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Molecular switches based on the 2H-1-benzopyran (chromene) scaffold have been widely developed for their desirable photochromic and mechanochromic properties. Extended π-conjugation is necessary to stabilize the ring-opened merocyanine dye at room temperature leading to efficient switching under ambient conditions. To this end, naphthopyrans represent a special class of benzo-annulated benzopyrans that have been studied extensively as both photoswitches and more recently as mechanophores, generating intensely colored merocyanine dyes upon exposure to ultraviolet light or mechanical force, respectively. Alternative annulation strategies with judicious heteroatom substitution have also been studied in the photochemistry literature, but the mechanochemistry of 2H-1-benzopyrans has yet to be explored. Here, we report the mechanochemical activation of an indole-fused 2H-1-benzopyran mechanophore that generates a yellow-colored merocyanine dye in polymers that is subsequently transformed to a purple-colored dye upon treatment with acid. Neutralization with base recovers the yellow-colored merocyanine isomer with trans exocyclic alkene geometry through an unusual acid-mediated alkene isomerization. This study expands the repertoire of mechanochromic mechanophores based on (hetero)annulated benzopyrans to enable multicolor chromomorphic behavior in response to both mechanical force and acid for applications in stimuli-responsive polymeric materials with complex switching properties. 

Abstract While fiducial inference was widely considered a big blunder by R.A. Fisher, the goal he initially set—‘inferring the uncertainty of model parameters on the basis of observations’—has been continually pursued by many statisticians. To this end, we develop a new statistical inference method called extended Fiducial inference (EFI). The new method achieves the goal of fiducial inference by leveraging advanced statistical computing techniques while remaining scalable for big data. Extended Fiducial inference involves jointly imputing random errors realized in observations using stochastic gradient Markov chain Monte Carlo and estimating the inverse function using a sparse deep neural network (DNN). The consistency of the sparse DNN estimator ensures that the uncertainty embedded in observations is properly propagated to model parameters through the estimated inverse function, thereby validating downstream statistical inference. Compared to frequentist and Bayesian methods, EFI offers significant advantages in parameter estimation and hypothesis testing. Specifically, EFI provides higher fidelity in parameter estimation, especially when outliers are present in the observations; and eliminates the need for theoretical reference distributions in hypothesis testing, thereby automating the statistical inference process. Extended Fiducial inference also provides an innovative framework for semisupervised learning. 

Abstract A mooring array has been maintained across the West Greenland shelf and slope since 2014 as part of the Overturning in the Subpolar North Atlantic Program (OSNAP). Here, we use the first 8 years of data to investigate the interannual variability of the two overflow water components of the deep western boundary current (DWBC): the Denmark Strait Overflow Water (DSOW) and the Northeast Atlantic Deep Water (NEADW). While the velocity structure has remained similar throughout the record, both water masses have freshened considerably, especially the NEADW salinity core. Using revised density criteria to define these two components, their transports decreased significantly between 2014 and 2022: from 6.2 to 3.8 Sv (1 Sv ≡ 10⁶m³s⁻¹) (−0.33 Sv yr⁻¹) for the DSOW and from 5.4 to 4.1 Sv (−0.19 Sv yr⁻¹) for the NEADW. Since the overflows across the Denmark Strait and the Faroe Bank Channel have remained steady over this period, this points to decreased entrainment downstream of the sills as a possible mechanism for the observed transport reduction south of Greenland. Using shipboard and mooring data from the two sills, and a hydrographic database for the surrounding region, we predict the downstream transport of the two DWBC components via the framework of a streamtube model. The predicted transport explains 94% of the observed DSOW trend and 63% of the observed NEADW trend. This implies that further entrainment of the NEADW must occur during its long pathlength, which would also help explain the fresher-than-predicted NEADW salinity observed at the OSNAP array. 

This study was grounded in the spatial computational thinking model developed by the 3D Weather project funded by the NSF STEM+C program. The model reflects a discipline-based perspective towards computational thinking and captures the spatial nature of computational thinking in meteorology and the reliance of computational thinking on spatial thinking for geospatial analysis. The research was conducted among nineteen teachers attending the summer workshop offered by the project in its third project year to prepare them for teaching spatial computational thinking with IDV (Integrated Data Viewer, downloadable at https://www.unidata.ucar.edu/software/idv/) visualization of weather data. Quantitative survey data were collected measuring these teachers’ meteorology content knowledge, spatial computational thinking, self-efficacy for teaching spatial computational thinking, and epistemic cognition of teaching meteorology. The data were analyzed to examine the effects of the workshop in terms of these variables and the correlations among them were also explored.