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Nickel-based single crystal superalloys are widely used for hot components of advanced turbine engines due to their excellent mechanical properties at elevated temperature. Ru is the symbol element of the fourth and fifth generation nickel-based superalloys. During the past decades, many efforts have been made to provide insight into the role of Ru addition in superalloys. In this paper, the research progress in the effect of Ru on the solidification characteristics, as-cast microstructures, precipitation of topologically close-packed (TCP) phases, and creep properties of nickel-based single crystal superalloy was reviewed. The effect of Ru on the solidification characteristics such as solidification path, solidification transition temperature, micro-segregation, and the solidification microstructures such as eutectic, carbide were analyzed systematically. And the reasons why the addition of Ru can inhibit the precipitation of TCP phase and improve the creep property of superalloy were emphatically studied. The composition design and optimization of Ru-containing superalloy are more challenging due to the complexity of the multi-component interaction on microstructure and properties. It was suggested that the future research on Ru-containing superalloys could be explored from the following aspects: the cause of the precipitation of the new Ru-rich phases and the method of inhibiting its precipitation, the effect of Ru addition on solidification defects, the mechanism of the interaction between Ru and other elements on the "reverse partitioning effect" and the precipitation of TCP phases. It could provide significant guidance for the composition optimization and performance improvement of the new Ru-containing nickel-based single crystal superalloys.

The surface [C/N] ratios of evolved giants are strongly affected by the first dredge-up (FDU) of nuclear-processed material from stellar cores. C and N also have distinct nucleosynthetic origins and serve as diagnostics of mixing and mass-loss. We use subgiants to find strong trends in the birth [C/N] with [Fe/H], which differ between the low-α and high-α populations. We demonstrate that these birth trends have a strong impact on the surface abundances after the FDU. This effect is neglected in current stellar models, which use solar-scaled C and N. We map out the FDU as a function of evolutionary state, mass, and composition using a large and precisely measured asteroseismic data set in first-ascent red giant branch (RGB) and core He-burning, or red clump (RC), stars. We describe the domains where [C/N] is a useful mass diagnostic and find that the RC complements the RGB and extends the range of validity to higher mass. We find evidence for extra mixing on the RGB below [Fe/H] = −0.4, matching literature results, for high-α giants, but there is no clear evidence of mixing in the low-α giants. The predicted signal of mass-loss is weak and difficult to detect in our sample. We discuss implications for stellar physics and stellar population applications. [ABSTRACT FROM AUTHOR]

One challenge for applying current weak lensing analysis tools to the Nancy Grace Roman Space Telescope is that individual images will be undersampled. Our companion paper presented an initial application of Imcom – an algorithm that builds an optimal mapping from input to output pixels to reconstruct a fully sampled combined image – on the Roman image simulations. In this paper, we measure the output noise power spectra, identify the sources of the major features in the power spectra, and show that simple analytic models that ignore sampling effects underestimate the power spectra of the coadded noise images. We compute the moments of both idealized injected stars and fully simulated stars in the coadded images, and their one- and two-point statistics. We show that the idealized injected stars have root-mean-square ellipticity errors (1–6) × 10−4 per component depending on the band; the correlation functions are ≥2 orders of magnitude below requirements, indicating that the image combination step itself is using a small fraction of the overall Roman second moment error budget, although the fourth moments are larger and warrant further investigation. The stars in the simulated sky images, which include blending and chromaticity effects, have correlation functions near the requirement level (and below the requirement level in a wide-band image constructed by stacking all four filters). We evaluate the noise-induced biases in the ellipticities of injected stars, and explain the resulting trends with an analytical model. We conclude by enumerating the next steps in developing an image coaddition pipeline for Roman. [ABSTRACT FROM AUTHOR]

The upcoming Nancy Grace Roman Space Telescope will carry out a wide-area survey in the near-infrared. A key science objective is the measurement of cosmic structure via weak gravitational lensing. Roman data will be undersampled, which introduces new challenges in the measurement of source galaxy shapes; a potential solution is to use linear algebra-based coaddition techniques such as imcom that combine multiple undersampled images to produce a single oversampled output mosaic with a desired 'target' point spread function (PSF). We present here an initial application of imcom to 0.64 square degrees of simulated Roman data, based on the Roman branch of the Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) simulation. We show that imcom runs successfully on simulated data that includes features such as plate scale distortions, chip gaps, detector defects, and cosmic ray masks. We simultaneously propagate grids of injected sources and simulated noise fields as well as the full simulation. We quantify the residual deviations of the PSF from the target (the 'leakage'), as well as noise properties of the output images; we discuss how the overall tiling pattern as well as Moiré patterns appear in the final leakage and noise maps. We include appendices on interpolation algorithms and the interaction of undersampling with image processing operations that may be of broader applicability. The companion paper ('Paper II') explores the implications for weak lensing analyses. [ABSTRACT FROM AUTHOR]

The stability of symbiotic flora is an important indicator of the health of an organism. Symbiotic bacteria have been proven to be closely involved in the immune process of organisms. The pathogenicity of Beauveria bassiana was studied in relation to symbiotic bacteria on the surface and inside of the migratory locust (Locusta migratoria). The results showed that the surface disinfection of test locusts contributed to the pathogenicity of B. bassiana to locusts. Most of the surface bacteria of L. migratoria caused some inhibition of B. bassiana growth, and LM5-4 (Raoultella ornithinolytica), LM5-2 (Enterobacter aerogenes), and LM5-13 (Citrobacter freundii) showed the highest inhibitory effect on the growth of B. bassiana. The inoculation of locusts with additional surface symbiotic bacteria reduced the virulence of B. bassiana to L. migratoria. Infection by different strains of B. bassiana caused similar changes in the symbiotic flora of migratory locusts. The inoculation of locusts with additional intestinal symbiotic bacteria (Enterobacter sp.) reduced the virulence of B. bassiana to L. migratoria. These findings illustrate the effect of bacterial communities on fungal infections in L. migratoria when seen from the perspective of ecology in a microenvironment. The active antifungal substances of such bacteria and their mechanisms of action need further study. [ABSTRACT FROM AUTHOR]

Dynamically relaxed galaxy clusters have long played an important role in galaxy cluster studies because it is thought their properties can be reconstructed more precisely and with less systematics. As relaxed clusters are desirable, there exist a plethora of criteria for classifying a galaxy cluster as relaxed. In this work, we examine 9 commonly used observational and theoretical morphological metrics extracted from |$54\, 000$| mock-X synthetic X-ray images of galaxy clusters taken from the IllustrisTNG, BAHAMAS, and MACSIS simulation suites. We find that the simulated criteria distributions are in reasonable agreement with the observed distributions. Many criteria distributions evolve as a function of redshift, cluster mass, numerical resolution, and subgrid physics, limiting the effectiveness of a single relaxation threshold value. All criteria are positively correlated with each other, however, the strength of the correlation is sensitive to redshift, mass, and numerical choices. Driven by the intrinsic scatter inherent to all morphological metrics and the arbitrary nature of relaxation threshold values, we find the consistency of relaxed subsets defined by the different metrics to be relatively poor. Therefore, the use of relaxed cluster subsets introduces significant selection effects that are non-trivial to resolve. [ABSTRACT FROM AUTHOR]

Tree species classification is important for the management and sustainable development of forest resources. Traditional object-oriented tree species classification methods, such as support vector machines, require manual feature selection and generally low accuracy, whereas deep learning technology can automatically extract image features to achieve end-to-end classification. Therefore, a tree classification method based on deep learning is proposed in this study. This method combines the semantic segmentation network U-Net and the feature extraction network ResNet into an improved Res-UNet network, where the convolutional layer of the U-Net network is represented by the residual unit of ResNet, and linear interpolation is used instead of deconvolution in each upsampling layer. At the output of the network, conditional random fields are used for post-processing. This network model is used to perform classification experiments on airborne orthophotos of Nanning Gaofeng Forest Farm in Guangxi, China. The results are then compared with those of U-Net and ResNet networks. The proposed method exhibits higher classification accuracy with an overall classification accuracy of 87%. Thus, the proposed model can effectively implement forest tree species classification and provide new opportunities for tree species classification in southern China. [ABSTRACT FROM AUTHOR]