Letter to the Editor

The paper by Alfons, Croux and Gelper (2013), Sparse least trimmed squares regression for analyzing high-dimensional large data sets, considered a combination of least trimmed squares (LTS) and lasso penalty for robust and sparse high-dimensional regression. In a recent paper [She and Owen (2011)], a method for outlier detection based on a sparsity penalty on the mean shift parameter was proposed (designated by "SO" in the following). This work is mentioned in Alfons et al. as being an "entirely different approach." Certainly the problem studied by Alfons et al. is novel and interesting.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS640 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Self-Propelled Micro/Nanomotors (MNMs) and Their Applications

The majority of the micro/nanomotors use the precious noble metal platinum for propulsion. However, platinum suffers from high-cost, scarcity, and possibility of deactivation in various media. In this thesis, we explored the MnO2 based materials for the fabrication of the high-performance and low-cost micro/nanomotors. These newly developed MnO2 based micromotors show great potential for replacing Pt and will greatly improves the applications of micro/nanomotors for biomedical science and environmental remediations areas