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In order to research the structure and morphology of silica after calcined at different tempreture, as well as the influence of particles shape with coupling agent. Use TEOS as raw material, prepared inorganic silica particles by acid-catalyzed, Characterization methods have FT-IR, XRD, TG-DSC, SEM. The results showed that: only the presence of characteristic absorption peak of SiO2 when the calcination temperature reached 1000°C, tetragonal α-cristobalite formed when reached 1500°C. porous structures densified when 1500°C, obtained by a small particle size and a good degree of dispersion. KH550 will affect particle morphology characteristics.

With the rapid growth and wide application of Internet, everyday there are many of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. The recommendation algorithm is the process to alleviative the problem. Collaborative filtering algorithm is one successful personalized recommendation technology, and is widely used in many fields. But traditional collaborative filtering algorithm has the problem of sparsity, which will influence the efficiency of prediction. In this paper, a collaborative filtering recommendation algorithm based on bipartite graph is proposed. The algorithm takes users, items and tags into account, and also studies the degree of tags which may affect the similarity of users. The collaborative filtering recommendation algorithm based on bipartite graph can alleviate the sparsity problem in the electronic commerce recommender systems.

This paper investigates the problem of automated defect detection for textile fabrics and proposes a new optimal filter design method for solving this problem. Wavelet is used to extract the texture features of textile fabrics. In view of this optimal filter, a news semi-supervised segmentation algorithm is then proposed. The performance of the scheme is evaluated by using a variety of homogeneous textile images with different types of common defects. The test results exhibit accurate defect detection with low false alarm, thus confirming the robustness and effectiveness of the proposed scheme.

With the advance of society, more and more attention is paid on food safety problem. As the main tool of refrigerated transport, refrigerated truck is the most significant part in cold chain. The current situation of Chinese food cold chain industry and the development of refrigerated truck are presented, and main problems existing are analyzed. Some proposals, which may gaide the development of Chinese cold chain in some degree, are tabled.

With the rapid growth and wide application of electronic commerce, lots of information comes forth to people. However, our experiences and knowledge often do not enough to process the vast amount of information. The problem of obtaining useful information becomes more and more serious. To deal with the problem, the personalized service and recommender system play a more important role in many fields and collaborative filtering is one of the most successful technologies in recommender systems. However, with the tremendous growth in the amount of items and users, most collaborative filtering algorithms suffer from data sparsity which leads to inaccuracy of recommendation. Aiming at the problem of data sparsity, a hybrid personalized recommendation method based on dynamic Bayesian networks is presented. This method uses the dynamic Bayesian network technique to fill the vacant ratings at first, and then employs the user-based collaborative filtering to produce recommendations.