Mcnallybinderup3431

MAIN RESULTS The results showed that with PV, a 1.5-cm rotation could be corrected with an average of 3.1 ± 1.5 interactive adjustments, equivalent to around 15.5 ± 7.5 seconds, which was greatly reduced compared to retraining. There was no significant difference in the real-time control performance between before the armband displacement and after the PV correction. SIGNIFICANCE To the best of our knowledge, this study was the first maintaining pattern recognition-based myoelectric control performance in the presence of electrode shifts without recollecting the entire training data. It suggested the feasibility of the PV framework used in the myoelectric armband and MCI for practical applications. © 2020 IOP Publishing Ltd.Although the 1T' phase is rare in the transition metal dichalcogenides (TMDCs) family, it has attracted rapid growing research interest due to the coexistence of superconductivity, unsaturated magneto-resistance, topological phases etc. Among them, the quantum spin Hall (QSH) state in monolayer 1T'-TMDCs is especially interesting because of its unique van der Waals crystal structure, bringing advantages in the fundamental research and application. For example, the van der Waals two-dimensional (2D) layer is vital in building novel functional vertical heterostructure. The monolayer 1T'-TMDCs has become one of the widely studied QSH insulator. In this review, we review the recent progresses in fabrications of monolayer 1T'-TMDCs and evidences that establish it as QSH insulator. © 2020 IOP Publishing Ltd.Myocardial perfusion (MP) PET imaging plays a key role in risk assessment and stratification of patients with coronary artery disease. In this work, we proposed a patch-based artificial neural network (ANN) fusion approach that integrates information from the maximum-likelihood (ML) and the post-smoothed ML reconstruction to improve MP PET imaging. To enhance quantification and tasked-based MP defect detection, the proposed method fused features from patches of the ML and the post-smoothed ML reconstructed images with different noise levels and spatial resolution. Using the XCAT phantom, we simulated three MP PET datasets, one with normal perfusion and the other two with non-transmural and transmural regionally reduced perfusion of the left ventricular (LV) myocardium. ABL001 The proposed ANN fusion technique was quantitatively evaluated in terms of noise-bias and noise-contrast tradeoff, and compared with the post-smoothed ML reconstruction. Using the channelized Hotelling observer, we evaluated the detectability of the non-transmural and transmural defects through a receiver operating characteristic analysis. The quantitative results demonstrated that the ANN enhancement method reduced bias and improved contrast while reaching comparable noise to that of the post-smoothed ML reconstruction. Moreover, the ANN fusion technique significantly improved the defect detectability of both non-transmural and transmural defects. In addition to the simulation study, we further evaluated the ANN enhancement method on patient data. Compared with the post-smoothed ML reconstruction, the ANN fusion method improved the tradeoff between noise and mean on the LV myocardium, indicating its potential clinical value in MP PET imaging. © 2020 Institute of Physics and Engineering in Medicine.OBJECTIVE Estimating the ongoing phase of oscillations in electroencephalography (EEG) recordings is an important aspect of understanding brain function, as well as for the development of phase dependent closed-loop real-time systems that deliver stimuli. Such stimuli may take the form of direct brain stimulation (for example transcranial magnetic stimulation), or sensory stimuli (for example presentation of an auditory stimulus). We identify two linked problems related to estimating the phase of EEG rhythms with a specific focus on the alpha-band 1) when the signal after a specific stimulus is unknown (real-time case), or 2) when it is corrupted by the presence of the stimulus itself (offline analysis). We propose methods to estimate the phase at the presentation time of these stimuli. APPROACH Machine learning methods are used to learn the causal mapping from an unprocessed EEG recording to a phase estimate generated with a non-causal signal processing chain. This mapping is then used to predict the phase causally where non-causal methods are inappropriate. MAIN RESULTS We demonstrate the ability of these machine learning methods to estimate instantaneous phase from an EEG signal subjected to very minor pre-processing with higher accuracy than commonly used signal-processing methods. SIGNIFICANCE Neural oscillations have been implicated in a wide variety of sensory, cognitive and motor functions. The instantaneous phase of these rhythms may reflect specific processes of computation which can be acted upon if they can be estimated with sufficient accuracy. Such brain-state dependent paradigms are of increasing medical and scientific interest. © 2020 IOP Publishing Ltd.Oxygen evolution reaction (OER) a sluggish multistep process in electrochemical water splitting is still a challenging issue to achieve with cheap, earth-abundant non-precious and non-polluting materials. In this work, the three different electrocatalysts namely NiCo2O4, NiCo2S4, and NiCo2Se4 synthesized by simple hydrothermal process show excellent OER activity. This report not only projects OER performances but also demonstrates a modified method for the transformation of NiCo2O4 to NiCo2S4 and NiCo2Se4 via sulfidation and selenization reactions. The well crystalline, porous nature of NiCo2O4, NiCo2S4, and NiCo2Se4 electrocatalysts with one dimensional (1-D) structural morphology affords overpotentials of 346 mV, 309 mV and 270 mV at current density of 10 mA cm-2 in 1M KOH. In particular, NiCo2Se4 exhibits a low overpotential as well as smaller Tafel slope of 63 mV dec-1, leading to the robust stability in alkaline condition. The abundant active sites in NiCo2Se4 with large mass and size enhances the OER performance. This type of selenide based materials with low toxicity is also an advantage for eco-friendly applications. © 2020 IOP Publishing Ltd.