Accounting for the uncertainties in the estimation of average shear wave velocity using V S– N correlations

Site-specific seismic hazard analysis is crucial for designing earthquake resistance structures, particularly in seismically active regions. Shear wave velocity ( V _S) is a key parameter in such analysis, although the economy and other factors restrict its direct field measurement in many cases. Various V _S–SPT– N correlations are routinely incorporated in seismic hazard analysis to estimate the value of V _S. However, many uncertainties question the reliability of these estimated V _S values. This paper comes up with a statistical approach to take care of such uncertainties involved in V _S calculations. The measured SPT– N values from all the critical boreholes were converted into statistical parameters and passed through various correlations to estimate V _S at different depths. The effect of different soil layers in the boreholes on the Vs estimation was also taken into account. Further, the average shear wave velocity of the top 30 m soil cover ( V _S30) is estimated after accounting for various epistemic and aleatoric uncertainties. The scattering nature of the V _S values estimated using different V _S– N correlations was reduced significantly with the application of the methodology. Study results further clearly demonstrated the potential of the approach to eliminate various uncertainties involved in the estimation of V _S30 using general and soil-specific correlations.     

Two- or three-stage least squares?

Two elements enter the choice between 2 and 3SLS for full-system estimation: statistical efficiency and computational cost. 2SLS always has the computational edge, but 3SLS can be more efficient, a relative advantage that increases with the strength of the interrelations among the error terms. A measure of these interrelations is thus helpful in making the choice, and, when there are only two equations, this has suggested using a high pairwise error correlation as an indicator of when to use 3SLS. In larger systems of equations, however, these pairwise correlations can remain small even though more general interrelations give 3SLS the relative advantage. More general indicators are therefore needed, and this paper suggests three such and demonstrates their efficacy.Professor of Economics, Boston College, and Principal Research Associate, Center for Computational Research in Economics and Management Science, MIT. All computation was done on the TROLL system at MIT. My thanks go to Josh Charap for his able research assistance. This research was sponsored in part by the National Science Foundation under grant #IST-8420614.     

Does the alliance cause good outcome? Recommendations for future research on the alliance.

Research has consistently documented that the quality of the therapeutic alliance is related to the outcome of diverse psychotherapies. Insufficient attention, however, has been directed at identifying the nature and magnitude of the causal relationship between the alliance and outcome. In this commentary, we discuss the major threats to causal interpretation of alliance-outcome correlations and provide suggestions for future research that would help clarify the extent to which the alliance causes positive outcomes. Assuming the alliance is a causal factor in relation to outcome, we provide recommendations for research on the alliance that would attempt to improve patient care by enhancing the alliance. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Convective cooling of supercritical carbon dioxide inside tubes: heat transfer analysis through neural networks

In a previous work, convective heating of carbon dioxide was studied with neural networks (NN), obtaining a totally heuristical heat transfer equation from the direct regression of experimental data. In the present work, the analysis focuses on the cooling process, which has a technical relevance in various applications, as for example in transcritical refrigeration cycles. Heat transfer around the critical zone presents a marked enhancement, that follows the peaks in thermophysical properties like thermal conductivity and heat capacity. Similarly, other properties like density and enthalpy, present a strong variation in narrow temperature intervals around the critical point.This constitutes then a highly non-linear phenomenon, for which it is advisable to use a very flexible function approximator like the NNs. NN models were applied both in terms of dimensionless numbers and of physical quantities, obtaining the two corresponding NN architectures. The choice of the optimal number of neurons in the NN hidden layer is discussed. The NN models are then compared with a recent correlation from literature, for which the validation results present an AAD of 27% and a bias of −26% with an evident prediction shifting. On the other hand the NN models in terms of dimensionless numbers and of physical quantities have AAD and bias of 14% and −4%, and of 7% and −2%, respectively, showing a largely better performance.     

一种基于图卷积网络的文本多标签学习方法

多标签学习广泛应用于文本分类、标签推荐、主题标注等.最近,基于深度学习技术的多标签学习受到广泛关注,针对如何在多标签学习中有效挖掘并利用高阶标签关系的问题,提出一种基于图卷积网络探究标签高阶关系的模型TMLLGCN.该模型采用GCN的映射函数从数据驱动的标签表示中生成对象分类器挖掘标签高阶关系.首先,采用深度学习方法提取文本特征,然后以数据驱动方式获得基础标签关联表示矩阵,为更好地建模高阶关系及提高模型效果,在基础标签关联表示矩阵上考虑未标记标签集对已知标签集的影响进行标签补全,并以此相关性矩阵指导GCN中标签节点之间的信息传播,最后将提取的文本特征应用到学习高阶标签关系的图卷积网络分类器进行端到端训练,综合标签关联和特征信息作为最终的预测结果.在实际多标签数据集上的实验结果表明,提出的模型能够有效建模标签高阶关系且提升了多标签学习的效果.     

用人工神经网络方法预测鼓泡塔气含率

A new correlation for the prediction of gas hod up in bubble columns was proposed based on an extensive experimental database set up from the literature published over last 30 years .The updated estimation method relying on artificial neural network,dimensional analysis and phenomenological approaches was used and the model prediction agreed with the experimental data with average relative error less than 10%.     

A unified model for the structure of polymers in semidilute solution

A model is developed to analyse the concentration dependence of the range ξ_? of the monomer pair correlation function. In semidilute solution, three concentration regimes are found for semiflexible molecules and the crossover points between the various regimes are predicted in terms of the characteristic ratio of the chains in dilute solution and in terms of the Flory interaction parameter χ. A simple physical interpretation is given which explains the concentration dependence of ξ_? based on binary contacts initially and then ternary contacts at higher concentration. Temperature-concentration diagrams are developed for several common polymer-solvent systems.     

多标记分类和标记相关性的联合学习

提出了多标记分类和标记相关性的联合学习(JMLLC),在JMLLC中,构建了基于类别标记变量的有向条件依赖网络,这样不仅使得标记分类器之间可以联合学习,从而增强各个标记分类器的学习效果,而且标记分类器和标记相关性可以联合学习,从而使得学习得到的标记相关性更为准确.通过采用两种不同的损失函数:logistic回归和最小二乘,分别提出了JMLLC-LR(JMLLC with logistic regression)和JMLLC-LS(JMLLC with least squares),并都拓展到再生核希尔伯特空间中.最后采用交替求解的方法求解JMLLC-LR和JMLLC-LS.在20个基准数据集上基于5种不同的评价准则的实验结果表明,JMLLC优于已提出的多标记学习算法.     

Graph regularized multiset canonical correlations with applications to joint feature extraction

Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.     

海拔高度对变频器散热影响的研究

根据高海拔地区空气的流动特点,引入海拔高度参数。结合不同海拔高度空气特性对散热器温度的影响,对文献[11]提出的板翅式散热器对流换热系数关联式做了修正。根据修正后的关联式计算了散热器出风口空气平均温度和散热器平均压降。修正后的理论计算结果与仿真结果的相对误差小于5％,表明该修正合理。文中的结果可为高海拔地区变频器等电气产品的热设计和可靠性评估提供计算方法和参考数据。