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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Seminar for the KEPCO International Nuclear Graduate School (KINGS) in South Korea. The presentation will cover a brief introduction to INL, opportunities for advanced nuclear, and integrated energy systems.
Wrapping a 3D object with a 2D sheet is not uncommon, but the wrapping behavior becomes complex and interesting when the sheet is bestowed with strong interfacial activities. Amphiphilic lignin macromolecules, isolated from biomass, form a scalable thin surfactant sheet at an oil/water interface through the interfacial jamming process. The process marks three distinct stages of interfacial behavior: a) diffusive assembly, b) viscous assembly with retarded mobility, and c) flexible yet irreversibly jammed rigid sheets. The surfactant sheet wraps, traps, and promptly stabilizes both oil and water droplets in nonequilibrium morphologies upon environmental stimulus. Finally, beyond preserving exotic morphologies, the highly interfacially active surfactant sheet also participates in morphology evolution by creating a vanishing interfacial tension and driving interfacial instability around the wrapped content, leading to novel morphologies as well as many potential applications.
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It is possible to describe fermionic phases of matter and spin-topological field theories in 2+1d in terms of bosonic “shadow” theories, which are obtained from the original theory by “gauging fermionic parity”. Furthemore, the fermionic/spin theories are recovered from their shadow by a process of fermionic anyon condensation: gauging a one-form symmetry generated by quasi-particles with fermionic statistics. We apply the formalism to theories which admit gapped boundary conditions. We obtain Turaev-Viro-like and Levin-Wen-like constructions of fermionic phases of matter. Here, we describe the group structure of fermionic SPT phases protected by Z 2 f × G. The quaternion group makes a surprise appearance.
We present a case of 57-year-old patient with three liver metastases from a primary neuroendocrine duodenal tumor, who underwent bland embolization with excellent response to therapy, followed by surgical resection. The purpose of our case report is to describe the histological characteristics of tumoral response to therapy after bland embolization focusing on intralesional necrosis and microsphere distribution.
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Previous studies have shown that acute inflammation is associated with increased sympathetic activity, which in turn increases the inflammatory response and leads to organ damage. The present study aimed to investigate whether dexmedetomidine administration during acute pancreatitis (AP) lessens pancreatic pathological and functional injury and the inflammatory response, and to explore the underlying mechanisms.
This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many practical engineering problems. This class of inverse problems is extremely challenging due to their high-dimensional, nonlinear, and ill-posed nature. To overcome these technical hurdles, we introduce a geometric regularization approach for deep neural networks (DNN) based on non-uniform rational B-splines (NURBS) and capable of predicting complex 2D scatterer geometries in a parsimonious dimensional representation. Then, this geometric regularization is combined with physics-embedded learning and integrated within a robust convolutional autoencoder (CAE) architecture to accurately predict the shape of 2D scatterers in the context of identification and inverse design problems. Further, an extensive numerical study is presented in order to showcase the remarkable ability of this approach to handle complex scatterer geometries while generating physically-consistent acoustic fields. The study also assesses and contrasts the role played by the (weakly) embedded physics in the convergence of the DNN predictions to a physically consistent inverse design.