Research on the Macro-Mesoscopic Response Mechanism of Multisphere Approximated Heteromorphic Tailing Particles

AbstractThe shape of tailing particles is essential factors of their macroscopic mechanical properties. Scholars have studied the effects of controllable factors, such as loading method, confining pressure, and strain rate, on the strength of tailing sand. However, research on the tailing particle structure and shape through laboratory tests has proved to be difficult due to the uncertain and discrete tailing particle distribution. Thus, the macro-mesoscopic response of heteromorphic tailing particles is rarely investigated. In this paper, the macro-mesoscopic response of heteromorphic tailing particles is studied using multisphere approximation, and numerical simulation of triaxial tests on the particles is conducted. Nonlinear evolution patterns of porosity, internal friction angle, and cohesion of heteromorphic tailing particles with the variation of angularity were investigated using the flexible boundary program developed in this study, which revealed the intrinsic relationship between the mesostructure evolution mechanism and the macroscopic engineering characteristics of heteromorphic tailing particles. The research results showed that (1) changes in angularity led to tailing particle rearrangements and, in turn, porosity changes. With increased angularity and confining pressure, particle sphericity decreased, and the deviatoric and peak stress increased accordingly. In the meantime, the softening was more significant as the peak stress was exceeded, while the cohesive force generally increased. (2) With fixed particle shape and angularity, the internal friction angle decreased slightly as the effective confining pressure increased. (3) In the shearing process, the simulated contact force chain evolution of tailing particles with different shapes was similar. The disordered contact force chains gradually undergo directional connection, i.e., the increased confining pressure reduced the number of free tailing particles and increased the number of stressed particles. (4) The triaxial stress-strain and peak stress in rigid boundary simulations under different confining pressures were slightly lower than those in the flexible boundary simulations. However, the difference was insignificant, indicating the good feasibility and reasonability of rigid boundary simulations for the macroscopic mechanical behaviors in triaxial tests. The research results could offer more direct insights into the macro-mesoscopic response and mechanical mechanisms of nonspherical particles and provide references for the simulation of tailings at the microscopic levels

Experimental Study on Failure Model of Tailing Dam Overtopping under Heavy Rainfall

AbstractUnusual rainfall is the primary cause of the failure of the tailing dams, and overtopping is the most representative model of the tailing dam failure. The upstream tailing dam was selected as the research object to study the whole process of breach extension and the overtopping dam-failure mechanism under the full-scale rainfall condition. The results showed that the significant size grading phenomenon in the front, middle, and end of the tailing pond was obvious due to the flow separation effect, and its average particle diameter was D50. At different moments of rainfall, the height of the infiltration line at different positions of the dam body was different; at the rainfall of 3600 s, the height of the infiltration line lagged behind the height of the tailing pond, and this phenomenon from the tail of pond to the outside of the dam slope became more obvious. After the rainfall of 3600 s, the height of the infiltration line lagging behind the water level in the pond basically disappeared, and the rate of infiltration line rise kept pace with the rate of water level. The process of overtopping dam-failure experienced dam overtopping (gully erosion), formation of a multistepped small “scarp,” breach rapid expansion, formation of large “scarp,” and burst (fan-shaped formation). The width and depth of the breach showed a positive correlation, and the widening rate of the breach was 3 to 8 times of the deepening rate, especially in the middle of the dam break, widening behavior occupied the dominant factor. The shape of the dam body after failure was parabolic, and the dam body had obvious elevation changes. These results provide the theoretical guidance and engineering application value for improving the theory and early warning model of the upstream tailing dam

Nitrogen addition mediates the effect of soil microbial diversity on microbial carbon use efficiency under long-term tillage practices

peer reviewedTillage practices can influence soil microbial carbon use efficiency (CUE), which is critical for carbon cycling in terrestrial ecosystems. The effect of tillage practices could also be regulated by nitrogen (N) addition. However, the soil microbial mechanism relating to N fertilizer effect on microbial CUE under no-tillage (zero-tillage) is still unclear. We investigated how N fertilizer regulates the effect of tillage management on microbial CUE through changing microbial properties and further assessed the impact of microbial CUE on particulate (POC) and mineral-associated organic matter carbon (MAOC). For this we used a 16-year field experiment with no-tillage (NT) and conventional tillage (CT), both of which combined with 105 (N1), 180 (N2), and 210 kg N ha−1 (N3) N application. We found that microbial CUE increased with increasing N application rate. NT increased microbial CUE compared with CT in the 0–10 cm. The bacterial and fungal diversities of NT were higher than CT and N application decreased their diversities in 0–10 cm. The partial least squares path model showed that bacterial and fungal diversity had a significant influence on microbial CUE. Furthermore, POC and MAOC under NT were higher than CT and they also increased with increasing N application rate. It suggested that increasing microbial CUE induced by N application had the potential to increase POC and MAOC. Overall, this study highlights that N addition can alter the effect of soil microbial diversity on CUE, which further improves our understanding to explain and predict the fractions of SOC (i.e., POC and MAOC) in tillage systems

Identifying the Species of Seeds in Traditional Chinese Medicine Using DNA Barcoding

Seed is not only the main reproductive organ of most of herbal plants but also an important part of Traditional Chinese Medicine (TCM). Seed TCMs possess important medicinal properties and have been widely used as components of pharmaceutical products. In parallel with the increasing popularity and accessibility of seeds as medicinal products in recent years, numerous substitutes and adulterants have also appeared on the market. Due to the small volume and similar appearances of many seed TCMs, they are very difficult to accurately identify the constituent plant species through organoleptic methods. Usage of the wrong herb may be ineffective or may worsen the condition and even cause death. Correct identification of seed herbal medicines is therefore essential for their safe use. Here, we acquired 177 ITS2 sequences and 15 psbA-trnH sequences from 51 kinds of seed TCMs belonging to 64 species that have been described in the Chinese Pharmacopoeia. Tree-building analysis showed that the ITS2 sequences of 48 seed TCMs can be differentiated from each other, and they formed distinct, non-overlapping groups in the maximum-likelihood tree. Furthermore, all of the sequences acquired in this study have been submitted to the public DNA barcoding system for herbal medicine, and this integrated database was used to identify 400 seed TCM samples purchased from medicinal markets, drug stores, and the Internet, enabling the identification of 7.5% of the samples as containing non-declared species. This study provides a brief operating procedure for the identification of seed TCMs found in herbal medicine. In the future, researchers and traditional herbal medicine enterprises can use this system to test their herbal materials

International benchmarking of terrestrial laser scanning approaches for forest inventories

The last two decades have witnessed increasing awareness of the potential of terrestrial laser scanning (TLS) in forest applications in both public and commercial sectors, along with tremendous research efforts and progress. It is time to inspect the achievements of and the remaining barriers to TLS-based forest investigations, so further research and application are clearly orientated in operational uses of TLS. In such context, the international TLS benchmarking project was launched in 2014 by the European Spatial Data Research Organization and coordinated by the Finnish Geospatial Research Institute. The main objectives of this benchmarking study are to evaluate the potential of applying TLS in characterizing forests, to clarify the strengths and the weaknesses of TLS as a measure of forest digitization, and to reveal the capability of recent algorithms for tree-attribute extraction. The project is designed to benchmark the TLS algorithms by processing identical TLS datasets for a standardized set of forest attribute criteria and by evaluating the results through a common procedure respecting reliable references. Benchmarking results reflect large variances in estimating accuracies, which were unveiled through the 18 compared algorithms and through the evaluation framework, i.e., forest complexity categories, TLS data acquisition approaches, tree attributes and evaluation procedures. The evaluation framework includes three new criteria proposed in this benchmarking and the algorithm performances are investigated through combining two or more criteria (e.g., the accuracy of the individual tree attributes are inspected in conjunction with plot-level completeness) in order to reveal algorithms’ overall performance. The results also reveal some best available forest attribute estimates at this time, which clarify the status quo of TLS-based forest investigations. Some results are well expected, while some are new, e.g., the variances of estimating accuracies between single-/multi-scan, the principle of the algorithm designs and the possibility of a computer outperforming human operation. With single-scan data, i.e., one hemispherical scan per plot, most of the recent algorithms are capable of achieving stem detection with approximately 75% completeness and 90% correctness in the easy forest stands (easy plots: 600 stems/ha, 20 cm mean DBH). The detection rate decreases when the stem density increases and the average DBH decreases, i.e., 60% completeness with 90% correctness (medium plots: 1000 stem/ha, 15 cm mean DBH) and 30% completeness with 90% correctness (difficult plots: 2000 stems/ha, 10 cm mean DBH). The application of the multi-scan approach, i.e., five scans per plot at the center and four quadrant angles, is more effective in complex stands, increasing the completeness to approximately 90% for medium plots and to approximately 70% for difficult plots, with almost 100% correctness. The results of this benchmarking also show that the TLS-based approaches can provide the estimates of the DBH and the stem curve at a 1–2 cm accuracy that are close to what is required in practical applications, e.g., national forest inventories (NFIs). In terms of algorithm development, a high level of automation is a commonly shared standard, but a bottleneck occurs at stem detection and tree height estimation, especially in multilayer and dense forest stands. The greatest challenge is that even with the multi-scan approach, it is still hard to completely and accurately record stems of all trees in a plot due to the occlusion effects of the trees and bushes in forests. Future development must address the redundant yet incomplete point clouds of forest sample plots and recognize trees more accurately and efficiently. It is worth noting that TLS currently provides the best quality terrestrial point clouds in comparison with all other technologies, meaning that all the benchmarks labeled in this paper can also serve as a reference for other terrestrial point clouds sources.</p

Autonomous Vehicle Localization with Prior Visual Point Cloud Map Constraints in GNSS-Challenged Environments

Accurate vehicle ego-localization is key for autonomous vehicles to complete high-level navigation tasks. The state-of-the-art localization methods adopt visual and light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) to estimate the position of the vehicle. However, both of them may suffer from error accumulation due to long-term running without loop optimization or prior constraints. Actually, the vehicle cannot always return to the revisited location, which will cause errors to accumulate in Global Navigation Satellite System (GNSS)-challenged environments. To solve this problem, we proposed a novel localization method with prior dense visual point cloud map constraints generated by a stereo camera. Firstly, the semi-global-block-matching (SGBM) algorithm is adopted to estimate the visual point cloud of each frame and stereo visual odometry is used to provide the initial position for the current visual point cloud. Secondly, multiple filtering and adaptive prior map segmentation are performed on the prior dense visual point cloud map for fast matching and localization. Then, the current visual point cloud is matched with the candidate sub-map by normal distribution transformation (NDT). Finally, the matching result is used to update pose prediction based on the last frame for accurate localization. Comprehensive experiments were undertaken to validate the proposed method, showing that the root mean square errors (RMSEs) of translation and rotation are less than 5.59 m and 0.08°, respectively

Autonomous Vehicle Localization with Prior Visual Point Cloud Map Constraints in GNSS-Challenged Environments

Accurate vehicle ego-localization is key for autonomous vehicles to complete high-level navigation tasks. The state-of-the-art localization methods adopt visual and light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) to estimate the position of the vehicle. However, both of them may suffer from error accumulation due to long-term running without loop optimization or prior constraints. Actually, the vehicle cannot always return to the revisited location, which will cause errors to accumulate in Global Navigation Satellite System (GNSS)-challenged environments. To solve this problem, we proposed a novel localization method with prior dense visual point cloud map constraints generated by a stereo camera. Firstly, the semi-global-block-matching (SGBM) algorithm is adopted to estimate the visual point cloud of each frame and stereo visual odometry is used to provide the initial position for the current visual point cloud. Secondly, multiple filtering and adaptive prior map segmentation are performed on the prior dense visual point cloud map for fast matching and localization. Then, the current visual point cloud is matched with the candidate sub-map by normal distribution transformation (NDT). Finally, the matching result is used to update pose prediction based on the last frame for accurate localization. Comprehensive experiments were undertaken to validate the proposed method, showing that the root mean square errors (RMSEs) of translation and rotation are less than 5.59 m and 0.08°, respectively

A Bidirectional Analysis Method for Extracting Glacier Crevasses from Airborne LiDAR Point Clouds

A crevasse is an important surface feature of a glacier. This study aims to detect crevasses from high-density airborne LiDAR point clouds. However, existing methods continue to suffer from the data holes within the crevasse region and the influence of the undulating non-crevasse glacier surfaces. Therefore, a bidirectional analysis method is proposed to robustly extract the crevasses from the point clouds, which utilizes their vertical and horizontal characteristics. First, crevasse points are separated from non-crevasse points using a hybrid-entity method, where the height difference and the nearly vertical characteristic of a crevasse sidewall are considered, to better distinguish the crevasses from the undulating non-crevasse glacier surfaces. Second, the crevasse regions/edges are adaptively delineated by a local statistical analysis method that is based on a novel feature of the Delaunay triangulation mesh of non-crevasse points in the horizontal plane. Last, the pseudo-crevasse points and regions are removed by a cross-analysis method. To test the performance of the proposed method, this study selected airborne LiDAR point clouds from two sites of Alaskan glaciers (i.e., Tyndall Glacier and Seward Glacier) as the experimental datasets. The results were verified by a comparison with the ground truth that was manually delineated. The proposed method achieved acceptable results: the recall, precision, and F 1 scores of both sites exceeded 94.00%. Moreover, a comparative experiment was carried out and the results confirmed that the proposed method achieved superior performance

Dense 3D surface reconstruction of large-scale streetscape from vehicle-borne imagery and LiDAR

Accurate and efficient three-dimensional (3D) streetscape reconstruction is the fundamental ability for an exploration vehicle to navigate safely and perform high-level tasks. Recently, remarkable progress has been made in streetscape reconstruction with visual images and light detection and ranging (LiDAR), but they have difficulties either in scaling and reconstructing large-scale outdoors or in efficient processing. To address these issues, this paper proposed an automatic method for incremental dense reconstruction of large-scale 3D streetscapes from coarse to fine at near real time. Firstly, the pose of vehicle is estimated by visual and laser odometry (VLO) and the state-of-the-art pyramid stereo matching network (PSMNet) is introduced to estimate depth information. Then, incremental dense 3D streetscape reconstruction is conducted by key-frame selection and coarse registration with local optimization. Finally, redundant and noise points are removed through multiple filtering, resulting good quality of dense reconstruction. Comprehensive experiments were undertaken to check the visual effect, trajectory pose error and multi-scale model to model cloud comparison (M3C2) based on reference trajectories and reconstructions provided by the state-of-the-art method, showing the precision, recall and F-score of sampling core points (SCPs) are over 80.42%, 71.68% and 77.19%, respectively, which verified the proposed method

Modeling the Effects of Rice-Vegetable Cropping System Conversion and Fertilization on GHG Emissions Using the DNDC Model

The cropping system conversion, from rice to vegetable, showed various influences on the greenhouse gases (GHG) emission with conversion time and fertilizer/irrigation management. In this study, we evaluated the DeNitrification-DeComposition (DNDC) model for predicting carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) emissions and crop yields as rice converted to vegetable cropping system under conventional or no fertilization from 2012 to 2014. Then, we quantified the long-term (40 years) impacts of rice-vegetable cropping system conversions and fertilization levels (0, 50, 100 and 150% conventional fertilization rate) on GHGs emissions and global warming potentials (GWP) using the calibrated model. The DNDC model-simulated daily GHG emission dynamics were generally consistent with the measured data and showed good predictions of the seasonal CH4 emissions (coefficient of determination (R2) = 0.96), CO2 emissions (R2 = 0.75), N2O emissions (R2 = 0.75) and crop yields (R2 = 0.89) in response to the different cropping systems and fertilization levels across the two years. The overall model performance was better for rice than for vegetable cropping systems. Both simulated and measured two-year data showed higher CH4 and CO2 emissions and lower N2O emissions for rice than for vegetable cropping systems and showed positive responses of the CO2 and N2O emissions to fertilizations. The lowest GWP for vegetable without fertilization and highest the GWP for rice with fertilization were obtained. These results were consistent with the long-term simulation results. In contrast to the two-year experimental data, the simulated long-term CH4 emissions increased with fertilization for the rice-dominant cropping systems. The reasonable cropping systems and fertilization levels were recommended for the region