低比转速离心及旋涡冷媒泵性能预测研究

利用损失模型及计算流体力学方法对低比转速R32离心和旋涡泵性能预测进行研究,结果表明:损失模型与计算流体力学方法预测出的性能曲线吻合较好;低比转速离心泵中轮阻损失占总流动损失的80%以上,对泵性能有重要影响;旋涡泵的纵向旋涡强度随流量增大而减弱;离心泵比旋涡泵具有较宽的运行范围,在小流量情况下旋涡泵比离心泵具有较高的效率和扬程。研究工作对丰富低比转速泵流动理论与设计方法具有参考价值。     

Cavity-mediated stationary atom–mirror entanglement in the presence of photothermal effects

We study stationary entanglement properties of an optomechanical system containing an atomic ensemble. We focus onto the case of the movable mirror strongly coupled to the cavity field through both radiation pressure and photothermal force. Exploiting a quantum Langevin equation approach we investigate the bipartite entanglement properties of various bipartite subsystems as well as stationary tripartite entanglement of the system. We particularly study robustness of the atom–mirror entanglement against temperature. We show that, even though the photothermal force is a dissipative force, it can significantly improve the cavity mediated atom–mirror entanglement.     

Limitation of predictive 2-D liquid chromatography in reducing the database search space in shotgun proteomics: in silico studies

A two-dimensional (2-D) liquid chromatography (LC) separation of complex peptide mixtures that combines a normal phase utilizing hydrophilic interactions and a reversed phase offers reportedly the highest level of 2-D LC orthogonality by providing an even spread of peptides across multiple LC fractions. Matching experimental peptide retention times to those predicted by empirical models describing chromatographic separation in each LC dimension leads to a significant reduction in a database search space. In this work, we calculated the retention times of tryptic peptides separated in the C18 reversed phase at different separation conditions (pH 2 and pH 10) and in TSK gel Amide-80 normal phase. We show that retention times calculated for different 2-D LC separation schemes utilizing these phases start to correlate once the mass range of peptides under analysis becomes progressively narrow. This effect is explained by high degree of correlation between retention coefficients in the considered phases.     

Considerations for the automated collection of thermodynamic data in gas chromatography

Thermodynamic modeling of retention times in gas chromatography depends on the accurate estimation of thermodynamic parameters. Previous research has used manual injections of samples with coinjection of a dead time marker to obtain accurate measurements of the retention factor of analytes. Ideally this process would be automated. Herein an approach is presented by which thermodynamic parameters can be estimated both autonomously and accurately. This method also allows for a consistent estimation of thermodynamic parameters regardless of factors such as data system delays and the nature of the void time marker employed. Ignoring these factors can lead to significant errors in the prediction of retention times when using thermodynamic models.     

Some properties of the vertical structure operators for hydrostatic atmospheric models

Hydrostatic atmospheric models in a generalized vertical coordinate are considered. The governing equations are linearized with respect to a general basic state including arbitrary vertical variation of the reference temperature profile. Vertical structure equations are derived and studied for both continuous and discretized models. Some properties of vertical normal modes are defined and a comparison between continuous and discrete cases is made.     

New insights into churn prediction in the telecommunication sector: A profit driven data mining approach

Customer churn prediction models aim to indicate the customers with the highest propensity to attrite, allowing to improve the efficiency of customer retention campaigns and to reduce the costs associated with churn. Although cost reduction is their prime objective, churn prediction models are typically evaluated using statistically based performance measures, resulting in suboptimal model selection. Therefore, in the first part of this paper, a novel, profit centric performance measure is developed, by calculating the maximum profit that can be generated by including the optimal fraction of customers with the highest predicted probabilities to attrite in a retention campaign. The novel measure selects the optimal model and fraction of customers to include, yielding a significant increase in profits compared to statistical measures.In the second part an extensive benchmarking experiment is conducted, evaluating various classification techniques applied on eleven real-life data sets from telecom operators worldwide by using both the profit centric and statistically based performance measures. The experimental results show that a small number of variables suffices to predict churn with high accuracy, and that oversampling generally does not improve the performance significantly. Finally, a large group of classifiers is found to yield comparable performance.     

A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data

The availability of abundant data posts a challenge to integrate static customer data and longitudinal behavioral data to improve performance in customer churn prediction. Usually, longitudinal behavioral data are transformed into static data before being included in a prediction model. In this study, a framework with ensemble techniques is presented for customer churn prediction directly using longitudinal behavioral data. A novel approach called the hierarchical multiple kernel support vector machine (H-MK-SVM) is formulated. A three phase training algorithm for the H-MK-SVM is developed, implemented and tested. The H-MK-SVM constructs a classification function by estimating the coefficients of both static and longitudinal behavioral variables in the training process without transformation of the longitudinal behavioral data. The training process of the H-MK-SVM is also a feature selection and time subsequence selection process because the sparse non-zero coefficients correspond to the variables selected. Computational experiments using three real-world databases were conducted. Computational results using multiple criteria measuring performance show that the H-MK-SVM directly using longitudinal behavioral data performs better than currently available classifiers.     

基于MTS特征选择的神经网络集成方法

在Bagging算法基础上,运用马田系统进行特征选择,形成双重扰动改善神经网络集成的分类性能.实验表明,双重扰动增加了集成网络个体精度和差异度,基于MTS-Bagging算法的分类性能相比于Bagging有明显提高.     

中国能源消费预测灰色建模法

选取2001—2007年中国能源消费实际值为依据,应用灰色系统理论建立煤炭能源和能源消费总量GM(1,1)预测模型,并分别进行精度检验和对比分析.检验结果表明:所建立的预测模型均为一级精度,适合于具有灰色特征的能源消费原始数据序列的模拟和预测分析.通过预测模型的计算结果可以知道:中国煤炭能源和能源消费总量呈指数增长规律,2008—2012年期间分别以16.33%和14.94%的速度增长,在整体能源消费结构中,煤炭能源所占比例将以0.29%的速度增长(为合理规划我国未来的能源消费战略,制定能源供应短缺和能源环境污染对策).     

基于支持向量回归机的水文混沌时间序列预测

支持向量机在系统辨识和分类研究方面比较成熟,目前尚没有提出有效的支持向量回归理论来解决非线性、时变、干扰的复杂问题.支持向量回归机主要用于因果关系点对的回归预测,把支持向量回归机应用于水文混沌时间序列的预测研究是一个有意义的工作.在支持向量机一般理论基础上,提出了水文混沌时间序列支持向量回归机模型,并就模型进行仿真计算,讨论了模型参数对支持向量回归机预测精度的影响,为模型参数寻优提供一般指导原则.直门达水文站径流量混沌时间序列支持向量回归机预测实验表明,水文混沌时间序列支持向量回归机模型是有效的.