On Lattice Distortion in High Entropy Alloys

Lattice distortion in high entropy alloys (HEAs) is an issue of fundamental importance but yet to be fully understood. In this article, we first focus on the recent research dedicated to lattice distortion in HEAs with an emphasis on the basic understanding derived from theoretical modeling and atomistic simulations. After that, we discuss the implications of the recent research findings on lattice distortion, which can be related to the phase transformation, dislocation dynamics and yielding in HEAs

Solving Robotic Trajectory Sequential Writing Problem via Learning Character’s Structural and Sequential Information

The writing sequence of numerals or letters often affects aesthetic aspects of the writing outcomes. As such, it remains a challenge for robotic calligraphy systems to perform, mimicking human writers’ implicit intention. This article presents a new robot calligraphy system that is able to learn writing sequences with limited sequential information, producing writing results compatible to human writers with good diversity. In particular, the system innovatively applies a gated recurrent unit (GRU) network to generate robotic writing actions with the support of a prelabeled trajectory sequence vector. Also, a new evaluation method is proposed that considers the shape, trajectory sequence, and structural information of the writing outcome, thereby helping ensure the writing quality. A swarm optimization algorithm is exploited to create an optimal set of parameters of the proposed system. The proposed approach is evaluated using Arabic numerals, and the experimental results demonstrate the competitive writing performance of the system against state-of-the-art approaches regarding multiple criteria (including FID, MAE, PSNR, SSIM, and PerLoss), as well as diversity performance concerning variance and entropy. Importantly, the proposed GRU-based robotic motion planning system, supported with swarm optimization can learn from a small dataset, while producing calligraphy writing with diverse and aesthetically pleasing outcomes

Accurate segmentation algorithm of acoustic neuroma in the cerebellopontine angle based on ACP-TransUNet

Acoustic neuroma is one of the most common tumors in the cerebellopontine angle area. Patients with acoustic neuroma have clinical manifestations of the cerebellopontine angle occupying syndrome, such as tinnitus, hearing impairment and even hearing loss. Acoustic neuromas often grow in the internal auditory canal. Neurosurgeons need to observe the lesion contour with the help of MRI images, which not only takes a lot of time, but also is easily affected by subjective factors. Therefore, the automatic and accurate segmentation of acoustic neuroma in cerebellopontine angle on MRI is of great significance for surgical treatment and expected rehabilitation. In this paper, an automatic segmentation method based on Transformer is proposed, using TransUNet as the core model. As some acoustic neuromas are irregular in shape and grow into the internal auditory canal, larger receptive fields are thus needed to synthesize the features. Therefore, we added Atrous Spatial Pyramid Pooling to CNN, which can obtain a larger receptive field without losing too much resolution. Since acoustic neuromas often occur in the cerebellopontine angle area with relatively fixed position, we combined channel attention with pixel attention in the up-sampling stage so as to make our model automatically learn different weights by adding the attention mechanism. In addition, we collected 300 MRI sequence nuclear resonance images of patients with acoustic neuromas in Tianjin Huanhu hospital for training and verification. The ablation experimental results show that the proposed method is reasonable and effective. The comparative experimental results show that the Dice and Hausdorff 95 metrics of the proposed method reach 95.74% and 1.9476 mm respectively, indicating that it is not only superior to the classical models such as UNet, PANet, PSPNet, UNet++, and DeepLabv3, but also show better performance than the newly-proposed SOTA (state-of-the-art) models such as CCNet, MANet, BiseNetv2, Swin-Unet, MedT, TransUNet, and UCTransNet

Solving Robotic Trajectory Sequential Writing Problem via Learning Character’s Structural and Sequential Information

The writing sequence of numerals or letters often affects aesthetic aspects of the writing outcomes. As such, it remains a challenge for robotic calligraphy systems to perform, mimicking human writers’ implicit intention. This article presents a new robot calligraphy system that is able to learn writing sequences with limited sequential information, producing writing results compatible to human writers with good diversity. In particular, the system innovatively applies a gated recurrent unit (GRU) network to generate robotic writing actions with the support of a prelabeled trajectory sequence vector. Also, a new evaluation method is proposed that considers the shape, trajectory sequence, and structural information of the writing outcome, thereby helping ensure the writing quality. A swarm optimization algorithm is exploited to create an optimal set of parameters of the proposed system. The proposed approach is evaluated using Arabic numerals, and the experimental results demonstrate the competitive writing performance of the system against state-of-the-art approaches regarding multiple criteria (including FID, MAE, PSNR, SSIM, and PerLoss), as well as diversity performance concerning variance and entropy. Importantly, the proposed GRU-based robotic motion planning system, supported with swarm optimization can learn from a small dataset, while producing calligraphy writing with diverse and aesthetically pleasing outcomes

Cooperative Stabilization of the [Pyridinium-CO2-Co] Adduct on a Metal-Organic Layer Enhances Electrocatalytic CO2 Reduction.

Pyridinium has been shown to be a cocatalyst for the electrochemical reduction of CO2 on metal and semiconductor electrodes, but its exact role has been difficult to elucidate. In this work, we create cooperative cobalt-protoporphyrin (CoPP) and pyridine/pyridinium (py/pyH+) catalytic sites on metal-organic layers (MOLs) for an electrocatalytic CO2 reduction reaction (CO2RR). Constructed from [Hf6(μ3-O)4(μ3-OH)4(HCO2)6] secondary building units (SBUs) and terpyridine-based tricarboxylate ligands, the MOL was postsynthetically functionalized with CoPP via carboxylate exchange with formate capping groups. The CoPP group and the pyridinium (pyH+) moiety on the MOL coactivate CO2 by forming the [pyH+--O2C-CoPP] adduct, which enhances the CO2RR and suppresses hydrogen evolution to afford a high CO/H2 selectivity of 11.8. Cooperative stabilization of the [pyH+--O2C-CoPP] intermediate led to a catalytic current density of 1314 mA/mgCo for CO production at -0.86 VRHE, which corresponds to a turnover frequency of 0.4 s-1

Inhibition of COX2/PGD2-Related Autophagy Is Involved in the Mechanism of Brain Injury in T2DM Rat

The present study was designed to observe the effect of COX2/PGD2-related autophagy on brain injury in type 2 diabetes rats. The histopathology was detected by haematoxylin–eosin staining. The learning and memory functions were evaluated by Morris water maze. The levels of insulin and PGD2 were measured by enzyme-linked immunosorbent assay. The expressions of COX2, p-AKT(S473), p-AMPK(T172), Aβ, Beclin1, LC3BII, and p62 were measured by immunohistochemistry and Western blotting. In model rats, we found that the body weight was significantly decreased, the blood glucose levels were significantly increased, the plasma insulin content was significantly decreased, the learning and memory functions were impaired and the cortex and hippocampus neurons showed significant nuclear pyknosis. The levels of COX2, p-AKT(S473), PGD2, Aβ, Beclin1 and p62 were significantly increased, whereas the expression of p-AMPK(T172) and LC3BII was significantly decreased in the cortex and hippocampus of model rats. In meloxicam-treated rats, the body weight, blood glucose and the content of plasma insulin did not significantly change, the learning and memory functions were improved and nuclear pyknosis was improved in the cortex and hippocampus neurons. The expression of p-AMPK(T172), Beclin1 and LC3BII was significantly increased, and the levels of COX2, p-AKT(S473), PGD2, Aβ, and p62 were significantly decreased in the cortex and hippocampus of meloxicam-treated rats. Our results suggested that the inhibition of COX2/PGD2-related autophagy was involved in the mechanism of brain injury caused by type 2 diabetes in rats

Landscape composition and configuration relatively affect invasive pest and its associator across multiple spatial scales

Landscape structures affect pests, depending on compositional heterogeneity (the number and proportions of different habitats), configurational heterogeneity (spatial arrangement of habitats), and spatial scales. However, there is limited information on the relative effects of compositional and configurational heterogeneity on invasive pests and their associates (species that can benefit from invasive pests), and how they vary across spatial scales. In this study, we assayed the invasive pest Bactrocera dorsalis (Hendel) and its associated fly Drosophila melanogaster in 15 landscapes centered on mango orchards. We calculated landscape composition (forest percentage, mango percentage, and Shannon's diversity) and configuration (edge density) using two methods: spatial distance scales and combined scales. Spatial distance scales included buffer rings with radii of 0.5, 1.0, and 1.5 km, and combined scales referred to cutting or not cutting a smaller ring from larger ones. Our results shown that compositional heterogeneity positively affected B. dorsalis and D. melanogaster due to forest cover percentage, whereas configurational heterogeneity with high edge density negative effect on B. dorsalis. Forest cover had less of an effect on B. dorsalis than configurational heterogeneity, but the opposite effect was observed for D. melanogaster. Importantly, the direction and strength of forest cover and configurational heterogeneity to species did not vary with spatial distance scales or spatial combined scales. Thus, compositional and configurational heterogeneity exhibit differential effects on this invasive pest and its associator, and revealed that the relative effects of landscape structures are consistent across multiple scales. These results provide new insights into landscape effects on interconnected species using a diverse spatial-scale approach