Thanks to their sprawled posture and multi-legged support, stability is not as hard to achieve for hexapedal robots as it is for bipeds and quadrupeds. A key engineering challenge with hexapods has been to produce insect-like agility and maneuverability, of which steering is an essential part. However, the mechanisms of multi-legged steering are not always clear, especially for robots with underactuated legs. Here we propose a formal definition of steering, and show why steering is difficult for robots with 6 or more underactuated legs. We show that for many of these robots, steering is impossible without slipping, and present experimental results which demonstrate the importance of allowing for slipping to occur intentionally when optimizing steering ability. Our results suggest that a non-holonomic multi-legged slipping model might be more appropriate than dynamic models for representing such robots, and that conventional non-slip contact models might miss significant parts of the performance envelope. © 2020 IOP Publishing Ltd.OBJECTIVE In this study, we proposed a state-based probabilistic method for decoding hand positions during unilateral and bilateral movements using the ECoG signals recorded from the brain of Rhesus monkey. APPROACH A customized electrode array was implanted subdurally in the right hemisphere of the brain covering from the primary motor cortex to the frontal cortex. Three different experimental paradigms were considered ipsilateral, contralateral, and bilateral movements. During unilateral movement, the monkey was trained to get food with one hand, while during bilateral movement, the monkey used its left and right hands alternately to get food. To estimate the hand positions, a state-based probabilistic method was introduced which was based on the conditional probability of the hand movement state (i.e., idle, right hand movement, and left hand movement) and the conditional expectation of the hand position for each state. Moreover, a hybrid feature extraction method based on linear discriminant analysis (LDAplications of the brain-machine interface technologies. © 2020 IOP Publishing Ltd.The study of zinc oxide, within the homogeneous electron gas approximation, results in overhybridization of zinc3dshell with oxygen2pshell, a problem shown for most transition metal chalcogenides. This problem can be partially overcome by using LDA+U(or, GGA+U) methodology. However, in contrast to the zinc3dorbital, Hubbard type correction is typically excluded for the oxygen2porbital. In this work, we provide results of electronic structure calculations of an oxygen vacancy in ZnO supercell fromab initioperspective, with two Hubbard type corrections,UZn-3dandUO-2p. The results of our numerical simulations clearly reveal that the account ofUO-2phas a significant impact on the properties of bulk ZnO, in particular the relaxed lattice constants, effective mass of charge carriers as well as the bandgap. For a set of validated values ofUZn-3dandUO-2pwe demonstrate the appearance of a localized state associated with the oxygen vacancy positioned in the bandgap of the ZnO supercell. Our numerical findings suggest that the defect state is characterized by the highest overlap with the conduction band states as obtained in the calculations with no Hubbard-type correction included. We argue that the electronic density of the defect state is primarily determined by Zn atoms closest to the vacancy. © 2020 IOP Publishing Ltd.Chirality, which has long been known as an intrinsic property of living organisms, has caught the interest of researchers due to the rapid emergence of chiral metamaterials. The chiroptical response of noble metal nanostructures in visible and near-infrared regions has been widely investigated. Herein, we propose a bilayer Ag metastructure, in which a chiral L-shaped nanostructure at the bottom is coupled with an achiral nanorod acquiring different positions in the top layer with respect to the long and/or short arm of the chiral L-shaped nanostructure at the bottom layer. The metastructure generates a giant circular dichroism (CD) signal resulting from the strong coupling of the multipolar and dipolar resonant modes on the two layers, in the visible and near-infrared regions. With changing the position of the achiral nanorod, an unusual reversal of the CD spectra is observed, along with a fourfold increase in CD intensity in the short wavelength range due to the multipolar resonant modes. The position of the achiral nanorod is tailored by the azimuthal angle of the substrate during the fabrication of the metastructure using the oblique angle deposition method. This study provides insights into the variation of the coupling strength between a chiral L-shaped nanostructure and an achiral nanorod. The results can be useful in designing chiral-achiral composite nanoantennas for sensing devices. © 2020 IOP Publishing Ltd.OBJECTIVE Methods based on Riemannian geometry have proven themselves to be good models for decoding in brain-computer interfacing (BCI). However, these methods suffer from the curse of dimensionality and are not possible to deploy in high-density online BCI systems. In addition, the lack of interpretability of Riemannian methods leaves open the possibility that artifacts drive classification performance, which is problematic in the areas where artifactual control is crucial, e.g., neurofeedback and BCIs in patient populations. APPROACH We rigorously proved the exact equivalence between any linear function on the tangent space and corresponding derived spatial filters. Upon which, we further proposed a set of dimension reduction solutions for Riemannian methods without intensive optimization steps. The proposed pipelines are validated against classic common spatial patterns and tangent space classification using an open-access BCI analysis framework, which contains over seven datasets and 200 subjects in total. At last, the robustness of our framework is verified via visualizing the corresponding spatial patterns. MAIN RESULTS Proposed spatial filtering methods possess competitive, sometimes even slightly better, performances comparing to classic tangent space classification while reducing the time cost up to 97\% in the testing stage. Importantly, the performances of proposed spatial filtering methods converge with using only four to six filter components regardless of the number of channels which is also cross validated by the visualized spatial patterns. learn more These results reveal the possibility of underlying neuronal sources within each recording session. SIGNIFICANCE Our work promotes the theoretical understanding about Riemannian geometry based BCI classification and allows for more efficient classification as well as the removal of artifact sources from classifiers built on Riemannian methods. Creative Commons Attribution license.