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A hybrid fuzzy inference-quantum particle swarm optimization (FI-QPSO) algorithm is developed to estimate the temperature-dependent thermal properties of grain. The fuzzy inference scheme is established to determine the contraction-expansion coefficient according to the aggregation degree of particles. The heat transfer process in the grain bulk is solved using the finite element method (FEM), and the estimation task is formulated as an inverse problem. Numerical experiments are performed to study the effects of the surface heat flux, number of measurement points, measurement errors and the individual space on the estimation results. Comparison with the quantum particle swarm optimization (QPSO) algorithm and conjugate gradient method (CGM) is also conducted, and it shows the validity of the estimation method established in this paper.

Abstract. This paper takes Yunnan as the object of a tailing, by theoretical analysis and numerical calculation method of the effect of seismic load effect of elevation on the stability of the tailing, to analyse the stability of two point driven safety factor and liquefaction area. The Bishop method is adopted to simplify the calculation of dynamic safety factor and liquefaction area analysis using comparison method of shear stress to analyse liquefaction, so we obtained the influence of elevation on the stability of the tailing. Under the earthquake, with the elevation increased, the safety coefficient of dam body decreases, shallow tailing are susceptible to liquefy. Liquefaction area mainly concentrated in the bank below the water surface, to improve the scientific basis for the design and safety management of the tailing.

The SeDeM Expert System was first known as a galenic pre-formulation system, which was based on the experimental research and quantitative determination of powdered substances. And the mathematical formula provided by the SeDeM Expert System has plays an important role in the study of powder properties. The system can be used not only to evaluate the powder direct compression (DC) of excipients and active pharmaceutical ingredients (API's), but also to predict the possible formulations, so it can reduce unnecessary research and trials, and shorten the time of development. In this paper, the research development and application of SeDeM Expert System in DC was summarized, and the results showed that with a few exceptions, the system was skilled in predicting acceptable tablet formulations. Finally, the new application prospect of the system is presented, including the application of the Internet traffic and content management (iTCM) database and the new co-processed excipients.

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