Search Results (3,517)

Hurricane Helene triggered 1792 landslides across western North Carolina and has caused damage to 79 bridges to date. Helene hit western North Carolina days after a low-pressure system dropped up to 254 mm of rain in some locations of western North Carolina (e.g., Asheville Regional Airport). The already waterlogged region experienced devastation as significant additional rainfall occurred during Helene, where some areas, like Asheville, North Carolina received an additional 356 mm of rain (National Weather Service, 2024). In this study, machine learning (ML)-generated multi-hazard landslide susceptibility maps are compared to the documented landslides from Helene. The landslide models use the North Carolina landslide database, soil survey, rainfall, USGS digital elevation model (DEM), and distance to rivers to create the landslide variables. From the DEM, aspect factors and slope are computed. Because recent research in western North Carolina suggests fault movement is destabilizing slopes, distance to fault was also incorporated as a predictor variable. Finally, soil types were used as a wildfire predictor variable. In total, 4794 landslides were used for model training. Random Forest and logistic regression machine learning algorithms were used to develop the landslide susceptibility map. Furthermore, landslide susceptibility was also examined with and without consideration of wildfires. Ultimately, this study indicates heavy rainfall and debris-laden floodwaters were critical in triggering both landslides and scour, posing a dual threat to bridge stability. Field investigations from Hurricane Helene revealed that bridge damage was concentrated at bridge abutments, with scour and sediment deposition exacerbating structural vulnerability. We evaluated the assumed flooding potential (AFP) of damaged bridges in the study area, finding that bridges with lower AFP values were particularly vulnerable to scour and submersion during flood events. Differentiating between landslide-induced and scour-induced damage is essential for accurately assessing risks to infrastructure. The findings emphasize the importance of comprehensive hazard mapping to guide infrastructure resilience planning in mountainous regions. Full article

The accurate estimation of Digital Elevation Models (DEMs) derived from satellite data is critical for numerous environmental applications. This study evaluates the accuracy and reliability of two satellite-derived elevation models, the ALOS World 3D and SRTM DEMs, specifically for their application in hydrological modeling. A comparative analysis with Terrestrial Laser Scanning (TLS) measurements assessed the agreement between these datasets. Multiple linear regression models were utilized to evaluate the relationships between the datasets and provide detailed insights into their accuracy and biases. The results indicate significant correlations between satellite DEMs and TLS measurements, with adjusted R-square values of 0.8478 for ALOS and 0.955 for the SRTM. To quantify the average difference, root mean square error (RMSE) values were calculated as 10.43 m for ALOS and 5.65 m for the SRTM. Additionally, slope and aspect analyses were performed to highlight terrain characteristics across the DEMs. Slope analysis showed a statistically significant negative correlation between SRTM and TLS slopes (R² = 0.16, p < 4.47 × 10⁻¹⁰ indicating a weak relationship, while no significant correlation was observed between ALOS and TLS slopes. Aspect analysis showed significant positive correlations for both ALOS and the SRTM with TLS aspect, capturing 30.21% of the variance. These findings demonstrate the accuracy of satellite-derived elevation models in representing terrain features relative to high-resolution terrestrial data. Full article

Activities, particularly harbor construction, often exert significant and non-negligible impacts on coastal environments. Therefore, it is of great practical importance to quantitatively assess the effects of such construction on the surrounding topography, such as tidal flats. This study focuses on the coast of Jiangsu Binhai Harbor. Using multi-source and multi-temporal remote sensing images, digital elevation models of tidal flats surrounding Binhai Harbor were generated for the years 2013, 2015, and 2017 through the waterline method. A quantitative analysis was conducted utilizing GIS spatial analysis techniques to examine erosion–deposition patterns, contour changes, and typical cross-sectional comparisons. The findings reveal that, although the overall coastline is in a state of erosion, the localized impacts of harbor construction are evident. Between 2013 and 2017, the northern tidal flats experienced overall erosion, whereas deposition occurred near the harbor’s root areas. Compared to 2013–2015, there was a significant decrease in erosion between 2015 and 2017, indicating that the construction of the project had a significant impact on the northern tidal flats. Throughout the five-year study period, the tidal flats within the breakwater underwent continuous adjustment, shifting from being close to the shoreline to being concentrated on both sides of the breakwater. Significant siltation was observed on the inner side of the breakwater at Binhai Harbor between 2015 and 2017, with an increase of 0.86 km² in the area above −2 m. This study demonstrates that remote sensing technology is highly effective in monitoring changes in coastal topography, especially under the influence of human activities. Full article

The calibration and validation of input parameters in the Discrete Element Method (DEM) are crucial for accurately simulating physical processes, typically achieved through experimental particle behavior analysis. Enhancing the accuracy of DEM models allows for more reliable predictions of material behavior, which is essential for optimizing engineering applications that involve particulate materials. In this study, we present a methodology for analyzing the movement properties of particulate materials, employing a combination of Caliscope software to obtain the real-world co-ordinates based on pixel values from both cameras and artificial neural networks for regression as straightforward and efficient tools. This approach enables the validation and calibration of digital twins of particulate matter systems with respect to motion characteristics. The method of contrast points was utilized to acquire spatial co-ordinates of particulate material movement from experimental measurements, facilitating precise trajectory determination and the subsequent verification of simulation predictions. The neural network analysis demonstrated high accuracy, achieving R² values of 0.9988, 0.9972, and 0.9982 for the X–, Y–, and Z–axes, respectively. The standard deviation between the predicted and actual co-ordinates was found to be 1.8 mm. A comparative analysis of particle trajectories from both the model and experimental data indicated strong agreement, underscoring the soundness and reliability of this approach. Full article

Monitoring groundwater pollution is an important issue in terms of analyzing threats to protected, environmentally valuable areas. The topographical and environmental characteristics of a given area are often mentioned among the factors affecting the dynamics and chemistry of groundwater. In this study, the random forest regression (RFR) model was used to determine the spatial distribution of selected metals, such as aluminum, calcium, iron, potassium, magnesium, manganese, sodium, and zinc. In the role of indicators describing terrain variability, derivatives of the digital elevation model (DEM) were employed, with a spatial resolution of 5 m, describing the topography of the terrain on a local scale, such as, among others, slopes, the aspect and curvatures of slopes, the topographic position index, and the SAGA wetness index, as well as generalized values determined for each sampling point of the areas contributing their runoff. In addition, environmental parameters were taken into consideration: forest habitat types, the structure of soil cover, and the seasons when samples were collected. This study used samples collected from 15 wells located in forested areas of the Wielkopolska National Park on seven dates. The results obtained show that random forest can be used with very good results to model the spatial variability of the concentrations of aluminum, potassium, magnesium, manganese, and sodium in groundwater. However, in the case of calcium and zinc, no correlations were found between the adopted indicators describing the spatial variability of the area and their concentrations in groundwater. In addition, the degree of importance of each predictor was determined in order to rank their importance in modeling the concentration of each of the metals in groundwater. The summary ranking of predictors indicates that the strongest influence on the predicted concentration of metals in groundwater is exhibited by profile curvatures, planar curvatures, multiscale TPI, and then the habitat type of the forest. On the other hand, curvature classifications, soil composition, and seasonality exhibit the smallest generalized impact on the results of modeling. Full article

This study examined the effect of atmospheric, topographic, and Bidirectional Reflectance Distribution Function (BRDF) corrections of Sentinel-2 images implemented in Google Earth Engine (GEE) for use in land cover classification. The study was carried out in an area of complex orography in northern Spain and made use of the Spanish National Forest Inventory plots and other systematically located plots to cover non-forest classes. A total of 2991 photo-interpreted ground plots and 15 Sentinel-2 images, acquired in summer at a spatial resolution of 10–20 m per pixel, were used for this purpose. The overall goal was to determine the optimal level of image correction in GEE for subsequent use in time series analysis of images for accurate forest cover classification. Particular attention was given to the classification of cover by the major commercial forest species: Eucalyptus globulus, Eucalyptus nitens, Pinus pinaster, and Pinus radiata. The Second Simulation of the Satellite Signal in the Solar Spectrum (Py6S) algorithm, used for atmospheric correction, provided the best compromise between execution time and image size, in comparison with other algorithms such as Sentinel-2 Level 2A Processor (Sen2Cor) and Sensor Invariant Atmospheric Correction (SIAC). To correct the topographic effect, we tested the modified Sun-canopy-sensor topographic correction (SCS + C) algorithm with digital elevation models (DEMs) of three different spatial resolutions (90, 30, and 10 m per pixel). The combination of Py6S, the SCS + C algorithm and the high-spatial resolution DEM (10 m per pixel) yielded the greatest precision, which demonstrated the need to match the pixel size of the image and the spatial resolution of the DEM used for topographic correction. We used the Ross-Thick/Li-Sparse-Reciprocal BRDF to correct the variation in reflectivity captured by the sensor. The BRDF corrections did not significantly improve the accuracy of the land cover classification with the Sentinel-2 images acquired in summer; however, we retained this correction for subsequent time series analysis of the images, as we expected it to be of much greater importance in images with larger solar incidence angles. Our final proposed dataset, with image correction for atmospheric (Py6S), topographic (SCS + C), and BRDF (Ross-Thick/Li-Sparse-Reciprocal BRDF) effects and a DEM of spatial resolution 10 m per pixel, yielded better goodness-of-fit statistics than other datasets available in the GEE catalogue. The Sentinel-2 images currently available in GEE are therefore not the most accurate for constructing land cover classification maps in areas with complex orography, such as northern Spain. Full article

Agricultural pesticides may play a crucial role in the selection of resistance in field populations of mosquito vectors. This study aimed to determine the susceptibility level of An. gambiae s.l. to pyrethroids and neonicotinoids in vegetable farms in northern Benin, in West Africa, and the underlying insecticide resistance mechanisms. A survey on agricultural practices was carried out on 85 market gardeners chosen randomly in Malanville and Parakou. Anopheles gambiae s.l. larvae were collected, reared to adult stages, and identified to species level. Susceptibility was tested with impregnated papers (WHO bioassays) or CDC bottles according to the insecticides. Synergists (PBO, DEM, and DEF) were used to screen resistance mechanisms. Allelic frequencies of the kdr (L1014F), kdr (L1014S), N1575Y, and ace-1R G119S mutations were determined in mosquitoes using Taqman PCR. Fertilizers and pesticides were the agrochemicals most used with a rate of 97.78% and 100%, respectively, in Malanville and Parakou. Anopheles coluzzii was the predominant species in Malanville, while An. gambiae was the only species found in Parakou. Bioassays revealed a high resistance of An. gambiae s.l. to pyrethroids and DDT, while a susceptibility to bendiocarb, pyrimiphos-methyl, malathion, and clothianidin was recorded. Resistance to acetamiprid was suspected in mosquitoes from both localities. A lower resistance level was observed when mosquitoes were pre-treated with synergists, then exposed to insecticides. The kdr L1014F mutation was observed in both locations at moderate frequencies (0.50 in Malanville and 0.55 in Parakou). The allelic frequencies of N1575Y and G119S were low in both study sites. This study confirmed the resistance of An. gambiae s.l. to insecticides used in agriculture and public health. It reveals a susceptibility of vectors to bendiocarb, pyrimiphos-methyl, malathion, and clothianidin, thus indicating that these insecticides can be used as an alternative in Benin to control malaria vectors. Full article

Ground collapse is one of the common geological hazards in modern cities. With the development of urbanization, the risk of ground collapse increases, which has a great impact on urban public safety. Ground collapse accidents typically occur due to the presence of unstable cavities under the surface, or the generation and expansion of cavities induced by triggering factors. Investigating the stability of cavities in the strata is significant for identifying subsidence risks and mitigating the consequences of subsidence. This study proposed a method for predicting ground subsidence settlement based on the ARMA model. Firstly, CFD-DEM coupled simulation is employed to simulate the mechanism of cavity changes in the soil layers under the influence of triggering factors and to calculate the safety coefficient for ground subsidence stability. Subsequently, the safety coefficient data at different time points are fitted to predict the subsequent stability of the subsidence. We selected a subway permeable collapse accident in Foshan City, Guangdong Province for experimental verification, and compared the predicted results with the actual situation. The result shows that this method can effectively predict the changes in ground collapse safety factor and the collapse time point. With 40% of the data, high accuracy prediction can be achieved, improving the efficiency of collapse evolution prediction and providing strong support for ground collapse risk prevention and control. Full article

Background: Anaplastic lymphoma kinase (ALK) plays a role in the development of lymphoma, lung cancer and neuroblastoma. While tyrosine kinase inhibitors (TKIs) have improved treatment outcomes, relapse remains a challenge due to on-target mutations and off-target resistance mechanisms. ALK-positive (ALK+) tumors can evade the immune system, partly through tumor-associated macrophages (TAMs) that facilitate immune escape. Cancer cells use “don’t eat me” signals (DEMs), such as CD47, to resist TAMs-mediated phagocytosis. TKIs may upregulate pro-phagocytic stimuli (i.e., calreticulin, CALR), suggesting a potential therapeutic benefit in combining TKIs with an anti-CD47 monoclonal antibody (mAb). However, the impact of this combination on both TKIs-sensitive and resistant ALK+ tumors requires further investigation. Methods: A panel of TKIs-sensitive and resistant ALK+ cancer subtypes was assessed for CALR and CD47 expression over time using flow cytometry. Flow cytometry co-culture and fluorescent microscopy assays were employed to evaluate phagocytosis under various treatment conditions. Results: ALK inhibitors increased CALR expression in both TKIs-sensitive and off-target resistant ALK+ cancer cells. Prolonged TKIs exposure also led to CD47 upregulation. The combination of ALK inhibitors and anti-CD47 mAb significantly enhanced phagocytosis compared to anti-CD47 alone, as confirmed by flow cytometry and fluorescent microscopy. Conclusions: Anti-CD47 mAb can quench DEMs while exposing pro-phagocytic signals, promoting tumor cell phagocytosis. ALK inhibitors induced immunogenic cell damage by upregulating CALR in both sensitive and off-target resistant tumors. Continuous TKIs exposure in off-target resistant settings also resulted in the upregulation of CD47 over time. Combining TKIs with a CD47 blockade may offer therapeutic benefits in ALK+ cancers, especially in overcoming off-target resistance where TKIs alone are less effective. Full article

To enhance the accuracy and reliability of the discrete element simulation software EDEM 2023 for pavement asphalt mixture simulation, three representative coarse aggregate particles were modeled in 3D using the SolidWorks 2018 software and imported into the EDEM 2023 software for particle filling. The Hertz–Mindlin with bonding contact model was used to construct the EDEM simulation model of asphalt mixtures, and the quadratic regression model of asphalt mixtures’ splitting tensile strength and four bonding parameters, namely, normal stiffness per unit area, shear stiffness per unit area, critical normal stress, and critical shear stress, was found by the response surface methodology. The results show that the relationship between the significance magnitude of the four bonding parameters on the splitting tensile strength of the asphalt mixture simulation model is as follows: critical normal stress > shear stiffness per unit area > normal stiffness per unit area > critical shear stress. The calibration results of the bonding parameters were used for simulation verification, and the relative error between the simulation and actual splitting tensile strength was only −2.48%. The feasibility of this bonding parameter calibration method is demonstrated, and it can lay a foundation for EDEM to simulate the performance of asphalt mixtures on pavements with high-precision simulation. Full article

Slow-moving landslides are often precursors of catastrophic failure, posing a major threat to human life and property safety. Interferometric synthetic aperture radar (InSAR) has become a crucial tool for investigating slow-moving landslides hazard because of its high-precision detection capability for slow surface deformation. However, landslides usually occur in alpine canyon areas and vegetation coverage areas where InSAR measurements are still limited by temporal and spatial decorrelation and atmospheric influences. In addition, there are several difficulties in monitoring the multiscale characterization of landslides from the InSAR results. To address this issue, this paper proposes a novel method for slow-moving landslide hazard assessment in low-coherence regions. A window-based atmosphere correction method is designed to highlight the surface deformation signals of InSAR results in low-coherence regions and reduce false alarms in landslide hazard assessment. Then, the deformation annual velocity rate map, coherence map and DEM are used to construct the InSAR sample set. A landslide hazard assessment model named Landslide-SE-Unilab is subsequently proposed. The global–local relationship aggregation structure is designed to capture the spatial relationship between local pixel-level deformation features and global landslides, which can reduce the number of missed assessments and false assessments of small-scale landslides. Additionally, a squeeze-and-excitation network is embedded to adjust the weight relationship between the features of each channel in order to enhance the performance of network evaluation. The method was evaluated in Kangding city and the Jinsha River Valley in the Hengduan Mountains, where a total of 778 potential landslides with slow deformation were identified. The effectiveness and accuracy of this approach for low-coherence landslide hazard assessment are demonstrated through comparisons with optical images and previous research findings, as well as evaluations via time-series deformation results. Full article

A batch mixer is an important piece of equipment for polymer filling modification, and the kinematics of agglomerate breakup and distribution are necessary for the structure design and mixing process optimization of the rotor, particularly in light of the cohesive forces that exist within the agglomerate. In this paper, computational fluid dynamics (CFD) was coupled with discrete element method (DEM) to simulate the mixing process, including breakup and distribution, which was further quantitatively evaluated by the post-processing involving numerical method. To study the mixing process of an agglomerate composed of massive spherical particles (individual particle ratio was r), the coordinates of the particles were exported from the CFD-DEM simulation results. Then, the coordinate data were automatically processed with an automate custom-built post-processing program to obtain the average radius of gyration (R_gy) and the particle distribution density (ε). The kinematics analyzation of breakup and distribution was represented by curve of R_gy/r versus mixing time (t) and curve of ε versus t, respectively. The value of R_gy/r and ε decreased over time until they reached an equilibrium and vibrated around a certain value. In particular, a notable decline in the value of R_gy/r was observed following an increase prior to critical time. The increase in R_gy/r stated that the agglomerate or aggregates undergo stretching deformation. Additionally, mixing processes of rotors with different pressurization coefficients (S) and rotation speeds could be facilitated and intensified by large S and high rotation speed. Finally, a “breakup-line” was developed by considering the influence of cohesive force and rotation speed on the agglomerate breakup process. The agglomerate could be broken if the combination of rotation speed and bonding strength was above the “breakup line”, otherwise the agglomerate was not broken. Furthermore, rotors with larger slopes exhibited stronger breakup ability. Full article

Background: Volatile compounds have a deep influence on the quality and application of the medicinal herb Artemisia argyi; however, little is known about the effect of UV-B radiation on volatile metabolites. Methods: We herein investigated the effects of UV-B exposure on the volatile compounds and transcriptome of A. argyi to assess the potential for improving its quality and medicinal characteristics. Results: Out of 733 volatiles obtained, a total of 133 differentially expressed metabolites (DEMs) were identified by metabolome analysis. These were classified into 16 categories, primarily consisting of terpenoids, esters, heterocyclic compounds, alcohols, and ketones. Sensory odor analysis indicated that green was the odor with the highest number of annotations. Among the 544 differentially expressed genes (DEGs) identified by transcriptome analysis, most DEGs were linked to “metabolic pathways” and “biosynthesis of secondary metabolites”. Integrated analysis revealed that volatiles were mainly synthesized through the shikimate pathway and the MEP pathway. RNA-seq and qPCR results indicated that transcription factors HY5, bHLH25, bHLH18, bHLH148, MYB114, MYB12, and MYB111 were upregulated significantly after UV-B radiation, and were therefore considered key regulatory factors for volatiles synthesis under UV-B radiation. Conclusions: These findings not only provide new insights into UV-induced changes in volatile compounds, but also provide an exciting opportunity to enhance medicinal herbs’ value, facilitating the development of products with higher levels of essential oils, flavor, and bioactivity. Full article

In addition to improving the efficacy of the furrow opener by ensuring consistent seeding depth, the gryllulus jaw geometry curve was integrated into the furrow opener. Soil particles were modeled using the DEM combined with the Hertz–Mindlin with JKR model, and simulation tests were conducted using the DEM corn stover model. Three geometric curves of gryllulus jaws were extracted. The effect of each curve and magnification on the manipulation results was clarified by the simulation test. Subsequently, field trials were conducted to evaluate the stability of the seeding depth of the bionic structure. The experiment showed that the No. 1 structure with a magnification of 1000 was the best, and the stability was 42.10% higher than that of the original structure. The results of this research can provide key structural and simulation parameters for the development of planter furrow openers with both efficient straw crushing and stable sowing depth functions, which is of great significance for the improvement of agricultural machinery. Full article

Remote sensing provides an efficient roadmap in geological analysis and interpretation. However, some challenges arise when remote sensing techniques are integrated with machine learning in geological surveys. Factors including irregular spatial distribution, sample imbalance, interclass resemblances, regolith, and geochemical similarities impede geological feature diagnosis, interpretation, and identification across varied remote sensing datasets. To address these limitations, a hybrid-attention-integrated long short-term memory (LSTM) network is employed to diagnose, interpret, and identify lithological feature representations in a remote sensing-based geological analysis using multisource data fusion. The experimental design integrates varied datasets including Sentinel-2A, Landsat-9, ASTER, ALOS PALSAR DEM, and Bouguer anomaly gravity data. The proposed model incorporates a hybrid attention mechanism (HAM) comprising channel and spatial attention submodules. HAM utilizes an adaptive technique that merges global-average-pooled features with max-pooled features, enhancing the model’s accuracy in identifying lithological units. Additionally, a channel separation operation is employed to allot refined channel features into clusters based on channel attention maps along the channel dimension. The comprehensive analysis of results from comparative extensive experiments demonstrates HAM-LSTM’s state-of-the-art performance, outperforming existing attention modules and attention-based models (ViT, SE-LSTM, and CBAM-LSTM). Comparing HAM-LSTM to baseline LSTM, the HAM module’s integrated configurations equip the proposed model to better diagnose and identify lithological units, thereby increasing the accuracy by 3.69%. Full article