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Prepared a series of castor oil modified NaHSO3 waterborne blocked polyurethane, discussed the influence on the emulsion stability by C.O. addition amount, R value, DMPA and the influences on latex film mechanical property and water absorption by C.O. content, R value. Then analyzed the characterization by FTIP and DSC, its shown that emulsion is stable when the R value controlled in the range of 1.4~1.8 and the stability will become poor with the increase of C.O. content; and with the increase of R value the elongation at break of latex film reduced and tensile strength increased. In short the C.O.s mixing can make latex film initial decomposition temperature increased by 60, reduced elongation at break, increased tensile strength and reduced the water absorption.

Dynamic response analysis is carried out for an aqueduct structure of the South-to-North Water Transfer Project. The interaction of water and aqueduct wall is simplified using Housner method. Six different water depths (empty aqueduct, 1/4 water depth, 1/2 water depth, 3/4 water depth, designed water depth and full water depth) are considered and calculation is conducted using time-history analysis method. The variation rule of dynamic stress and dynamic displacement are gained under different water depths. Results show that water has great influence on aqueduct body and its dynamic response. Dynamic displacement and dynamic stress of the aqueduct structure increase with the aqueduct water level increases. When water depth is bigger, dynamic displacement response and dynamic stress response are later than corresponding earthquake excitation. The relative stiffness of the longitudinal beam and the transverse beam should be fully considered in order to reduce stress concentration of aqueduct body.

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The solution space of a frequent itemset generally presents exponential explosive growth because of the high-dimensional attributes of big data. However, the premise of the big data association rule analysis is to mine the frequent itemset in high-dimensional transaction sets. Traditional and classical algorithms such as the Apriori and FP-Growth algorithms, as well as their derivative algorithms, are unacceptable in practical big data analysis in an explosive solution space because of their huge consumption of storage space and running time. A multi-objective optimization algorithm was proposed to mine the frequent itemset of high-dimensional data. First, all frequent 2-itemsets were generated by scanning transaction sets based on which new items were added in as the objects of population evolution. Algorithms aim to search for the maximal frequent itemset to gather more non-void subsets because non-void subsets of frequent itemsets are all properties of frequent itemsets. During the operation of algorithms, lethal gene fragments in individuals were recorded and eliminated so that individuals may resurge. Finally, the set of the Pareto optimal solution of the frequent itemset was gained. All non-void subsets of these solutions were frequent itemsets, and all supersets are non-frequent itemsets. Finally, the practicability and validity of the proposed algorithm in big data were proven by experiments.