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黑龙江省老沟-二根河成矿带金矿床地质特征及找矿方向
,,石书校
矿床地质 , 2010,
Abstract: 老沟-二根河成矿带是大兴安岭北部重要的金成矿带,位于蒙古-鄂霍茨克造山带的东南缘。近年来发现了砂宝斯、老沟、砂宝斯林场、三十二站、二根河等金矿床和一些金矿点,矿床(点)严格受NEE向漠河推覆构造带控制,矿体常赋存于其次级张扭性断裂构造带中。矿石中硫化物以黄铁矿为主(含量≤3%),为少硫化物矿石。矿石中黄铁矿等的δ34S值为-8.3‰~+7.8‰;成矿流体的δ18O值为5.6‰~18.7‰,δD值为-135‰~-89‰;铅同位素表现出造山带铅同位素特征。流体包裹体类型有气液两相、含CO2三相和纯CO2包裹体3种。包裹体气相成分主要为CO2、H2O、CH4和N2,总体上属CO2-H2O-N2±CH4±H2±CO体系。流体包裹体的盐度平均为5.0%~7.0%,密度平均为0.75~0.86g/cm3,属低盐度、低密度流体;均一温度为225.9~295.2℃,属中温热液矿床;成矿压力平均为65~82MPa,成矿深度平均为6.0~7.0km。金矿床的地质-地球化学特征与造山型金矿类似,应属造山型,形成于蒙古-鄂霍茨克陆-陆碰撞造山环境,并以CMF模式解释了其形成机制。在上述研究的基础上,提出了找矿方向。
使用多特征联合变量的支持向量机方法预测外膜蛋白
邹凌云?,正志?,
生物工程学报 , 2008,
Abstract: 外膜蛋白(outermembraneproteins,omps)是一类具有重要生物功能的蛋白质,通过生物信息学方法来预测omps能够为预测omps的二级和三级结构以及在基因组发现新的omps提供帮助。文中提出计算蛋白质序列的氨基酸含量特征、二肽含量特征和加权多阶氨基酸残基指数相关系数特征,将三类特征组合,采用支持向量机(supportvectormachine,svm)算法来识别omps。计算了包括四种残基指数的多种组合特征的识别结果,并且讨论了相关系数的阶次和权值对预测性能的影响。在数据集上的十倍交叉验证测试和独立性测试结果显示,组合特征识别方法对omps和非omps的识别精度最高分别达到96.96%和97.33%,优于现有的多种方法。在五种细菌基因组内识别omps的结果显示,组合特征方法具有很高的特异性,并且对pdb数据库中已知结构的omps识别准确度超过99%。表明该方法能够作为基因组内筛选omps的有效工具。
垂直圆管内向上稀疏层流泡状流相分布的实验研究
宋蔷,罗锐,,,
工程热物理学报 , 2001,
Abstract: 采用三维照相法对垂直圆管内稀疏层流泡状流充分发展段的相分布进行了实验研究。得到了8个流动工况下均匀尺寸气泡形成的泡状流的空泡率分布以及6个流动工况下非均匀尺寸气泡形成的泡状流的总体和大、小气泡组各自的空泡率分布。实验结果表明当气泡组的平均直径小于约3.5mm时,其空泡率分布在管壁附近出现尖峰;当气泡组的平均直径大于约3.5mm时,其空泡率分布的尖峰移向管中心;气泡尺寸对泡状流的相分布有重要影响。
泡状流运动的湍流模型及液相速度和湍动能的分布预测
宋蔷,,罗锐,
工程热物理学报 , 1999,
Abstract:
绝热层流泡状流运动的双流体模型
宋蔷,罗锐,,
化工学报 , 2001,
Abstract: 绝热层流泡状流是泡状流研究中的一个基础范例.目前描述绝热层流泡状流常采用的双流体模型由于相间作用考虑欠缺而适用性差.本文结合理论和实验研究结果导出了描述壁面“排斥”作用的表达式,并建立了一个封闭的双流体模型.模型预测值和实验值的比较表明,由于相间作用的合理考虑,扩大了该模型的适用范围
基于二次开发平台的阳极溶出伏安仪
,,,朱果逸
分析化学 , 2009,
Abstract: 建立了一种适合研发电化学仪器的新型二次开发平台,开发了一系列仪器模块和组件,并利用此平台研制了一种操作方便、成本低廉的微型半自动阳极溶出伏安仪(MASV).仪器采用了20位数模转换器和16位模数转换器,电流测量范围为1pA~20mA;支持线形扫描法和微分脉冲伏安法等电化学方法.电解池可原位清洗,搅拌均匀高效且速度可调;仪器体积小,且不需要外接电源,操作方便.利用MASV进行了一系列阳极溶出实验,获得了Cd2+和Pb2+溶液的标准曲线,其相关系数均大于0.999,检出限可达μg/L数量级,重现性较好.测试了环境水样中的Cd2+和Pb2+的含量.
一种新的矿产储量计算方法
刘树惠, , 丁浩
金属矿山 , 2008,
Abstract: 针对传统矿产储量计算方法和地质统计学矿产储量计算方法存在的不足,提出了一种均值与插值相结合的新的矿产储量计算方法。该方法先建立网格,再将网格分成内部包含样品和内部不包含样品两类,然后分别用平均值法和距离平方反比法计算其品位值,进而得出网格乃至整个矿体的储量。这种储量计算方法既解决了传统算法准确度不够高的缺点,又克服了地质统计学方法计算量太大的弊端。对紫金山金矿床某矿体储量的实际计算结果证明了新算法的可行性。
Prediction of Outer Membrane Proteins Using Support Vector Machine with Combined Features
使用多特征联合变量的支持向量机方法预测外膜蛋白

Lingyun Zou,Zhengzhi Wang,Yongxian Wang,
邹凌云
,正志,

微生物学报 , 2008,
Abstract: Outer membrane proteins (OMPs) are embedded in the outer membrane of Gram-negative bacteria, mitochondria, and chloroplasts. The cellular location and functional diversity of OMPs makes them an important protein class. Researches on prediction of OMPs by bioinformatics methods can bring helpful methodologies for identifying OMPs from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this paper, three feature classes were calculated from protein sequences: amino acid compositions, dipeptide compositions and weighted amino acid index correlation coefficients. Then, three feature classes were combined and inputted into a support vector machine (SVM) based predictor to identify OMPs from other folding types of proteins. The results of discrimination using several combined features including four amino acid index categories were calculated, and the influence on discrimination accuracy using different correlation coefficients with different orders and weights was discussed. In cross-validated tests and independent tests for identifying OMPs from a dataset of 1087 proteins belonging to all different types of globular and membrane proteins, the method using combined features obtains an overall accuracy of 96.96% and 97.33% respectively. And these results outperform that of other methods in the literature. Using this method, high specificities are shown from the results of identifying OMPs in five bacterial genomes, and over 99% OMPs with known three-dimensional structures in the PDB database are correctly discriminated. These results indicate that the method is a powerful tool for OMPs discrimination in genomes.
Prediction of Outer Membrane Proteins Using Support Vector Machine with Combined Features
使用多特征联合变量的支持向量机方法预测外膜蛋白

Lingyun Zou,Zhengzhi Wang,Yongxian Wang,
邹凌云
,正志,

生物工程学报 , 2008,
Abstract: Outer membrane proteins (OMPs) are embedded in the outer membrane of Gram-negative bacteria, mitochondria, and chloroplasts. The cellular location and functional diversity of OMPs makes them an important protein class. Researches on prediction of OMPs by bioinformatics methods can bring helpful methodologies for identifying OMPs from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this paper, three feature classes were calculated from protein sequences: amino acid compositions, dipeptide compositions and weighted amino acid index correlation coefficients. Then, three feature classes were combined and inputted into a support vector machine (SVM) based predictor to identify OMPs from other folding types of proteins. The results of discrimination using several combined features including four amino acid index categories were calculated, and the influence on discrimination accuracy using different correlation coefficients with different orders and weights was discussed. In cross-validated tests and independent tests for identifying OMPs from a dataset of 1087 proteins belonging to all different types of globular and membrane proteins, the method using combined features obtains an overall accuracy of 96.96% and 97.33% respectively. And these results outperform that of other methods in the literature. Using this method, high specificities are shown from the results of identifying OMPs in five bacterial genomes, and over 99% OMPs with known three-dimensional structures in the PDB database are correctly discriminated. These results indicate that the method is a powerful tool for OMPs discrimination in genomes.
Uniprocessor Performance Tuning of a Structured Grid Based Parallel CFD Application
一个结构网格并行CFD程序的单机性能优化

车永刚,张理论,,徐传福,刘巍,正华,刘化
计算机科学 , 2013,
Abstract: 从单机性能优化角度对一个高阶精度结构网格CFI)并行程序进行了优化。通过识别关键变量并对其进行 常量参数化优化,使编译器能够实现更高级别的针对性优化;根据程序数据结构特点及访问模式,设计了分级数据缓 存技术,使程序主要计算代码能够以更优的方式访问主要数据结构,提高了访存空间局部性;进行了各种循环变换,以 优化访存性能。在国家超算长沙中心“`Tianhe—lA',并行机上的测试结果表明,相对于采用Intel编译器最高优化级别 的版本,其对10。万网格点二维翼型算例,串行程序性能提高约22.2%-28.9%;对1. 12亿网格点三角翼算例,并行 程序性能提高约13.9%-20.2%。
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