，全面实施综合温控防裂措施，达到了预期效果，避免了大坝基础约束区产生危害性贯穿裂缝，并为顺利完成混凝土年度计划和形象面貌创造了有利条件。     

基于栅格法-模拟退火法的机器人路径规划

路径规划是机器人技术中的重要组成部分,分全局路径规划和局部路径规划。本文将栅格法与模拟退火法结合,采用栅格法表示环境信息。局部路径规划主要基于模拟退火法,使路径跳出局部极小点,到达目标位置。     

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Kirkpatrick于1982年认识到固体粒子退火过程和组合优化问题有相似性，从而提出了模拟退火算法，并从数学上已证明了模拟退火算法可以取得全局最优解。相比之下，传统的最优化法只能求局部最优值。在计算物质平衡方程时，存在很多不确定的参数，给精确计算带来了一定困难。因此，本文引入模拟退火算法求解物质平衡方程。     

基于模拟退火算法的旅行商问题的实现

旅行商问题TSP是一类典型的NP完全问题．围绕着这个问题有各种不同的求解方法,已有的算法例如动态规划法、分支限界法、回溯法等,这些精确式方法都是指数级的,根本无法解决目前的实际问题．贪心法是近似方法．无法达到比较满意的近似比。常用的遗传算法也是求解这类问题的常用方法之一。由于该问题的解是一种特殊的序列．所以遗传算法在求解该问题时的性能也并不理想。模拟退火算法具有描述简单、使用灵活、运用广泛、运行效率高和较少受到初始条件约束等优点．是解决旅行商问题的一种很好的算法。     

Extremal optimization vs. learning automata: Strategies for spin selection in portfolio selection problems

Nowadays, various imitations of natural processes are used to solve challenging optimization problems faster and more accurately. Spin glass based optimization, specifically, has shown strong local search capability and parallel processing. But, spin glasses have a low rate of convergence since they use Monte Carlo simulation techniques such as simulated annealing (SA). Here, we propose two algorithms that combine the long range effect in spin glasses with extremal optimization (EO-SA) and learning automata (LA-SA). Instead of arbitrarily flipping spins at each step, these two strategies aim to choose the next spin and selectively exploiting the optimization landscape. As shown in this paper, this selection strategy can lead to faster rate of convergence and improved performance. The resulting two algorithms are then used to solve portfolio selection problem that is a non-polynomial (NP) complete problem. Comparison of test results indicates that the two algorithms, while being very different in strategy, provide similar performance and reach comparable probability distributions for spin selection. Furthermore, experiments show there is no difference in speed of LA-SA or EO-SA for glasses with fewer spins, but EO-SA responds much better than LA-SA for large glasses. This is confirmed by tests results of five of the world's major stock markets. In the last, the convergence speed is compared to other heuristic methods such as Neural Network (NN), Tabu Search (TS), and Genetic Algorithm (GA) to approve the truthfulness of proposed methods.