Search Results (3,840)

The Jiaodong gold cluster region (JGCR) at the southeastern edge of the North China Craton (NCC), holding approximately 5000 t gold reserve, is the third largest gold cluster region in the world. The Bidimensional Empirical Mode Decomposition (BEMD) is applied in extracting gravity anomaly components showing deep geological architectures and geodynamics. The research results illustrate that (a) at a depth of about 27 km, there are three tectonic units, namely the mantle uplift (I) with gravity values ranging from 2 to 14 μm/s², a mantle depression (II) with gravity values varying from 0 to −13 μm/s², and a mantle flat (III) with gravity values ranging from −2 to 2 μm/s². All giant gold deposits are distributed within the mantle depression. This implies that mantle uplift can trigger the concentration of hot, ore-forming fluids in mantle depressions, leading to the accumulation of large amounts of gold and the formation of giant deposits. (b) At about 17.1–12.5 km, there are three tectonic units: the Jiaolai–Jiaobei mantle uplift (I), showing a strong positive gravity anomaly with gravity values ranging from 1.5 to 10 μm/s², the Sulu ultra-high pressure metamorphic block (II), displaying a negative gravity anomaly with gravity values ranging from −10 to −1.5 μm/s², and the Jiaoxibei gold cluster region (III), exhibiting gravity background with gravity values varying from −1.5 to 1.5 μm/s². (c) At about 8.9–5.3 km, there are a series of positive and negative gravity anomalies. Most granites with low density display negative gravity anomalies, among which there are some negative anomalies with positive anomalous edges which contain gold deposits. This illustrates an ore-forming pattern, a granite with negative gravity anomaly, around which there is alteration mineralization with positive gravity anomaly. Combined with other studies, it was concluded that the geological architectures at different depths as and the giant Jiaodong gold cluster region were formed by the asthenosphere upwelling triggered by NNW-ward subduction of the Izanagi Plate over a time period of approximately 200–100 Ma. Full article

The paper studies and analyzes electric vehicle engines powered by hydrogen under the WLTP standard driving protocol. The driving range extension is estimated using a specific protocol developed for FCEV compared with the standard value for battery electric vehicles. The driving range is extended by 10 km, averaging over the four protocols, with a maximum of 11.6 km for the FTP-75 and a minimum of 7.7 km for the WLTP. This driving range extension represents a 1.8% driving range improvement, on average. Applying the FCEV current weight, the driving range is extended to 18.9 km and 20.4 km, on average, when using power source energy capacity standards for BEVs and FCEVs. Full article

The lower Mahanadi basin in eastern India is experiencing significant land and soil transformations that directly influence agricultural sustainability and ecosystem resilience. In this study, we used geospatial techniques to analyze the spatial-temporal variability of soil quality and land cover between 2011 and 2020 in the lower Mahanadi basin. The results revealed that the cropland decreased from 39,493.2 to 37,495.9 km², while forest cover increased from 12,401.2 to 13,822.2 km², enhancing soil organic carbon (>290 g/kg) and improving fertility. Grassland recovered from 4826.3 to 5432.1 km², wastelands declined from 133.3 to 93.2 km², and water bodies expanded from 184.3 to 191.4 km², reflecting positive land–soil interactions. Soil quality was evaluated using the Simple Additive Soil Quality Index (SQI), with core indicators bulk density, organic carbon, and nitrogen, selected to represent physical, chemical, and biological components of soil. These indicators were chosen as they represent the essential physical, chemical, and biological components influencing soil functionality and fertility. The SQI revealed spatial variability in texture, organic carbon, nitrogen, and bulk density at different depths. SQI values indicated high soil quality (SQI > 0.65) in northern and northwestern zones, supported by neutral to slightly alkaline pH (6.2–7.4), nitrogen exceeding 5.29 g/kg, and higher organic carbon stocks (>48.8 t/ha). In contrast, central and southwestern regions recorded low SQI (0.15–0.35) due to compaction (bulk density up to 1.79 g/cm³) and fertility loss. Clay-rich soils (>490 g/kg) enhanced nutrient retention, whereas sandy soils (>320 g/kg) in the south increased leaching risks. Integration of LULC with soil quality confirms forest expansion as a driver of resilience, while agricultural intensification contributed to localized degradation. These findings emphasize the need for depth-specific soil management and integrated land-use planning to ensure food security and ecological sustainability. Full article

Compost amendments are widely recognized as an effective strategy for improving soil quality, modulating enzyme activities, and enhancing nitrogen cycling. Urease, a key enzyme in nitrogen transformation, is characterized by kinetic parameters such as the maximum reaction rate (V_max) and Michaelis constant (K_m), as well as thermodynamic attributes including temperature sensitivity (Q₁₀), activation energy (E_a), enthalpy change (ΔH), Gibbs free energy change (ΔG), and entropy change (ΔS). However, how different compost sources regulate urease kinetics, thermodynamics, and nitrogen availability remains poorly understood. In this study, we evaluated the effects of three compost amendments—mushroom residue (MR), mushroom residue–straw mixture (MSM), and leaf litter (LL)—on urease kinetics and thermodynamics in a temperate agroecosystem. The MSM treatment significantly enhanced urea hydrolysis capacity and catalytic efficiency. In contrast, LL treatment resulted in the highest Km value, indicating a substantially lower enzyme-substrate affinity. Furthermore, MSM reduced the E_a and increased the thermal stability of urease, thereby supporting enzymatic performance under fluctuating temperatures. Collectively, our findings highlight that compost composition is a critical determinant of urease function and nitrogen turnover. By elucidating the coupled kinetic and thermodynamic responses of urease to compost inputs, this study provides mechanistic insights to guide optimized soil management and sustainable nitrogen utilization in temperate agricultural systems. Full article

This study investigates spatiotemporal b value variations and seismic interaction networks preceding the M_wg 7.1 Dingri earthquake that struck southern Tibet on 7 January 2025. Using relocated earthquake catalogs (2021–2025) and dual-method analysis combining b value mapping with Granger causality network modeling, we reveal systematic precursory patterns. Spatial analysis shows that the most significant b value reduction (Δb > 0.5) occurred north of the mainshock epicenter at seismogenic depths (5–15 km), closely aligning with subsequent aftershock concentration zones. Granger causality analysis reveals a progressive network simplification: from 73 causal links among 28 nodes during the background period (2021–2023) to 49 links among 34 nodes pre-mainshock (2023–2025) and finally to 6 localized links post-rupture. This transition from distributed system-wide interactions to localized “locked-in” dynamics reflects the stress concentration onto the primary asperity approaching critical failure. The convergence of b value anomalies and network evolution provides a comprehensive framework linking quasi-static stress states with dynamic system behavior. These findings offer valuable insights for understanding earthquake nucleation processes and improving seismic hazard assessment in the Tibetan Plateau and similar complex tectonic environments. Full article

We present two case studies of tropospheric aerosol transport observed over the high-altitude Helmos observatory (1800–2300 m a.s.l.) in Greece during September 2021. Two cases were linked to Saharan dust intrusions, of which one was additionally linked to a mixture of biomass-burning and continental aerosols. Aerosol vertical profiles from the AIAS mobile backscatter/depolarization lidar (532 nm, NTUA) revealed distinct aerosol layers between 2 and 6 km a.s.l., with particle linear depolarization ratio values of up to 0.30–0.40, indicative of mineral dust. The elevated location of Helmos allows lidar measurements in the free troposphere, minimizing planetary boundary layer influence and improving the attribution of long-range transported aerosols. Radiative impacts were quantified using the LibRadtran model. For the 27 September dust outbreak, simulations showed strong shortwave absorption within 3–7 km, peaking at 5–6 km, with surface forcing reaching −25 W m⁻² and TOA forcing around −12 W m⁻², thus, implying a net cooling by 13 W m⁻² on the Earth’s atmosphere system. In contrast, the 30 September mixed aerosol case produced substantial solar attenuation, a surface heating rate of 2.57 K day⁻¹, and a small positive forcing aloft (~0.05 K day⁻¹). These results emphasize the contrasting radiative roles of dust and smoke over the Mediterranean and the importance of high-altitude observatories for constraining aerosol–radiation interactions. Full article

Advancing ecological civilization in the Yellow River Basin requires a nuanced understanding of the spatiotemporal evolution of ecosystem service value (ESV) and its underlying drivers, which are fundamental to regional sustainable development. This study examines the Henan section of the Yellow River Basin, applying the equivalent factor method to estimate ESV in 2020 at three grid scales: 3 km × 3 km, 5 km × 5 km, and 10 km × 10 km. Spatial patterns of land-averaged ESV at each scale are characterized using autocorrelation analysis, while the geodetector model is employed to identify and quantify the influence of driving factors on ESV spatial heterogeneity. The findings reveal that (1) ESV displays both consistent and variable spatial patterns, with higher values in the west and north, lower values in the east and south, and a distinct high-value belt along water bodies; (2) strong spatial positive correlation and aggregation of ESV are observed at all grid scales, though these effects weaken as grid cell size increases; and (3) human activities exert a significant influence on regional ESV, with the interaction of multiple factors providing robust explanatory power for ESV variation, which diminishes with increasing scale. These results offer insights for optimizing ecosystem management and promoting sustainable development in the Yellow River Basin. Full article

Populus adenopoda, an endemic tree species in China with considerable ecological and industrial value, is threatened by climate change-induced habitat loss. Understanding its spatial response is critical for conservation. This study employed the MaxEnt model with 181 occurrence records and seven environmental variables to project its current and future suitable habitats under multiple climate scenarios (SSP126, SSP245, SSP370, SSP585 for the 2050s and 2090s). The model exhibited high predictive performance (AUC = 0.947 and TSS = 0.817). Annual precipitation and the minimum temperature of the coldest month were the dominant factors shaping its distribution. Currently, the total suitable habitat spans approximately 228.19 × 10⁴ km², predominantly in subtropical China. Future projections consistently revealed a stark degradation of highly suitable habitat, with losses of up to 78.81% under SSP585 by the 2090s, partially offset by an expansion of low-suitability areas. A pronounced northwestward shift of the habitat centroid indicates a potential migration toward higher elevations. These results provide a critical scientific foundation for developing climate-adaptive conservation strategies, including identifying priority areas and planning assisted migration, to ensure the long-term sustainability of P. adenopoda. Full article

Background and Objectives: This study aimed to assess the week-by-week predictive value of Acute:Chronic Workload Ratios (ACWRs) for non-contact injury risk in professional soccer players. Materials and Methods: A cohort of 40 elite players was monitored using GPS over two competitive seasons. Binomial logistic regression and ROC curve analyses were performed on ACWR metrics—including total distance, moderate-to high-speed running, sprinting, acceleration, and deceleration—during the four weeks prior to injury (W4 to W1). p-values were further adjusted for multiple comparisons using the false discovery rate (FDR) correction (q < 0.05). Results: Significant predictive models emerged mainly for ACWR metrics related to moderate-speed running (15–20 km/h), sprinting (>25 km/h), and acceleration/deceleration. The ACWR for 15–20 km/h (DSR15–20) demonstrated the highest predictive accuracy, particularly in Week 3 (AUC = 0.811, p = 0.004). Sprinting (DSR>25) was also significantly associated with injury occurrence across Weeks 1–4 (AUC = 0.709–0.755, p = 0.011–0.024). Acceleration (ACC) and deceleration (DEC) metrics showed significant associations prior to correction—ACC in Weeks 3–4 (AUC = 0.737–0.755, p = 0.020–0.026) and DEC in Weeks 3–4 (AUC = 0.720–0.741, p = 0.029–0.043)—but these associations did not retain significance following FDR adjustment (q = 0.052–0.086). In contrast, total distance (ACWR TD) and high-speed running (20–25 km/h) were weaker predictors, reaching only marginal or nonsignificant levels (e.g., Week 3, AUC = 0.675, p = 0.054). After FDR correction, only DSR15–20 and DSR>25 remained statistically significant (q < 0.05), confirming them as robust predictors of non-contact injury risk. Multivariable models adjusted for age and playing position confirmed these findings, with DSR15–20 and DSR>25 retaining their predictive value independent of confounding factors. Injury risk thresholds were established through Estimated Marginal Means (EMMs), defining the “Sweet Spot” and “Danger Zone” for each metric, whereas the “Low Load” zone was treated as exploratory. Conclusions: This weekly ACWR monitoring approach enables practical injury risk profiling, helping training staff optimize load management and minimize non-contact injury risk in elite soccer. Full article

This study investigates landslide occurrence in Taiwan, a region highly susceptible to landslides due to steep mountains and frequent typhoons (TYPs). The primary objective is to understand how both geomorphological factors and TYP characteristics contribute to landslide occurrence, which is essential for improving hazard prediction and risk management. The research analyzed landslide events that occurred during the TYP seasons of 2019 and 2023. The methodology involved using satellite-derived landslide inventories from SPOT imagery for events larger than 0.1 hectares, tropical cyclone track and intensity data from IBTrACS v4 (classified by Saffir–Simpson Hurricane Scale), and detailed topographic variables (elevation, slope, aspect, Stream Power Index) extracted from a 30 m Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM). Land use and land cover classifications were based on Landsat imagery. To establish a timeline, landslides were matched with TYPs within a ±3-day window, and proximity was analyzed using buffer zones ranging from 50 to 500 km around storm centers. Key findings revealed that landslide susceptibility results from a complex interplay of meteorological, topographic, and land cover factors. The critical controls identified include elevations above 2000 m, slope angles between 30 and 45 degrees, southeast- and south-facing aspects, and low Stream Power Index values typical of headwater and upper slope locations. Landslides were most frequent during Category 3 TYPs and were concentrated 300 to 350 km from storm centers, where optimal rainfall conditions for slope failures exist. Interestingly, despite the stronger storms in 2023, the number of landslides was higher in 2019. This emphasizes the importance of interannual variability and terrain preparedness. These findings support sustainable disaster risk reduction and climate-resilient development, aligning with Sustainable Development Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action). Furthermore, they provide a foundation for improving hazard assessment and risk mitigation in Taiwan and similar mountainous, TYP-prone regions. Full article

In the eastern part of India, specifically in the coastal districts of Odisha, the Puri central canalsystem’s Phulnakhara distributary command, which is split between the districts of Cuttack and Khurda, is where the study was taken up during 2020 and 2021. The flow modelling of the Phulnakhara distributary command, covering a 49.03 km² area, was done by Visual MODFLOW (VMOD). The command area’s conceptual model was created by assigning various input data, and the developed model was calibrated with 1-year data (2020) and validated with 1-year data (2021) on a fortnightly basis for simulating the groundwater flow using VMOD. Both steady state and transient state circumstances were used to calibrate the hydraulic conductivity and storage coefficient for the various layers in 2020. The calibrated hydraulic conductivity values vary from 1.16 × 10⁻³ ms⁻¹ to 4.86 × 10⁻⁴ ms⁻¹, and the calibrated values (2.00 × 10⁻² m⁻¹ to 4.00 × 10⁻⁶ m⁻¹) of specific storage varied from the first to third layer in both state scenarios. The validated model could forecast the groundwater condition and the flow head for the following ten years, assuming a 0.5% annual drop in recharge by increasing the pumping rate five, six, and seven times throughout the validation period (2021). The modelling study suggested that the command area will not remain safe for 10 years from the point of future groundwater development. The model performance showed strong agreement between simulated and observed groundwater heads, with R² values ranging from 0.68 to 0.91 and NSE values between 0.64 and 0.88. Predictive simulations indicated groundwater drawdowns of 4.82 m, 5.72 m, and 6.11 m under 5×, 6×, and 7× pumping scenarios, respectively, over the next decade, highlighting a significant risk of depletion unless conjunctive use strategies are adopted. Full article

This paper presents the design of a seed-pressing mechanism for a high-speed suction-type precision peanut planter to address the issue of poor seeding performance at high travel speeds and to reduce seed bounce within furrows. To clarify the working principle of the mechanism, a force analysis of peanut seeds in the furrow and a numerical study using discrete element analysis were conducted under high-speed operating conditions. Simulation results show that when the distance between the center of the seed-pressing wheel and the seeding-tube outlet (DCSPW-STO) is 146.11 mm, the seed-pressing wheel diameter is 198.13 mm, and the machine operating velocity is 6.45 km h⁻¹, the plant spacing qualification index and seeding depth compliance index for peanuts planted after rolling reach their maximum values. The corresponding germination rates of 93.78% and 90.65% indicate satisfactory sowing performance. Field validation trials demonstrate that when DCSPW-STO (l_fz) is 146 mm, the seed-pressing wheel diameter (d_fz) is 198 mm, and the machine operating velocity (v) is 6.45 km h⁻¹, the post-seeding plant-spacing qualification index and the seeding-depth compliance index reach 90.31% and 89.18%, respectively. Although slightly lower than the simulation results, these values meet the operational requirements for peanut seeding. Field performance comparisons with non-pressure seeding units further confirm that units equipped with the seed-pressing and soil-covering mechanisms significantly improve both the plant-spacing qualification index and the seeding-depth compliance index, satisfying agronomic requirements for high-speed peanut cultivation. Full article

Assessing soil fertility is a complex task as it is determined by natural and anthropogenic factors, including specific agronomic interventions (e.g., fertilization and crop rotation) and broader soil management (e.g., tillage and drainage). For agricultural management, soil represents a primary production factor whose chemical–physical characteristics and macro-elements content must be known. This work presents the maps of three macronutrients, i.e., N, K, and P, in the topsoils (0–30 cm layer) of the Emilia-Romagna (21,710.1 km²) region in NE Italy. The maps and associated uncertainty at 100 m resolution were obtained via digital soil mapping (DSM) resorting to Quantile Random Forests using topsoil data from the regional soil database (N = 34,750). As Emilia-Romagna is characterized by two distinct major landforms, i.e., the intensively cultivated alluvial plain and the extensively managed mountain range of the Northern Apennines, each representing nearly half of the region, two distinct sets of numerical and categorical covariates were used as predictors for the DSM estimation of each macronutrient. Results highlight an average N content of approximately 1.57 ± 0.83 (standard deviation) g kg⁻¹ in the alluvial plain and of 1.63 ± 0.49 g kg⁻¹ in the Apennines. For exchangeable potassium (K), concentrations were 275.90 ± 92.6 mg kg⁻¹ and 210.2 ± 86.3 mg kg⁻¹ in the plain and Apennines, respectively. A stark contrast was observed for available phosphorus (P), with mean values of 40.4 ± 11.0 mg kg⁻¹ in the alluvial plain, dropping to 15.2 ± 6.1 mg kg⁻¹ in the Apennines. Such results provide useful information for assessing the fertility of regional soils and provide a reference baseline for soil quality monitoring. The resulting macronutrient maps were eventually compared with those based on the Land Use and Cover Area frame Survey (LUCAS), which represents the reference baselines at the EU scale. Full article

Digital soil mapping has become increasingly important for large-scale soil organic carbon (SOC) assessments, yet the choice of accuracy assessment method significantly influences model performance interpretation. This study investigates the impact of cross-validation fold numbers on accuracy metrics and compares cross-validation with split-sample validation approaches in national-scale SOC mapping. Five machine learning algorithms (Random Forest, Cubist, Support Vector Regression, Bayesian Regularized Neural Networks, and ensemble modeling) were evaluated to predict SOC content across France (539,661 km²) and Czechia (78,873 km²) using 2731 and 445 soil samples, respectively. Environmental covariates included satellite imagery (Sentinel-1, Sentinel-2, and MODIS), climate data (CHELSA), and topographic variables. Four cross-validation approaches (k = 2, 4, 5, 10) were utilized with 100 repetitions each and the results were compared with the existing literature using both cross-validation and split-sample methods. Ensemble models consistently produced the highest prediction accuracy and lowest variance per fold across all validation approaches. Higher fold numbers (k = 10) also produced higher accuracy estimates compared to lower folds (k = 2) and had the greatest value ranges of accuracy assessment metrics. This confirmed the observations from previous studies, in which split-sample validation reported higher R² values (0.10–0.90) compared to cross-validation studies (0.03–0.68), suggesting a strong effect of randomness in training and test data split in the split-sample approach. This suggests that k-fold cross-validation should preferably be used in reporting prediction accuracy in similar studies, with the split-sample approach being strongly affected by value properties from training and test data from particular splits used for validation. Full article

Maglev trains represent an advanced form of modern rail transportation. The guideway irregularity presents a common disturbance to the safe and reliable operation of the maglev train. Variations in the air gap between the train and the guideway, induced by the guideway irregularities, exert a significant influence on the train’s dynamic performance, thereby impacting both ride comfort and operational safety. Although previous studies have acknowledged the importance of guideway irregularity, the stochastic effects on the car body vibration across different speeds have not been quantitatively assessed. To fill in this gap, this paper presents a 10-degree-of-freedom maglev train model based on multibody dynamics. The guideway is modelled via the finite element method using Euler–Bernoulli beam theory, and a linearized electromagnetic force equation is employed to couple the guideway and the train dynamics. Furthermore, the measurement data of guideway irregularity from the Shanghai Maglev commercial line are incorporated to evaluate their stochastic effect. Analysis results under varying speeds and irregularity wavelengths identify a resonance speed of 127.34 km/h, attributed to the interplay between guideway periodicity and the train’s natural frequency. When the ratio of the train speed versus irregularity wavelength satisfies the train’s natural frequency, a significant resonance can be observed, leading to an increase in train vibration. Based on the Monte Carlo method, stochastic analysis is conducted using 150 simulations per speed in 200–600 km/h. The maximum vertical acceleration remains relatively stable at 200–400 km/h but increases significantly at higher speeds. When the irregularity is present, greater dispersion is observed with increasing speed, with the standard deviation at 600 km/h reaching 2.7 times that at 200 km/h. Across all tested cases, acceleration values are consistently higher than those without irregularities within the corresponding confidence intervals. Full article