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By using the principles of porous media seepage mechanics and solute transport theories, a seepage–erosion theory model was developed to uncover the dynamics of mud and water inrush in fault rupture zones during the construction of tunnels. This model consists of a mass conservation equation, a flow transformation equation, a porosity evolution equation, and a permeability evolution equation. These components illustrate the interaction between seepage–erosion particle loss and the transformation of seepage flow patterns throughout the mud and water inrush evolution in the fault fracture zone. This model proves to be effective in illustrating the catastrophic process of mud and water inrushes within tunnels located in fault rupture zones. To address the spatial and temporal variations, the implicit difference and Galerkin finite element schemes were utilized, and the Newton–Raphson iteration method was applied to handle the nonlinear attributes of the equations. The theoretical model underwent further development and numerical simulations were performed using COMSOL multi-field coupling software. A comparison with existing indoor water inrush mud model test results validated the effectiveness of our model. The theoretical model was then applied to the Yong Lian tunnel scenario within the fault rupture zone. This computational analysis exposed the sequence of flow pattern transformations and the instability in seepage–erosion evolution within the fault rupture zone, ultimately leading to the emergence of mud and water inrush disasters. The findings of this study offer valuable insights for addressing tunnel engineering challenges related to underwater inrush disasters. [ABSTRACT FROM AUTHOR]

Optimizing agricultural carbon compensation zoning is crucial for establishing robust mechanisms in agricultural carbon compensation management, with significant implications for achieving national "dual carbon" strategic objectives. This study employs K-means and the three-dimensional magic cube approach to construct a novel evaluation index system for comprehensive carbon compensation zoning. By combining spatial land-use zoning, we delineate carbon compensation zones in Guangshui City, Hubei Province, and analyze the spatiotemporal variations of agricultural carbon balance, proposing optimization strategies. The results show that (1) from 2000 to 2021, agricultural carbon emissions and absorption exhibit a trend of increasing followed by decreasing, with spatial patterns of "higher in the northwest, lower in the southeast" and "higher in the southwest, lower in the northeast"; (2) the Gini coefficient of agricultural carbon emissions averages at 0.24, with economic contribution coefficients and ecological carrying coefficients ranging from 0.04–16.1 and 0.39–1.99, respectively, from 2000 to 2021; and (3) in 2021, Guangshui City comprises seven payment zones, four balance zones, and six compensation zones, ultimately forming eight optimized agricultural carbon compensation zones in alignment with regional agricultural carbon balance objectives. This study provides theoretical references for enhancing county-level agricultural carbon comprehensive compensation management mechanisms. [ABSTRACT FROM AUTHOR]

This study proposes a two-dimensional analytical framework based on urban spatial form and spatial service perspectives, utilizing data on buildings and points of interest (POIs). It integrates fishnet analysis, kernel density analysis, the categorization of POI functionalities, and mixture calculations to enhance our understanding of urban spatial form and function. Taking the main urban area of Zhengzhou as an example, this study identifies image elements that can describe urban spatial characteristics through the results of two-dimensional analysis and enriches the city image in the form of a portrait. The experimental findings demonstrate that the elements of the annular layer, functional landmarks, ring line boundaries, and special districts can fully convey the spatial picture of Zhengzhou City. The performance of the four types of image elements has a high degree of matching with the content of the urban spatial planning of Zhengzhou City, which can effectively identify the urban multi-center structure and development pattern. This paper explores and tests the development status of the city from a new perspective, which can provide an effective reference for the future planning and sustainable development of the city. [ABSTRACT FROM AUTHOR]

Granite residual soil (GRS) has prominent disintegration characteristics which have induced various geological disasters and engineering problems. The initial moisture content is believed to affect the disintegration of GRS significantly. To explore the effects of the initial moisture content on the soil disintegration characteristics and disintegration mechanism, disintegration tests were performed on remolded GRS with different initial moisture contents via the balance method, and the quantitative disintegration indices were corrected, considering the effects of water-absorption weight gain, in combination with a parallel water-absorption test. The disintegration characteristics and mechanism were thoroughly investigated, starting with the disintegration process curves and disintegration morphology, and combined with strength theory, X-ray diffraction (XRD) and X-ray fluorescence (XRF), the matric suction test, and the triaxial shear test. The results are as follows. (1) The corrected method improves the accuracy of the quantitative disintegration evaluation. (2) During the two disintegration stages, the forms of disintegration are dispersive fragmentation and progressive or block separation, and the soil matric suction and weakening of intergranular joining forces, respectively, are the drivers of disintegration. The first stage is usually completed within 1.5–2 min, and the disintegration ratio is usually within 20%. (3) The trends of change within the disintegration during the two stages show opposite water-content-dependent modes, and the soil samples with lower moisture contents have better water stability and slower disintegration in the second stage. The average disintegration rate of the soil with a moisture content of 24.4% in the first and second stages was approximately 1/5 and 13 times, respectively, that of the soil with a moisture content of 6.1%; these values can be rendered as 0.049%/s and 0.82%/s, respectively. The results provide some theoretical references for soil and water conservation and engineering applications in the GRS field. [ABSTRACT FROM AUTHOR]

It is necessary to predict the spatial variation in particle number concentration (PNC) and black carbon (BC) because they are considered air pollutants associated with traffic and many diseases. In this study, land use regression (LUR) models for PNC and BC were developed based on a mobile monitoring campaign in January 2020 in Lanzhou, and the performance of models was evaluated with hold-out validation (HV) and leave-one-out cross-validation (LOOCV). The results show that the adjusted R2 of the LUR models for PNC and BC are 0.51 and 0.53, respectively. The R2 of HV and LOOCV are 0.43 and 0.44, respectively, for the PNC model and 0.42 and 0.50, respectively, for the BC model. The performances of the LUR models are of a moderate level. The spatial distribution of the predicted PNC is related to the distance from water bodies. The high PNC is related to industrial pollution. The BC concentration decreases from south to north. High BC concentrations are associated with freight distribution centres and coal-fired power plants. The range of PNC particle sizes in this study is larger than in most studies. As one of few studies in Lanzhou to develop LUR models of air pollutants, it is important to accurately estimate pollutant concentrations to improve air quality and provide health benefits for residents. [ABSTRACT FROM AUTHOR]

Investigating the vertical distribution of mineral dust masses and their microphysical properties is crucial for accurately assessing the climate effects of dust. However, there are limited studies related to relevant in situ observations over dust source areas. In this study, the near-surface vertical characteristics (within 500 m a.g.l) of dust mass concentrations in five size fractions (PMs: TSP, PM10, PM4, PM2.5, and PM1) were investigated using an unmanned aerial vehicle (UAV) in Tazhong (TZ) in the Taklimakan Desert (TD) in July 2021. To the best of our knowledge, the vertical profiles of particle number concentration (PNC), effective radius (Reff), and volume concentration (Cv) were obtained for the first time by UAV over the TD. Four scenarios of clear sky, floating dust, blowing sand, and dust storm were selected based on the classification criteria for PMs. The PMs, PNC, Reff, and Cv decreased with height for all scenarios. From clear-sky to dust-storm scenarios PMs, PNC, Reff, and Cv in the column gradually increased. Reff (Cv) increased from 1.15 μm (0.08 μm3/μm2) to 4.53 μm (0.74 μm3/μm2). The diurnal variations of PMs, PNC, and Reff (Cv) revealed a unimodal pattern, with the peak occurring between 13:00 and 16:00, due to the evolution of wind speed and the atmospheric boundary layer in TZ. Unexpectedly, among the three postprecipitation scenarios (P1, P2, and P3), the PNC of P2 was smaller than those of P1 and P3. The Reff (Cv) for P2 was similar to or greater than that for dust storms, which may be associated with moist dust particles on the ground surface being carried into the air by wind. These investigations add to our understanding of the mineral dust vertical characteristics over the source area, and provide a meaningful reference for colocated lidar inversion and dust simulations. [ABSTRACT FROM AUTHOR]

As a core task of computer vision perception, 3D scene understanding has received widespread attention. However, the current research mainly focuses on the semantic understanding task at the level of entity objects and often neglects the semantic relationships between objects in the scene. This paper proposes a 3D scene graph prediction model based on deep learning methods for scanned point cloud data of indoor scenes to predict the semantic graph about the class of entity objects and their relationships. The model uses a multi-scale pyramidal feature extraction network, MP-DGCNN, to fuse features with the learned category-related unbiased meta-embedding vectors, and the relationship inference of the scene graph uses an ENA-GNN network incorporating node and edge cross-attention; in addition, considering the long-tail distribution effect, a category grouping re-weighting scheme is used in the embedded prior knowledge and loss function. For the 3D scene graph prediction task, experiments on the indoor point cloud 3DSSG dataset show that the model proposed in this paper performs well compared with the latest baseline model, and the prediction effectiveness and accuracy are substantially improved. [ABSTRACT FROM AUTHOR]

Accurate translation of aerial imagery to maps is a direction of great value and challenge in mapping, a method of generating maps that does not require using vector data as traditional mapping methods do. The tremendous progress made in recent years in image translation based on generative adversarial networks has led to rapid progress in aerial image-to-map translation. Still, the generated results could be better regarding quality, accuracy, and visual impact. This paper proposes a supervised model (SAM-GAN) based on generative adversarial networks (GAN) to improve the performance of aerial image-to-map translation. In the model, we introduce a new generator and multi-scale discriminator. The generator is a conditional GAN model that extracts the content and style space from aerial images and maps and learns to generalize the patterns of aerial image-to-map style transformation. We introduce image style loss and topological consistency loss to improve the model's pixel-level accuracy and topological performance. Furthermore, using the Maps dataset, a comprehensive qualitative and quantitative comparison is made between the SAM-GAN model and previous methods used for aerial image-to-map translation in combination with excellent evaluation metrics. Experiments showed that SAM-GAN outperformed existing methods in both quantitative and qualitative results. [ABSTRACT FROM AUTHOR]

This study is oriented towards machine autonomous mapping and the need to improve the efficiency of map point symbol recognition and configuration. Therefore, an intelligent recognition method for point symbols was developed using the You Only Look Once Version 3 (YOLOv3) algorithm along with the Convolutional Block Attention Module (CBAM). Then, the recognition results of point symbols were associated with the point of interest (POI) to achieve automatic configuration. To quantitatively analyze the recognition effectiveness of this study algorithm and the comparison algorithm for map point symbols, the recall, precision and mean average precision (mAP) were employed as evaluation metrics. The experimental results indicate that the recognition efficiency of point symbols is enhanced compared to the original YOLOv3 algorithm, and that the mAP is increased by 0.55%. Compared to the Single Shot MultiBox Detector (SSD) algorithm and Faster Region-based Convolutional Neural Network (Faster RCNN) algorithm, the precision, recall rate, and mAP all performed well, achieving 97.06%, 99.72% and 99.50%, respectively. On this basis, the recognized point symbols are associated with POI, and the coordinate of point symbols are assigned through keyword matching and enrich their attribute information. This enables automatic configuration of point symbols and achieves a relatively good effect of map configuration. [ABSTRACT FROM AUTHOR]

Accurately determining the atmospheric boundary layer height (ABLH) is needed when one is addressing the air quality-related issues in highly urbanized areas, as well as when one is investigating issues that are related to the emission and transport of dust aerosols over the source region. In this study, we propose a new ABLH retrieval method, which is named ADEILP (ABLH that is determined by polarization lidar); it is based on the short-term polarized lidar observation that took place during the intensive field campaign in July 2021 in Tazhong, the hinterland of Taklimakan Desert. Furthermore, we conducted comparisons between the ABLH that was identified using a radiosonde (ABLHsonde), the ABLH that was identified by ERA5 (ABLHERA5) and the ABHL that was identified by ADELIP (ABLHADELIP), and we discussed the implications of the dust events. The ADELIP method boasts remarkable advancements in two parts: (1) the lidar volume linear depolarization ratio (VLDR) that represented the aerosol type was adopted, which is very effective in distinguishing between the different types of boundary layers (e.g., mixing layer and residual layer); (2) the idea of breaking up the entire layer into sub-layers was applied on the basis of the continues wavelet transform (CWT) method, which is favorable when one is considering the effect of fine stratification in an aerosol layer. By combining the appropriate height limitations, these factors ensured that there was good robustness of the ADELIP method, thereby enabling it to deal with complex boundary layer structures. The comparisons revealed that ABLHADELIP shows good consistency with ABLHsonde and ABLHERA5 for non-dust events. Nevertheless, the ADELIP method overestimated the stable boundary layer and underestimated the heights of the mixing layer. The dust events seem to be a possible reason for the great difference between ABLHERA5 and ABLHsonde. Thus, it is worth suggesting that the influence that is caused by the differences of the vertical profile in the ERA5 product should be carefully considered when the issues on dust events are involved. Overall, these findings support the climatological analysis of the atmosphere boundary layer and the vertical distribution characteristics of aerosols over typical climatic zones. [ABSTRACT FROM AUTHOR]

The community is the basic spatial unit for urban residents to live and rest. It is a crucial direction of city image research to explore people's cognitive characteristics of community space image. Aiming at the lack of cognitive quantification of community spatial images, a new method that can quantify community spatial data into cognitive results is proposed. By employing spatial analysis tools, eleven spatial indicators from the perspective of community spatial form and spatial services are selected, and an image structure is constructed based on the characteristics of the indicator results. The results of multiple indicators are organized through the improved technique for order preference by similarity to ideal solution (TOPSIS) and overlay analysis method to produce a spatial image map of the community. The study displays that the spatial image characteristics of the community scale can be comprehensively expressed through three types of elements: district, path (edge), and node (landmark). These three types of elements constitute the image structure at the community scale and present apparent elements' characteristics. This scrutiny is also aimed to demonstrate the construction and use process of the methodology and to provide new ideas for the cognitive research of urban spatial image at the community scale. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) is an essential nutrient for crop growth and development. Hyperspectral satellite images with comprehensive spectral band coverage and high spectral resolution can be used to estimate and draw a spatial distribution map of SOM content in the region, which can provide a scientific management basis for precision agriculture. This study takes Xinzheng City, Henan Province's agricultural area, as the research object. Based on ZY1-02D hyperspectral satellite image data, the first derivative of reflectance (FDR) was processed on the original reflectance (OR). The SOM characteristic spectral bands were extracted using the correlation coefficient (CC) and least absolute shrinkage and selection operator (Lasso) methods. The prediction model of SOM content was established by multiple linear regression (MLR), partial least squares regression (PLSR), and random forest (RF) algorithms. The results showed that: (1) FDR processing can enhance SOM spectral features and reduce noise; (2) the Lasso feature band extraction method can reduce the model's input variables and raise the estimation precision; (3) the SOM content prediction ability of the RF model was significantly better than that of the MLR and PLSR models. The FDR-Lasso-RF model was the best SOM content prediction model, and the validation set R2 = 0.921, MAEV = 0.512 g/kg, RMSEV = 0.645 g/kg; (4) compared with laboratory hyperspectral data-SOM prediction methods, hyperspectral satellite data can achieve accurate, rapid, and large-scale SOM content prediction and mapping. This study provides an efficient, accurate, and feasible method for predicting and mapping SOM content in an agricultural region. [ABSTRACT FROM AUTHOR]

The adsorption/desorption characteristics for light and heavy rare earth elements (REEs) on halloysite and illite (which are beneficial for the utilization of ion-adsorption RE ore) were systematically investigated and compared. Additionally, halloysite and illite were fully charactered by XRD, SEM, microscope, zeta potential, nitrogen adsorption–desorption isotherms and buffer pH to build the relationship between adsorption/desorption mechanisms and the minerals' properties. The results of experiments show that the adsorption rate of halloysite is higher than illite, although they are both very fast and follow the pseudo-second-order kinetic model. The adsorption capacity of halloysite and illite increases with an increase in adsorption pH and remains constant when pH is higher than 4. Due to the narrow interlamellar spacing of halloysite and the fact that it is a nanotube, RE ions are adsorbed only through electrostatic attraction, whereas the adsorption and desorption pH have a significant effect on the recovery of RE ions from illite, because of the diverse adsorption mechanism. The results illustrated that the structure and surface properties of clays are also the key factors that affect RE ions leaching. [ABSTRACT FROM AUTHOR]

Featured Application: The results of this paper are suitable for the construction of landfill expansion, especially for high-steep slope engineering due to limited construction space. The design scheme and analysis method in this paper have significance for the safety assessment and management of landfill sites. This study presents a stability analysis of an extended berm reinforced by geotextiles, with a steep slope of 1V:1.1H (vertical: horizontal). Finite element (FE) analyses were carried out to explore the failure mechanism and factor of safety (FOS) of the berm, on which the effect of the strength of geotextiles, leachate level, and anti-slide pile arrangement located at the toe of the berm were considered. It was found that: (1) failure surfaces developed along the interface between the new and the existing berms; (2) the FOS decreased as the leachate increased, and an FOS value of 1.42 could be obtained if the leachate level was controlled at a height of 20 m; (3) the tensile force of geotextiles was far lower than the available strength, which suggested that the geotextile had enough of a safety reserve; and (4) one row of longer piles at the toe of the berm performed better than two rows of shorter piles if the total length of piles was the same. The design and analysis of this project can be used as a reference for landfill expansion. Especially for a site condition with limited space, a geosynthetic-reinforced soil (GRS) berm is a safe, reliable and promising alternative. [ABSTRACT FROM AUTHOR]

The galvanic interaction between chalcopyrite and monoclinic pyrrhotite and its effect on flotation separation were studied using monomineral flotation tests, adsorption capacity tests, X-ray photoelectron spectroscopy (XPS) characterization, and scanning electron microscopy (SEM) test. These results showed that the interaction promoted the reduction of O2 on the cathodic chalcopyrite surface and accelerated the generation of Fe(OH)3, which was not conducive to collector adsorption; hence, the flotation recovery decreased by 10–16%. On the other hand, galvanic interaction accelerated the oxidation of S on the anodic monoclinic pyrrhotite surface to S0 or SO42− and produced a large amount of H+, thus preventing the formation of Fe(OH)3. Meanwhile, the Cu2+ eluted from chalcopyrite surface activated monoclinic pyrrhotite; hence, the flotation recovery increased by 3–10%. Galvanic interaction reduced the floatability difference between the two minerals, and the separation difficulty was significantly increased. Even with an increase in the amount of lime, the separation could not be improved. [ABSTRACT FROM AUTHOR]

This paper revisits the spectrally negative Lévy risk process embedded with the general tax structure introduced in Kyprianou and Zhou (2009). A joint Laplace transform is found concerning the first down-crossing time below level 0. The potential density is also obtained for the taxed Lévy risk process killed upon leaving [ 0 , b ] . The results are expressed using scale functions. [ABSTRACT FROM AUTHOR]

A tailings dam failure can lead to disastrous impacts on people's livelihood and the surrounding ecological environment. Due to interactions among water, tailings and ground, the mechanism of a tailings flow is more complicated than that of a flood flow. In this paper, the tailings flow is regarded as a homogeneous and incompressible non-Newtonian fluid. Its rheological properties were studied through rheological tests conforming to the Bingham model. The rheological parameters were further used in a Computational Fluid Dynamics (CFD) simulation over complex terrain to explore the tailings flow characteristics. The method was validated with experimental results of a non-Newtonian dam-break flow from literature. The flow characteristics, including flow velocity, runout distance, inundation area and depth, were analyzed in the case of the Dagangding tailings dam. The results showed that the downstream railway and village would not be affected in a conservative scenario. Finally, the effects of two measures for preventing tailings flow hazards were discussed. Setting the check dam and planting grasses and trees can effectively mitigate the damage of tailings flow.