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This paper proposes a novel classification technology—fuzzy rule-based oblique decision tree (FRODT). The neighborhood rough sets-based FAST feature selection (NRS_FS_FAST) is first introduced to reduce attributes. In the axiomatic fuzzy set theory framework, the fuzzy rule extraction algorithm is then proposed to dynamically extract fuzzy rules. And these rules are regarded as the decision function during the tree construction. The FRODT is developed by expanding the unique non-leaf node in each layer of the tree, which results in a new tree structure with linguistic interpretation. Moreover, the genetic algorithm is implemented on σ to obtain the balanced results between classification accuracy and tree size. A series of comparative experiments are carried out with five classical classification algorithms (C4.5, BFT, LAD, SC and NBT), and recently proposed decision tree HHCART on 20 UCI data sets. Experiment results show that the FRODT exhibits better classification performance on accuracy and tree size than those of the rival algorithms. [ABSTRACT FROM AUTHOR]

Soybean protein was taken as a model protein to investigate two aspects of the protein extraction by sodium bis(2-ethylhexyl) sulfosuccinate (AOT) reverse micelles: (1) the forward protein extraction from the solid state, and the effect of pH, AOT concentration, alcohol and water content (W) on the transfer efficiency; (2) the back-transfer, the capability of the protein to be recovered from the micellar solution. The experimental results led to the conclusion that the highest forward extraction efficiency of soybean protein was reached at AOT concentration 180 mmol l, aqueous pH 7.0, KCl concentration 0.05 mol l, 0.5 % (v/v) alcohol, W 18. Under these conditions, the forward extraction efficiency of soybean protein achieved 70.1 %. It was noted that the percentage of protein back extraction depended on the salt concentration and pH value. Around 92 % of protein recovery was obtained after back extraction. [ABSTRACT FROM AUTHOR]

The relationship between TMS and general logic programs is an important issue in non-monotonic logic programming. In this paper, we prove that, after we translate the TMS theory into a general logic program, the TMS's well-founded assignment (or extension) is equivalent to the corresponding general logic program's stable model. It means that TMS can be completely integrated into a non-monotonic logic programming environment. [ABSTRACT FROM AUTHOR]