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Composites printed using material extrusion additive manufacturing (AM) typically exhibit alignment of high- aspect-ratio reinforcements parallel to the print direction. This alignment leads to highly anisotropic stiffness, strength, and transport properties. In many cases, it would be desirable to increase mechanical and transport properties transverse to the print direction, for example, in 3D-printed heat sinks or heat exchangers where heat must be moved efficiently between printed roads or layers. Rotational direct ink writing (RDIW), where the deposition nozzle simultaneously rotates and translates during deposition, provides a method to reorient fibers transverse to the print direction during the printing process. In the present work, carbon fiber-reinforced epoxy composites were printed by RDIW with a range of nozzle rotation rates and the in-plane and through-thickness thermal conductivity was measured. In addition, the orientation of carbon fiber (CF) in the composites was measured using optical microscopy and image analysis, from which second-order fiber orientation tensors were calculated. These results showed that the orientation of CF became less anisotropic as nozzle rotation rate increased, leading to increased through-thickness thermal conductivity, which increased by 40% at the highest rotation rate. The orientation tensors also showed that RDIW was more effective at reorienting fibers within the in-plane transverse direction compared to the through-thickness transverse direction. The results presented here demonstrate that a current weakness of material extrusion AM composites—poor thermal conductivity in the through-thickness direction—can be significantly improved with RDIW. 

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system’s own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndromecoronavirus2, influenza, and human immunodeficiency virus, highlight antigen-specific receptors, and reveal distinct characteristics of systemic lupus erythematosus and type-1 diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of immune responses. 

Abstract The Iceland Greenland Seas Project (IGP) is a coordinated atmosphere–ocean research program investigating climate processes in the source region of the densest waters of the Atlantic meridional overturning circulation. During February and March 2018, a field campaign was executed over the Iceland and southern Greenland Seas that utilized a range of observing platforms to investigate critical processes in the region, including a research vessel, a research aircraft, moorings, sea gliders, floats, and a meteorological buoy. A remarkable feature of the field campaign was the highly coordinated deployment of the observing platforms, whereby the research vessel and aircraft tracks were planned in concert to allow simultaneous sampling of the atmosphere, the ocean, and their interactions. This joint planning was supported by tailor-made convection-permitting weather forecasts and novel diagnostics from an ensemble prediction system. The scientific aims of the IGP are to characterize the atmospheric forcing and the ocean response of coupled processes; in particular, cold-air outbreaks in the vicinity of the marginal ice zone and their triggering of oceanic heat loss, and the role of freshwater in the generation of dense water masses. The campaign observed the life cycle of a long-lasting cold-air outbreak over the Iceland Sea and the development of a cold-air outbreak over the Greenland Sea. Repeated profiling revealed the immediate impact on the ocean, while a comprehensive hydrographic survey provided a rare picture of these subpolar seas in winter. A joint atmosphere–ocean approach is also being used in the analysis phase, with coupled observational analysis and coordinated numerical modeling activities underway.