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This paper is concerned with the estimation of correlated noise and packet dropout for information fusion in distributed sensing networks. By studying the problem of the correlation of correlated noise in sensor network information fusion, a matrix weight fusion method with a feedback structure is proposed to deal with the interrelationship between multi-sensor measurement noise and estimation noise, and the method can achieve optimal estimation in the sense of linear minimum variance. Based on this, a method is proposed using a predictor with a feedback structure to compensate for the current state quantity to deal with packet dropout that occurs during multi-sensor information fusion, which can reduce the covariance of the fusion results. Simulation results show that the algorithm can solve the problem of information fusion noise correlation and packet dropout in sensor networks, and effectively reduce the fusion covariance with feedback.

Currently, the improvement of LiDAR poses estimation accuracy is an urgent need for mobile robots. Research indicates that diverse LiDAR points have different influences on the accuracy of pose estimation. This study aimed to select a good point set to enhance accuracy. Accordingly, the sensitivity and uncertainty of LiDAR point residuals were formulated as a fundamental basis for derivation and analysis. High-sensitivity and low -uncertainty point residual terms are preferred to achieve higher pose estimation accuracy. The proposed selection method has been theoretically proven to be capable of achieving a global statistical optimum. It was tested on artificial data and compared with the KITTI benchmark. It was also implemented in LiDAR odometry (LO) and LiDAR inertial odometry (LIO), both indoors and outdoors. The experiments revealed that utilizing selected LiDAR point residuals simultaneously enhances optimization accuracy, decreases residual terms, and guarantees real-time performance.

Histone deacetylase (HDAC) inhibitors are known to induce multiple epigenetic modifications affecting signaling networks and act synergistically with phosphatidylinositol 3-kinase (PI3K) inhibitors for the treatment of cancer. Herein we present a novel design approach for cancer drug development by incorporating HDAC inhibitory functionality into a PI3K inhibitor pharmacophore to construct dual-acting inhibitors. The designed compounds were synthesized and showed inhibitory activities against PI3K and HDAC. The representative dual PI3K/HDAC inhibitors, compounds 12a–j, showed potent antiproliferative activities against K562 and Hut78 in cellular assays. This work may lay the foundation for developing novel dual PI3K/HDAC inhibitors as potential anticancer therapeutics.

This paper proposed a Mixed Match SLAM embedded with two match methods, planar patch based and visual descriptor based. Planar patch based method eliminates the cost of feature extraction for motion estimation. Visual descriptor based method is more robust for wide baseline match and loop closing. We use a reasonable statistic criterion to determine keyframe for redundancy avoidance. An efficient outlier detection method is employed to pick out feature match outliers trapped in local minimum. Quantitative comparison experiments with the state-of-the-art ORB-SLAM2 show that the proposed method achieves better performance in large region dynamic scenes due to a less redundant keyframe network. It also provides better performance than ORB-SLAM2 in most static benchmark scenes with higher efficiency.