On June 4, 2023, a small-scale landslide occurred in Shiban Valley, Jinkouhe district, Leshan City, Sichuan Province, china. The Shiban Valley landslide destroyed a miners' dormitory andresulted in 19 casualties....
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On June 4, 2023, a small-scale landslide occurred in Shiban Valley, Jinkouhe district, Leshan City, Sichuan Province, china. The Shiban Valley landslide destroyed a miners' dormitory andresulted in 19 casualties. The sliding material traveled at a maximum distance of approximately 280 m with a total volume of approximately 2 x 104 m3. In this study, comprehensive field investigations are conducted to gain insight into the characteristics, causes, andrestoration measures of the Shiban Valley landslide. The runout process is revealed through a combined analysis of rAMMS numerical simulations and landquake signal inversion. The results indicate that the Shiban Valley landslide is likely the result of a combination of natural geographic conditions, hydrometeorological factors, and engineering works. The numerical results are verified with landquake signals and field investigations, and show that the Shiban Valley landslide travels a maximum distance of 291 m in 99.7 s, with a peak velocity of 16 m/s. The simulated sliding mass begins with a volume of 1.18 x 104 m3 andresults in a total deposition volume of 1.83 x 104 m3 and average depth of 8 m, which exhibits obvious erosion effect. The runout evolution process can be divided into four phases (sudden onset, impact shoveling, main deposition, and trailing end accumulation), and each phase exhibits distinct frequency characteristics. The findings of this study provide a significant reference for future prevention, mitigation, and early warning of similar landslides in this area. The characteristics andrestoration measures of the Shiban Valley landslide may also serve as a reference for the management of similar landslides.
data augmentation is a widely usedregularization strategy in deep neural networks to mitigate overfitting and enhance *** the context of point clouddata,mixing two samples to generate new training examples has prove...
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data augmentation is a widely usedregularization strategy in deep neural networks to mitigate overfitting and enhance *** the context of point clouddata,mixing two samples to generate new training examples has proven to be *** this paper,we propose a novel and effective approach called Farthest Point Sampling Mix(FPSMix)for augmenting point cloud *** method leverages farthest point sampling,a technique used in point cloud processing,to generate new samples by mixing points from two original point *** key innovation of our approach is the introduction of a significance-based loss function,which assigns weights to the soft labels of the mixed samples based on the classification loss of each part of the new sample that is separated from the two original point *** way,our method takes into account the importance of different parts of the mixed sample during the training process,allowing the model to learn better global *** results demonstrate that our FPSMix,combined with the significance-based loss function,improves the classification accuracy of point cloud models and achieves comparable performance with state-of-the-art data augmentation ***,our approach is complementary to techniques that focus on local features,and their combined use further enhances the classification accuracy of the baseline model.
Underwater object detection is an important research area with wide-ranging applications, from underwater exploration to ecological monitoring. However, this field faces multiple challenges, particularly the significa...
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Underwater object detection is an important research area with wide-ranging applications, from underwater exploration to ecological monitoring. However, this field faces multiple challenges, particularly the significant degradation of underwater image quality and variations in target scales. Traditional object detection algorithms struggle to accurately extract key features of underwater targets, leading to poordetection performance. This study aims to enhance the performance of underwater object detection, especially for small-scale underwater targets, to adapt to complex underwater environments. In this paper, we propose a novel underwater object detector called MPEdet based on multi-branch attention mechanism and path enhancement. Specifically, to improve the capability of the model to extract key features in complex underwater environments, we propose a multi-branch attention mechanism called MBAM, which fully utilizes the dependency information between input features and input keys to strengthen the semantic representation capability during the encoding phase. In addition, we use the designed path enhancement module to facilitate the information interaction between high and low features andreduce the loss of detailed information in the propagation of high-level features within the network. Finally, after training the proposed MPEdet underwaterdetector for only 24 epochs, it achieved AP50 values of 84.4% and 74.8% on the rUOd and UTdAC underwater test sets, respectively. The results demonstrate that the proposed MPEdet detector can effectively handle the task of underwater.
Sulfurized polyacrylonitrile (SPAN) is recognized as a promising organic cathode for long-lifespan lithium metal batteries. Nevertheless, the irreversible cleavage/formation of multiple sulfur-sulfur (S-S) bonds of SP...
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Sulfurized polyacrylonitrile (SPAN) is recognized as a promising organic cathode for long-lifespan lithium metal batteries. Nevertheless, the irreversible cleavage/formation of multiple sulfur-sulfur (S-S) bonds of SPAN within conventional ether-based electrolytes results in loss of active S species, severe capacity fading and shuttle effects. Herein, we propose a new electrolyte based on dipropyl ether (PE) solvent for Li-SPAN batteries. Benefiting from the particular chain-coordination structure and weak dipole interactions with Li+ and active species, the resulting electrolyte not only achieves low desolvation energy barrier and high Li+ transference number, but also displays stable electrolyte-electrode interface (EEI). Consequently, the full cells utilizing this electrolyte exhibit good cyclability, outstanding capacity retention and superior extreme-temperature (-50 degrees C to 50 degrees C) performance. Furthermore, the Ah-scale pouch cell with lean electrolyte (2.5 g Ah-1) achieves record cycle stability with 96.5 % capacity retention after 75 cycles, which deliver an initial specific energy density of 150 Wh kg-1 (based on the weight of the entire cell). Impressively, this strategy demonstrates universality in a series of organic electrodes employing with PE-based electrolytes. This work highlights the strategy for modulating the dipole interaction at EEI for long-lifespan Li-organic batteries at extreme conditions.
To enhance the post-disaster highway driving speed evaluation system for steel-concrete composite beam bridge (SCCBB), a human-vehicle-bridge coupleddriving adaptability analysis method is proposed. This method integ...
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To enhance the post-disaster highway driving speed evaluation system for steel-concrete composite beam bridge (SCCBB), a human-vehicle-bridge coupleddriving adaptability analysis method is proposed. This method integrates considerations of both driving safety and comfort. Numerical simulation is employed to utilize a damaged SCCBB as the engineering context for studying vehicle rollover safety, vibration acceleration limits, anddriving comfort. The analysis considers various vehicle types, including sedan cars, mini vans, motor buses, and trucks, resulting in recommended speed limits for each category. The study reveals that trucks demonstrate lower susceptibility to rollover accidents compared to sedan cars, mini vans, and motor buses under constant damage grade and vehicle speed conditions. However, increaseddamage grade androad surface roughness elevate rolloverrisks anddiminish driver comfort. To ensure smooth post-disaster traffic flow on SCCBB and maintain acceptable driving comfort and speed levels, it is recommended to enforce speed limits: below 60 km/h for sedan cars, below 40 km/h for mini vans and motor buses, and below 80 km/h for trucks. The primary aim of this study is to provide technical guidance for the safe operation and maintenance of post-disaster SCCBB.
Understanding the reactivation causes of ancient landslides is imperative for the prevention of landslides. However, the reasons for the reactivation of thick loess-mudstone ancient landslides and evolutionary mechani...
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Understanding the reactivation causes of ancient landslides is imperative for the prevention of landslides. However, the reasons for the reactivation of thick loess-mudstone ancient landslides and evolutionary mechanisms are unclear. This paper investigates the Gaojiawan thick loess-mudstone ancient landslide as an example using field investigation, InSAr time series analysis, and laboratory testing methods to analyze the reactivation deformation characteristics andreactivation causes of the thick loess mudstone ancient landslides, which were and verified by numerical simulation. The results show that fault fracture zones and groundwater primarily control the reactivation of Gaojiawan's thick loess-mudstone ancient landslide. due to the fragmentation of rock mass and the development of structural planes in the fault fracture zones, as well as the excavation and unloading zone formed by the surrounding rock of the tunnel, it is beneficial to the enrichment of groundwater. It intensifies the interaction of groundwater-rock-fault fracture zones, especially for the red mudstone with more clay mineral content. The strength degradation is significant after encountering water, resulting in an imbalance in the stress state in deep strata and the reactivation of the landslide.
A 20-kiloton liquid scintillatordetector is under construction at the Jiangmen Underground Neutrino Observatory(JUNO)for several physics *** neutrinos released from nuclearreactors,the sun,supernova bursts,and Earth...
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A 20-kiloton liquid scintillatordetector is under construction at the Jiangmen Underground Neutrino Observatory(JUNO)for several physics *** neutrinos released from nuclearreactors,the sun,supernova bursts,and Earth's atmosphere across a wide energy range necessitates efficient reconstruction *** this study,we introduce a novel method forreconstructing event energy by counting 3-inch photomultiplier tubes(PMTs)with or without *** proposed algorithm demonstrated excellent performance in accurate energy reconstruction,validated with electron Monte Carlo samples covering kinetic energies ranging from 10 MeV to 1 GeV.
Finite element method (FEM) is one of the essential means of structural analysis. However, the existing finite element modelling relies on manual anddesign drawings. Therefore, this study proposes an automated method...
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Finite element method (FEM) is one of the essential means of structural analysis. However, the existing finite element modelling relies on manual anddesign drawings. Therefore, this study proposes an automated method for the numerical analysis of in-service bridges represented by point clouds. The proposed method includes two main innovations: first, an improved finite cell method (FCM) is introduced to generate finite element meshes from point clouds directly. This method eliminates the need for intricate computations involving uniformly distributed grid points as division criteria, significantly reducing the modelling time. Second, to overcome FCM's limitations in handling structures with multiple material properties, this paper introduces a combination of a three-way topological relationship determination method (TrdM) andrandLA-Net. This approach automatically classifies material properties at integration points within the bridge structure's physical domain. A model of an arch bridge is subjected to indoor experiments. Through comparative experimentation and ANSYS outcomes, proposed methoddemonstrates a level of precision akin to that of conventional modelling approaches.
The accuracy of geometric control during the construction phase of main cables and cable clamps in suspension bridges directly impacts the safety performance of the completed structure. Traditionally, this process rel...
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The accuracy of geometric control during the construction phase of main cables and cable clamps in suspension bridges directly impacts the safety performance of the completed structure. Traditionally, this process relies on total stations for measurement, which is cumbersome and inefficient. To address this, the paper proposed an automated measurement method for the main cable shape of large-span suspension bridges, integrating laser and camera. This method introduces two innovations: (1) To tackle the challenges of point cloudreconstruction and processing for large-span bridges, a radar-camera-inertial sensor fusion-based method for mapping large-scale heterogeneous data is proposed;(2) To address point cloud aliasing during data mapping, an automated segmentation method based on deep learning and Euclidean clustering is introduced, combined with a geometric processing framework that considers bridge features, enabling precise segmentation and shape extraction of cable clamps. This method is successfully applied to the Xianxin road Bridge in china, accurately calculating the geometric information of the main cables. The results show an average shape error of 1.6 cm, validating the method's efficiency andreliability and providing a novel approach for point cloud segmentation and construction quality assessment of large-span suspension bridges.
Pre-harvest sprouting (PHS) of wheat (Triticum aestivum L.) is one of the complex traits that result in rainfall-dependent reductions in grain production and quality worldwide. Breeding new varieties and germplasm wit...
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Pre-harvest sprouting (PHS) of wheat (Triticum aestivum L.) is one of the complex traits that result in rainfall-dependent reductions in grain production and quality worldwide. Breeding new varieties and germplasm with PHS resistance is of great importance to reduce this problem. However, research on markers and genes related to PHS resistance is limited, especially in marker-assisted selection (MAS) wheat breeding. To this end, we studied PHS resistance in recombinant inbred line (rIL) population and in 171 wheat germplasm accessions in different environments and genotyped using the wheat Infinium 50 K/660 K SNP array. Quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) identified 59 loci controlling PHS. Upon comparison with previously reported QTL affecting PHS, 16 were found to be new QTL, and the remaining 43 loci were co-localized with QTL from previous studies. We also pinpointed 12 candidate genes within these QTL intervals that share functional similarities with genes previously known to influence PHS resistance. In addition, we developed and validated two kompetitive allele-specific PCr (KASP) markers within the chromosome 7B region identified by linkage analysis. These QTL, candidate genes, and the KASP marker identified in this study have the potential to improve PHS resistance of wheat, and they may enhance our understanding of the genetic basis of PHS resistance, thus being useful for MAS breeding.
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