Chemical Modification of Fast-Grown Poplar by Urea Formaldehyde and Polyacrylic Resin

In this study, methylolurea and polyacrylic resin were used to modify fast-grown poplar. The dimensional stability and mechanical properties of natural and modified wood were investigated. Also, the modified samples were characterized by scanning electron microscopy, X-ray diffraction (XRD), Fourier transform IR spectroscopy (FTIR), and thermogravimetric analysis (TGA). The results showed that the chemical modifier was impregnated into the wood inner structure, which improved the chemical and mechanical performance of wood. FTIR demonstrated that the hydroxyl groups of wood were decreased, which decreased the water absorption of natural wood. XRD tests indicated that the crystallinity of wood increased but the structure of cellulose was not disrupted after modification. TGA analysis showed that the thermal stability of the hemicelluloses and cellulose was enhanced after modification. The prepolymer was not only impregnated into the wood cell, but also reacted with the wood fiber. A crosslinking reaction occurred between the wood modifier and wood fiber

Identification of driving factors of algal growth in the South-to-North Water Diversion Project by Transformer-based deep learning

Accurate and credible identification of the drivers of algal growth is essential for sustainable utilization and scientific management of freshwater. In this study, we developed a deep learning-based Transformer model, named Bloomformer-1, for end-to-end identification of the drivers of algal growth without the needing extensive a priori knowledge or prior experiments. The Middle Route of the South-to-North Water Diversion Project (MRP) was used as the study site to demonstrate that Bloomformer-1 exhibited more robust performance (with the highest R$^{2}$, 0.80 to 0.94, and the lowest RMSE, 0.22–0.43 ​μg/L) compared to four widely used traditional machine learning models, namely extra trees regression (ETR), gradient boosting regression tree (GBRT), support vector regression (SVR), and multiple linear regression (MLR). In addition, Bloomformer-1 had higher interpretability (including higher transferability and understandability) than the four traditional machine learning models, which meant that it was trustworthy and the results could be directly applied to real scenarios. Finally, it was determined that total phosphorus (TP) was the most important driver for the MRP, especially in Henan section of the canal, although total nitrogen (TN) had the highest effect on algal growth in the Hebei section. Based on these results, phosphorus loading controlling in the whole MRP was proposed as an algal control strategy

2-[Hy­droxy(4-meth­oxy­phen­yl)methyl­idene]indane-1,3-dione

In the title compound, C17H12O4, there is an intra­molecular O—H⋯O hydrogen bond. The dihedral angle between the indane ring system [maximun deviation = 0.023 (2) Å] and the benzene ring is 37.42 (9)°

A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image

Dynamic medical imaging is usually limited in application due to the large radiation doses and longer image scanning and reconstruction times. Existing methods attempt to reduce the dynamic sequence by interpolating the volumes between the acquired image volumes. However, these methods are limited to either 2D images and/or are unable to support large variations in the motion between the image volume sequences. In this paper, we present a spatiotemporal volumetric interpolation network (SVIN) designed for 4D dynamic medical images. SVIN introduces dual networks: first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from two-image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures. We also introduce an adaptive multi-scale architecture to capture the volumetric large anatomy motions. Experimental results demonstrated that our SVIN outperformed state-of-the-art temporal medical interpolation methods and natural video interpolation methods that have been extended to support volumetric images. Our ablation study further exemplified that our motion network was able to better represent the large functional motion compared with the state-of-the-art unsupervised medical registration methods.Comment: 10 pages, 8 figures, Conference on Computer Vision and Pattern Recognition (CVPR) 202

Soft Scattering Evaporation of Dark Matter Subhalos by Inner Galactic Gases

The large gap between a galactic dark matter subhalo's velocity and its own gravitational binding velocity creates the situation that dark matter soft-scattering on baryons to evaporate the subhalo, if kinetic energy transfer is efficient by low momentum exchange. Small subhalos can evaporate before dark matter thermalize with baryons due to the low binding velocity. In case dark matter acquires an electromagnetic dipole moment, the survival of low-mass subhalos requires stringent limits on the photon-mediated soft scattering. We calculate the subhalo evaporation rate via soft collision by ionization gas and accelerated cosmic rays, and show the stability of subhalos lighter than $10^{-5}M_{\odot}$ in the gaseous inner galactic region is sensitive to dark matter's effective electric and magnetic dipole moments below current direct detection limits.Comment: 8 pages, 4 figure

4-(4-Oxopent-2-en-2-yl­amino)-1,2,4-triazol-1-ium-5-thiol­ate

In the title compound, C8H12N4OS, an intra­molecular N—H⋯O hydrogen bond links the imine N atom to the oxo O atom. In the crystal, mol­ecules are linked by inter­molecular N—H⋯O and N—H⋯S hydrogen bonds, forming a two-dimensional framework