Search for: All records

{"Abstract":[""Somatosensory input plays a critical role in motor learning. Noise reduces the neural activation threshold and enhances the sensitivity of sensory neurons. While research has demonstrated that peripheral electrical stimulation with noise waveform improves motor performance and functions, the effects of noise electrical stimulation on motor learning remain unknown. This study aimed to investigate the immediate effects of peripheral noise electrical stimulation on motor learning and corresponding neural activities in the motor cortex. Eighteen healthy adults participated in 2 experimental sessions (i.e., noise and sham electrical stimulation conditions) on 2 separate days. Participants performed a grip force tracking task to follow a 0.5 Hz continuous sine wave with amplitudes of 10, 20, and 30% maximal voluntary isometric contraction while the electroencephalogram (EEG) of the sensorimotor cortex and the electromyography (EMG) of the right finger flexors were recorded. The differences (force error) between the actual and the targeted force were calculated, and motor learning was achieved by reducing the force error to a plateau. The efficiency of motor learning was defined as how fast the force error reached a plateau. Two-way (conditions [noise vs sham stimulation] by time [during vs post]) analysis of variance with repeated measures was used to compare the differences in force error, EEG power spectrum density (PSD), and EEG-EMG (corticomuscular) coherence (CMC). The significance level was set at 0.05. Noise electrical stimulation significantly reduced the force error both during and post motor learning (p < 0.05) and required less time to reach a plateau of force error (p < 0.05); however, for percipients who received sham stimulation first, the effect of noise on learning may not be optimal and thus not represent the net effect of stochastic resonance. For neural activities in the brain, noise electrical stimulation induced an immediate reduction in the EEG beta (15-30 Hz) band and gamma (> 30 Hz) CMC. We also observed that motor learning resulted in a decrease in EEG PSD beta band and gamma CMC. This study demonstrated that noise electrical stimulation during motor learning significantly reduced the time required to learn a motor task. We also identified neurophysiological signatures that associate with motor learning, including desynchronization of EEG beta power and reduced functional connectivity between the brain and muscles. These findings could potentially help develop novel motor training strategies and precision interventions.""]} 

This work presents a systematic study of the relationship between structural stochasticity and the crush energy absorption capability of lattice structures, with controlled stiffness and weight. We develop a Voronoi tessellation-based approach to generate multiple series of lattice structures with either equal weight or equal stiffness, smoothly transitioning from periodic to stochastic configurations for crush energy absorption analysis. The generated lattice series fall into two categories, originating from periodic honeycomb and diamond lattice structures. A new stochasticity metric is proposed for quantifying the structural stochasticity and is compared with the state-of-the-art stochasticity metrics to ensure a consistent measurement. The crush energy absorption properties are obtained using explicit finite element analysis and we observe similar stochasticity-property trends in simulations using both elastic-plastic and hyperelastic materials. We report a new observation that an intermediate level of stochasticity between periodic and high randomness leads to the best crush energy absorption performance. Our analysis reveals that this optimal performance arises from enhanced activation of deformation hinges, promoting efficient energy absorption. 

Roll-to-roll (R2R) mechanical dry transfer is an enabling technology for high-throughput, environmentally friendly fabrication of advanced thin-film devices. However, precise control is required to ensure high-quality transfer, presenting a significant challenge due to nonlinear peeling dynamics, abrupt material property changes, and input constraints. This study proposes a sequential quadratic programming iterative learning control (SQP ILC) approach to regulate the R2R mechanical dry transfer process. The method leverages the system's iterative structure to improve the performance across successive transfer tasks while rigorously accounting for nonlinear dynamics and input constraints. Experimental validation on a lab-scale testbed and a case study on the chemical vapor deposition-grown graphene transfer show that the SQP ILC significantly improves transfer quality with minimal online computation, making it a scalable solution for industrial R2R applications. 

A series of perovskite oxides (Ln = La, Pr, Nd, Gd; A = Ba, Sr) was investigated to understand the effects of A-site cation size on oxygen vacancy formation. Quasirandom mixed structures were generated using Alloy Theoretic Automated Toolkit (ATAT), followed by density functional theory (DFT) calculations. While mixing the orthorhombic structures with the hexagonal AMnO3 structures leads to lattices and global symmetries closer to cubic, the average volume generally increases with the average ionic size, and the local bond and angles exhibit more variations due to A-site mixing. DFT calculations and a statistical model were combined to predict oxygen reduction abilities. Thermogravimetric analysis (TGA) provided experimental validation of these predictions by measuring changes in oxygen non-stoichiometry under controlled conditions. Both indicated that larger A-site ionic size differences lead to greater, consistent with the larger variation in local structures, and enhanced redox capabilities. This combined computational-experimental approach highlights the importance of local structure variation, instead of average properties, in A-site cation engineering to optimize perovskite oxides for different devices relying on oxygen vacancy redox activity. 

How people reason about the mechanics of the physical world is an important question for several different cognitive sciences. Education, cognitive psychology, and developmental psychology have each conducted large numbers of studies over the last several decades, largely in isolation from one another (especially in the last quarter century). The results have suggested that cognitive mechanics may be subserved by a number of mechanisms that are differentially involved in different tasks. Here, we report converging results from factor analysis of a large compendium of mechanics questions. 

Abstract Although the existing causal inference literature focuses on the forward-looking perspective by estimating effects of causes, the backward-looking perspective can provide insights into causes of effects. In backward-looking causal inference, the probability of necessity measures the probability that a certain event is caused by the treatment given the observed treatment and outcome. Most existing results focus on binary outcomes. Motivated by applications with ordinal outcomes, we propose a general definition of the probability of necessity. However, identifying the probability of necessity is challenging because it involves the joint distribution of the potential outcomes. We propose a novel assumption of monotonic incremental treatment effect to identify the probability of necessity with ordinal outcomes. We also discuss the testable implications of this key identification assumption. When it fails, we derive explicit formulas of the sharp large-sample bounds on the probability of necessity.