Prevalence, genetic diversity, and antimicrobial susceptibility profiles of Staphylococcus aureus isolated from bovine mastitis in Zhejiang Province, China

This study was conducted to determine genetic diversity and antimicrobial susceptibility profiles of Staphylococcusaureus recovered from bovine mastitis in Zhejiang Province, China. Out of 3178 quarter milk samples from 846 lactating cows, among which 459 cows (54.3%) were found HMT positive, 890 quarters (28%) were found having subclinical mastitis. From 75 representative S. aureus isolates, 16 distinct types were identified by pulsed-field gel electrophoresis (PFGE). Four major PFGE types (A, B, C, and D) accounted for 82.7% of all isolates, and type A (41.3%) was observed in multiple herds across the studied areas. Each region was found to have a predominant type: Hangzhou type A (64.1%), Ningbo type C (34.5%) and type B (23.1%), Jinhua type D (53.3%), and Taizhou type C (62.5%). Results of antimicrobial susceptibility tests showed that 90.7% of the isolates were resistant to at least one antimicrobial. Resistance to penicillin and ampicillin (77.3%), tetracycline (60.0%), or erythromycin (48.0%) was observed. The bacteria resistant to multiple antibiotics such as penicillin, ampicillin, tetracycline, and erythromycin were commonly found. The information obtained from this study is useful for designing specific control programs for bovine S. aureus mastiffs in this region.     

Parameters optimization and nonlinearity analysis of grating eddy current displacement sensor using neural network and genetic algorithm

A grating eddy current displacement sensor （GECDS） can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions. The parameters optimization of the sensor is essential for economic and efficient production. This paper proposes a method to combine an artificial neural network （ANN） and a genetic algorithm （GA） for the sensor parameters optimization. A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS, and then a GA is used in the optimization process to determine the design parameter values, resulting in a desired minimal nonlinearity error of about 0.11%. The calculated nonlinearity error is 0.25%. These results show that the proposed method performs well for the parameters optimization of the GECDS.     

函数优化的遗传算法

遗传算法是一种有效的模拟进化算法,针对不同问题,编码方式多种多样。针对函数优化问题,阐述了不同的编码方式及遗传操作,在Matlab环境中用遗传算法实现求解函数优化问题。     

江苏种群橄榄蚶同工酶的生化遗传分析

采用聚丙烯酰胺凝胶不连续垂直平板电泳技术对江苏种群橄榄蚶不同个体的苹果酸脱氢酶、苹果酸酶、醇脱氢酶、乳酸脱氢酶、淀粉酶、酯酶、超氧化物歧化酶和细胞色素氧化酶8种同工酶进行分析,得到基本图谱,并对其进行了生化遗传分析．共记录了21个基因座位、38个等位基因,其中有11个基因座位（Me-1、Adh-2、Adh-3、Ldh-1、Ldh-2、Amy-1、Amy-2、Est-2、Sod-2、Cod-1和Cod-2）为多态,多态座位比例（P）为0．5238,该比例接近一般海产无脊椎动物的多态座位比例,表明江苏种群橄榄蚶具有一定的遗传变异能力．     

Science Letters: Low-cost fault tolerance in evolvable multiprocessor systems: a graceful degradation approach

The evolvable multiprocessor (EvoMP), as a novel multiprocessor system-on-chip (MPSoC) machine with evolvable task decomposition and scheduling, claims a major feature of low-cost and efficient fault tolerance. Non-centralized control and adaptive distribution of the program among the available processors are two major capabilities of this platform, which remarkably help to achieve an efficient fault tolerance scheme. This letter presents the operational as well as architectural details of this fault tolerance scheme. In this method, when a processor becomes faulty, it will be eliminated of contribution in program execution in remaining run-time. This method also utilizes dynamic rescheduling capability of the system to achieve the maximum possible efficiency after processor reduction. The results confirm the efficiency and remarkable advantages of the proposed approach over common redundancy based techniques in similar systems.     

一种矩形件布局问题的求解方法

布局问题来源于生产实际,在提高板材利用率以节约原材料,降低成本,提高经济效益,对各行业均有重要意义。针对遗传算法在矩形件布局问题中的实际特点,并分析了矩形件布局给定排放顺序的排放算法,并提出了一种新的排放算法——最低水平线旋转搜索法,并将这种算法和遗传算法结合应用于矩形件布局问题的求解。计算实例表明这种新的排放算法可以和遗传蚁群算法有效的结合。     

在Direct 3D程序中运用碰撞检测

本文分析了三维场景中的物体之间的碰撞和干涉的原因,提出了一个针对物体与场景之间的碰撞检测的算法,并阐述了它的原理及其如何具体实现,为编写程序代码做了充分准备。     

基于遗传算法的分层覆盖网络组播树构建

依据NICE网络协议组织网络节点,提出一种分层覆盖网络组播树模型,其基于K-Mediods和遗传算法对组播服务节点MSNs选择,构建覆盖网络分层组播树。根据覆盖网络中组播服务节点的特点,引入基因差异控制和变异精英控制策略。对遗传算法中的交叉和变异算子进行修正,限制适应度差的个体生成,在缩小搜索空间、加快收敛速度的同时,提高算法的全局寻优能力。理论分析和仿真结果也表明,该模型不仅有效克服了传统K-Mediods算法模型易陷入局部极小值的特点,而且明显避免了对初始中心选值敏感的问题。     

基于IP网络流量路由算法的设计与实现

本文提出当网络流量达到阈值时采用"基于多线程的分时段静态路由算法"来解决网络出现拥塞时浏览器页面打开很慢的问题。在网络流量未达到阈值时,采用"基于单播 选播的动态路由算法"来优化路由以满足用户的服务质量(QoS)需求。在路由发生改变后,本文采用多播技术使同一网段内的所有路由器在短时间内收到路由更新信息。     

粒子群算法与遗传算法的结合研究

自上世纪80年代以来,智能优化算法(粒子群算法、遗传算法等)通过模拟或者揭示某些自然现象和过程而发展起来,为优化理论提供了新的思路.通过将不同算法结合来达到更好的优化效果日益成为当前优化研究领域的热点.在粒子群算法中引入遗传算法的交叉和变异算子及种群分割策略,将两种算法有机结合,提出了粒子群算法与遗传算法的混合优化算法(GA-PSO).为了验证该GA-PSO混合优化算法是否具有良好性能,将该算法用于一些标准测试函数的优化.