Substantially enhanced plasticity of bulk metallic glasses by densifying local atomic packing

Common wisdom to improve ductility of bulk metallic glasses (BMGs) is to introduce local loose packing regions at the expense of strength. Here the authors enhance structural fluctuations of BMGs by introducing dense local packing regions, resulting in simultaneous increase of ductility and strength

Ship Detection in Synthetic Aperture Radar Images under Complex Geographical Environments, Based on Deep Learning and Morphological Networks

Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like speckle noise, coastlines, and islands, which may result in false positives or missed detections. This article introduces a ship detection method for SAR images, which employs deep learning and morphological networks. Initially, adaptive preprocessing is carried out by a morphological network to enhance the edge features of ships and suppress background noise, thereby increasing detection accuracy. Subsequently, a coordinate channel attention module is integrated into the feature extraction network to improve the spatial awareness of the network toward ships, thus reducing the incidence of missed detections. Finally, a four-layer bidirectional feature pyramid network is designed, incorporating large-scale feature maps to capture detailed characteristics of ships, to enhance the detection capabilities of the network in complex geographic environments. Experiments were conducted using the publicly available SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID). Compared with the baseline model YOLOX, the proposed method increased the recall by 3.11% and 0.22% for the SSDD and HRSID, respectively. Additionally, the mean Average Precision (mAP) improved by 0.7% and 0.36%, reaching 98.47% and 91.71% on these datasets. These results demonstrate the outstanding detection performance of our method

Introduction Performance of New Labor-saving Grape Variety Miguang in Southern Guangxi and Its Two-harvest-a-year Cultivation Technique

In order to alleviate the difficulty of labor employment for grape cultivation Guangxi, Nanning Comprehensive Experiment Station of China Grape Industry Technology System introduced the labor-saving variety Miguang, made an observation and record of the botanical characteristics, fruit traits, phenology, disease resistance, and yield of the new grape variety. Finally, it summarized the two-harvest-a-year cultivation technique of the labor-saving grape variety Miguang

Conservation of the endangered freshwater mussel Solenaia carinata (Bivalvia, Unionidae) in China

Volume: 26Start Page: 33End Page: 5

Analysis of genetic differentiation coefficient (<i>F</i>_st) calculated using COI mtDNA sequence data among seven collection locations of <i>Nodularia douglasiae</i> from the middle and lower Yangtze River drainage.

<p>Analysis of genetic differentiation coefficient (<i>F</i>_st) calculated using COI mtDNA sequence data among seven collection locations of <i>Nodularia douglasiae</i> from the middle and lower Yangtze River drainage.</p

Estimated gene flow (<i>N</i>m) (below diagonal) calculated using genotypes form 13 microsatellite loci and among seven collection locations of <i>N</i>. <i>douglasiae</i> from the middle and lower Yangtze River drainage.

<p>Estimated gene flow (<i>N</i>m) (below diagonal) calculated using genotypes form 13 microsatellite loci and among seven collection locations of <i>N</i>. <i>douglasiae</i> from the middle and lower Yangtze River drainage.</p

Mismatch distribution analysis (MDA) for <i>N</i>. <i>douglasiae</i> collection locations in the middle and lower reaches of Yangtze River.

<p>Mismatch distribution analysis (MDA) for <i>N</i>. <i>douglasiae</i> collection locations in the middle and lower reaches of Yangtze River.</p

Analysis of genetic differentiation coefficient (<i>F</i>_st) calculated using COI mtDNA sequence data among seven collection locations of <i>Nodularia douglasiae</i> from the middle and lower Yangtze River drainage.

<p>Analysis of genetic differentiation coefficient (<i>F</i>_st) calculated using COI mtDNA sequence data among seven collection locations of <i>Nodularia douglasiae</i> from the middle and lower Yangtze River drainage.</p

Population genetic parameters in seven populations of <i>N</i>. <i>douglasiae</i> in the middle and lower Yangtze River drainage calculated using 13 microsatellites.

<p>Population genetic parameters in seven populations of <i>N</i>. <i>douglasiae</i> in the middle and lower Yangtze River drainage calculated using 13 microsatellites.</p