A novel chitosan-based composite nanofibers which was composited poly (vinylpyrrolidone) (PVP) and poly (vinylalcohol) (PVA) was prepared by electrospinning, and the spinnability and morphology of CS-based composite nanofibers were investigated. The result of SEM showed that with the introduction of PVP to composite fibers the spinnability of the system was improved. And the CS/PVA/PVP composite fibers displayed mean diameter of 71 nm and narrower diameter distribution than CS/PVA composite fibers. IR demonstrated that there were intermolecular hydrogen bonds in CS/PVA/PVP fibers. XRD showed that the crystalline microstructure of the fibers was not well developed.
Precision is selected unwillingly by human being when dealing with imprecise objects because of the limitation of human cognitive ability, which deviates from the substance of the processed object when it gets the feasible way of solution. Nowadays, in terms of the research in the Ontology and the Semantic Web, the time for the transformation from the “precise phase” to the “imprecise phase” is ripe. The interoperability among ontologies is seriously blocked by the heterogeneity of ontologies constructed under distributed environment. In this case, Ontology merging in the same domain is the most effective method to solve ontology heterogeneity. Firstly, the improved fuzziness and the R-improved roughness are respectively defined and verified as the more efficient measure way for the fuzziness and roughness. Secondly, a composite appraisal method of fuzzy-rough relevancy in combination of the fuzzy set theory and the rough set theory is proposed, which can serve as the basis of the inquiry and reasoning of the imprecise ontology, the transformation reference of the fuzzy roughness set or the rough fuzziness set. Lastly, by employing semantic bridge generator and conflict processor, a novel multiple-mapping-based imprecise ontology merging framework is proposed. The example verification reveals that both the imprecise ontology merging efficiency can be improved and the merging source imprecise ontologies into object imprecise ontology can be done automatically under the semantic web environment.
This paper tries to find a more feasible method to achieve core and reduction. Against concepts "distinguishable relation of attribute set" and "distinguishable unit set of attribute set", it defines a concept "important index", and proposes an effective and quick approach for important index. After drawn out the involved theory and equivalent proposition, also presents algorithms for core and reduction upon the important index. The heuristic reduction algorithm adopts the bottom-up design, and gets reduction based on the heuristic information "important index of attribute set". The complexity of the algorithm in space is O(m), and the complexity in time is O(mn2). The theoretical analysis and results show that the ways proposed here simplify the relevant operations and are suitable to deal with the huge volume of data.
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