Expression model for multiple relationships in the ontology of traditional Chinese medicine knowledge

Objective:To explore multiple relationships in traditional Chinese medicine(TCM)knowledge by comparing binary and multiple relationships during knowledge organization.Methods:Characteristics of binary and multiple semantic relationships as well as their associations are described.A method to classify multiple relationships based on the involvement of time is proposed and theoretically validated using examples from the ancient TCM classic Important Formulas Worth a Thousand Gold Pieces.The classification includes parallel multiple relationships,restricted multiple relationships,multiple relationships that involve time,and multiple relationships that involve time restriction.Next,construction of multiple semantic relationships for TCM concepts in each classification using Protege,an ontology editing tool is described.Results:Protege is superior to a binary relationship and less than ideal with multiple relationships during the constitution of concept relationships.Conclusion:When applied in TCM,the semantic relationships constructed by Protege are superior than those constructed by correlation and/or attribute relationships,but less ideal than those constructed by the human cognitive process.

Practical Application of Two-Way Multiple Overlapping Relationships in a BDM Network

Today, most construction projects in urban environments are complex high-rise buildings that present unique challenges, including local building ordinances and restrictions, adjoining public and residential areas, narrow sidewalks and streets, and underground utilities, all of which require extensive planning and tight schedules. A major problem facing such projects is to formulate realistic schedules that will make it possible to meet contractual completion dates with limited resources and budgets. The scheduling software products currently used in construction projects, which include Primavera P6, Microsoft Project, etc., are not actually applied as a scheduling tool in practical construction projects, which instead generally depend on Microsoft Excel or a bar-chart. This is because the existing scheduling programs cannot provide more user-oriented schedule format such as representing two-way multiple overlapping relationships. To overcome this deficiency, the BDM （beeline diagramming method） is proposed as a new networking technique in 2010. But two-way multiple overlapping relationships generate the loop in a conventional schedule computation process. This paper addresses the loop phenomenon of two-way multiple overlapping relationships in a BDM network as well as proposes the solutions of them, and then presents a practical application of two-way multiple overlapping relationships at a real project.

Quantitative Structure-biodegradability Relationship Study about the Aerobic Biodegradation of Some Aromatic Compounds

10 quantum chemical descriptors of 21 aromatic compounds have been calculated by the semi-empirical quantum chemical method AM1. The Quantitative Structure-Biodegradability Relationships （QSBR） studies were performed by the multiple linear regression （MLR）, principal component regression （PCR） and back propagation artificial neural network （BP-ANN）, respectively. The root mean square error （RMSE） of the training and validation sets of the BP-ANN model are 0.1363 and 0.0244, the mean absolute percentage errors （MAPE） are 0.1638 and 0.0326, the squared correlation coefficients （R^2） are 0.9853 and 0.9996, respectively. The results show that the BP-ANN model achieved a better prediction result than those of MLR and PCR. In addition, some insights into the structural factors affecting the aerobic biodegradation mechanism were discussed in detail.

Research on FDI and China＇s Economic Growth

In this paper, the relationship between FDI and China＇s economic growth is analyzed by Granger causality test and multiple regression model. It is found that relationship is bi-directional causal. It is suggested that the utilization of FDI should be focused on not only the quantity, but also the quality of FDI with its rapid development.

Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds

In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection （SPA） strategy, is used as the descriptor selection and model development method. Then, the support vector machine （SVM） and multiple linear regression （MLR） model are utilized to construct the non-linear and linear quantitative structure-property relationship models. The results obtained using the SVM model are compared with those obtained using MLR reveal that the SVM model is of much better predictive value than the MLR one. The root-mean-square errors for the training set and the test set for the SVM model were 0.1911 and 0.2569, respectively, while by the MLR model, they were 0.4908 and 0.6494, respectively. The results show that the SVM model drastically enhances the ability of prediction in QSPR studies and is superior to the MLR model.