Simulation of the Metal Melt Convection and its Viscoelastic Interaction with Dielectric Film in an Alternating Magnetic Field

Illarion Leonidovich Nikulin,Vitalii Anatolevich Demin 대한금속·재료학회 2022 METALS AND MATERIALS International Vol.28 No.9

This article refers to the problem of the formation of oxide films during induction melting. The mutual influence of theforced convection of the melt in an alternating electromagnetic field and the elastic-stress state of the dielectric film on thesurface are considered. The assumptions of the mathematical model, governing equations and film properties are discussedin detail. The mechanisms that can create mechanical stresses in the film are considered, and it is shown that the greatestcontribution to stresses comes from viscous forces arising from the movement of the melt under the film. The influence ofelectromagnetic field frequency (its non-dimensional analogue) on melt flows is investigated numerically, it is shown howthese flows deform films of different configurations. It was found that there are ranges of parameters, such as the frequencyof the magnetic field (and its spatial distribution), at which films of certain sizes are in stable equilibrium. On the plane ofparameters frequency and film size the maps of film deformation are plotted, from which one can see favourable and unfavourableregimes in terms of formation and stability of the surface dielectric films.

Structure and Mechanical Properties of a Layered Composite Based on Fe–Cr–V Alloy and High-Nitrogen High-Chromium Steel After Hot Pressing and Annealing

V. M. Khatkevich,S. O. Rogachev,S. A. Nikulin,E. N. Tokmakova 대한금속·재료학회 2022 METALS AND MATERIALS International Vol.28 No.2

A hot pressing method was used to obtain a layered composite consisting of alternating layers of AISI 439 type steel witha high nitrogen content (0.8%) and Fe-20% Cr-5%V alloy with a low nitrogen content (less than 0.02%). Annealing of thecomposite at T = 700–850 °C after hot pressing leads to diffusion of nitrogen from the nitrided AISI 439 layers into theFe–Cr–V layers, the formation of diffusion zones containing dispersed vanadium nitrides in the Fe–Cr–V layers and theformation of diffusion zones with a low nitrogen content in the AISI 439 layers. A tendency towards a decrease in the sizesof the vanadium nitride particles with decreasing annealing temperature was revealed. Annealing at T = 750 °C increasesthe plastic characteristics and reduces the strength characteristics of the composite compared to the state after hot pressing. The presence of dispersed vanadium nitrides precipitated in the composite structure during annealing increases the strengthof the composite by 15%.

Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation

Vidaurre, C.,Ramos Murguialday, A.,Haufe, S.,Gó,mez, M.,Mü,ller, K.-R.,Nikulin, V.V. ACADEMIC PRESS 2019 NEUROIMAGE Vol.199 No.-

<P><B>Abstract</B></P> <P>An important goal in Brain-Computer Interfacing (BCI) is to find and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) sensory threshold neuromuscular electrical stimulation during performance of motor imagery (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MI or BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition, we observed that the performance in MI condition could be predicted on the basis of a more pronounced connectivity within sensorimotor areas in the frequency bands providing the best performance in BOTH. These finding might offer a new avenue for training SMR-based BCI systems particularly for users having difficulties to achieve efficient BCI control. It might also be an alternative strategy for users who cannot perform real movements but still have remaining afferent pathways (e.g., ALS and stroke patients).</P> <P><B>Highlights</B></P> <P> <UL> <LI> Afferent stimulation (STM) in the calibration phase was used to enhance BCI performance. </LI> <LI> Concurrent motor imagery and STM had stronger modulation of sensorimotor oscillations. </LI> <LI> STM significantly improved BCI accuracy particularly for poorly performing subjects. </LI> <LI> Classifiers trained with STM can be successfully used online even without stimulation. </LI> <LI> These findings ease the practical applicability of STM-based BCI systems. </LI> </UL> </P>

Multiscale temporal neural dynamics predict performance in a complex sensorimotor task

Samek, Wojciech,Blythe, Duncan A.J.,Curio, Gabriel,Mü,ller, Klaus-Robert,Blankertz, Benjamin,Nikulin, Vadim V. Elsevier 2016 NeuroImage Vol.141 No.-

<P><B>Abstract</B></P> <P>Ongoing neuronal oscillations are pivotal in brain functioning and are known to influence subjects' performance. This modulation is usually studied on short time scales whilst multiple time scales are rarely considered. In our study we show that Long-Range Temporal Correlations (LRTCs) estimated from the amplitude of EEG oscillations over a range of time-scales predict performance in a complex sensorimotor task, based on Brain-Computer Interfacing (BCI). Our paradigm involved eighty subjects generating covert motor responses to dynamically changing visual cues and thus controlling a computer program through the modulation of neuronal oscillations. The neuronal dynamics were estimated with multichannel EEG. Our results show that: (a) BCI task accuracy may be predicted on the basis of LRTCs measured during the preceding training session, and (b) this result was not due to signal-to-noise ratio of the ongoing neuronal oscillations. Our results provide direct empirical evidence in addition to previous theoretical work suggesting that scale-free neuronal dynamics are important for optimal brain functioning.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Functional relevance of Long-Range Temporal Correlations (LRTCs) was investigated. </LI> <LI> LRTCs were measured with EEG during complex sensorimotor task. </LI> <LI> Alpha-band LRTCs predicted task performance. </LI> <LI> Power-law neuronal dynamics are likely to be beneficial for brain functioning. </LI> </UL> </P>

Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets

Vidaurre, C.,Nolte, G.,de Vries, I.E.J.,Gó,mez, M.,Boonstra, T.W.,Mü,ller, K.-R.,Villringer, A.,Nikulin, V.V. Elsevier 2019 NeuroImage Vol.201 No.-

<P><B>Abstract</B></P> <P>Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We developed a novel multivariate method for the detection of neural synchronization. </LI> <LI> Canonical Coherence (caCOH) maximizes coherence between two datasets. </LI> <LI> caCOH was validated in simulations and real data. </LI> <LI> caCOH is applicable for diverse brain-brain or brain-periphery signals (EEG/MEG/LFP/EMG). </LI> </UL> </P>