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Insights from data-driven surrogate models reveal that the Sherwood number characterizes mass transport conditions independent of the convection method, offering design guidelines for scaling up organic electrosynthesis.

 

With the recent success of representation learning methods, which includes deep learning as a special case, there has been considerable interest in developing techniques that incorporate known physical constraints into the learned representation. As one example, in many applications that involve a signal propagating through physical media (e.g., optics, acoustics, fluid dynamics, etc.), it is known that the dynamics of the signal must satisfy constraints imposed by the wave equation. Here we propose a matrix factorization technique that decomposes such signals into a sum of components, where each component is regularized to ensure that it nearly satisfies wave equation constraints. Although our proposed formulation is non-convex, we prove that our model can be efficiently solved to global optimality. Through this line of work we establish theoretical connections between wave-informed learning and filtering theory in signal processing. We further demonstrate the application of this work on modal analysis problems commonly arising in structural diagnostics and prognostics. 

The long-term goal of this work is to predict and control the microstructure evolution in metal additive manufacturing processes. In pursuit of this goal, we developed and applied an approach which combines physics-based thermal modeling with data-driven machine learning to predict two important microstructure-related characteristics, namely, the meltpool depth and primary dendritic arm spacing in Nickel Alloy 718 parts made using the laser powder bed fusion (LPBF) process. Microstructure characteristics are critical determinants of functional physical properties, e.g., yield strength and fatigue life. Currently, the microstructure of LPBF parts is optimized through a cumbersome build-and-characterize empirical approach. Rapid and accurate models for predicting microstructure evolution are therefore valuable to reduce process development time and achieve consistent properties. However, owing to their computational complexity, existing physics-based models for predicting the microstructure evolution are limited to a few layers, and are challenging to scale to practical parts. This paper addresses the aforementioned research gap via a novel physics and data integrated modeling approach. The approach consists of two steps. First, a rapid, part-level computational thermal model was used to predict the temperature distribution and cooling rate in the entire part before it was printed. Second, the foregoing physics-based thermal history quantifiers were used as inputs to a simple machine learning model (support vector machine) trained to predict the meltpool depth and primary dendritic arm spacing based on empirical materials characterization data. As an example of its efficacy, when tested on a separate set of samples from a different build, the approach predicted the primary dendritic arm spacing with root mean squared error ≈ 110 nm. This work thus presents an avenue for future physics-based optimization and control of microstructure evolution in LPBF. 

Long-read sequencing is revolutionizingde-novogenome assemblies, with continued advancements making it more readily available for previously understudied, non-model organisms. Stony corals are one such example, with long-readde-novogenome assemblies now starting to be publicly available, opening the door for a wide array of ‘omics-based research. Here we present a newde-novogenome assembly for the endangered Caribbean star coral,Orbicella faveolata, using PacBio circular consensus reads. Our genome assembly improved the contiguity (51 versus 1,933 contigs) and complete and single copy BUSCO orthologs (93.6% versus 85.3%, database metazoa_odb10), compared to the currently available reference genome generated using short-read methodologies. Our newde-novoassembled genome also showed comparable quality metrics to other coral long-read genomes. Telomeric repeat analysis identified putative chromosomes in our scaffolded assembly, with these repeats at either one, or both ends, of scaffolded contigs. We identified 32,172 protein coding genes in our assembly through use of long-read RNA sequencing (ISO-seq) of additionalO. faveolatafragments exposed to a range of abiotic and biotic treatments, and publicly available short-read RNA-seq data. With anthropogenic influences heavily affectingO. faveolata, as well as itsincreasing incorporation into reef restoration activities, this updated genome resource can be used for population genomics and other ‘omics analyses to aid in the conservation of this species.

 

The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large data sets becoming available in the near future, we develop a likelihood-free Deep Learning technique using convolutional neural networks (CNNs) to infer broad-scale physical properties of a galaxy’s CGM and its halo mass for the first time. Using CAMELS (Cosmology and Astrophysics with MachinE Learning Simulations) data, including IllustrisTNG, SIMBA, and Astrid models, we train CNNs on Soft X-ray and 21-cm (H i) radio two-dimensional maps to trace hot and cool gas, respectively, around galaxies, groups, and clusters. Our CNNs offer the unique ability to train and test on ‘multifield’ data sets comprised of both H i and X-ray maps, providing complementary information about physical CGM properties and improved inferences. Applying eRASS:4 survey limits shows that X-ray is not powerful enough to infer individual haloes with masses log (Mhalo/M⊙) < 12.5. The multifield improves the inference for all halo masses. Generally, the CNN trained and tested on Astrid (SIMBA) can most (least) accurately infer CGM properties. Cross-simulation analysis – training on one galaxy formation model and testing on another – highlights the challenges of developing CNNs trained on a single model to marginalize over astrophysical uncertainties and perform robust inferences on real data. The next crucial step in improving the resulting inferences on the physical properties of CGM depends on our ability to interpret these deep-learning models.

 

Quenching of star formation in the central galaxies of cosmological halos is thought to result from energy released as gas accretes onto a supermassive black hole. The same energy source also appears to lower the central density and raise the cooling time of baryonic atmospheres in massive halos, thereby limiting both star formation and black hole growth, by lifting the baryons in those halos to greater altitudes. One predicted signature of that feedback mechanism is a nearly linear relationship between the central black hole’s mass (M_BH) and the original binding energy of the halo’s baryons. We present the increasingly strong observational evidence supporting a such a relationship, showing that it extends up to halos of massM_halo∼ 10¹⁴M_⊙. We then compare current observational constraints on theM_BH–M_halorelation with numerical simulations, finding that black hole masses in IllustrisTNG appear to exceed those constraints atM_halo< 10¹³M_⊙and that black hole masses in EAGLE fall short of observations atM_halo∼ 10¹⁴M_⊙. A closer look at IllustrisTNG shows that quenching of star formation and suppression of black hole growth do indeed coincide with black hole energy input that lifts the halo’s baryons. However, IllustrisTNG does not reproduce the observedM_BH–M_halorelation because its black holes gain mass primarily through accretion that does not contribute to baryon lifting. We suggest adjustments to some of the parameters in the IllustrisTNG feedback algorithm that may allow the resulting black hole masses to reflect the inherent links between black hole growth, baryon lifting, and star formation among the massive galaxies in those simulations.

 

Ultrafast affinity extraction (UAE) is a form of microscale affinity HPLC that can be employed to quickly measure equilibrium constants for solute-binding agent interactions in solution. This study used chromatographic and equilibrium theory with universal plots to examine the general conditions that are needed in UAE to obtain accurate, precise, and robust measurements of equilibrium constants for such interactions. The predicted results were compared to those obtained by UAE in studies that examined the binding of various drugs with two transport proteins: human serum albumin and α1-acid glycoprotein. The most precise and robust conditions for these binding studies occurred for systems with intermediate values for their equilibrium free fraction for the solute (F0 ≈ 0.20-0.80). These trends showed good agreement with those seen in prior studies using UAE. It was further determined how the apparent free fraction of a solute was related to the dissociation rate of this solute, the time allowed for solute dissociation during UAE, and the equilibrium free fraction for the solute. These results also agreed with experimental results, as obtained for the binding of warfarin and gliclazide with human serum albumin. The final section examined how a change in the apparent free fraction, as caused by solute dissociation, affected the accuracy of an equilibrium constant that was measured by UAE. In addition, theoretical plots were generated to allow the selection of conditions for UAE that provided a given level of accuracy during the measurement of an equilibrium constant. The equations created and trends identified for UAE were general ones that can be extended in future work to other solutes and binding agents. 

By combining the James Webb Space Telescope (JWST)/NIRCam JADES and CEERS extragalactic data sets, we have uncovered a sample of 21 T and Y brown dwarf candidates at best-fit distances between 0.1 and 4.2 kpc. These sources were selected by targeting the blue 1–2.5μm colors and red 3–4.5μm colors that arise from molecular absorption in the atmospheres ofT_eff< 1300 K brown dwarfs. We fit these sources using multiple models of substellar atmospheres and present the resulting fluxes, sizes, effective temperatures, and other derived properties for the sample. If confirmed, these fits place the majority of the sources in the Milky Way thick disk and halo. We observe proper motions for seven of the candidate brown dwarfs, with directions in agreement with the plane of our Galaxy, providing evidence that they are not extragalactic in nature. We demonstrate how the colors of these sources differ from selected high-redshift galaxies, and explore the selection of these sources in planned large-area JWST NIRCam surveys. Deep imaging with JWST/NIRCam presents an an excellent opportunity for finding and understanding these ultracool dwarfs at kiloparsec distances.

 

The power-law slope of the rest-ultraviolet (UV) continuum (fλ ∝ λβ) is a key metric of early star-forming galaxies, providing one of our only windows into the stellar populations and physical conditions of z ≳ 10 galaxies. Expanding upon previous studies with limited sample sizes, we leverage deep imaging from the JWST Advanced Deep Extragalactic Survey (JADES) to investigate the UV slopes of 179 z ≳ 9 galaxies with apparent magnitudes of mF200W ≃ 26–31, which display a median UV slope of β = −2.4. We compare to a statistical sample of z ≃ 5–9 galaxies, finding a shift towards bluer rest-UV colours at all $M_{\rm UV}$. The most UV-luminous z ≳ 9 galaxies are significantly bluer than their lower redshift counterparts, representing a dearth of moderately red galaxies within the first 500 Myr. At yet earlier times, the z ≳ 11 galaxy population exhibits very blue UV slopes, implying very low impact from dust attenuation. We identify a robust sample of 44 galaxies with β ≲ −2.8, which have spectral energy distributions requiring models of density-bounded H ii regions and median ionizing photon escape fractions of 0.51 to reproduce. Their rest-optical colours imply that this sample has weaker emission lines (median mF356W − mF444W = 0.19 mag) than typical galaxies (median mF356W − mF444W = 0.39 mag), consistent with the inferred escape fractions. This sample consists of relatively low stellar masses (median $\log (M/{\rm M}_{\odot })=7.5\pm 0.2$), and specific star formation rates (sSFRs; median $=79 \, \rm Gyr^{-1}$) nearly twice that of our full galaxy sample (median sSFRs $=44 \, \rm Gyr^{-1}$), suggesting these objects are more common among systems experiencing a recent upturn in star formation. We demonstrate that the shutoff of star formation provides an alternative solution for modelling of extremely blue UV colours, making distinct predictions for the rest-optical emission of these galaxies. Future spectroscopy will be required to distinguish between these physical pictures.