Tree destruction induced by heavy rainfall, an overlooked type of forest degradation, has been exacerbated along with global climate change. On the Chinese Loess Plateau, especially in afforested gully catchments dominated by Robinia pseudoacacia, destructive rainfall events have increasingly led to widespread forest damage. Previous study has manifested the severity of heavy rainfall-induced tree destruction and its association with topographic change, yet the contributions of tree structure and forest structure remain poorly understood. In this study, we quantified the destroyed trees induced by heavy rainfall using light detection and ranging (LiDAR) techniques. We assessed the influence of tree structure (tree height, crown diameter, and crown area), forest structure (tree density, gap fraction, leaf area index, and canopy cover), and terrain parameters (elevation, slope, and terrain relief) using machine learning models (random forest and logistic regression). Based on these, we aimed to clarify the respective and combined contributions of structural and topographic factors to rainfall-induced tree destruction. Key findings revealed that when considered in isolation, greater tree height, crown diameter, crown area, leaf area index (LAI), and canopy cover suppressed tree destruction, whereas higher gap fractions increased the probability of tree destruction. However, the synergistic increases of tree structural factors (tree height, crown diameter, and crown area) and forest structural factors (LAI and canopy cover) significantly promoted tree destruction, which can counteract the inhibitory effect of terrain on destruction. In addition, increases in tree structure or canopy density (LAI and canopy cover) also increased the probability of tree destruction at the same elevation. Our findings challenge conventional assumptions in forest management by demonstrating the interaction of tree structure and canopy density can significantly promote tree destruction during heavy rainfall. This highlights the need to avoid overly dense afforestation in vulnerable landscapes and supports more adaptive, climate-resilient restoration strategies.

Rice bakanae disease is a soil-borne disease mainly caused by Fusarium fujikuroi, which seriously damages the yield and quality of rice. Phenamacril targets Myosin-5, thereby inhibiting its ATPase activity to exert an antifungal effect, demonstrating significant bioactivity against Fusarium species. However, the resistance of Fusarium fujikuroi field populations to phenamacril in Jiangsu Province in recent years remains unclear. In this study, a total of 223 Fusarium fujikuroi isolates were collected in Jiangsu Province from 2022 to 2023, with the resistance frequency increase from 25.88 % to 49.28 %. Additionally, a novel mutation type (S420I) in FfMyosin-5 was identified and confirmed by genetic transformation. The compound fitness index (CFI) revealed that the fitness of FfMyosin5(S420I) point mutants (1 x 10(5) < CFI <= 2 x 10(5)) was significantly lower than sensitive strain (CFI = 10.26 x 10(5)) in terms of mycelial growth rate, conidia production and conidia germination. In summary, the S420I mutation in FfMyosin-5 induces resistance to phenamacril while also decreased the fitness of Fusarium fujikuroi.

In view of the pollution of unpaved road dust in the current mines, this study demonstrated the excellent dust suppression performance of the dust suppressant by testing the dynamic viscosity, penetration depth and mechanical properties of the dust suppressant, and apply molecular dynamics simulations to reveal the interactions between substances. The results showed that the maximum dust suppression rate was 97.75 % with a dust suppressant formulation of 0.1 wt% SPI + 0.03 wt% Paas + NaOH. The addition of NaOH disrupts the hydrogen bonds between SPI molecules, which allows the SPN to better penetrate the soil particles and form effective bonding networks. The SPI molecules rapidly absorb onto the surface of soil particles through electrostatic interactions and hydrogen bonds. The crosslinking between SPI molecules connects multiple soil particles, forming larger agglomerates. The polar side chain groups in the SPN interact with soil particles through dipole-dipole interactions, further stabilizing the agglomerates and resulting in an enhanced dust suppression effect. Soil samples treated with SPN exhibited higher compressive strength values. This is primarily attributed to the stable network structure formed by the SPN dust suppressant within the soil. Additionally, the SPI molecules and sodium polyacrylate (Paas) molecules in SPN contain multiple active groups, which interact under the influence of NaOH, restricting the rotation and movement of molecular chains. From a microscopic perspective, the SPN dust suppressant further strengthens the interactions between soil particles through mechanisms such as liquid bridge forces, which contribute to the superior dust suppression effect at the macroscopic level.

Granite residual soils (GRS) are often encountered in geotechnical projects in the Guangdong-Hong Kong-Macao Greater Bay Area (briefly written as the Greater Bay Area, or abbreviated as GBA). The rea experiences frequent rainfall, leading to wetting-drying cycles that progressively diminish the shear strength of GRS. This weakening effect is not only significant but also accumulates, exhibiting a direct positive correlation with the number of cycles. Current studies on the soil strength attenuation due to wetting-drying cycles are typically limited to no more than 10 cycles, which is rather insufficient to uncover the long-term water-weakening behaviors and their accumulative impacts on GRS. To address this gap, typical GRS samples were first taken from the GBA and then prepared by making them go through a certain number of wetting-drying cycles (maximum of up to 100). Next, a total of 552 small- and large-scale direct shear tests were conducted to investigate the mechanisms of water-weakening effects on soil internal friction angle, cohesion, and shear strength. The degree of saturation and number of cycles were also examined to see their effects on the cumulation of water weakening. Based on results from the small-scale direct shear tests, a model was developed for assessing the weakening impact of water on soil strength. The accuracy of the model prediction was statistically evaluated. Last, the effectiveness and efficiency of the proposed model were demonstrated by validating against the results from the large-scale direct shear tests.

Soft clay soils inherently exhibit low mechanical strength, imposing significant challenges for various engineering applications. The present research explores various techniques and stabilizers to enhance soft clay's suitability for construction purposes. This study evaluates the mechanism of stabilizing kaolin using recycled macro-synthetic fibers (RMSF) for the first time. Samples were prepared with 5 % LKD, with 25 % replaced by VA, and varying RMSF amounts of 0, 0.5 %, 1 %, and 1.5 % in lengths ranging from 4 to 6 mm. The specimens were cured for 7, 28, and 56 days and exposed to 0, 1, 4, and 10 freeze-thaw (F-T) cycles. Laboratory investigations were conducted through standard compaction, Unconfined Compressive Strength (UCS), Indirect Tensile Strength (ITS), Scanning Electron Microscope (SEM), California Bearing Ratio (CBR), X-ray diffraction (XRD), and Fourier Transform Infrared Spectroscopy (FTIR) tests on the samples at various stages of stabilizer addition, both before and after F-T cycles. The optimal mixture was 5 % LKD, with 25 % VA replacement and 1 % RMSF, which led to a considerable 11-fold enhancement in ITS and a 14-fold improvement in UCS compared to the untreated sample. Additionally, the secant modulus (E50) and energy absorption capacity (Eu) of the sample with the optimal combination content increased in comparison to the stabilized sample without RMSF. The CBR of the optimal sample reached 81 %, allowing for an 87 % reduction in pavement thickness compared to the untreated sample. According to the findings of this research, the combination of LKD, VA, and RMSF increased the compressive and tensile strength properties, bearing capacity, and durability of kaolin, making it an appropriate option for use in various practical civil projects like road construction.

The extensive utilization of agricultural machinery in China has made it a prominent contributor to particulate matter (PM). However, there still exist significant knowledge gaps in understanding optical characteristics and molecular composition of chromophores of brown carbon (BrC) in PM emitted from agricultural machinery. Therefore, BrC in PM from six typical agricultural machines in China were measured to investigate the light absorption, chromophore characteristics, and influencing factors. Results showed that the average emission factors of methanol-soluble organic carbon (MSOC) and water-soluble organic carbon (WSOC) were 0.96 and 0.21 g (kg fuel)-1, respectively, exhibiting clear decreasing trends with increasing engine power and improving emission standards. Despite the light absorption coefficient of methanol-extracted BrC (Abs365,M) being approximately 2.2 times higher than that of water (Abs365,W), mass absorption efficiency of water-extracted BrC (MAE365,W) exhibited significantly greater values than MAE365,M. Among the detected chromophores, nitro-aromatic compounds (NACs) exhibited the highest contribution to light absorption that was about 14.5 times more than to total light absorption compared to their mass contributions to MSOC (0.04%), followed by polycyclic aromatic hydrocarbons (PAHs) and oxygenated PAHs (OPAHs). Besides, the average integrated simple forcing efficiency values were estimated to be 1.5 W g-1 for MSOC and 3.7 W g-1 for WSOC, indicating significant radiative forcing absorption of agricultural machinery. The findings in this study not only provide fundamental data for climate impact estimation of but also propose effective strategies to mitigate BrC emissions, such as enhancing emission standards and promoting the adoption of high-power agricultural machinery.

Floods are devastating natural disasters causing significant damage worldwide, especially in southern Latin America, where recurrent river floods lead to severe impacts. This study proposes an innovative flood modelling approach using a naive Bayes classifier to simulate flood extents at a regional scale, incorporating spatial and temporal variability. Using 12 features, including topography, soil properties, precipitation and discharge, the model was trained with multiple flood events, avoiding sampling limitations and evaluating optimal pre-processing strategies for continuous data. The predictive capacity resulted in high performance metrics, with temporal validation accuracy (AC) up to 0.98 and a critical success index (CSI) of 0.58, and spatial validation achieved an AC up to 0.97 and CSI of 0.56, outperforming the hydrodynamic model by 65%. A reduced model with significant features improved computational efficiency and achieved a CSI exceeding 0.60. This practical tool supports flood risk management and enhances resilience in vulnerable regions.

Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the carbon cycle, hydrological processes, ecosystems, and the safety of engineering structures in cold regions. This study constructs a Stefan CatBoost-ET (SCE) model through machine learning and Blending integration, leveraging multi-source remote sensing data, the Stefan equation, and measured ALT data to focus on the ALT in the Qinghai-Tibet Plateau (QTP). Additionally, the SCE model was verified via ten-fold cross-validation (MAE: 20.713 cm, RMSE: 32.680 cm, R2: 0.873, and MAPE: 0.104), and its inversion of QTP's ALT data from 1958 to 2022 revealed 1998 as a key turning point with a slow growth rate of 0.25 cm/a before 1998 and a significantly increased rate of 1.26 cm/a afterward. Finally, based on multiple model input factor analysis methods (SHAP, Pearson correlation, and Random Forest Importance), the study analyzed the ranking of key factors influencing ALT changes. Meanwhile, the importance of Stefan equation results in SCE model is verified. The research results of this paper have positive implications for eco-hydrology in the QTP region, and also provide valuable references for simulating the ALT of permafrost.

Heliotropium L. genus belongs to the Boraginaceae family and is represented by approximately 250 species found in the temperate warm regions of the world, and there are 15 species of these species recorded in Turkiye. Heliotropium hirsutissimum Grauer grows in Bulgaria, Greece, N. Africa, Syria, and Turkiye. There is no record showing that H. hirsutissimum is a heat-tolerant plant. However, in our field studies, it was observed that H. hirsutissimum, which is also distributed in Hisaralan Thermal Springs of Sindirgi-Balikesir, Turkiye, grows in the thermal area with extremely high soil temperature (57.6 degrees C (similar to 60 degrees C)). It was thought that it would be useful to investigate the tolerance mechanism of the H. hirsutissimum plant to extremely high temperatures. For this purpose, the plant seeds were obtained from a geothermal area in the thermal spring. Growing plants were exposed to 20, 40, 60, and 80 +/- 5 degrees C soil temperature gradually for 15 days under laboratory conditions. We measured the effect of high soil temperature on some morphological changes, relative water content, thiobarbituric acid reactive substances, cell membrane stability, and hydrogen peroxide analysis to determine stress levels on leaves and roots. Changes in osmolyte compounds, some antioxidant enzyme activities, ascorbate content, and chlorophyll fluorescence and photosynthetic gas exchange parameters were also determined. As a result of the study carried out to determine the stress level, it was observed that there was not much change and it was understood that the plant was tolerant to high soil temperature. In addition, there was a general increase in osmolytes accumulation, antioxidant enzyme activities, and ascorbate level. There was no significant difference in photosynthetic gas exchange and chlorophyll fluorescence parameters of plants grown at different soil temperatures. The high temperature did not negatively impact the photosynthetic yield of H. hirsutissimum because this plant was found to enhance its antioxidant capacity. The increase in antioxidant activity helped reduce oxidative damage and protect the photosynthetic mechanism under high temperature conditions, while the significant increase in the osmolyte level helped maintain the water status and cell membrane integrity of plants, thus enabling them to effectively withstand high soil temperatures.

Moisture accumulation within road pavements, particularly in unbound granular materials with or without thin sprayed seals, presents significant challenges in high-rainfall regions such as Queensland. This infiltration often leads to various forms of pavement distress, eventually causing irreversible damage to the pavement structure. The moisture content within pavements exhibits considerable dynamism and directly influenced by environmental factors such as precipitation, air temperature, and relative humidity. This variability underscores the importance of monitoring moisture changes using real-time climatic data to assess pavement conditions for operational management or incorporating these effects during pavement design based on historical climate data. Consequently, there is an increasing demand for advanced, technology-driven methodologies to predict moisture variations based on climatic inputs. Addressing this gap, the present study employs five traditional machine learning (ML) algorithms, K-nearest neighbors (KNN), regression trees, random forest, support vector machines (SVMs), and gaussian process regression (GPR), to forecast moisture levels within pavement layers over time, with varying algorithm complexities. Using data collected from an instrumented road in Brisbane, Australia, which includes pavement moisture and climatic factors, the study develops predictive models to forecast moisture content at future time steps. The approach incorporates current moisture content, rather than averaged values, along with seasonality (both daily and annual), and key climatic factors to predict next step moisture. Model performance is evaluated using R2, MSE, RMSE, and MAPE metrics. Results show that ML algorithms can reliably predict long-term moisture variations in pavements, provided optimal hyperparameters are selected for each algorithm. The best-performing algorithms include KNN (the number of neighbours equals to 15), medium regression tree, medium random forest, coarse SVM, and simple GPR, with medium random forest outperforming the others. The study also identifies the optimal hyperparameter combinations for each algorithm, offering significant advancements in moisture prediction tools for pavement technology.