A survey of experiments and experimental facilities for active control of flexible structures

A brief survey of large space structure control related experiments and facilities was presented. This survey covered experiments performed before and up to 1982, and those of the present period (1982-...). Finally, the future planned experiments and facilities in support of the control-structure interaction (CSI) program were reported. It was stated that new, improved ground test facilities are needed to verify the new CSI design techniques that will allow future space structures to perform planned NASA missions

SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States

The annual area burned due to wildfires in the western United States (WUS) increased by more than 300 % between 1984 and 2020. However, accounting for the nonlinear, spatially heterogeneous interactions between climate, vegetation, and human predictors driving the trends in fire frequency and sizes at different spatial scales remains a challenging problem for statistical fire models. Here we introduce a novel stochastic machine learning (SML) framework, SMLFire1.0, to model observed fire frequencies and sizes in 12 km × 12 km grid cells across the WUS. This framework is implemented using mixture density networks trained on a wide suite of input predictors. The modeled WUS fire frequency matches observations at both monthly (r=0.94) and annual (r=0.85) timescales, as do the monthly (r=0.90) and annual (r=0.88) area burned. Moreover, the modeled annual time series of both fire variables exhibit strong correlations (r≥0.6) with observations in 16 out of 18 ecoregions. Our ML model captures the interannual variability and the distinct multidecade increases in annual area burned for both forested and non-forested ecoregions. Evaluating predictor importance with Shapley additive explanations, we find that fire-month vapor pressure deficit (VPD) is the dominant driver of fire frequencies and sizes across the WUS, followed by 1000 h dead fuel moisture (FM1000), total monthly precipitation (Prec), mean daily maximum temperature (Tmax), and fraction of grassland cover in a grid cell. Our findings serve as a promising use case of ML techniques for wildfire prediction in particular and extreme event modeling more broadly. They also highlight the power of ML-driven parameterizations for potential implementation in fire modules of dynamic global vegetation models (DGVMs) and earth system models (ESMs).</p

Quasiparticle dynamics and phonon softening in FeSe superconductors

Quasiparticle dynamics of FeSe single crystals revealed by dual-color transient reflectivity measurements ({\Delta}R/R) provides unprecedented information on Fe-based superconductors. The amplitude of fast component in {\Delta}R/R clearly tells a competing scenario between spin fluctuations and superconductivity. Together with the transport measurements, the relaxation time analysis further exhibits anomalous changes at 90 K and 230 K. The former manifests a structure phase transition as well as the associated phonon softening. The latter suggests a previously overlooked phase transition or crossover in FeSe. The electron-phonon coupling constant {\lambda} is found to be 0.16, identical to the value of theoretical calculations. Such a small {\lambda} demonstrates an unconventional origin of superconductivity in FeSe.Comment: Final published version; 5 pages; 4 figure

Three-dimensional optical method for integrated visualization of mouse islet microstructure and vascular network with subcellular-level resolution

Microscopic visualization of islets of Langerhans under normal and diabetic conditions is essential for understanding the pathophysiology of the disease. The intrinsic opacity of pancreata, however, limits optical accessibility for high-resolution light microscopy of islets in situ. Because the standard microtome-based, 2-D tissue analysis confines visualization of the islet architecture at a specific cut plane, 3-D representation of image data is preferable for islet assessment. We applied optical clearing to minimize the random light scattering in the mouse pancreatic tissue. The optical-cleared pancreas allowed penetrative, 3-D microscopic imaging of the islet microstructure and vasculature. Specifically, the islet vasculature was revealed by vessel painting-lipophilic dye labeling of blood vessels-for confocal microscopy. The voxel-based confocal micrographs were digitally processed with projection algorithms for 3-D visualization. Unlike the microtome-based tissue imaging, this optical method for penetrative imaging of mouse islets yielded clear, continuous optical sections for an integrated visualization of the islet microstructure and vasculature with subcellular-level resolution. We thus provide a useful imaging approach to change our conventional planar view of the islet structure into a 3-D panorama for better understanding of the islet physiology. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3470241

Spatial Symmetry of Superconducting Gap in YBa2Cu3O7-\delta Obtained from Femtosecond Spectroscopy

The polarized femtosecond spectroscopies obtained from well characterized (100) and (110) YBa2Cu3O7-\delta thin films are reported. This bulk-sensitive spectroscopy, combining with the well-textured samples, serves as an effective probe to quasiparticle relaxation dynamics in different crystalline orientations. The significant anisotropy in both the magnitude of the photoinduced transient reflectivity change and the characteristic relaxation time indicates that the nature of the relaxation channel is intrinsically different in various axes and planes. By the orientation-dependent analysis, d-wave symmetry of the bulk-superconducting gap in cuprate superconductors emerges naturally.Comment: 8 pages, 4 figures. To be published in Physical Review B, Rapid Communication

Unveiling the hidden nematicity and spin subsystem in FeSe

The nematic order (nematicity) is considered one of the essential ingredients to understand the mechanism of Fe-based superconductivity. In most Fe-based superconductors (pnictides), nematic order is reasonably close to the antiferromagnetic order. In FeSe, in contrast, a nematic order emerges below the structure phase transition at T_s = 90 K with no magnetic order. The case of FeSe is of paramount importance to a universal picture of Fe-based superconductors. The polarized ultrafast spectroscopy provides a tool to probe simultaneously the electronic structure and the magnetic interactions through quasiparticle dynamics. Here we show that this approach reveals both the electronic and magnetic nematicity below and, surprisingly, its fluctuations far above Ts to at least 200 K. The quantitative pump-probe data clearly identify a correlation between the topology of the Fermi surface (FS) and the magnetism in all temperature regimes, thus providing profound insight into the driving factors of nematicity in FeSe and the origin of its uniqueness.Comment: Supplementary Information include