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Smart devices and Internet of Things (IoT) technologies are replacing or being incorporated into traditional devices at a growing pace. The use of digital interfaces to interact with these devices has become a common occurrence in homes, work spaces, and various industries around the world. The most common interfaces for these connected devices focus on mobile apps or voice control via intelligent virtual assistants. However, with augmented reality (AR) becoming more popular and accessible among consumers, there are new opportunities for spatial user interfaces to seamlessly bridge the gap between digital and physical affordances. In this paper, we present a human-subject study evaluating and comparing four user interfaces for smart connected environments: gaze input, hand gestures, voice input, and a mobile app. We assessed participants’ user experience, usability, task load, completion time, and preferences. Our results show multiple trade-offs between these interfaces across these measures. In particular, we found that gaze input shows great potential for future use cases, while both gaze input and hand gestures suffer from limited familiarity among users, compared to voice input and mobile apps. 

Abstract We test the impact of an evolving supermassive black hole mass scaling relation (M_BH–M_bulge) on the predictions for the gravitational-wave background (GWB). The observed GWB amplitude is 2–3 times higher than predicted by astrophysically informed models, which suggests the need to revise the assumptions in those models. We compare a semi-analytic model’s ability to reproduce the observed GWB spectrum with a static versus evolving-amplitudeM_BH–M_bulgerelation. We additionally consider the influence of the choice of galaxy stellar mass function (GSMF) on the modeled GWB spectra. Our models are able to reproduce the GWB amplitude with either a large number density of massive galaxies or a positively evolvingM_BH–M_bulgeamplitude (i.e., theM_BH/M_bulgeratio was higher in the past). If we assume that theM_BH–M_bulgeamplitude does not evolve, our models require a GSMF that implies an undetected population of massive galaxies (M_⋆≥ 10¹¹M_⊙atz> 1). When theM_BH–M_bulgeamplitude is allowed to evolve, we can model the GWB spectrum with all fiducial values and anM_BH–M_bulgeamplitude that evolves asα(z) =α₀(1 +z)^1.04±0.5. 

Abstract We present the first targeted searches for continuous gravitational waves (CWs) from 114 active galactic nuclei that may host supermassive black hole binaries, using the NANOGrav 15 yr dataset. By incorporating electromagnetic priors on sky location, distance, redshift, and CW frequency, our strain and chirp-mass upper limits are typically improved by a factor of ∼2 (median 2.2) relative to all-sky limits at the same frequency. Bayesian comparisons against a model including only a Hellings–Downs-correlated background disfavors a CW signal for all targets, with a mean Bayes factor of 0.73 ± 0.32. Two targets have Bayes factors slightly above unity, but coherence tests, random-targeting experiments, and a conservative accounting of the 114-target trials factor all indicate that they are consistent with noise. We use these two candidates as worked examples to illustrate an end-to-end targeted CW search analysis and a suite of follow-up tests that future promising candidates would need to pass. We find that the electromagnetic interpretations of both candidates are ambiguous, and we update the constraints on a putative binary in 3C 66B, ruling out part of its previously allowed parameter space. Ultimately, our results demonstrate the current sensitivity of targeted pulsar timing array searches for CWs and define a road map for future multimessenger CW detections. 

There is increasing interest in broadening participation in computational thinking (CT) by integrating CT into pre-college STEM curricula and instruction. Science, in particular, is emerging as an important discipline to support integrated learning. This highlights the need for carefully designed assessments targeting the integration of science and CT to help teachers and researchers gauge students’ proficiency with integrating the disciplines. We describe a principled design process to develop assessment tasks and rubrics that integrate concepts and practices across science, CT, and computational modeling. We conducted a pilot study with 10 high school students who responded to integrative assessment tasks as part of a physics-based computational modeling unit. Our findings indicate that the tasks and rubrics successfully elicit both Physics and CT constructs while distinguishing important aspects of proficiency related to the two disciplines. This work illustrates the promise of using such assessments formatively in integrated STEM and computing learning contexts.