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The trajectory tracking performance of an actual robot is usually used as an important standard to evaluate a nonlinear controller algorithm. The aim of this article is to design a new experimental robot system based on the Robot Operating System-MATLAB framework. To validate the performance, a nonlinear tracking controller includes the following features. Firstly, the surface robot model is in Lie algebra S O (3) format, which keeps the potential of cooperation control between space vehicles. Secondly, the nonlinear controller has good robustness under unknown disturbances. Thirdly, the control algorithm focuses on the practical robot, Turtlebot2. The robot system experimental results demonstrate the effectiveness of the proposed controller for trajectory tracking under unknown disturbance. The controller's potential applications include autonomous robots for indoor examination, extreme condition rescuing, and cooperation between unmanned aerial vehicle and unmanned surface vehicle autodrives. [ABSTRACT FROM AUTHOR]

Identification of cancer patient subgroups using high throughput genomic data is of critical importance to clinicians and scientists because it can offer opportunities for more personalized treatment and overlapping treatments of cancers. In spite of tremendous efforts, this problem still remains challenging because of low reproducibility and instability of identified cancer subgroups and molecular features. In order to address this challenge, we developed Integrative Genomics Robust iDentification of cancer subgroups (InGRiD), a statistical approach that integrates information from biological pathway databases with high-throughput genomic data to improve the robustness for identification and interpretation of molecularly-defined subgroups of cancer patients. We applied InGRiD to the gene expression data of high-grade serous ovarian cancer from The Cancer Genome Atlas and the Australian Ovarian Cancer Study. The results indicate clear benefits of the pathway-level approaches over the gene-level approaches. In addition, using the proposed InGRiD framework, we also investigate and address the issue of gene sharing among pathways, which often occurs in practice, to further facilitate biological interpretation of key molecular features associated with cancer progression. The R package "InGRiD" implementing the proposed approach is currently available in our research group GitHub webpage ( https://dongjunchung.github.io/INGRID/ ). [ABSTRACT FROM AUTHOR]

The maximal procedure is a restricted randomization method that maximizes the number of feasible allocation sequences under the constraints of the maximum tolerated imbalance and the allocation sequence length. It assigns an equal probability to all feasible sequences. However, its implementation is not easy due to the lack of the Markovian property of the conditional allocation probabilities. In this paper, we propose the asymptotic maximal procedure, which replaces the sequence-length-dependent conditional allocation probabilities with their asymptotic values. The new randomization procedure is compared with the original maximal procedure and few other randomization procedures with the maximum tolerated imbalance via simulations and is found to be a practical choice for future clinical trials. [ABSTRACT FROM AUTHOR]