半绝缘砷化镓EPR谱的异常退火特性

国家自然科学基金

The mean value concept in mono-linear regression of multi-variables and its application to trace studies in geosciences

IEECAS SKLLQG

CO2 Flux Estimation by Different Regression Methods from an Alpine Meadow on the Qinghai-Tibetan Plateau

National Key Research and Development Program [2010CB833500]; National Natural Science Foundation of China [30590381]; Chinese Academy of Sciences [KZCX2-YW-432]

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

Influence of the degree of grafting on the morphology and mechanical properties of blends of poly(butylene terephthalate) and glycidyl methacrylate grafted poly(ethylene-co-propylene) (EPR)

Poly(ethylene-co-propylene) (EPR) was functionalized to varying degrees with glycidyl methacrylate (GMA) by melt grafting processes. The EPR-graft-GMA elastomers were used to toughen poly(butylene terephthalate) (PBT). Results showed that the grafting degree strongly influenced the morphology and mechanical properties of PBT/EPR-graft-GMA blends. Compatibilization reactions between the carboxyl and/or hydroxyl of PBT and epoxy groups of EPR-graft-GMA induced smaller dispersed phase sizes and uniform dispersed phase distributions. However, higher degrees of grafting (>1.3) and dispersed phase contents (>10 wt%) led to higher viscosities and severe crosslinking reactions in PBT/EPR-graft-GMA blends, resulting in larger dispersed domains of PBT blends. Consistent with the change in morphology, the impact strength of the PBT blends increased with the increase in EPR-graft-GMA degrees of grafting for the same dispersion phase content when the degree of grafting was below 1.8. However, PBT/EPR-graft-GMA1.8 displayed much lower impact strength in the ductile region than a comparable PBT/EPR-graft-GMA1.3 blend (1.3 indicates degree of grafting).

Selection for variables in quantitative structure-activity/property relationship studies - Comparison between methods of orthogonal descriptors and leaps-and-bounds regression analysis

In this paper, the comparison of orthogonal descriptors and Leaps-and-Bounds regression analysis is performed. The results obtained by using orthogonal descriptors are better than that obtained by using Leaps-and-Bounds regression for the data set of nitrobenzenes used in this study. Leaps-and-Bounds regression can be used effectively for selection of variables in quantitative structure-activity/property relationship(QSAR/QSPR) studies. Consequently, orthogonalisation of descriptors is also a good method for variable selection for studies on QSAR/QSPR.

La_(1-x)Li_xMnO_3体系的输运特性及EPR研究

利用固相反应法合成了掺杂锂的La1-xLixMnO3(x=0,0.10,0.15,0.20,0.25,0.30)钙钛矿氧化物．XRD测试表明，所有样品均为菱方结构．除x=0,0.3外，其它样品均随温度的降低在液氮温区可观察到从绝缘态到金属态的转变，其中x=0.15样品的转变温度最高为167K．在H=1T的磁场下，出现了负磁阻现象．EPR谱上的ｇ＝2.00信号与Mn3+和Mn4+组成的复合团簇有关

PP-EPR-PP增容PP/EPT的研究

采用δ－ＴｉＣｌ３－Ｅｔ２ＡｌＣｌ催化合成了三元乙丙嵌段共聚物ＰＰ－ＥＰＲ－ＰＰ，作为乙丙橡胶（ＥＰＴ）增韧聚丙烯（ＰＰ）的增容剂，以改善ＰＰ的低温冲击性能。实验发现，ＰＰ－ＥＰＲ－ＰＰ中ＥＰＲ段较长，有利于提高其增容效果，ＰＰ－ＥＰＲ－ＰＰ的加入量仅为４％，就可以显著的改善ＰＰ和ＥＰＴ相容性，使ＰＰ／ＥＰＴ／ＰＰ－ＥＰＲ－ＰＰ共混物的无缺口冲击强度比相应的ＰＰ／ＥＰＴ在－２０℃时提高４０％，在－４０℃时提高了１２０％，达到较高的耐冲击等级。形态观察表明，ＰＰ－ＥＰＲ－ＰＰ同时又起到了“乳化剂”的作用，使ＥＰＴ在ＰＰ中均匀分散。还提出了ＰＰ－ＥＰＲ－ＰＰ的增容模型，完满地解释了实验结果。

Studies on the method of leaps and bounds regression

In this paper, we introduce the method of leaps and bounds regression which can be used to select variables quickly and obtain the best regression models. These models contain one variable, two variables, three variables and so on. The results obtained by using leaps and bounds regression were compared with those achieved by using stepwise regression to lead to the conclusion that leaps and bounds regression is an effective method.

Prediction of molar absorptivities of color reagents during their color reactions with cerium using multiple regression analysis and neural network

A quantitative structure-property study has been made on the relationship between molar absorptivities (epsilon) of asymmetrical phosphone bisazo derivatives of chromotropic acid and their color reactions with cerium by multiple regression analysis and neural network. The new topological indices A(x1) - A(x3) suggested in our laboratory and molecular connectivity indices of 43 compounds have been calculated. The results obtained from the two methods are compared. The neural network model is superior to the regression analysis technique and gave a prediction which was sufficiently accurate to estimate the molar absorptivities of color reagents during their color reactions with cerium.

Study on Phenol Compounds Using Multiple Regression and Neural Network Methods

In this paper, the molecular connectivity indices and the electronic charge parameters of forty-eight phenol compounds nave been calculated. and applied for studying the relationship between partition coefficients and structure of phenol compounds. The results demonstrate that the properties of compounds can be described better with selective parameters, and the results obtained by neural network are superior to that by multiplle regression.