MECHANICAL RESPONSE ANALYSIS AND TREATMENT MEASURES OF PARTITION WALL OF METRO STATION

In the process of metro station construction, the partition wall is usually used to prevent the displacement of the soil behind the wall to ensure the construction platform. This research aims to analyze the response of partition wall to keep the safety of the structure. The time-horizontal displacement and cracks of the partition wall are measured during the process of metro station construction. The finite element software FLAC 3D is used to simulate the response of partition wall, and the reasons of partition wall cracking and displacement are analyzed by finite element simulation and measurement data. The stiffness and self-stability of the partition wall in the station is powerful, the stress concentration appeared on the central area and caused cracks of the partition wall. Reducing the pressure exerted on the partition wall and shifting partial pressure exerted on the partition wall by the construction of the medium plate can solve the tress concentration problem. Through the data analysis of the strengthened partition wall, the reinforcement effect is good, ensuring the safety of the subsequent construction. The causes of partition wall cracking are found during process of metro station construction, and the solution proposed in this paper is effective. Engineers can refer to this paper to analyze the response of partition wall in the construction of similar structures, which can ensure construction safety and reduce construction cost

Saccharomyces cerevisiae Est3p dimerizes in vitro and dimerization contributes to efficient telomere replication in vivo

In Saccharomyces cerevisiae at least five genes, EST1, EST2, EST3, TLC1 and CDC13, are required for telomerase activity in vivo. The telomerase catalytic subunit Est2p and telomerase RNA subunit Tlc1 constitute the telomerase core enzyme. Est1p and Est3p are the other subunits of telomerase holoenzyme. In order to dissect the function of Est3p in telomere replication, we over-expressed and purified recombinant wild-type and mutant Est3 proteins. The wild-type protein, as well as the K71A, E104A and T115A mutants were able to dimerize in vitro, while the Est3p-D49A, -K68A or -D166A mutant showed reduced ability to dimerize. Mutations in Est3p that decreased dimerization also appeared to cause telomere shortening in vivo. Double point mutation of Est3p-D49A-K68A and single point mutation of Est3p-K68A showed similar telomere shortening, suggesting that the K68 residue might be more important for telomerase activity. The ectopic co-expression of K71A or T115A mutant with wild-type Est3p using centromere plasmids caused telomere shortening, while co-expression of the D49A, K68A, D86A or F103A mutants with wild-type Est3p had no effect on telomere length regulation. These data suggested that dimerization is important for Est3p function in vivo

KRF: Keypoint Refinement with Fusion Network for 6D Pose Estimation

Existing refinement methods gradually lose their ability to further improve pose estimation methods' accuracy. In this paper, we propose a new refinement pipeline, Keypoint Refinement with Fusion Network (KRF), for 6D pose estimation, especially for objects with serious occlusion. The pipeline consists of two steps. It first completes the input point clouds via a novel point completion network. The network uses both local and global features, considering the pose information during point completion. Then, it registers the completed object point cloud with corresponding target point cloud by Color supported Iterative KeyPoint (CIKP). The CIKP method introduces color information into registration and registers point cloud around each keypoint to increase stability. The KRF pipeline can be integrated with existing popular 6D pose estimation methods, e.g. the full flow bidirectional fusion network, to further improved their pose estimation accuracy. Experiments show that our method outperforms the state-of-the-art method from 93.9\% to 94.4\% on YCB-Video dataset and from 64.4\% to 66.8\% on Occlusion LineMOD dataset. Our source code is available at https://github.com/zhanhz/KRF