BC-DUnet-Based Segmentation of Fine Cracks in Bridges under a Complex Background

Crack is the external expression form of potential safety risks in bridge construction. Currently, automatic detection and segmentation of bridge cracks remains the top priority of civil engineers. With the development of image segmentation techniques based on convolutional neural networks, new opportunities emerge in bridge crack detection. Traditional bridge crack detection methods are vulnerable to complex background and small cracks, which is difficult to achieve effective segmentation. This study presents a bridge crack segmentation method based on a densely connected U-Net network (BC-DUnet) with a background elimination module and cross-attention mechanism. First, a dense connected feature extraction model (DCFEM) integrating the advantages of DenseNet is proposed, which can effectively enhance the main feature information of small cracks. Second, the background elimination module (BEM) is proposed, which can filter the excess information by assigning different weights to retain the main feature information of the crack. Finally, a cross-attention mechanism (CAM) is proposed to enhance the capture of long-term dependent information and further improve the pixel-level representation of the model. Finally, 98.18% of the Pixel Accuracy was obtained by comparing experiments with traditional networks such as FCN and Unet, and the IOU value was increased by 14.12% and 4.04% over FCN and Unet, respectively. In our non-traditional networks such as HU-ResNet and F U N-4s, SAM-DUnet has better and higher accuracy and generalization is not prone to overfitting. The BC-DUnet network proposed here can eliminate the influence of complex background on the segmentation accuracy of bridge cracks, improve the detection efficiency of bridge cracks, reduce the detection cost, and have practical application value

Study on water loss settlement law of loose aquifer based on distributed optical fiber

Indirect water loss caused by disturbance from coal mining can cause compression of loose layers and surface subsidence, which poses a threat to coal mine safety. To analyze the effects of such non-mining factors on water loss and subsidence of loose aquifers, the study area was divided into seven layers from top to bottom based on existing geological and hydrological data. Using distributed fiber optic monitoring technology, hydrological observation techniques, and soil mechanics experiments, the loose aquifer in the study area was comprehensively observed and the deformation characteristics of each layer under non-mining conditions were analyzed. The weakening law of the deep aquitard was explored, and the relationship between the deformation of the deep aquifer and the water head height of that layer was determined. The results show that: ① continuous compression of the fourth aquifer and its upper part of the aquitard is the main cause of surface subsidence in the study area. The two layers that contribute the most to the deformation of the strata are the fourth and first aquifers, with the latter showing seasonal deformation characteristics. The fourth aquifer exhibits a continuous subsidence trend during the observation period. ② By combining hydrological boreholes, distributed fiber optic and soil mechanics experiments, monitoring of the degree of clay weakening of the target layer was achieved. The clay layer above the fourth aquifer in the study area is weakened by the impact of the groundwater in the fourth aquifer. The degree of weakening is inversely proportional to the burial depth and directly proportional to the permeability of the groundwater, and the weakening of the clay layer will cause compression of the aquitard at the top of the aquifer and exacerbate surface subsidence. ③ The deformation of the fourth aquifer is consistent with the trend of changes in the water head of the fourth aquifer, and the two are linearly related. The observation results are in agreement with the theoretical calculation results, indicating that water loss from the fourth aquifer is the main cause of its compression deformation

Similar simulation experiment of water loss and settlement in thick loose aquifer

The in-depth research on the breaking and deformation law of overburden rock under the geological and mining conditions of thick loose aquifer are lacking at present. Taking 11111 working face of Pansidong Coal Mine in Huainan mining area as the engineering background, the similar material model is constructed, and the digital photogrammetry extraction displacement method is used to record the overburden rock breaking process and overburden rock deformation during the model roadway heading. The causes of water loss and settlement of aquifer are analyzed. The overburden rocks form two main longitudinal diversion fissure zones under the action of W-type shear stress arch. The further development of the diversion fissure zone causes water loss and consolidation of the aquifer, and the aquifer is further compacted under the action of gravity of the thick loose layer. With the intensification of the overburden rock breaking movement, О type shear stress arch is formed under the joint extrusion of bending zone and overburden rock, which compresses the thin space and leads to the large amount of surface subsidence. The damage of overburden rock under water loss condition is analyzed. After the roadway heading work of the working face is completed and the overburden rock reaches a steady state, the front caving angle is 57°, the rear caving angle is 62°, and the height of the diversion fissure zone is 63 m. Under the action of stress concentration, the overburden rock above the open-cut hole and the stop-mining line is broken to produce longitudinal fissure, and the overburden rock in the area of the collapse zone above the open-cut hole and the stop-mining line produces lateral separation fissure. The longitudinal fissures and lateral separation fissures intensify the hydraulic connection between overburden rock and the aquifer. The dynamic movement law of overburden rock under water loss state is given. With the advance of mining face, the overburden settlement of each observation line increases gradually, and the overburden settlement of the observation line close to the working face is the largest. The trend of the subsidence curves of the observation lines in the overburden rock above the working face is basically similar, and the jump of the subsidence curves is consistent. The trend of the subsidence curves of the observation lines above the aquifer is basically consistent, and the jump of the subsidence curves is synchronous. The jump of the subsidence curves of observation lines in the overburden rock above the working face and the one of the observation line above the aquifer are asynchronous, indicating that the aquifer plays an important role in the movement and deformation of the overburden rock

Spatio-temporal variation and prediction of land use and carbon storage in high groundwater level mining area

Mining activities and urbanisation in high dive mining areas can lead to significant changes in land use types, which in turn affect the carbon sequestration capacity of mining areas. Based on the land use data of Panxie mining area from 2002 to 2021, and used the FLUS (Future Land Use Simulation) model to predict land use changes in 2028 under two scenarios: natural development and ecological conservation, using mining, socio-economic and climatic data as drivers, and then The historical carbon stocks in the Panshet mine area from 2002 to 2021 and the future carbon stocks in 2028 under different scenarios were calculated by combining the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model, and the spatial and temporal characteristics of the carbon stocks in the Panxie mine area were analysed. The spatial and temporal variability of carbon stocks in the Panxie mine was also analysed. The results show that: ① from 2002 to 2021, land use changes in the Panxai mining area show a continuous decrease in arable land and a continuous increase in wetland and building land, with a decrease of 147.93 km2 in arable land and an increase of 71.01 km2 and 75.76 km2 in wetland and building land, respectively. during this period, the carbon stock in the Panxai mining area decreases from 1.62×105 t, a decrease of 3.83%, with the fastest decrease in carbon reserves from 2018 to 2021. ② The predicted results show that the land use changes in the study area under both scenarios in 2028 are a continuous increase in wetlands and building land, and a continuous decrease in arable land. However, compared to the natural development scenario, the ecological conservation scenario protects and increases the area of arable land in the mine area, while the growth of wetlands and building land slows down. Compared with 2021, the carbon stock in the natural development scenario decreases by 0.74×105 t and the ecological conservation scenario decreases by 0.53×105 t. The results of the study indicate that the decrease in arable land due to sinking water and the expansion of construction land is the main reason for the decrease in carbon stock due to the influence of coal mining subsidence and urban development, and that the adoption of ecological conservation measures can slow down the decrease in carbon stock to a certain extent. Ecological conservation measures can slow down the decline of carbon stocks to a certain extent

MMDGAN: A Fusion Data Augmentation Method for Tomato-Leaf Disease Identification

Tomato disease control is of great significance to ensure crop production and tomato disease classification study is an essential tool for doing so. In this paper, we propose a new data augmentation method based on deep threshold multi-feature extraction convolution GAN with Mixed Attention and Markovian Discriminator (MMDGAN) for tomato disease leaf classification. Firstly, in the generator of MMDGAN, a deep threshold multi-feature extraction module was proposed to improve the feature extraction of tomato disease leaves. Then, a mixed attention mechanism combined cross attention module with fused features-highlighting module was proposed to coordinate the overall generation of images. Finally, for the discriminator, Markov discriminator was used to strengthen the similarity judgment of local texture of images. Based on the open datasets PlantVillage, the Frechet Inception Distance (FID) score of healthy tomato leaf image, Leaf Mold, Leaf Curl and Spider Mite generated by MMDGAN were 159.3010, 164.4744, 230.3825 and 254.9866 respectively. Thereafter, a B-ARNet model is trained on synthetic and real images using transfer learning to classify the four categories of tomato diseases. The proposed method achieved an accuracy of 97.12%, with and F1 value of 97.78%. The proposed approach shows its superiority over the existing methodologies

Cinnamaldehyde regulates mitochondrial quality against hydrogen peroxide induced apoptosis in mouse lung mesenchymal stem cells via the PINK1/Parkin signaling pathway

Background Idiopathic pulmonary fibrosis (IPF) is a fatal respiratory disease without effective treatments. Mitochondrial dysfunction weakens the ability of mesenchymal stem cells (MSCs) to repair the distal lung epithelium, which is a probable pathogenesis of IPF. In previous research, we found that cinnamaldehyde (CA) can maintain the mitochondrial morphology of MSCs. Methods This present study evaluated the effect and mechanism of CA on murine lung MSCs using the hydrogen peroxide model. Antioxidant effects and mitochondrial function were determined using flow cytometry. The mRNA levels of mitochondrial dynamics and the expressions of autophagy-related proteins were also detected. Results CA can increase the levels of SOD, MMP and ATP, decrease the rate of ROS and apoptosis, and restore the mitochondrial structure. CA can also improve the mRNA expression of MFN1, MFN2, FIS1, DRP1, OPA1, and PGC-1α, increase the expression of LC3 II and p62 and promote the PINK1/Parkin signaling pathway. Our results demonstrated that CA can control mitochondrial quality and avoid apoptosis, which may be associated with the regulation of the PINK1/Parkin signaling pathway

The Production–Living–Ecological Land Classification System and Its Characteristics in the Hilly Area of Sichuan Province, Southwest China Based on Identification of the Main Functions

Production–living–ecological land (PLEL) is one of the research focuses of land planning and regional sustainable development in China. This paper builds a three-level classification system of PLEL based on the identification of the main land use functions (LUFs). Taking 215 typical towns in the hilly area of Sichuan Province, Southwest China as samples, the quantitative, spatial, and functional characteristics and impact factors of PLEL were studied. The results showed that (1) production land holds a dominant role in the hilly area of Sichuan Province, and production land (PL), living land (LL) and ecological land (EL) account for 66.06%, 7.60%, and 26.34% of the area, respectively. The area of agricultural production land is the largest; forestland and rural living land rank second and third. (2) The spatial patterns of PLEL in different regions of hilly area have differences. The proportion of PL gradually decreases from north to south, while the proportion of EL gradually increases from north to south, and the difference in LL is not obvious. The EL is mainly distributed in the upper and middle parts of hills, and the PL and LL are mainly distributed in the foot slopes and valleys. (3) The main functions of PLEL in the hilly area of Sichuan are production and ecology. The production function is mainly for agricultural and forestry products, and the living function is mainly for cultural leisure and residential functions. There are little differences among the ecological sub-functions. (4) There is a strong correlation between PLEL and natural–social–economic factors in the hilly area of Sichuan. Natural conditions such as latitude, relative height, and surface roughness have significant impacts on PL and EL. Social and economic factors such as population density, location and total industrial output value have a significant impact on LL. The results of this study provide valuable implications for the spatial planning and sustainable development in the Sichuan Basin and upstream of the Yangtze River

MDMASNet: A Dual-task Interactive Semi-supervised Remote Sensing Image Segmentation Method

Remote sensing image (RSIs) segmentation is widely used in urban planning, natural disaster detection and many other fields. Compared with natural scene images, RSIs have higher resolution, complex imaging, and diverse object shapes and sizes, while semantic segmentation methods based on deep learning often require many data labels. In this paper, we propose a semi-supervised RSIs segmentation network with multi-scale deformable threshold feature extraction module and mixed attention (MDMANet). First, a pyramid ensemble structure is used, which incorporates deformable convolution and bole convolution, to extract features of objects with different shapes and sizes and reduce the influence of redundant features. Meanwhile, a mixed attention (MA) is proposed to aggregate long-range contextual relationships and fuse low-level features with high-level features. Second, an FCN-based full convolution discriminator task network is designed to help evaluate the feasibility of unlabeled image prediction results. We performed experimental validation on three datasets, and the results show that MDMANet segmentation provides more significant improvement in accuracy and better generalization than existing segmentation networks