Search for: All records

Abstract Drawing inspiration from cohesive integration of skeletal muscles and sensory skins in vertebrate animals, we present a design strategy of soft robots, primarily consisting of an electronic skin (e-skin) and an artificial muscle. These robots integrate multifunctional sensing and on-demand actuation into a biocompatible platform using an in-situ solution-based method. They feature biomimetic designs that enable adaptive motions and stress-free contact with tissues, supported by a battery-free wireless module for untethered operation. Demonstrations range from a robotic cuff for detecting blood pressure, to a robotic gripper for tracking bladder volume, an ingestible robot for pH sensing and on-site drug delivery, and a robotic patch for quantifying cardiac function and delivering electrotherapy, highlighting the application versatilities and potentials of the bio-inspired soft robots. Our designs establish a universal strategy with a broad range of sensing and responsive materials, to form integrated soft robots for medical technology and beyond. 

Summary We consider forecasting a single time series using a large number of predictors in the presence of a possible nonlinear forecast function. Assuming that the predictors affect the response through the latent factors, we propose to first conduct factor analysis and then apply sufficient dimension reduction on the estimated factors to derive the reduced data for subsequent forecasting. Using directional regression and the inverse third-moment method in the stage of sufficient dimension reduction, the proposed methods can capture the nonmonotone effect of factors on the response. We also allow a diverging number of factors and only impose general regularity conditions on the distribution of factors, avoiding the undesired time reversibility of the factors by the latter. These make the proposed methods fundamentally more applicable than the sufficient forecasting method of Fan et al. (2017). The proposed methods are demonstrated both in simulation studies and an empirical study of forecasting monthly macroeconomic data from 1959 to 2016. Also, our theory contributes to the literature of sufficient dimension reduction, as it includes an invariance result, a path to perform sufficient dimension reduction under the high-dimensional setting without assuming sparsity, and the corresponding order-determination procedure. 

Abstract In many dimension reduction problems in statistics and machine learning, such as principal component analysis, canonical correlation analysis, independent component analysis, and sufficient dimension reduction, it is important to determine the dimension of the reduced predictor, which often amounts to estimating the rank of a matrix. This problem is called order determination. In this paper, we propose a novel and highly effective order-determination method based on the idea of predictor augmentation. We show that, if we augment the predictor by an artificially generated random vector, then the part of the eigenvectors of the matrix induced by the augmentation display a pattern that reveals information about the order to be determined. This information, when combined with the information provided by the eigenvalues of the matrix, greatly enhances the accuracy of order determination. 

Many notable female sociologists have vanished from the canonical history of American sociology. As the most influential crowd-sourced encyclopedia, Wikipedia promises – but does not necessarily deliver – a democratic corrective to the generation of knowledge, including academic knowledge. This article explores multiple mechanisms by which women either enter or disappear from the disciplinary record by analyzing the unfolding interaction between the canonical disciplinary history of sociology and Wikipedia. We argue that the uneven representation of women sociologists as (1) remembered, (2) neglected, (3) erased or, finally, (4) recovered is shaped by the emerging interactional space of knowledge production. 

Abstract Most reported cathodes of nonaqueous dual‐ion batteries (DIBs) host anions via insertion reactions. It is necessary to explore new cathode chemistry to increase the battery energy density. To date, transition metals have yet to be investigated for nonaqueous DIBs, albeit they may offer high capacity in anodic conversion reactions. Here, we report that bulk copper powder exhibits a high reversible capacity of 762 mAh g⁻¹at 3.2 V vs. Li⁺/Li and relatively stable cycling in common organic electrolytes. The operation of the copper electrode is coupled with the transfer of anion charge carriers. An anion exchange membrane separator is employed to prevent Cu²⁺from crossing from the catholyte to the anode side. We designed an unbalanced electrolyte with a more concentrated anolyte than a catholyte. This addresses the concentration overpotential ensued during charge and facilitates the high specific capacity and enhanced reversibility. This finding provides a promising direction for high‐energy DIBs.