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  1. ( , Sampled-Data Observer Based Dynamic Surface Control of Delayed Neuromuscular Functional Electrical Stimulation)
    Functional electrical stimulation (FES) is a potential technique for reanimating paralyzed muscles post neurological injury/disease. Several technical challenges including difficulty in measuring and compensating for delayed muscle activation levels inhibit its satisfactory control performance. In this paper, an ultrasound (US) imaging approach is proposed to measure delayed muscle activation levels under the implementation of FES. Due to low sampling rates of US imaging, a sampled data observer (SDO) is designed to estimate the muscle activation in a continuous manner. The SDO is combined with continuous-time dynamic surface control (DSC) approach that compensates for the electromechanical delay (EMD) in the tibialismore »anterior (TA) activation dynamics. The stability analysis based on the Lyapunov-Krasovskii function proves that the SDO-based DSC plus delay compensation (SDO-DSC-DC) approach achieves semi-global uniformly ultimately bounded (SGUUB) tracking performance. Simulation results on an ankle dorsiflexion neuromusculoskeletal system show the root mean square error (RMSE) of desired trajectory tracking is reduced by 19.77 % by using the proposed SDO-DSC-DC compared to the DSC-DC without the SDO. The findings provide potentials for rehabilitative devices, like powered exoskeleton and FES, to assist or enhance human limb movement based on the corresponding muscle activities in real-time.« less
    Free, publicly-accessible full text available October 1, 2023
  2. ; ; ; ; ; ( , The Astrophysical Journal)
    Abstract The formation, development, and impact of slow shocks in the upstream regions of reconnecting current layers are explored. Slow shocks have been documented in the upstream regions of magnetohydrodynamic (MHD) simulations of magnetic reconnection as well as in similar simulations with the kglobal kinetic macroscale simulation model. They are therefore a candidate mechanism for preheating the plasma that is injected into the current layers that facilitate magnetic energy release in solar flares. Of particular interest is their potential role in producing the hot thermal component of electrons in flares. During multi-island reconnection, the formation and merging of flux ropesmore »in the reconnecting current layer drives plasma flows and pressure disturbances in the upstream region. These pressure disturbances steepen into slow shocks that propagate along the reconnecting component of the magnetic field and satisfy the expected Rankine–Hugoniot jump conditions. Plasma heating arises from both compression across the shock and the parallel electric field that develops to maintain charge neutrality in a kinetic system. Shocks are weaker at lower plasma β , where shock steepening is slow. While these upstream slow shocks are intrinsic to the dynamics of multi-island reconnection, their contribution to electron heating remains relatively minor compared with that from Fermi reflection and the parallel electric fields that bound the reconnection outflow.« less
    Free, publicly-accessible full text available February 1, 2023
  3. ; ; ; ; ; ; ; ; ( , ACM SIGMOD)
    Free, publicly-accessible full text available January 1, 2023
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Climate of the past)
    Free, publicly-accessible full text available December 8, 2022
  5. ; ; ; ; ( , The Astrophysical Journal)
    Full Text Available 
  6. ( , Atmospheric chemistry and physics discussion)
    Full Text Available 
  7. ; ; ; ; ; ( , Conference on Robot Learning)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; ; ; ; ( , Nature reviews)
    Hailstorms are dangerous and costly phenomena that are expected to change in response to a warming climate. In this Review, we summarize current knowledge of climate change effects on hailstorms. As a result of anthropogenic warming, it is generally anticipated that low-level moisture and convective instability will increase, raising hailstorm likelihood and enabling the formation of larger hailstones; the melting height will rise, enhancing hail melt and increasing the average size of surviving hailstones; and vertical wind shear will decrease overall, with limited influence on the overall hailstorm activity, owing to a predominance of other factors. Given geographic differences andmore »offsetting interactions in these projected environmental changes, there is spatial heterogeneity in hailstorm responses. Observations and modelling lead to the general expectation that hailstorm frequency will increase in Australia and Europe, but decrease in East Asia and North America, while hail severity will increase in most regions. However, these projected changes show marked spatial and temporal variability. Owing to a dearth of long-term observations, as well as incomplete process understanding and limited convection-permitting modelling studies, current and future climate change effects on hailstorms remain highly uncertain. Future studies should focus on detailed processes and account for non-stationarities in proxy relationships.« less
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; ; ; ; ; ; et al ( , Few-Body Systems)
    Free, publicly-accessible full text available June 1, 2023
  10. ; ; ; ; ; ( , the International Joint Conference on Neural Networks)
    Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally efficient uncertainty quantification approach that captures layerwise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubaturemore »points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.« less
    Accepted Manuscript Full Text Available