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"Albert, A."
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Comparing attentional disengagement between Prolific and MTurk samples
Attention often disengages from primary tasks in favor of secondary tasks (i.e., multitasking) and task-unrelated thoughts (i.e., mind wandering). We assessed whether attentional disengagement, in the context of a cognitive task, can substantially differ between samples from commonly used online participant recruitment platforms, Prolific and Mechanical Turk (MTurk). Initially, eighty participants were recruited through Prolific to perform an attention task in which the risk of losing points for errors was varied (high risk = 80% chance of loss, low risk = 20% chance of loss). Attentional disengagement was measured via task performance along with self-reported mind wandering and multitasking. On Prolific, we observed surprisingly low levels of disengagement. We then conducted the same experiment on MTurk. Strikingly, MTurk participants exhibited more disengagement than Prolific participants. There was also an interaction between risk and platform, with the high-risk group exhibiting less disengagement, in terms of better task performance, than the low-risk group, but only on MTurk. Platform differences in individual traits related to disengagement and relations among study variables were also observed. Platform differences persisted, but were smaller, after increasing MTurk reputation criteria and remuneration in a second experiment. Therefore, recruitment platform and recruitment criteria could impact results related to attentional disengagement.
Journal Article
The reproductive number of COVID-19 is higher compared to SARS coronavirus
Teaser: Our review found the average R0 for 2019-nCoV to be 3.28, which exceeds WHO estimates of 1.4 to 2.5.
Journal Article
Value of strong ties to disconnected others
Knowledge creation requires the combination and exchange of diverse and overlapping knowledge inputs as individuals interact with exchange partners to create new knowledge. In this study, we examine knowledge creation among university research scientists as a function of their professional (ego) networks—those others with whom they collaborate for the purpose of creating new knowledge. We propose that knowledge creation relies, in part, on two attributes of a researcher's professional network structure—average tie strength and ego network density—and we provide insights into how these attributes jointly affect knowledge creation. Our study of over 7,300 scientific publications by 177 research scientists working with more than 14,000 others over an 11-year period provides evidence that the relationship between a research scientist's professional network and knowledge creation depends on both ego network density and average tie strength. Our evidence suggests that both attributes affect knowledge creation. Moreover, average tie strength interacts with density to affect knowledge creation such that researchers who maintain mostly strong ties with research collaborators who themselves comprise a sparse network have the highest levels of new knowledge creation.
Journal Article
The Impact of Three-Dimensional Effects on the Simulation of Turbulence Kinetic Energy in a Major Alpine Valley
The correct simulation of the atmospheric boundary layer (ABL) is crucial for reliable weather forecasts in truly complex terrain. However, common assumptions for model parametrizations are only valid for horizontally homogeneous and flat terrain. Here, we evaluate the turbulence parametrization of the numerical weather prediction model COSMO with a horizontal grid spacing of Δx=1.1km for the Inn Valley, Austria. The long-term, high-resolution turbulence measurements of the i-Box measurement sites provide a useful data pool of the ABL structure in the valley and on slopes. We focus on days and nights when ABL processes dominate and a thermally-driven circulation is present. Simulations are performed for case studies with both a one-dimensional turbulence parametrization, which only considers the vertical turbulent exchange, and a hybrid turbulence parametrization, also including horizontal shear production and advection in the budget of turbulence kinetic energy (TKE). We find a general underestimation of TKE by the model with the one-dimensional turbulence parametrization. In the simulations with the hybrid turbulence parametrization, the modelled TKE has a more realistic structure, especially in situations when the TKE production is dominated by shear related to the afternoon up-valley flow, and during nights, when a stable ABL is present. The model performance also improves for stations on the slopes. An estimation of the horizontal shear production from the observation network suggests that three-dimensional effects are a relevant part of TKE production in the valley.
Journal Article
SLEEPY: a comprehensive Python module for simulating relaxation and dynamics in nuclear magnetic resonance
Nuclear magnetic resonance is a powerful method for characterizing dynamics of biological systems in a native-like environment. Accurate dynamics characterization, however, often requires simulations of complex NMR experiments. While a number of simulation programs exist for NMR simulation (SIMPSON, Spinach, SpinEvolution), none of these are focused on easy simulation of motional effects on NMR experiments. The SLEEPY Python module makes it straightforward to simulate arbitrary pulse sequences while including both relaxation and exchange processes. SLEEPY furthermore allows simulation of solid-state (static and spinning) and solution NMR experiments, using both truncated and full Hamiltonians (rotating frame/lab frame). We demonstrate its application to a wide variety of experiments, including transverse (T
1
ρ
), and longitudinal relaxation (T
1
), nuclear Overhauser effect magnetization transfers, recoupling, and paramagnetic effects. We also provide an extensive online tutorial that explains how to use the various capabilities of SLEEPY. This tool can then be used for both better understanding of the impact of dynamics on NMR and in reproduction of experimental results.
Simulation is a critical tool in NMR development, and while powerful simulation software exists, simulating dynamic and relaxation effects remains challenging. Here, the authors describe SLEEPY, a Python module to simplify simulation of such effects.
Journal Article