Tannase (Tan410) from a soil metagenomic library was immobilized on different supports, including mesoporous silica SBA-15, chitosan, calcium alginate, and amberlite IRC 50. Entrapment in calcium alginate beads was comparatively found to be the best method and was further characterized. The optimum pH of the immobilized Tan410 was shifted toward neutrality compared with the free enzyme (from pH 6.4 to pH 7.0). The optimum temperature was determined to be 45°C for the immobilized enzyme and 30°C for the free enzyme, respectively. The immobilized enzyme had no loss of activity after 10 cycles, and retained more than 90% of its original activity after storage for 30 days. After immobilization, the enzyme activity was only slightly affected by Hg(2+), which completely inhibited the activity of the free enzyme. The immobilized tannase was used to remove 80% of tannins from a green tea infusion on the first treatment. The beads were used for six successive runs resulting in overall hydrolysis of 56% of the tannins.

Constraint-based clustering utilizes pairwise constraints to improve clustering performance. In this paper, we propose a novel formulation algorithm to generate more informative pairwise constraints from limited queries for the constraint-based clustering. Our method consists of two phases: pre-clustering and marking. The pre-clustering phase introduces the fuzzy c-means clustering (FCM) to generate the cluster knowledge that is composed of the membership degree and the cluster centers. In the marking phase, we first propose the weak sample with the larger uncertainty expressed by the entropy of the membership degree. Then, we study the strong sample that contains less uncertainty and should be closest to its cluster center. Finally, given weak samples in descending order of entropy, we formulate informative pairs with strong samples and seek answers using the second minimal symmetric relative entropy priority principle, which leads to more efficient queries. Making use of the pairwise constraint k-means clustering (PCKM) as the underlying constraint-based clustering algorithm, further data experiments are conducted in several datasets to verify the improvement of our method.INDEX TERMS Constraint-based clustering, pairwise constraint, weak sample, strong sample, symmetric relative entropy.

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Several engineering practices have shown that the excavation of shallow-buried tunnels beneath major roads, as well as the selection of appropriate engineering measures and construction methods, has a significant impact on road surface settlement. Therefore, field monitoring and numerical simulation are adopted in this study to analyze the effect of the cross diaphragm (CRD) excavation method on surface settlement for the under-construction Yüan 1 railroad tunnel. The findings show that during the excavation of the four divisions of the CRD excavation method for shallow-buried tunnels, the amount of surface settlement caused by the excavation of part 1 accounts for 40% of the total surface settlement, followed by the excavation of part 3, accounting for 30% of the total surface settlement, and the difference between the excavation of parts 2 and 4 is insignificant, with part 2 slightly larger than part 4. The main influence of the CRD method on surface settlement for shallow-buried tunnels is 0.64–0.86 times the cavity diameter from the tunnel median, within which the final surface settlement caused by excavation is within the same horizontal range, and beyond which the surface settlement is prone to dramatically decline. By applying advanced grouting and adjusting the construction method of CRD based on the monitoring data, the effect of the CRD excavation method on surface settlement can be controlled.