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We use deep imaging from the JWST Advanced Deep Extragalactic Survey (JADES) to study the evolution of the ionizing photon production efficiency, ξion. We estimate ξion for a sample of 677 galaxies at z ∼ 4–9 using NIRCam (Near-Infrared Camera) photometry. Specifically, combinations of the medium and wide bands F 335 M–F 356 W and F 410 M–F 444 W to constrain emission lines that trace ξion: Hα and [O  iii ]. Additionally, we use the spectral energy distribution fitting code prospector to fit all available photometry and infer galaxy properties. The flux measurements obtained via photometry are consistent with FRESCO (First Reionisation Epoch Spectroscopic Complete Survey) and NIRSpec-derived fluxes. Moreover, the emission-line-inferred measurements are consistent with the prospector estimates. We also confirm the observed ξion trend with redshift and M UV, and find: log ξion(z, M UV) = (0.05 ± 0.02) z + (0.11 ± 0.02) M UV + (27.33 ± 0.37). We use prospector to investigate correlations of ξion with other galaxy properties. We see a clear correlation between ξion and burstiness in the star formation history of galaxies, given by the ratio of recent to older star formation, where burstiness is more prevalent at lower stellar masses. We also convolve our ξion relations with luminosity functions from the literature, and constant escape fractions of 10 per cent and 20 per cent, to place constraints on the cosmic ionizing photon budget. By combining our results, we find that if our sample is representative of the faint low-mass galaxy population, galaxies with bursty star formation are efficient enough in producing ionizing photons and could be responsible for the reionization of the Universe. [ABSTRACT FROM AUTHOR]

New robot-assisted surgery platforms being developed will be required to have proficiency-based simulation training available. Scoring methodologies and performance feedback for trainees are currently not consistent across all robotic simulator platforms. Also, there are virtually no prior publications on how VR simulation passing benchmarks have been established. This paper compares methods evaluated to determine the proficiency-based scoring thresholds (a.k.a. benchmarks) for the new Medtronic Hugo™ RAS robotic simulator. Nine experienced robotic surgeons from multiple disciplines performed the 49 skills exercises 5 times each. The data were analyzed in 3 different ways: (1) include all data collected, (2) exclude first sessions, (3) exclude outliers. Eliminating the first session discounts becoming familiar with the exercise. Discounting outliers allows removal of potentially erroneous data that may be due to technical issues, unexpected distractions, etc. Outliers were identified using a common statistical technique involving the interquartile range of the data. Using each method above, mean and standard deviations were calculated, and the benchmark was set at a value of 1 standard deviation above the mean. In comparison to including all the data, when outliers are excluded, fewer data points are removed than just excluding first sessions, and the metric benchmarks are made more difficult by an average of 11%. When first sessions are excluded, the metric benchmarks are made easier by an average of about 2%. In comparison with benchmarks calculated using all data points, excluding outliers resulted in the biggest change making the benchmarks more challenging. We determined that this method provided the best representation of the data. These benchmarks should be validated with future clinical training studies., (© 2024. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.)