Controlled Synthesis of Organic/Inorganic van der Waals Solid for Tunable Light-matter Interactions

Van der Waals (vdW) solids, as a new type of artificial materials that consist of alternating layers bonded by weak interactions, have shed light on fascinating optoelectronic device concepts. As a result, a large variety of vdW devices have been engineered via layer-by-layer stacking of two-dimensional materials, although shadowed by the difficulties of fabrication. Alternatively, direct growth of vdW solids has proven as a scalable and swift way, highlighted by the successful synthesis of graphene/h-BN and transition metal dichalcogenides (TMDs) vertical heterostructures from controlled vapor deposition. Here, we realize high-quality organic and inorganic vdW solids, using methylammonium lead halide (CH3NH3PbI3) as the organic part (organic perovskite) and 2D inorganic monolayers as counterparts. By stacking on various 2D monolayers, the vdW solids behave dramatically different in light emission. Our studies demonstrate that h-BN monolayer is a great complement to organic perovskite for preserving its original optical properties. As a result, organic/h-BN vdW solid arrays are patterned for red light emitting. This work paves the way for designing unprecedented vdW solids with great potential for a wide spectrum of applications in optoelectronics

Remote Sensing Image Segmentation of Mariculture Cage Using Ensemble Learning Strategy

In harbour areas, the irrational layout and high density of mariculture cages can lead to a dramatic deterioration of the culture’s ecology. Therefore, it is important to analyze and regulate the distribution of cages using intelligent analysis based on deep learning. We propose a remote sensing image segmentation method based on the Swin Transformer and ensemble learning strategy. Firstly, we collect multiple remote sensing images of cages and annotate them, while using data expansion techniques to construct a remote sensing image dataset of mariculture cages. Secondly, the Swin Transformer is used as the backbone network to extract the remote sensing image features of cages. A strategy of alternating the local attention module and the global attention module is used for model training, which has the benefit of reducing the attention computation while exchanging global information. Then, the ensemble learning strategy is used to improve the accuracy of remote sensing cage segmentation. We carry out quantitative and qualitative analyses of remote sensing image segmentation of cages at the ports of Li’an, Xincun and Potou in Hainan Province, China. The results show that our proposed segmentation scheme has significant performance improvement compared to other models. In particular, the mIoU reaches 82.34% and pixel accuracy reaches 99.71%

Intelligent Measurement of Morphological Characteristics of Fish Using Improved U-Net

In the smart mariculture, batch testing of breeding traits is a key issue in the breeding of improved fish varieties. The body length (BL), body width (BW) and body area (BA) features of fish are important indicators. They are of great significance in breeding, feeding and classification. To accurately and intelligently obtain the morphological characteristic sizes of fish in actual scenes, data augmentation is first used to greatly expand the published fish dataset, thereby ensuring the robustness of the training model. Then, an improved U-net segmentation and measurement algorithm is proposed, which uses a dilated convolution with a dilation rate 2 and a convolution to partially replace the convolution in the original U-net. This operation can enlarge the partial convolution receptive field and achieve more accurate segmentation for large targets in the scene. Finally, a line fitting method based on the least squares method is proposed, which is combined with the body shape features of fish and can accurately measure the BL and BW of inclined fish. Experimental results show that the Mean Intersection over Union (mIoU) is 97.6% and the average relative error of the area is 0.69%. Compared with the unimproved U-net, the average relative error of the area is reduced to about half. Moreover, with the improved U-net and the line fitting method, the average relative error of BL and the average relative error of BW of inclined fish decrease to 0.37% and 0.61%, respectively

Vibrio ziniensis sp. nov., isolated from mangrove sediments

A novel Gram-staining-negative, catalase- and oxidase-positive, facultatively anaerobic and rod-shaped motile bacterial strain, designated as ZWAL4003T, was isolated from mangrove sediments of the Zini Mangrove Forest, Zhangzhou City, PR China. Phylogenetic analysis based on its 16S rRNA gene sequence indicated that ZWAL4003T was grouped into a separated branch with Vibrio plantisponsor MSSRF60T (97.38% nucleotide sequence identity) and Vibrio diazotrophicus NBRC 103148T (97.27%). The major cellular fatty acids were C14 : 0 (12.6%), C16 : 0 (17.6%), and summed feature 3 (C16 : 1ω6c /C16 : 1 ω7c, 45.6%). Its genome had a length of 4650556 bp with 42.8% DNA G+C content, and contained genes involved in the biosynthesis of bacteriocin, β-lactone, resorcinol, N-acyl amino acid, and arylpolyene. The in silico DNA–DNA hybridization and average nucleotide identity values for whole-genome sequence comparisons between ZWAL4003T and V. plantisponsor LMG 24470T were clearly below the thresholds used for the delineation of a novel species. The morphological and chemotaxonomic characteristics and the genotypic data of ZWAL4003T indicated that it represented a novel species of the genus Vibrio . Its proposed name is Vibrio ziniensis sp. nov., and the type strain is ZWAL4003T (=KCTC 72971T=MCCC 1A17474T)

Research on a novel multi-database middleware for multiple applications

As the information construction in enterprise goes deeper, the data in need of management grows dramatically in number as well as in structural diversification. Supposed that heterogeneous data sources cannot achieve sharing with each other, departments in enterprise would turn to be isolated 'information islands', blocking the internal data circulation and sharing. Based on the existing multidatabase middleware technique, this paper puts forward a novel multi-database middleware for multiple applications. With the utilization of JAVA reflection, it supports cross-platform application. Besides, through the design of an application, the paper introduces several key technologies as JDBC connection, Socket + XML, connection pool and so forth

Infrared small target detection based on multiscale local contrast learning networks

Recently, model-driven deep networks have achieved excellent detection performance on infrared small targets in cluttered environments. However, its detection performance is sensitive to the hyperparameters in the embedded model-driven module. Therefore, we propose a novel multiscale local contrast learning network (MLCL-Net), which is an end-to-end fully convolutional infrared small target detection network. By constructing a local contrast learning (LCL) structure, it can learn to generate local contrast feature maps during training. Considering the difference in target size, we further build a multiscale local contrast learning (MLCL) module based on LCL. By extracting and fusing local contrast information of different scales from feature maps of the same level, the feature information of targets is fully excavated. At the same time, due to the small size of the target, a slight pixel shift will cause a severe loss of accuracy. We propose a bilinear feature pyramid network (BFPN) based on the feature pyramid network (FPN). Compared to state-of-the-art methods, the proposed MLCLNet achieves superior performance with an intersection-over-union (IoU) of 0.772 and normalized IoU (nIoU) of 0.755 on the public SIRST dataset

Volatile Organic Compounds of <i>Bacillus velezensis</i> GJ-7 against <i>Meloidogyne hapla</i> through Multiple Prevention and Control Modes

The Bacillus velezensis GJ-7 strain isolated from the rhizosphere soil of Panax notoginseng showed high nematicidal activity and therefore has been considered a biological control agent that could act against the root-knot nematode Meloidogyne hapla. However, little was known about whether the GJ-7 strain could produce volatile organic compounds (VOCs) that were effective in biocontrol against M. hapla. In this study, we evaluated the nematicidal activity of VOCs produced by the fermentation of GJ-7 in three-compartment Petri dishes. The results revealed that the mortality rates of M. hapla J2s were 85% at 24 h and 97.1% at 48 h after treatment with the VOCs produced during GJ-7 fermentation. Subsequently, the VOCs produced by the GJ-7 strain were identified through solid-phase micro-extraction gas chromatography mass spectrometry (SPME-GC/MS). Six characteristic VOCs from the GJ-7 strain fermentation broth were identified, including 3-methyl-1-butanol, 3-methyl-2-pentanone, 5-methyl-2-hexanone, 2-heptanone, 2,5-dimethylpyrazine, and 6-methyl-2-heptanone. The in vitro experimental results from 24-well culture plates showed that the six volatiles had direct-contact nematicidal activity against M. hapla J2s and inhibition activity against egg hatching. In addition, 3-methyl-1-butanol and 2-heptanone showed significant fumigation effects on M. hapla J2s and eggs. Furthermore, all six of the VOCs repelled M. hapla J2 juveniles in 2% water agar Petri plates. The above data suggested that the VOCs of B. velezensis GJ-7 acted against M. hapla through multiple prevention and control modes (including direct-contact nematicidal activity, fumigant activity, and repellent activity), and therefore could be considered as potential biocontrol agents against root-knot nematodes

Storage of Soil Organic Carbon and Its Spatial Variability in an Agro-Pastoral Ecotone of Northern China

Spatial distribution of soil organic carbon (SOC) is important for the development of ecosystem carbon cycle models and assessment of soil quality. In this study, a total of 732 soil samples from 122 soil profiles (0&ndash;10, 10&ndash;20, 20&ndash;40, 40&ndash;60, 60&ndash;80, and 80&ndash;100 cm) were collected by a combination of fixed-point sampling and route surveys in an agro-pastoral ecotone of northern China and the spatial variation of the SOC in the samples was analyzed through classical statistical and geostatistical approaches. The results showed that the SOC contents decreased from 4.31 g/kg in the 0&ndash;10 cm to 1.57 g/kg in the 80&ndash;100 cm soil layer. The spatial heterogeneity of the SOC exhibited moderate and strong dependence for all the soil layers owing to random and structural factors including soil texture, topography, and human activities. The spatial distributions of the SOC increased gradually from northeast to southwest in the 0&ndash;40 cm soil layers, but there was no general trend in deep soil layers and different interpolation methods resulted in the inconsistent spatial distribution of SOC. The storage of SOC was expected to be 25 Tg in the 0&ndash;100 cm soil depths for the whole area of 7692 km2. The SOC stocks estimated by two interpolation approaches were very close (25.65 vs. 25.86 Tg), but the inverse distance weighting (IDW) interpolation generated a more detailed map of SOC and with higher determination coefficient (R2); therefore, the IDW was recognized as an appropriate method to investigate the spatial variability of SOC in this region