Communication Roles, Perceived Effectiveness, and Satisfaction in an Environmental Management Program

Earlier research has shown a relationship between various forms of structural centrality and perceived leadership and role satisfaction in small experimental groups. The limited amount of research on this topic in naturally occurring social networks has yielded results that often conflict with one another. Different results have generally been attributed to possible differences in task environments. This paper examines the relationship between two types of structural centrality and perceived influence, role satisfaction, and perceived effectiveness in an environmental resource management program. Findings in this paper suggest that the observed differences in relationships between the network and other variables is partly a function of global network properties (e.g., marginality of subgroups) and related task environments. This revised version was published online in August 2006 with corrections to the Cover Date.     

神光装置放大自发辐射输出特性测量

讨论了大型激光装置放大自发辐射输出的一般特性，给出了神光装置放大自发辐射特性的测量结果。     

Size distribution of fractured areas in one-dimensional systems

We study a one-dimensional model for fracture, identifying fractured areas with intervals on which a stress field exceeds a threshold value. When is a diffusion process, the cumulative numberN(l) of fractured areas whose length is greater thanl obeys a power lawCl ^–p asl0 with probability one. The exponentp and the constantC are determined. The exponentp agrees with the Hausdorff dimension of the end points of fractured areas, i.e., ^–1(). Even if is self-similar with parameterH>0, i.e.,(cx)– is equivalent toc ^H {(x)–} for anyc>0, the exponentp does not depend solely onH;p=H, where(0, 1/H) is another parameter characterizing. Non-diffusion processes are given whereN(l) does not follow a power law.     

Establishing structure-property correlations and classification of base oils using statistical techniques and artificial neural networks

The present paper describes various classification techniques like cluster analysis, principal component (PC)/factor analysis to classify different types of base stocks. The API classification of base oils (Group I-III) has been compared to a more detailed NMR derived chemical compositional and molecular structural parameters based classification in order to point out the similarities of the base oils in the same group and the differences between the oils placed in different groups. The detailed compositional parameters have been generated using and nuclear magnetic resonance (NMR) spectroscopic methods. Further, oxidation stability, measured in terms of rotating bomb oxidation test (RBOT) life, of non-conventional base stocks and their blends with conventional base stocks, has been quantitatively correlated with their NMR and elemental (sulphur and nitrogen) data with the help of multiple linear regression (MLR) and artificial neural networks (ANN) techniques. The MLR based model developed using NMR and elemental data showed a high correlation between the ‘measured’ and ‘estimated’ RBOT values for both training (R=0.859) and validation (R=0.880) data sets. The ANN based model, developed using fewer number of input variables (only NMR data) also showed high correlation between the ‘measured’ and ‘estimated’ RBOT values for training (R=0.881), validation (R=0.860) and test (R=0.955) data sets.     

Case for projectile kinetic energy gain in stopping power studies

Under certain collision conditions, a swift ion projectile colliding with a target will gain rather than lose kinetic energy, contrary to the standard conception of stopping power. In this work, we consider the conditions for such a collision such that the energy loss is negative, that is, that there will be projectile kinetic energy gain. In particular, for a target initially in the ground state we find that the projectile gains kinetic energy only when charge exchange and de‐excitation processes are involved. This occurs when the electron affinity of the projectile is larger than the ionization potential of the target. Consequences of this effect are analyzed. © 2003 Wiley Periodicals, Inc. Int J Quantum Chem 94: 215–221, 2003     

丹麦生物质发电的现状和研究发展趋势

丹麦是世界上利用秸秆生物质燃烧发电技术开发、运行最好的国家，其经验值得中国借鉴。根据作者在丹麦对生物质燃烧发电的研究经历，系统地介绍了丹麦在秸秆生物质发电的经验、遇到的问题及相应的研究和今后的发展方向。     

n型T'-214相超导体的结构和性质

T＇－214相化合物R2-xCexCuO4（R为稀土离子）成为超导体决定于R3+的离子半径大小、Ce4+的取代量和化合物的热处理时的稳定性．从Raman光谱实验结果提出热处理过程中发生电荷转移．Ce4+的取代引起TN（Cu）下降，当TN（Cu）降到0时，超导性出现。不同的稀土离子TN（Cu）不同，稀土离子的反铁磁性（AFM）与超导性（SC）共存。热电势的测量结果可以用双通道模型解释，n型超导体中电子与空穴共存．     

Structure-property modelling of complex formation of strontium with organic ligands in water

The method of substructural molecular fragments based on representation of the molecular graph by ensembles of fragments and involving calculations of those contributions to the given property is applied to the modelling of stability constants of the complexes of strontium(II) with organic ligands in water. Reliability of predictions of developed structure-property models was examined using three different test sets of structurally diverse ligands. The obtained models have been used for generation and screening of combinatorial library of virtual ligands. Some hypothetical efficient Sr(II) binders were suggested.     

Partial least-squares regression and fuzzy clustering — A joint approach

Chemical and physical analyses of malt, the main ingredient of beer, have been used to predict the concentration of certain volatile compounds in the finished beer.The prediction was done by means of the partial least squares regression (PLS2) in SIMCA. The total data set as well as individual malt clusters were submitted to PLS analysis. Best prediction was obtained by separating the total object matrix in classes according to similarity found by fuzzy pattern recognition (FCV). FCV was also used to separate the beer variables in classes and to select the subset of variables to be predicted.A joint approach of fuzzy pattern recognition to identify groups of samples and SIMCA-PLS2 to predict several dependent variables is suggested as a powerful tool in process-analytical chemistry.     

predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance

Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site.