基于网络环境的计算机技能考试系统的设计与实现

介绍了河北省对口招生计算机技能测试考试系统的设计。该系统是一个基于网络环境的无纸化自动评分并实现网络收分的考试系统,采用C/S模式,对系统硬件要求低,软件容易安装,易于考试管理人员使用和管理。该系统在河北省对口招生计能考试中连续使用了2年,考试结果分析表明,考生的成绩分布曲线正常。     

A Statistical Analysis of Protein—Protein Interaction with Knowledge—Based Potential at Residue Level

Protein-protein recognition is an important step in biological processes, which still largely remains elusive. The inter-residue contact potential, CPij, describes the propensity of contact between two types of residue. In this study, several different CPij variants were examined with the objective of discriminating the binding potential of surface pairs. Using solvent mediated inter-molecule contact potential (SM-IMCPij), an evaluation model was deduced and tested. Using the evaluation model it was found that the SM-IMCPij gives a better performance than either residue mediated IMCPij(RM-IMCPij) or folding-residue contact potential (FCPij). The results suggest that the evaluation model provides a fast, effective, and discriminative method for the evaluation of proposed binding interfaces.     

Exponential family non-linear models for categorical data with errors of observation

We present a general framework for treating categorical data with errors of observation. We show how both latent class models and models for doubly sampled data can be treated as exponential family nonlinear models. These are extended generalized linear models with the link function substituted by an observationwise defined non-linear function of the model parameters. The models are formulated in terms of structural probabilities and conditional error probabilities, thus allowing natural constraints when modelling errors of observation. We use an iteratively reweighted least squares procedure for obtaining maximum likelihood estimates. This is faster than the traditionally used EM algorithm and the computations can be made in GLIM.¹ As examples we analyse three sets of categorical data with errors of observation which have been analysed before by Ashford and Sowden,² Goodman³ and Chen,⁴ respectively.     

Strategies to serve or receive the service in volleyball

We analyze the problem facing the team that wins the toss at the deciding fifth set of a volleyball match. The teams decision to serve or to receive the service can make a difference to the eventual outcome of the match. We characterize the conditions under which it is better to serve or to receive the service at that set. These conditions are obtained by first expressing the exact probability of winning a set in volleyball in terms of the Legendre polynomials.     

基于互信息和测度学习信度网结构

交叉熵是对一个分布与其近似分布的接近程度的度量。在许多关于信度网结构的学习文献中,都将交叉熵作为检验算法学习效果的一个指标。笔者直接从交叉熵最优这一指标出发,在分析已有测度的基础上,提出了一个新的测度－互信息和测度,并证明了该测度的可分解性质。最后,给出了利用互信息和测度进行信度网结构学习的两种启发式搜索算法。     

RI指数联合超声对孕晚期胎儿肾积水预后预测价值的研究

目的探讨RI指数联合超声对孕晚期胎儿肾积水预后预测的价值.方法选取行超声检查发现的晚孕期肾积水胎儿141例,检测胎儿的肾实质厚度、肾盂前后直径(APD)、肾盂肾盏形态,并给予评分,累计3项指标评分分值;对胎儿行超声检查时测定积水肾脏肾门位置的RI值.将以上检测结果与胎儿出生后1 a内行超声检查和临床诊断结果进行对照研究.结果 141例胎儿共有170只肾积水;出生后1 a内行超声检查和临床诊断显示,非病理性肾积水121只,病理性49只.随着超声评分值增加,病理性肾积水风险越高,其中超声评分≤3分均为非病理性肾积水,评分≥8分均为病理性肾积水.病理性肾积水与非病理性肾积水超声评分之间差异具有显著统计学意义(P0.01).超声评分为5分时,为辨别诊断非病理性肾积水和病理性肾积水的截断值,特异度为88.61%,灵敏度为95.00%.超声评分≤5分的肾积水胎儿的肾动脉RI值在0.60~0.67,而超声评分5分的肾积水胎儿肾动脉RI值在0.70~0.78.结论超声评分法联合RI指数评估胎儿肾积水预后客观准确,具有较高实用价值,超声评分5分为诊断胎儿病理性肾积水的最佳截断值.     

An Affordable Accurate Evaluation of the Binding Conformations Predicted by Molecular Docking Studies in Bio-System

正Recently,docking has been widely used to predict the binding-modes of protein-inhibitors,when the crystal complexes structure was absent.Most docking algorithms are able to generate a large number of probable conformations,it,however,is difficult to effectively evaluate these docking poses and identify the most reasonable bindingmode.In the present study,on the basis of the crystallographic data of human 3-hydroxy-3-methylglutaryl coenzyme     

Development and application of consumer credit scoring models using profit-based classification measures

This paper presents a new approach for consumer credit scoring, by tailoring a profit-based classification performance measure to credit risk modeling. This performance measure takes into account the expected profits and losses of credit granting and thereby better aligns the model developers’ objectives with those of the lending company. It is based on the Expected Maximum Profit (EMP) measure and is used to find a trade-off between the expected losses – driven by the exposure of the loan and the loss given default – and the operational income given by the loan. Additionally, one of the major advantages of using the proposed measure is that it permits to calculate the optimal cutoff value, which is necessary for model implementation. To test the proposed approach, we use a dataset of loans granted by a government institution, and benchmarked the accuracy and monetary gain of using EMP, accuracy, and the area under the ROC curve as measures for selecting model parameters, and for determining the respective cutoff values. The results show that our proposed profit-based classification measure outperforms the alternative approaches in terms of both accuracy and monetary value in the test set, and that it facilitates model deployment.     

A Bayesian statistical approach of improving knowledge‐based scoring functions for protein–ligand interactions

Knowledge‐based scoring functions are widely used for assessing putative complexes in protein–ligand and protein–protein docking and for structure prediction. Even with large training sets, knowledge‐based scoring functions face the inevitable problem of sparse data. Here, we have developed a novel approach for handling the sparse data problem that is based on estimating the inaccuracies in knowledge‐based scoring functions. This inaccuracy estimation is used to automatically weight the knowledge‐based scoring function with an alternative, force‐field‐based potential (FFP) that does not rely on training data and can, therefore, provide an improved approximation of the interactions between rare chemical groups. The current version of STScore, a protein–ligand scoring function using our method, achieves a binding mode prediction success rate of 91% on the set of 100 complexes by Wang et al., and a binding affinity correlation of 0.514 with the experimentally determined affinities in PDBbind. The method presented here may be used with other FFPs and other knowledge‐based scoring functions and can also be applied to protein–protein docking and protein structure prediction. © 2014 Wiley Periodicals, Inc.