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Most traditional methods have difficulty detecting landslide boundary accurately, and the existing methods based on deep learning often lead to insufficient training or overfitting due to insufficient samples. An end-to-end, semi-supervised adversarial network, which fully considers spectral and topographic features derived using unmanned aerial vehicle (UAV) photogrammetry, is proposed to extract landslides by semantic segmentation to address the abovementioned problem. In the generative network, a generator similar to pix2pix is introduced into the proposed adversarial nets to learn semantic features from UAV-photogrammetry-derived data by semi-supervised operation and a confrontational strategy to reduce the requirement of the number of labeled samples. In the discriminative network, DeepLabv3+ is improved by inserting multilevel skip connection architecture with upsampling operation to obtain the contextual information and retain the boundary information of landslides at all levels, and a topographic convolutional neural network is proposed to be inserted into the encoder to concatenate topographic features together with spectral features. Then, transfer learning with the pre-trained parameters and weights, shared with pix2pix and DeepLabv3+, is used to perform landslide extraction training and validation. In our experiments, the UAV-photogrammetry-derived data of a typical landslide located at Meilong gully in China are collected to test the proposed method. The experimental results show that our method can accurately detect the area of a landslide and achieve satisfactiory results based on several indicators including the Precision, Recall, F1 score, and mIoU, which are 13.07%, 15.65%, 16.96%, and 18.23% higher than those of the DeepLabV3+. Compared with state-of-the-art methods such as U-Net, PSPNet, and pix2pix, the proposed adversarial nets considering multidimensional information such as topographic factors can perform better and significantly improve the accuracy of landslide extraction. [ABSTRACT FROM AUTHOR]

Abstract: The Xiaotian-Mozitan fault (XMF) located north of the Dabie orogenic belt separates the North Dabie complex to the south from the Beihuaiyang low-grade metamorphic rocks to the north. It comprises several NW-striking ductile shear zones and brittle faults. The brittle faults obviously overprinted on the ductile shear zones and promoted the development of the volcanic basins in early Cretaceous to the north, which suggests that the brittle faults were normal faults formed in early Cretaceous during doming of the Dabie orogenic belt. The ductile shear zone superposed on the north Dabie gray gneiss, and it is an important channel where the Dabie HP–UHP rocks exhumed. For obtaining new structural constraint on exhumation of the HP–UHP rocks, we present here experimental results on the microstructure, quartz C-axis fabrics and the microprobe analyses of phengite. The ductile shear zone was determined to be formed at a temperature of 600–650°C and pressure of 1.1 GPa by the mineral deformation, microprobe analyses and geobarometry of Si-in-phengite of the mylonite, the results suggest that the mylonite now exposed on the surface experienced an upper amphibolite-facies metamorphism in the lower crust. The mineral stretching lineation varies from horizontal in the east segment to sub-dip in the west. Shear sense indicators from outcrop and thin sections of orientated specimen and quartz C-axis fabrics suggest that the XMF is a sinistral normal fault. The kinematics analysis of the ductile shear zone indicates that the exhumation of Dabie HP–UHP rocks is the results of a SE-directed extrusion and an anticlockwise rotation around its eastern pivot simultaneously. [Copyright &y& Elsevier]

[Display omitted] • Soil erosion intensity increased from North to South in the Wangdonggou watershed. • The soil erosion intensity varied with land use, vegetation coverage and slope. • Woodland and grassland in the study area caused more than 50% soil erosion. • Converting sloping woodland and grassland to terraces effectively control soil erosion. Soil is an essentially limited natural resource that natural and human-induced processes have both generated and damaged. Soil degradation has become one of the most crucial socio-economic and environmental problems since it produces deterioration in productivity and quality of soil resources. Soil erosion, a natural phenomenon that causes degradation of soil and, curves the soil surface away from natural physical forces. To reveal the main factors influencing the spatial distribution of soil erosion in the small watershed of the Loess Plateau, the present study has investigated the synergistic as well as the independent influence of land use, vegetation coverage, and slope on the spatial distribution characteristics of soil erosion in the Wangdonggou watershed in 2015. Soil samples have been collected and analyzed in the laboratory together with high-resolution satellite imagery and meteorological data and derived data from digital elevation model (DEM). The results have shown that soil erosion in Wangdonggou watershed in 2015 has been characterized by a slight erosion, highlighting a gradually increased intensity from North to South. Among different land-uses, woodland and grassland have caused more than 50% soil erosion in the study area, and the areas with vegetation coverage of ≥ 50% have been the main source of soil erosion, and they have been all affected by slope. Furthermore, the practice of expanding vegetation presence on the lower coverage of woodland and grassland, particularly where the slope is between 15°∼45°, and converting sloping woodland and grassland to the terrace have seemed to be effective strategies for controlling soil erosion in the Wangdonggou watershed. Finally, the current study has revealed that the RUSLE-GIS integrated model could be a useful tool to quantitatively and spatially map soil erosion at the watershed scale in the Loess Plateau, taking into account the provision of landscape services. [ABSTRACT FROM AUTHOR]

The Qianlishan Complex is a multiply deformed granulite terrane of the Khondalite Belt that represents a Paleoproterozoic continent-continent collisional orogen in the Western Block of the North China Craton. Two major stages of deformation (D 1 and D 2) were identified in the Qianlishan Complex. D 1 mainly produced NWW-trending overturned to recumbent intrafolial isoclinal folds F 1 , sub-horizontal penetrative foliations S 1 , NNE-SSW mineral lineations L 1 associated with top-to-the-SSW thrust shear, reflecting crustal thickening due to the NNE-SSW compression. D 1 was developed with the prograde (M 1) and peak (M 2) granulite-facies metamorphism. D 2 successively superposed the D 1 structures and was characterized by NWW-SEE-trending doubly plunging open to tight upright folds F 2 and minor sub-vertical axial planar foliations/cleavages S 2. D 2 was accompanied by post-peak near-isothermal decompression (M 3), probably related to exhumation following the crustal thickening. SIMS U–Pb zircon ages of deformation-related leucocratic dykes demonstrate that D 1 broadly occurred at 1976–1936 Ma and D 2 subsequently happened at 1936–1854 Ma. These results indicate that the Khondalite Belt underwent long-term (~100 Myr) polyphase deformation and high-grade metamorphism associated with magmatism. Timing constraints and kinematic compatibility suggest that the deformation sequence resulted from the NNE-SSW collision between the Yinshan and Ordos Blocks to form the Western Block at ~1.95 Ga. fx1 [ABSTRACT FROM AUTHOR]

In the research on energy-efficient networking methods for precision agriculture, a hot topic is the energy issue of sensing nodes for individual wireless sensor networks. The sensing nodes of the wireless sensor network should be enabled to provide better services with limited energy to support wide-range and multi-scenario acquisition and transmission of three-dimensional crop information. Further, the life cycle of the sensing nodes should be maximized under limited energy. The transmission direction and node power consumption are considered, and the forward and high-energy nodes are selected as the preferred cluster heads or data-forwarding nodes. Taking the cropland cultivation of ginseng as the background, we put forward a particle swarm optimization-based networking algorithm for wireless sensor networks with excellent performance. This algorithm can be used for precision agriculture and achieve optimal equipment configuration in a network under limited energy, while ensuring reliable communication in the network. The node scale is configured as 50 to 300 nodes in the range of 500 × 500 m2, and simulated testing is conducted with the LEACH, BCDCP, and ECHERP routing protocols. Compared with the existing LEACH, BCDCP, and ECHERP routing protocols, the proposed networking method can achieve the network lifetime prolongation and mitigate the decreased degree and decreasing trend of the distance between the sensing nodes and center nodes of the sensor network, which results in a longer network life cycle and stronger environment suitability. It is an effective method that improves the sensing node lifetime for a wireless sensor network applied to cropland cultivation of ginseng., Competing Interests: The authors have declared that no competing interests exist.

Foodborne Staphylococcus aureus , including methicillin-resistant S. aureus (MRSA), is increasingly threatening human health. Pooled prevalence rates of S. aureus contamination have been extensively studied in retail food products in mainland China, but data regarding antibiotic resistance rates of S. aureus remain scattered. This study was designed to collect researches published between 2007 and 2017 in mainland China and to evaluate the antibiotic resistance of S. aureus from retail foods using a meta-analytic approach. We systematically searched the China National Knowledge Infrastructure (CNKI) and Web of Science databases to identify peer-reviewed literature. A number of multilevel random-effects models were fitted to estimate mean occurrence rates of antibiotic-resistant S. aureus , and subgroup analyses were performed to compare antibiotic resistance rates of S. aureus throughout the years and among the methods to determine the antimicrobial susceptibility. Among the considered antibiotics, S. aureus showed the highest resistance rate to penicillin G (87%, 95% confidence interval [CI] 83-90%), followed by ampicillin (72%, 95% CI 62-81%) and erythromycin (41%, 95% CI 36-46%). MRSA showed the highest resistance rate to ampicillin (98%, 95% CI 89-100%), followed by oxacillin (97%, 95% CI 80-100%) and penicillin G (96%, 95% CI 89-99%). Multidrug resistance (MDR) of S. aureus was most frequently observed to three antibiotics (17%, 95% CI 12-22%), and MRSA showed the highest resistance rate to four antibiotics (24%, 95% CI 5-67%). Subgroup analyses results proved that sources of heterogeneity among studies were neither publication year nor detection method. In conclusion, the meta-analysis showed that β-lactam antibiotics resistance of S. aureus and MRSA strains isolated from retail foods remained the most serious, and MDR of S. aureus and MRSA were also observed. Therefore, it is important to monitor the antibiotic resistance of S. aureus and MRSA in food chain, and food safety measures should be taken to reduce the transmission of this bacterium from foods to human beings.