Phase-field simulations for dripping-to-jetting transitions: Effects of low interfacial tension and bulk diffusion

The dripping-to-jetting transitions in coaxial flows have been experimentally well studied for systems of high interfacial tension, where the capillary number of the outer fluid and the Weber number of the inner fluid are in control. Recent experiments have shown that in systems of low interfacial tension, the transitions driven by the inner flow are no longer dominated by the inertial force alone, and the viscous drag force due to the inner flow is also quantitatively important. In the present work, we carry out numerical simulations based on the Cahn-Hilliard-Navier-Stokes model, aiming for a more complete and quantitative study that is needed for understanding the effects of interfacial tension when it becomes sufficiently low. The Cahn-Hilliard-Navier-Stokes model is solved by using an accurate and efficient spectral method in a cylindrical domain with axisymmetry, and numerical results obtained for jet and drop radii demonstrate the accuracy of our computation. Plenty of numerical examples are systematically presented to show the dripping-to-jetting transitions driven by the outer flow and inner flow respectively. In particular, for transitions dominated by inner flow, detailed results reveal how the magnitude of interfacial tension quantitatively determines the relative importance of the inertial and viscous forces due to the inner flow at the transition point. Our numerical results are found to be consistent with the experimental observation. Finally, the degree of bulk diffusion is varied to investigate its quantitative effect on the condition for the occurrence of transition. Such effect is expected for systems of ultralow interfacial tension where interfacial motion is more likely to be driven by bulk diffusion.Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Phys. Fluids 35, 074105 (2023

Automated labeling and online evaluation for self-paced movement detection BCI

Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) allow users to use brain signals to control external instruments, and movement intention detecting BCIs can aid in the rehabilitation of patients who have lost motor function. Existing studies in this area mostly rely on cue-based data collection that facilitates sample labeling but introduces noise from cue stimuli; moreover, it requires extensive user training, and cannot reflect real usage scenarios. In contrast, self-paced BCIs can overcome the limitations of the cue-based approach by supporting users to perform movements at their own initiative and pace, but they fall short in labeling. Therefore, in this study, we proposed an automated labeling approach that can cross-reference electromyography (EMG) signals for EEG labeling with zero human effort. Furthermore, considering that only a few studies have focused on evaluating BCI systems for online use and most of them do not report details of the online systems, we developed and present in detail a pseudo-online evaluation suite to facilitate online BCI research. We collected self-paced movement EEG data from 10 participants performing opening and closing hand movements for training and evaluation. The results show that the automated labeling method can contend well with noisy data compared with the baseline labeling method. We also explored popular machine learning models for online self-paced movement detection. The results demonstrate the capability of our online pipeline, and that a well-performing offline model does not necessarily translate to a well-performing online model owing to the specific settings of an online BCI system. Our proposed automated labeling method, online evaluation suite, and dataset take a concrete step towards real-world self-paced BCI systems.</p

Towards a unified understanding of uncertainty quantification in traffic flow forecasting

Uncertainty is an essential consideration for time series forecasting tasks. In this work, we focus on quantifying the uncertainty of traffic forecasting from a unified perspective. We develop a novel traffic forecasting framework, namely Deep Spatio-Temporal Uncertainty Quantification (DeepSTUQ), which can estimate both aleatoric and epistemic uncertainty. Specifically, we first leverage a spatio-temporal model to model the complex spatio-temporal correlations of traffic data. Subsequently, two independent sub-neural networks maximizing the heterogeneous log-likelihood are developed to estimate aleatoric uncertainty. To estimate epistemic uncertainty, we combine the merits of variational inference and deep ensembling by integrating the Monte Carlo dropout and the Adaptive Weight Averaging re-training methods, respectively. Furthermore, to relax the Gaussianity assumption, mitigate overfitting, and improve horizon-wise uncertainty quantification performance, we define a new calibration method called Multi-horizon Conformal Calibration (MHCC). Finally, we provide a theoretical analysis of the proposed unified approach based on the PAC-Bayes theory. Extensive experiments are conducted on four public datasets, and the empirical results suggest that the proposed method outperforms state-of-the-art methods in terms of both point prediction and uncertainty quantification