Evaluating continuous chirality measure as a 3D descriptor in chemoinformatics applied to asymmetric catalysis

Continuous Chirality Measure (CCM) is a computational metric by which to quantify the chirality of a compound. In enantioselective catalysis, prior work has postulated that CCM is correlated to selectivity and can be used to understand which structural features dictate catalyst efficacy. Herein, the investigation of CCM as a metric capable of guiding catalyst optimization is explored. Conformer-dependent CCM is also explored. Finally, CCM is used with Sterimol parameters to significantly improve the performance of Random Forest models.     

不同直径碳纳米管的抗电化学氧化性

本文比较了由化学气相沉积法制备的不同直径(在100 nm以内)的多壁碳纳米管(CNT)的抗电化学氧化性.将CNT电极于1.2 V(vs.RHE)下电氧化120 h,记录氧化电流~时间变化曲线;X射线光电子能谱(XPS)分析氧化前后CNT的表面化学组成.结果表明,随着CNT直径的减小,其氧化电流降低,但其中以为10~20 nm的CNT电极氧化电流最小,表面氧的增量也最小,即被氧化的程度最低,抗电化学氧化性最强.根据不同直径CNT的缺陷位、不定型碳的丰度和碳原子的应力能,分析了其抗电化学氧化性差异的原因.     

一种基于距离比值的支持向量机增量训练算法

由于支持向量机具有较好地学习性能和泛化能力,目前已经得到了广泛的应用。如何使支持向量机进行有效的增量学习是目前支持向量机应用中需要解决的问题。深入研究了支持向量分布特点,提出了一种新的支持向量机增量训练淘汰机制——距离比值算法。该算法根据遗忘规则,设定一个合适的参数,按距离比值法中的定义计算各个样本中心距离与其到最优分类面距离的比值,舍弃对后续训练影响不大的样本,即可对训练数据进行有效的淘汰。对标准数据集的实验结果表明,使用该方法进行增量训练在保证分类精度的同时,能有效地提高训练速度。     

基于多分类器融合的语音识别方法研究

针对语音识别性能提高的问题,提出了一种基于多分类器融合的语音识别方法,该方法使用支持向量机(support vector machine,SVM),RBF神经网络与贝叶斯网络作为成员分类器,根据样本库中抽取的校验集计算各成员分类器的权值,以加权评分的投票策略进行决策融合.实验结果表明,通过多分类器融合的识别结果明显优于...     

基于向量式有限元的大跨度钢结构施工力学分析方法

针对大跨度钢结构施工容易产生刚体位移等强烈非线性过程的特点,采用向量式有限元分析方法进行施工过程模拟.基于向量式有限元的基本理论,引入张拉索单元,通过控制张拉索单元的原长,实现大跨度预应力钢结构张拉全过程模拟;引入千斤顶单元,实现大跨度钢结构的脱架模拟.在此基础上,利用MATLAB语言编制了基于向量式有限元理论的施工过程分析程序,并通过悬臂梁拼装、索桁架张拉成形以及大跨度张弦桁架的张拉施工过程模拟分析,与传统分析方法进行比较,验证理论推导和自编程序的正确性与有效性.结果表明,采用基于向量式有限元的施工力学分析方法能够准确模拟结构拼装、预应力张拉成形和结构脱架等施工过程.     

Establishing Efficient C2C E-alliance Based on Mobile Agent

In order to alleviate difficulties of conducting consumer-to-consumer(C2C)e-commerce transaction,establishing efficient e-alliance was proposed.E-alliance is the union of e-commerce sites.It is constructed by mobile agents.The mobile agent architecture was discussed.The process of selecting suitable e-commerce site to e-alliance was presented based on support vector machine(SVM)and fuzzy method.A prototype of the proposed system is implemented on a web platform.To enable data exchange between e-alliance and...     

基于3种建模法的食源性 ACE抑制二肽 QSAR 研究

以自组建的食源性血管紧张素转化酶（angiotensin I‐converting enzyme ,ACE）抑制二肽为研究样本,采用氨基酸描述子VHSE（principal component score vector of hydrophilicity ,steric ,and electronic properties）对ACE抑制二肽进行表征后,比较偏最小二乘（partial least square regression ,PLS）、支持向量机（support vector machine ,SVM）及主成分分析（principal component analysis ,PCA）‐SVM相结合的3种建模方法对ACE抑制二肽的QSAR（quantitative structure‐activity relationship）建模。结果显示,对于食源性ACE抑制二肽,3个模型的拟合能力无明显差异,SVM模型的预测能力略强;对其进行权重投影分析发现,C末端氨基酸较N末端氨基酸对其活性的影响更为明显。     

Predicting protein subcellular localization based on information content of gene ontology terms

Predicting the location where a protein resides within a cell is important in cell biology. Computational approaches to this issue have attracted more and more attentions from the community of biomedicine. Among the protein features used to predict the subcellular localization of proteins, the feature derived from Gene Ontology (GO) has been shown to be superior to others. However, most of the sights in this field are set on the presence or absence of some predefined GO terms. We proposed a method to derive information from the intrinsic structure of the GO graph. The feature vector was constructed with each element in it representing the information content of the GO term annotating to a protein investigated, and the support vector machines was used as classifier to test our extracted features. Evaluation experiments were conducted on three protein datasets and the results show that our method can enhance eukaryotic and human subcellular location prediction accuracy by up to 1.1% better than previous studies that also used GO-based features. Especially in the scenario where the cellular component annotation is absent, our method can achieved satisfied results with an overall accuracy of more than 87%.     

Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications.