Quantitative tests revealing hydrogen enhanced dislocation motion in α-iron

Hydrogen embrittlement jeopardizes the use of high-strength steels as critical load-bearing components in energy, transportation, and infrastructure applications. However, our understanding of hydrogen embrittlement mechanism is still obstructed by the uncertain knowledge of how hydrogen affects dislocation motion, due to the lack of quantitative experimental evidence. Here, by studying the well-controlled, cyclic, bow-out movements of individual screw dislocations, the key to plastic deformation in α-iron, we find that the critical stress for initiating dislocation motion in a 2 Pa electron-beam-excited H2 atmosphere is 27~43% lower than that under vacuum conditions, proving that hydrogen lubricates screw dislocation motion. Moreover, we find that aside from vacuum degassing, dislocation motion facilitates the de-trapping of hydrogen, allowing the dislocation to regain its hydrogen-free behavior. Atomistic simulations reveal that the observed hydrogen-enhanced dislocation motion arises from the hydrogen-reduced kink nucleation barrier. These findings at individual dislocation level can help hydrogen embrittlement modelling in steels.

Output voltage control of a PMSG with the DPC-SVM technique and high-order sliding mode

This paper aims to study the control of the output voltage of a wind turbine (WT) which is composed of a permanent magnet synchronous generator (PMSG) connected to an inverter/rectifier. The first tested control on PMSG is based on the classical direct power control (CDPC); this technique uses the active and reactive power as a control variable. Then, to improve the quality of energy and evaluate the performance of the system, we proposed a high-order sliding mode (HOSM) with space vector modulation (SVM) to controlthe output voltage. As a result, the proposed approach presents attractive features such as the chattering-free behavior of the sliding mode. This system was designed for a wind power conversion application in the case of an isolated site. The computer simulations were provided to verify the validity of the proposed control algorithm using the MATLAB/Simulink software.

An Exposition of Fractional Spurs Resulting From Nonlinear Distortion

The interaction between quantization noise intro-duced by the divider controller and memoryless nonlinearities in a fractional-N PLL causes fractional spurs to occur. This paper presents a comprehensive theory to explain why combinations of quantizers and memoryless nonlinearities produce fractional spurs. Necessary and sufficient conditions for spur-free behavior in the presence of an arbitrary memoryless nonlinearity or linear combinations of sets of arbitrary memoryless nonlinearities are derived. Finally, an upper limit on the number of nonlinearities for which a quantizer can exhibit spur-free performance is derived.

An Exposition of Fractional Spurs Resulting From Nonlinear Distortion

The interaction between quantization noise intro-duced by the divider controller and memoryless nonlinearities in a fractional-N PLL causes fractional spurs to occur. This paper presents a comprehensive theory to explain why combinations of quantizers and memoryless nonlinearities produce fractional spurs. Necessary and sufficient conditions for spur-free behavior in the presence of an arbitrary memoryless nonlinearity or linear combinations of sets of arbitrary memoryless nonlinearities are derived. Finally, an upper limit on the number of nonlinearities for which a quantizer can exhibit spur-free performance is derived.

Scale-free behavioral dynamics directly linked with scale-free cortical dynamics

Naturally occurring body movements and collective neural activity both exhibit complex dynamics, often with scale-free, fractal spatiotemporal structure, thought to confer functional benefits to the organism. Despite their similarities, scale-free brain activity and scale-free behavior have been studied separately, without a unified explanation. Here we show that scale-free dynamics of behavior and certain subsets of cortical neurons are one-to-one related. Surprisingly, the scale-free neural subsets exhibit stochastic winner-take-all competition with other neural subsets, inconsistent with prevailing theory of scale-free neural systems. We develop a computational model which accounts for known cell-type-specific circuit structure and explains our findings. Our results establish neural underpinnings of scale-free behavior and clear behavioral relevance of scale-free neural activity, which was previously thought to represent background noise in cerebral cortex.

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.

The cone method: Inferring decision times from single-trial 3D movement trajectories in choice behavior

Ongoing goal-directed movements can be rapidly adjusted following new environmental information, e.g., when chasing pray or foraging. This makes movement trajectories in go-before-you-know decision-making a suitable behavioral readout of the ongoing decision process. Yet, existing methods of movement analysis are often based on statistically comparing two groups of trial-averaged trajectories and are not easily applied to three-dimensional data, preventing them from being applicable to natural free behavior. We developed and tested the cone method to estimate the point of overt commitment (POC) along a single two- or three-dimensional trajectory, i.e., the position where the movement is adjusted towards a newly selected spatial target. In Experiment 1, we established a “ground truth” data set in which the cone method successfully identified the experimentally constrained POCs across a wide range of all but the shallowest adjustment angles. In Experiment 2, we demonstrate the power of the method in a typical decision-making task with expected decision time differences known from previous findings. The POCs identified by cone method matched these expected effects. In both experiments, we compared the cone method’s single trial performance with a trial-averaging method and obtained comparable results. We discuss the advantages of the single-trajectory cone method over trial-averaging methods and possible applications beyond the examples presented in this study. The cone method provides a distinct addition to existing tools used to study decisions during ongoing movement behavior, which we consider particularly promising towards studies of non-repetitive free behavior.