No abstract

No abstract

Norovirus monitoring and early warning can be used for diagnosis without etiological testing, and the treatment of this disease does not require the antibiotics. It often occurs in preschool children and affects their growth and development, so the coping measures for this disease are more prevention than treatment. In this study, the clinical data of 2133 children with diarrhea were collected. Based on the artificial intelligence (AI) algorithm of wavelet transform, a related model for data mining and processing of children’s intestinal ultrasound images and stool specimens was constructed. Then, the norovirus infection trend was warned based on the wavelet analysis algorithm model. The results showed that the intestinal ultrasound image processed by the wavelet transform algorithm was clearer. The positive detection rate of norovirus in children with clinical diarrhea was as high as 59%, and the children had different degrees of body damage, of which the probability of compensatory metabolic acidosis was the highest. The epidemiological analysis found that children with norovirus infection were mainly concentrated in the age group under 2 years old and over 5 years old and showed a peak of infection in December. In summary, the intelligent algorithm based on wavelet transform can realize the noise reduction of intestinal ultrasound, and it should protect children with susceptible age and susceptible seasons to reduce the clinical infection rate of norovirus.

Construction of the unified and shared domain ontology is significant for effective knowledge management. For the acquisition and sharing of scientific research knowledge under Web2.0, a novel approach of building Interval Valued Fuzzy Ontology (IVFO) in scientific research domain is presented. Through interval valued fuzzy theory, the definition and constructing framework of IVFO is proposed. Then IVFO is applied to semi-automatic extraction of information retrieval research domain. The preliminary constructing of research domain ontology is an essential base for the knowledge management system of scientific research. It can be effective methods for enhancing the efficiency and productivity of researching.

As the born and communication base of new science and new technology, scientific research requires effective information integration and knowledge management to improve the efficiency of scientific research. The growth of e-science in circumstance of Web 2.0 has created a need to integrate large quantities of diverse and heterogeneous data. To tackle with the scientific information management problems, we proposed an ontology-based scientific keywords recommendation system under web 2.0. The main goal achieved is to extract the meaningful information and recommend to web scholars through the proposed system. The components of the system are Integration Interface, Service Module, Text Processor, Recommendation Module and Ontology Database. Experimental results and performance evaluation shows that the proposed system provides the effective way to recommend semantic related keywords for scholars.