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Upper-limb amputees commonly cite difficulty of control as one of the main reasons why they abandon their prostheses. Combining myoelectric control with autonomous sensor-based control could improve prosthesis control. However, the cognitive and physical impact of shared control and semi-autonomous systems on users has yet to be fully explored. In this study we introduce a novel shared-control algorithm that blends proportional position control predicted from electromyography (EMG) with proportional position control predicted from an autonomous machine using infrared sensors embedded in the prosthetic hand’s fingers to detect the distance to objects. The user’s EMG control is damped in proportion to the machine’s prediction of an object’s position in relation to a given finger. The shared-control algorithm was validated using three intact individuals completing a holding task where they attempted to hold an object for as long as possible without dropping it. Shared control resulted in fewer object drops, 32% less cognitive demand, and 49% less physical effort (measured by EMG) relative to the participant’s EMG control alone. These results indicate that shared control can reduce the physiological burdens on the user as well as increase prosthetic control. 

We present a detailed chemical abundance analysis of the brightest star in the ultrafaint dwarf (UFD) galaxy candidate Cetus II from high-resolution Magellan/MIKE spectra. For this star, DES J011740.53-173053, abundances or upper limits of 18 elements from carbon to europium are derived. Its chemical abundances generally follow those of other UFD galaxy stars, with a slight enhancement of theα-elements (Mg, Si, and Ca) and low neutron-capture element (Sr, Ba, and Eu) abundances supporting the classification of Cetus II as a likely UFD. The star exhibits lower Sc, Ti, and V abundances than Milky Way (MW) halo stars with similar metallicity. This signature is consistent with yields from a supernova originating from a star with a mass of ∼11.2M_⊙. In addition, the star has a potassium abundance of [K/Fe] = 0.81, which is somewhat higher than the K abundances of MW halo stars with similar metallicity, a signature that is also present in a number of UFD galaxies. A comparison including globular clusters and stellar stream stars suggests that high K is a specific characteristic of some UFD galaxy stars and can thus be used to help classify objects as UFD galaxies.

 

Based on the prior O1–O2 observing runs, about 30% of the data collected by Advanced LIGO and Virgo in the next observing runs are expected to be single-interferometer data, i.e. they will be collected at times when only one detector in the network is operating in observing mode. Searches for gravitational-wave signals from supernova events do not rely on matched filtering techniques because of the stochastic nature of the signals. If a Galactic supernova occurs during single-interferometer times, separation of its unmodelled gravitational-wave signal from noise will be even more difficult due to lack of coherence between detectors. We present a novel machine learning method to perform single-interferometer supernova searches based on the standard LIGO-Virgo coherent WaveBurst pipeline. We show that the method may be used to discriminate Galactic gravitational-wave supernova signals from noise transients, decrease the false alarm rate of the search, and improve the supernova detection reach of the detectors.

 

Processing signals from open-loop system realizations can replace real-time operation using actuators in the design of closed-loop eigenstructures. One merit of the signal processing-based implementation is that it, in principle, allows virtual compensators of user-defined model order since the closed-loop systems are not to be realized during physical testing. The present paper explores the implication of the virtual compensator order in terms of the Fisher information on unknown parameters to be estimated in a model updating context. A numerical example with a structural system of engineering interest is presented that demonstrates the basic points outlined in the paper. 

We present Fermi Gamma-ray Burst Monitor (Fermi-GBM) and Swift Burst Alert Telescope (Swift-BAT) searches for gamma-ray/X-ray counterparts to gravitational-wave (GW) candidate events identified during the third observing run of the Advanced LIGO and Advanced Virgo detectors. Using Fermi-GBM onboard triggers and subthreshold gamma-ray burst (GRB) candidates found in the Fermi-GBM ground analyses, the Targeted Search and the Untargeted Search, we investigate whether there are any coincident GRBs associated with the GWs. We also search the Swift-BAT rate data around the GW times to determine whether a GRB counterpart is present. No counterparts are found. Using both the Fermi-GBM Targeted Search and the Swift-BAT search, we calculate flux upper limits and present joint upper limits on the gamma-ray luminosity of each GW. Given these limits, we constrain theoretical models for the emission of gamma rays from binary black hole mergers.