Search Results (1,688)

Nitrogen is a critical nutrient that influences plant growth and resistance to pathogens; however, its impact on disease dynamics, particularly downy mildew infection, and the associated physiological responses in cucumber during early growth stages remains poorly understood. To evaluate the combined effects of downy mildew (caused by Pseudoperonospora cubensis) infection and nitrogen application on cucumber growth and physiological traits during the seedling and vine development stages, two downy mildew treatments— infected (B0) and non-infected(B1)—and three nitrogen levels—T1 (N-50%), T2 (N-100%), and T3 (N-150%)—were applied. Significant differences were observed between all treatments (p < 0.05). Among them, the B1T3 treatment had the most pronounced stimulatory effect, particularly on growth parameters (such as plant height, stem diameter, and leaf area). Without any disease infection (B1), the B1T2 treatment showed an increasing trend in photosynthetic rate and a more notable rise in stomatal conductance. In contrast, with downy mildew infection (B0), photosynthetic rates declined under B0T1 and B0T2. Moreover, with downy mildew infection (B0), the intracellular CO₂ concentration, stomatal conductance, and transpiration rate of cucumber leaves decreased in the B0T1, B0T2, and B0T3 treatments. Plant height, stem diameter, and leaf area responded variably to nitrogen levels and downy mildew infection. The total root length, root surface area, average root diameter, total root volume, and total root tips of cucumber plants were significantly different under different experimental conditions (p < 0.05). Consequently, this study provides a theoretical basis for stress-resistant cucumber cultivation in greenhouses and has practical implications for advancing the sustainable development of the greenhouse cucumber industry. Full article

The inhibition mechanism and binding properties of four phenolic acids (ferulic acid (FA), p-coumaric acid (CA), gallic acid (GA), and protocatechuic acid (PA)) on xanthine oxidase (XOD) were investigated. All four phenolic acids acted via a mixed inhibition pattern, mainly influencing the hydrophobic regions and secondary conformation of XOD through hydrophobic bonding and hydrophobic association. Molecular dynamics simulations exhibited that the complexes of XOD with FA and CA revealed smaller radii of gyration (Rg) and solvent-accessible surface areas (SASA), along with lower variability in root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF), collectively indicating greater structural stability. FA, CA, and PA significantly reduced uric acid (UA) concentration in the 25–100 μM range. Although GA only reduced UA levels in cell models at 25 μM, this effect was likely due to its larger polar surface area, which limits cellular uptake. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation suggested that these phenolic acids have potential for development. Full article

Drought stress is one of the primary abiotic factors negatively affecting garlic growth, development, and yield formation. The application of plant growth-promoting bacteria (PGPB) could enhance plant tolerance to drought stress. The aim of this study was to explore the regulatory effect of the PGPB Pseudomonas sp. UW4 on growth and physiological indexes of garlic under drought stress. The results revealed that drought stresses significantly reduced total root length, total root surface area, root projection area and total root volume, chlorophyll content, antioxidant enzyme activity and osmolyte content (proline and soluble proteins), and increased relative electrical conductivity and malondialdehyde (MDA) content, all of which could be significantly improved by inoculating the roots with strain UW4. Under drought stress, an increase in total surface area of roots of 87.06% and an increase in root projected area of 40.71% were observed upon inoculation with strain UW4. The a, b, and total content of chlorophyll were increased significantly by 83.63%, 217.33% and 100.02%, respectively. The osmolyte content in leaves significantly increased, and decreased significantly in roots. The content of antioxidants also significantly increased. Moreover, the relative electrical conductivity in leaves and roots was decreased by 23.18% and 41.20%, respectively, upon strain UW4 inoculation. The content of malondialdehyde (MDA) was decreased by 25.23% and 54.08%, respectively, in the presence of strain UW4. The result of principal component analysis (PCA) revealed that the key factors influencing drought tolerance in garlic inoculated with Pseudomonas sp. UW4 could be summarized into two categories: photosynthetic pigments and root growth-related factors, and leaf osmotic adjustment and root antioxidant enzyme-related factors. Based on the result of the Mantel test, it can be inferred that there was a connection between the osmoregulation and antioxidant enzyme systems in the roots and leaves. Based on the D values, the comprehensive evaluation result of drought resistance was that the drought resistance of the garlic inoculated with strain UW4 under drought stress was lower than that of the garlic inoculated with UW4 under normal treatment and higher than that of the garlic under normal treatment. Therefore, Pseudomonas sp. UW4 enhanced the drought resistance of garlic seedlings by improving root phenotype and antioxidant enzyme activity, and increasing the content of shoot chlorophyll. Full article

The wide use of antibiotics in multiple fields leads to their entry into the environment, challenging agriculture and ecology and potentially affecting maize seedling growth. In this study, maize variety Longken 10 was chosen as the experimental material. Subsequently, two antibiotics commonly utilized in production, namely oxytetracycline (OTC) belonging to the tetracycline class and ciprofloxacin (CIP) from the quinolone class, were selected. To comprehensively examine the impacts of these antibiotics on the phenotype, photosynthetic enzymes, nitrogen metabolism, and endogenous hormone contents of maize seedlings, a series of different concentration gradients (0, 3, 5, 30, 60, and 120 mg·L⁻¹) were established, and the nutrient solution hydroponic method was employed. The results showed that, compared with the control group (CK), the activities of all indicators of maize seedlings were the strongest and the seedling growth was the most vigorous when the concentration of CIP was 5 mg·L⁻¹ and that of OTC was 3 mg·L⁻¹. The inhibitory effect of OTC on various indicators of maize seedlings was stronger than that of CIP. The underground parts of maize seedlings were more sensitive to OTC and CIP than the aboveground parts. Overall, maize seedlings exhibited a trend where high concentrations (30–120 mg·L⁻¹) of antibiotics inhibited growth, while low concentrations (3–5 mg·L⁻¹) promoted growth. The treatment groups with 3–5 mg·L⁻¹ of OTC and CIP increased maize seedling growth phenotypes, the robust growth of seedlings with enhanced vitality, and the relative water content of maize leaves; decreased the relative electrical conductivity of maize leaves, indicating reduced cell permeability; increased the activities of leaf photosynthetic enzymes (PEPCase, RUBPCase, PPDK, NADP-ME, and NADP-MDH); increased the levels of hormones (IAA, GA, and ZR) in maize leaves and roots; decreased the levels of ABA and MeJA; increased the levels of nitrogen metabolism-related enzymes (GS, GOGAT, and GAD) in roots and leaves; decreased the GDH level; enhanced root activity and increased various root parameters (including average diameter, number of root tips, total volume, total root length, and root surface area), indicating vigorous root growth. Compared with CK, the treatment groups with 30–120 mg·L⁻¹ of OTC and CIP reduced the phenotypes of maize seedlings, decreased the relative water content of maize leaves and increased the relative electrical conductivity of maize leaves, indicating enhanced cell permeability; reduced the activity of leaf photosynthetic enzymes, leading to weakened photosynthesis and decreased photosynthetic productivity; lowered the levels of IAA, GA, and ZR in leaves and roots of maize seedlings, and increased the levels of ABA and MeJA; decreased the levels of GS, GOGAT, and GAD in leaves and roots of maize seedlings, and increased the GDH level; reduced root activity, with the corresponding decrease in various root parameters. Full article

The fracture behavior of a locking pin used in the penultimate-stage blades of a 600 MW steam turbine in a thermal power plant was investigated through microstructural and microhardness characterization, fracture surface and energy-dispersive spectroscopy (EDS) analysis, as well as finite element load simulation. The microhardness values measured on the cross-section of the service pins ranged from 528 to 541 HV0.1, showing little difference from the unused pins. Scanning electron microscopy analysis revealed that approximately 70% of the fracture surfaces exhibited an intergranular “rock candy” morphology. The results indicate that pin failure was primarily caused by the combined effects of fretting wear and stress corrosion cracking (SCC). Specifically, vibration at the blade root, impeller, and pins due to start–stop cycles and load variations led to fretting wear, forming pits approximately 75 μm in size. Under the combined effects of weakly corrosive wet steam environments and shear stresses, SCC initiated at the high stress concentration points of these pits. Early crack propagation primarily followed original austenite grain boundaries, while later stages mainly extended along martensite plate boundaries. As cracks advanced, the cross-sectional area gradually decreased, causing the effective shear stress to increase until it exceeded the shear strength, ultimately leading to fracture. These findings not only provide a scientific basis for enhancing the reliability of steam turbine locking pins and extending their service life, but also contribute to a broader understanding of the failure mechanisms of key components operating under corrosive and fluctuating load environments. Full article

Natural geogenic effects lead to alterations in soil heavy metal concentrations. This study assesses the presence of elevated trace-element concentrations in the Oued Irriri watershed in southeastern Morocco. ASTER satellite imagery, geochemical, and aeromagnetic data are combined to determine the origin of these anomalies. Processing of ASTER images delineated alteration zones coinciding with areas of high heavy metal anomalies by detecting hydrothermal alteration minerals, including muscovite, montmorillonite, illite, hematite, jarosite, chlorite, and epidote. Principal Component Analysis (PCA) of geochemical data distribution in soils enabled the characterization of variations in trace-element concentrations, the extraction of geochemical anomalies, and the identification of potential sources of contamination. Comparing satellite image processing results with geochemical analyses facilitated the production of a geogenic enrichment map. The study results indicate high enrichment levels of zinc, Molybdenum, and bismuth in the western basin, of purely lithological origin. Hydrothermal alteration surfaces intersect geochemical anomaly zones in the north and northeast, primarily showing the impact of fault rooting on the surface deposition of Cu, Ba, Hg, and Pb-rich deposits. This study developed a geogenic enrichment map indicating naturally affected areas, identifying potential risks to eco-environmental systems, and better preventing the effects of geogenic enrichment. Full article

Understanding belowground carbon dynamics is essential for predicting the carbon balance of forest ecosystems. This study aimed to investigate links between soil CO₂ efflux (R_S), soil physicochemical properties, and fine-root morphology across four middle-aged plantations of different species (Robinia pseudoacacia, Quercus mongolica, Pinus koraiensis, and Metasequoia glyptostroboides) in Mt. Ansan, Seoul, Republic of Korea. Seasonal measurements of R_S, soil temperature (T_S), and soil water content (SWC) were conducted, and soils and fine roots (≤2.0 mm) were analyzed for physicochemical properties and morphological traits, with a focus on very-fine roots (≤0.5 mm). The results showed that R_S was positively correlated with T_S (r = 0.77) and negatively with SWC (r = −0.33). R_S normalized at 25 °C (R₂₅), differed significantly among plantations, and exhibited strong positive correlations with electrical conductivity (r = 0.81), as well as with total nitrogen and carbon concentrations and clay content. Among fine root traits, the length, surface area, and volume of very-fine roots exhibited the strongest associations with R₂₅, underscoring their pivotal role in regulating belowground respiration. These findings suggest that species-specific fine root strategies and soil conditions jointly control R_S dynamics, particularly under warmer conditions, and highlight very-fine root traits as key indicators of soil carbon flux in forest ecosystems. Full article

Rootstock choice offers a powerful lever for tailoring economically important trees to adverse environments. Camellia oleifera Abel., a premier oil-producing species cultivated widely on red-soil hills, suffers large yield losses under chronic phosphorus deficiency. We grafted a single elite scion (CL4) onto three contrasting rootstocks (CL4, CL3, CL53) and monitored growth and root transcriptomes for 1.5 years under adequate (1 mM) or limiting (0 mM) P supply. Under low-P stress, the rootstock identity reshaped the root architecture: CL4/CL3 produced the longest, most extensive network, increasing the total root length by 49.7%, the surface area by 52.9%, and the volume by 42.6% relative to the control, whereas leaf morphology responded solely to P supply, not to the graft combination. CL4/CL3 also accumulated up to more than 17.5% of root biomass and 28.25% of whole-plant biomass than any other combination. Physiologically, CL4/CL3 acted as an aggressive P miner, accumulating 67.8% more P in its roots than the self-grafted control under P limitation, while CL4/CL4 maximized the internal P use efficiency, showing a 44.74% higher root P use efficiency than CL4/CL53—two contrasting yet effective strategies for coping with low-P stress. Transcriptome profiling uncovered 1733 DEGs in the CL4/CL3 and 2585 in the CL4/CL4 roots, with 150 and 255 uniquely co-expressed genes, respectively. CL4/CL3 up-regulated organic-acid and phenylpropanoid pathways; CL4/CL4 activated defense and phosphate transport networks. qRT-PCR of six genes confirmed that CL4/CL3 mounted a stronger low-P response via MAPK, hormonal, and lipid–metabolic signaling. These results provide a mechanistic framework for rootstock-mediated P efficiency and establish a foundation for the molecular breeding of C. oleifera under nutrient-limited conditions. Full article

The economic and strategic value of high-latitude maritime regions is increasingly significant, yet traditional Global Navigation Satellite Systems remain constrained by unfavorable geometric configurations and slow convergence speeds at high latitudes, failing to meet the growing demand for real-time centimeter-level high-precision positioning in these areas. Benefiting from their rapid motion and superior coverage over high-latitude zones, Low Earth Orbit (LEO) satellites offer an effective means to enhance positioning performance in such regions. This paper uses the real BDS data collected by an unmanned surface vessel in the high-latitude waters of the Southern Hemisphere, jointly simulates polar and medium-inclination LEO constellations, and systematically assess the enhancement effects of LEO augmentation on Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) techniques. The results demonstrate that the polar-orbiting constellation markedly improves the observation environment, increasing the number of visible satellites by 70.2% and reducing the Position Dilution of Precision from 2.4 to 1.7, whereas the medium-inclination orbit constellation offered negligible improvement due to insufficient visibility. The rapid geometric change brought by LEO constellations is the core key to achieving fast convergence. Incorporating LEO observations drastically shortened the BDS PPP convergence time from 45.3 min to under 1 min, achieving a reduction of over 97%. Simultaneously, it improved the three-dimensional Root Mean Square accuracy by 54.7%, from 0.086 m to 0.039 m. Convergence within one minute was consistently achieved when at least 5.4 LEO satellites were included in the solution. Moreover, the addition of LEO signals increased the fixed solution rate of short-baseline RTK from 96.5% to 100%, while improving horizontal and vertical accuracy by 31.5% and 12.3%, respectively. This study confirms that LEO constellations, especially those in polar orbits, can substantially enhance BDS precise positioning performance in high-latitude maritime environments, thereby providing critical technical support for related navigation applications. Full article

Geophysical and remote sensing observations offer powerful means to monitor large-scale hydrological changes, particularly in regions where in situ data are scarce. In this study, we integrate satellite-derived water storage from the Gravity Recovery and Climate Experiment (GRACE) with land surface variables from the Global Land Data Assimilation System (GLDAS) to assess and forecast groundwater storage (GWS) dynamics across eight major regions in Sudan. Missing GRACE observations of terrestrial water storage (TWS) were first reconstructed using a Random Forest machine learning model, after which GWS anomalies were estimated by subtracting GLDAS-based surface and root-zone components from TWS. The resulting GWS time series was decomposed into trend, seasonal, and residual components, and the trend signals were used to train a bootstrapped Bidirectional Long Short-Term Memory (BiLSTM) model. This framework generated probabilistic forecasts accompanied by confidence intervals, which were generally narrow and consistent with the historical range. The forecasted GWS anomalies indicate positive recovery across all regions, with Sen’s slope values ranging from 0.014 to 0.051 per month. The strongest recoveries are evident in the southern and southwestern regions, while northern and eastern areas display more modest gains. This work represents one of the first applications of deep learning with uncertainty quantification for GRACE-based groundwater analysis in Sudan, demonstrating the potential of such an integrated approach to support informed and sustainable groundwater management in data-limited environments. Full article

Mining-induced surface subsidence is a typical geological hazard. Loose layer drainage disturbed by coal mining can exacerbate surface subsidence in terms of both the extent and amount, thereby increasing the risk of building deformation and environmental degradation in mining areas. However, currently the prediction results of surface subsidence considering these two factors are not precise enough, which contradicts the principles of green coal mining. Firstly, this paper introduces the probability integral method, which predicts mining-induced surface subsidence. Subsequently, based on the soil–water coupled theory and the derived characteristic curve of groundwater level decline, a surface subsidence prediction model that considers loose layer drainage is constructed using triple integral transformation. Finally, a more precise surface subsidence prediction model considering both factors is proposed based on the principle of superposition. The model is applied to the mining of working panel 1309 in Shanxi province, China, an area rich in coal yet scarce in water resources. When compared with the measured subsidence data, the proposed model achieves a root mean square error (RMSE) of 27 mm, while the RMSEs of existing models are 78 mm and 123 mm, respectively. The prediction accuracy has been significantly improved. In addition, the proposed model is further validated through fluid–solid coupling numerical calculations in FLAC3D. The subsidence results considering the single effect of each factor also demonstrated good validation accuracy. Overall, the proposed model can accurately describe the surface subsidence considering both factors. This research can provide a theoretical guide for assessing the environmental impact and building damage, while contributing to the sustainable development of land use and groundwater resource in mining areas. Full article

Sea surface wind has been listed as an essential climate variable, playing crucial roles in regulating the global and regional weather and climate. Spaceborne synthetic aperture radar (SAR) has demonstrated the advantages in observing the wind field given its all-weather measurement capability. In this study, we present a convolutional neural network (CNN)-based framework for retrieving 10 m wind speed ($U_{10}$) from Sentinel-1 SAR wave mode (WV) imagery. The model is trained on SAR data acquired in 2017 using collocated ERA5 reanalysis wind vectors as the reference, with final performance evaluated against a temporally independent dataset from 2016 and in situ wind measurements. The CNN approach demonstrates improved retrieval accuracy compared to the conventional CMOD5.N-based result, achieving lower root mean square error (RMSE) and bias across both WV1 and WV2 incidence angle modes. Residual diagnostics show a systematic overestimation at low wind speeds and a slight underestimation at higher wind speeds. Spatial analyses of retrieval bias reveal regional variations, particularly in areas characterized by ocean swell or convective atmospheric activity, highlighting the importance of geophysical features in retrieval accuracy. These results support the viability of deep learning approaches for SAR-based ocean surface wind estimation and suggest a path forward for the development of more accurate, data-driven wind products suitable for both scientific research and operational marine forecasting. Full article

Accurate stockpile volume estimation is essential for industries that manage bulk materials across various stages of production. Conventional ground-based methods such as walking wheels, total stations, Global Navigation Satellite Systems (GNSSs), and Terrestrial Laser Scanners (TLSs) have been widely used, but often involve significant safety risks, particularly when accessing hard-to-reach or hazardous areas. Unmanned Aerial Systems (UASs) provide a safer and more efficient alternative for surveying irregularly shaped stockpiles. This study evaluates UAS-based methods for estimating the volume of coal stockpiles at a storage facility near Cadiz, Ohio. Two sensor platforms were deployed: a Freefly Alta X quadcopter equipped with a Real-Time Kinematic (RTK) Light Detection and Ranging (LiDAR, active sensor) and a WingtraOne UAS with Post-Processed Kinematic (PPK) multispectral imaging (optical, passive sensor). Three approaches were compared: (1) LiDAR; (2) Structure-from-Motion (SfM) photogrammetry with a Digital Surface Model (DSM) and Digital Terrain Model (DTM) (SfM–DTM); and (3) an SfM-derived DSM combined with a kriging-interpolated DTM (SfM–intDTM). An automated boundary detection workflow was developed, integrating slope thresholding, Near-Infrared (NIR) spectral filtering, and Canny edge detection. Volume estimates from SfM–DTM and SfM–intDTM closely matched LiDAR-based reference estimates, with Root Mean Square Error (RMSE) values of 147.51 m³ and 146.18 m³, respectively. The SfM–intDTM approach achieved a Mean Absolute Percentage Error (MAPE) of ~2%, indicating strong agreement with LiDAR and improved accuracy compared to prior studies. A sensitivity analysis further highlighted the role of spatial resolution in volume estimation. While RMSE values remained consistent (141–162 m³) and the MAPE below 2.5% for resolutions between 0.06 m and 5 m, accuracy declined at coarser resolutions, with the MAPE rising to 11.76% at 10 m. This emphasizes the need to balance the resolution with the study objectives, geographic extent, and computational costs when selecting elevation data for volume estimation. Overall, UAS-based SfM photogrammetry combined with interpolated DTMs and automated boundary extraction offers a scalable, cost-effective, and accurate approach for stockpile volume estimation. The methodology is well-suited for both the high-precision monitoring of individual stockpiles and broader regional-scale assessments and can be readily adapted to other domains such as quarrying, agricultural storage, and forestry operations. Full article

Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology, with its advantages in large-scale and high-precision deformation monitoring, has become an essential tool for monitoring surface subsidence in coal mining areas. To address the issue of missing deformation values resulting from interferometric decoherence when using InSAR technology for surface subsidence monitoring in mining areas, this study proposes a combined approach integrating SBAS-InSAR with KTree Adaptive Inverse Distance Weighting (KTree-AIDW). The method constructs a dynamic neighborhood search mechanism through the KTree algorithm, considering the spatial heterogeneity between the interpolation points and adjacent sample points, and optimizes the weight distribution of heterogeneous sample points. The study is based on Sentinel-1 data with a 12-day revisit cycle, focusing on the 2021 grouting working face of the Liangbei Mine in Yuzhou, Henan Province, China. The results show the following: (1) Along both the strike and dip lines, the correlation coefficient between the SBAS-InSAR + KTree-AIDW results and leveling result is 0.95, with an overall root mean square error (RMSE) of 22.08 mm and a relative root mean square error (RRMSE) of 9.48%. The Mean Absolute Error (MAE) of characteristic points in the decoherence region is 19.05 mm, indicating a significantly improved accuracy in the decoherence region compared to traditional methods. (2) The cumulative maximum subsidence in the study area reached 233 mm, with an average maximum subsidence rate of 171 mm/yr. The maximum positive/negative inclines were 2.4 mm/m and −2.9 mm/m; the maximum positive/negative curvatures were ±0.18 mm/m². The surface structures are within the threshold values specified for Class I damage. The proposed method effectively addresses the decoherence issue that leads to missing deformation data in mining areas, providing a novel technical approach to accurate surface subsidence monitoring under grouting and backfilling conditions. Full article

Heat-related morbidity and mortality are increasing due to climate change, emphasizing the need to identify vulnerable areas and people exposed to extreme temperatures. To improve heat stress impact assessment, we developed a replicable machine learning model that integrates remote sensing, ground station, and geospatial data to estimate daily air temperature at a spatial resolution of 100 m × 100 m across the region of Tuscany, Italy. Using a two-stage approach, we first imputed missing land surface temperature data from MODIS using gradient-boosted trees and spatio-temporal predictors. Then, we modeled daily maximum and minimum air temperatures by incorporating monitoring station observations, satellite-derived data (MODIS, Landsat 8), topography, land cover, meteorological variables (ERA5-land), and vegetation indices (NDVI). The model achieved high predictive accuracy, with R² values of 0.95 for Tmax and 0.92 for Tmin, and root mean square errors (RMSE) of 1.95 °C and 1.96 °C, respectively. It effectively captured both temporal (R²: 0.95; 0.94) and spatial (R²: 0.92; 0.72) temperature variations, allowing for the creation of high-resolution maps. These results highlight the potential of integrating Earth Observation and machine learning to generate high-resolution temperature maps, offering valuable insights for urban planning, climate adaptation, and epidemiological studies on heat-related health effects. Full article