Search Results (679)

Predicting landslide displacement is important for geological-hazard early warning. In reservoir areas, displacement evolution is affected by rainfall, reservoir water level, vegetation variation, and the intrinsic non-stationarity of the displacement sequence, which makes accurate prediction difficult for conventional single-sequence models. To address this problem, this study proposes a residual-increment-oriented landslide displacement prediction framework that fuses multi-source monitoring variables. The displacement sequence is first processed into trend and periodic-related fluctuation representations, and the residual increment is used as the prediction target. Rainfall, reservoir water level, and the normalized difference vegetation index (NDVI) are incorporated as external monitoring variables. A cross-branch attention mechanism models interactions among heterogeneous feature branches, and a sparse MoE-based fusion module is introduced to adaptively adjust branch contributions under different deformation conditions. The model predicts the displacement residual increment, from which the final displacement is reconstructed. A case study using the Baishui River (Baishuihe) landslide monitoring dataset was conducted, together with additional validation on the related Bazimen Z110 landslide monitoring dataset and comparisons against conventional recurrent, convolutional, statistical, and Transformer-based baselines. The results show that the proposed model achieves lower RMSE and MAE than the compared methods on the tested datasets. These findings suggest that residual-increment modeling, multi-source monitoring variables, and condition-dependent branch fusion can improve short-term displacement prediction for the tested reservoir-area landslide cases. Full article

Fine particulate matter with a diameter ≤2.5 μm (PM_2.5) pollution poses a global public health crisis, demonstrating significant threats to human health. This study focused on the strategically important Chengdu-Chongqing Economic Circle in western China, systematically comparing the toxic effects of urban and rural PM_2.5 across five levels. PMF and regression analysis were used to identify source contributions, dual-omics to pinpoint key molecules, and epidemiological data with a GAM model to assess health risks. Findings demonstrate that rural PM_2.5 possesses greater biotoxicity than its urban counterpart. Cytotoxicity in urban and rural PM_2.5 originated from road dust/vehicle emissions and biomass burning, respectively. Subsequently, integrated omics and molecular biology analyses identify kinesin family member 20A (KIF20A) as a shared key target, which mediates toxicity induced by both urban and rural PM_2.5. Finally, epidemiological analysis reveals that females and ≥65 years old exhibit relatively high sensitivity to urban PM_2.5 exposure trends, with rhinitis showing a comparatively higher impact among various related diseases. The novelty of this work lies in its pioneering application of a multi-tiered investigative approach. This approach spans “environmental samples-cellular mechanisms-population health” within the Chengdu-Chongqing economic circle context, systematically elucidating common and distinct respiratory health risk of urban and rural PM_2.5. This work offers a vital scientific foundation for advancing region-specific, precise air pollution prevention and control measures. Full article

The beneficial utilization of small and medium floods requires a clear flood-control safety boundary before floodwater can be moderately stored and regulated as a water resource. For the Three Gorges Reservoir, a large river-type reservoir with long-distance backwater effects and tributary blocking, this boundary cannot be determined solely from the dam-front water level. This study developed a one-dimensional unsteady hydrodynamic model with dynamic roughness calibration to investigate the risk-constrained flood-season operating water level of the Three Gorges Reservoir. Typical flood events and the 20-year return period design flood were used to examine the responses of the maximum dam-front flood-regulation water level, excess flood volume, longitudinal water levels, and exceedance risk at key reservoir-area sections under different initial regulation water levels and release-discharge conditions. The results show that the Changshou reach is the main control section for high-water-level inundation risk under the study scenarios. When the initial regulation water level is at or below 155 m, the dam-front flood-regulation water level, the peak water level at Changshou, and the exceedance duration generally vary only slightly. When the initial regulation water level exceeds 155 m, these risk indicators increase markedly, indicating a reduced flood-control safety margin. Perturbation analysis further shows that the dam-front flood-regulation indicators are relatively insensitive to small roughness and dam-front boundary perturbations, whereas the Changshou water level and exceedance duration are more sensitive to roughness and flood-volume perturbations. Therefore, 155 m should be interpreted as a conservative operational reference boundary under the current design-flood framework, existing operation rules, and the assumption of no forecast-based pre-release, rather than as an absolute safety threshold. Increasing release discharge can reduce high-water-level risk in the reservoir area under preset release limits, but its practical application must remain conditional on downstream flood-control constraints and real-time flood-conveyance capacity. The results provide a hydrodynamic basis for risk-constrained flood-season operation of large river-type reservoirs. Full article

Accurate time series forecasting is fundamental to geohazard early warning, yet remains a major challenge. Conventional in situ geotechnical monitoring remains costly and spatially constrained, whereas deep learning applied to remote sensing data has become increasingly prevalent but often suffers from opacity of model decision-making. To address this issue, we propose a Transformer-based forecasting framework, namely PatchTST-Fusion, adapted for multivariate Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) time series. The framework integrates model interpretability analysis through TimeSHAP, providing temporal and feature-level attributions across the input sequence. Landslide deformation time series are first derived from Copernicus Sentinel-1 SAR data. Variational Mode Decomposition is then applied to decompose the non-linear signals into trend, seasonal, and noise components. The denoised displacement series are modeled and forecast using the proposed PatchTST-Fusion, which incorporates rainfall and reservoir water level fluctuations as feature-level drivers. Application to the Daping landslide cluster in the Three Gorges Reservoir Area in China demonstrates that our method captures both the long-term and transient non-linear coupling between deformation and its triggers, surpassing state-of-the-art models including CNN-BiGRU-Attention, Informer and original PatchTST with 7–55% improvements in MAE and 10–52% improvements in RMSE. Beyond predictive gains, feature attribution of environmental triggers via TimeSHAP reveals that rainfall and reservoir regulation exert temporally distinct influences on slope kinematics, with high relative importance concentrated in specific periods and characteristic lagged responses. This interpretable framework provides both enhanced forecasting accuracy and process-based insights, offering a broadly applicable tool for landslide early warning in reservoir regions. Full article

Highway slope instability under coupled traffic and environmental loading poses critical threats to transportation safety in mountainous regions, where dynamic vehicular forces interact with complex geological conditions in ways that single-modality monitoring cannot fully resolve. This study proposes MMDF-DEWS, a multi-modal data fusion and deep learning-based early-warning system that, for the first time, treats quantified traffic-loading parameters as a first-class input modality alongside Interferometric Synthetic Aperture Radar (InSAR) displacement, Global Navigation Satellite System (GNSS) measurements, and embedded geotechnical sensor outputs. A hybrid Transformer–bidirectional LSTM backbone with hierarchical attention-guided fusion enables the model to capture both long-range temporal deformation trends and short-term dynamic responses triggered by heavy-vehicle passage. To guard against over-fitting on a limited number of instability events, we adopt chronological training/validation/test partitioning, five-fold cross-validation for hyper-parameter selection, stratified focal-loss training, and cross-dataset evaluation on two independent public benchmarks: the Three Gorges Reservoir Area Landslide Monitoring Dataset (TGRA-LMD) and the European Ground Motion Service Sentinel-1 (EGMS-S1) dataset. The framework outperforms six state-of-the-art baselines by 4.7–11.2% in F1-score, and ablation studies confirm that the explicit inclusion of traffic-loading features alone improves Warning-class recall by 6.3 percentage points, demonstrating a direct and physically grounded link between cyclic vehicular loading and slope-state prediction. The system satisfies operationally relevant engineering targets for warning lead time and false-alarm rate, and provides interpretable attention maps suitable for transportation-authority decision support. Full article

Pine wilt disease, caused by Bursaphelenchus xylophilus, poses a serious threat to global pine forest ecosystems and forestry production. Thaumatin-like proteins (TLPs), which belong to the PR-5 family, are known to participate in plant defense, but their roles in pine have not been well characterized. In this study, a comprehensive genome-wide analysis of the TLP gene family was conducted in Pinus taeda. A total of 116 TLP genes were identified and classified into four major clades based on phylogenetic analysis. Gene structure and conserved motif analyses revealed that members within the same clade generally exhibited similar exon–intron organization patterns and conserved motif compositions. Promoter analysis identified numerous cis-regulatory elements associated with stress responses and phytohormone signaling. Transcriptome data from different stages of pine wood nematode infection identified eight TLP genes that exhibited continuous differential expression, and their expression patterns were further confirmed by qRT-PCR. A multilayer regulatory network highlighted MYB and other transcription factors as key upstream regulators, and yeast one-hybrid assays confirmed MYB-mediated regulation. Together, these findings improve our understanding of the TLP gene family in P. taeda and offer valuable candidate genes and regulatory information for future studies on pine resistance to pine wilt disease. Full article

In recent decades, flood drought abrupt alternation (FDAA) events have intensified markedly in the middle and lower reaches of the Yangtze River Basin (MLYRB), exposing limitations of the conventional single flood-limited water level (FLWL) operation of the Three Gorges Reservoir. To better address drought risk during the flood season, this study develops a phased and graded drought-limited water level (DLWL) operation framework. FDAA events were identified using a hybrid method combining the Short-term Flood-Drought Abrupt Alternation Index and the Standardized Runoff Index. A multi-objective optimization model solved by NSGA-III was employed to determine staged DLWLs across five operational periods with tiered thresholds prioritizing urban, ecological, and irrigation water demands. Results show that FDAA events are mainly concentrated in June–October and have intensified significantly since 2010. Compared with conventional operation, the optimized DLWL framework substantially improves irrigation water supply reliability and reservoir fullness, while maintaining urban and ecological water supply security. Validation during typical wet years indicates that the proposed strategy introduces no evident reduction in flood control safety. Full article

Large reservoir construction generates vast drawdown zones characterized by novel hydrological regimes that impose unprecedented selective pressures. While annual plants serve as pioneer colonists during secondary succession in these ecosystems, the mechanisms allowing their seeds to persist through prolonged anti-seasonal flooding remain poorly understood. We investigated how seed germination responses to extreme submergence are influenced by dormancy traits and phylogenetic history. We conducted a field experiment on 44 common annual plant species in the Three Gorges Reservoir drawdown zone. Seeds were subjected to maximum submergence depths of 0 m (control), 5 m, 10 m, 15 m, and 20 m, along the reservoir’s hydrological gradient. Post-submergence germination percentages were measured and analyzed using linear and Bayesian phylogenetic mixed-effects models, with seed dormancy status, seed type, season, and species’ phylogenetic relationships as explanatory variables. Submergence significantly reduced overall seed germination (p < 0.001), but more than 75% of species retained germination capacity even after 20 m of submergence. Germination percentage distributions shifted from near-normal to bimodal with increasing depth. Although the regression of squared PIC values against phylogenetic branch lengths showed a significant relationship, phylogenetic signal for germination percentages was weak and non-significant across all depths (Pagel’s λ < 0.101, Blomberg’s K < 0.228, p > 0.05). Bayesian models revealed that dormancy season significantly interacted with submergence depth (Estimate = −1.41, 95% CrI [−2.16, −0.67]). Seeds dormant during autumn-winter maintained stable germination percentages across depths, while germination of spring-summer dormant seeds declined significantly with increasing depth. Our findings demonstrate that annual plant seeds possess widespread, species-specific tolerance to extreme submergence. This tolerance is primarily driven by environmental filtering rather than phylogenetic history. The seasonality of dormancy is a crucial adaptive mechanism, enabling seeds, particularly those dormant in autumn-winter, to withstand the harsh conditions of the Three Gorges Reservoir drawdown zone. This study provides a functional trait-based framework for selecting suitable species for the ecological restoration of reservoir drawdown zones globally. Full article

Understanding the anisotropy in the physical and mechanical properties of layered rocks is essential for predicting and preventing instability in layered rock masses. However, in-situ sampling is often hindered by the difficulty of obtaining specimens with controlled bedding orientations. Cement-based 3D printing (3DP) offers an efficient approach for fabricating rock analogues, yet the inherent anisotropy induced by the layer-by-layer deposition process has not been well characterized, hindering its broader application. The objectives of this study are (i) to systematically evaluate the intrinsic anisotropy of cement-based 3DP rocks and (ii) to compare the mechanical anisotropy and failure modes of 3DP layered rocks with those of natural layered sandstone. The key findings are as follows: (1) The uniaxial compressive strength (UCS), P-wave velocity, and computed tomography (CT) number of the 3DP rock vary by less than 6% among the X-, Y-, and Z-directions, indicating lower intrinsic anisotropy compared to typical sandstones and several other natural rocks. (2) The UCS, elastic modulus, and secant modulus of the 3DP layered rocks all decrease initially and then increase with bedding dip angle, reaching a minimum at 60°. (3) The main fracture characteristics of the 3DP layered rocks are similar to those of layered sandstone; notably, the 3DP layered soft rock exhibits the most pronounced shear failure features. This study quantifies the low intrinsic anisotropy of cement-based 3DP rocks and validates their similarity to natural layered sandstone in both mechanical anisotropy and failure modes. It thereby provides a reliable, reproducible basis for physical modeling of layered rock masses using 3DP, offering a new approach for laboratory-scale investigations of layered rocks. Full article

A large number of reservoirs (or hydropower plants) have been constructed for flood control and energy production in the past several decades in the Yangtze River basin in China. The conventional scheduling rule curves (Scheme A) were designed in the reservoir construction period and did not consider river flow alternation, which needs to be modified to increase comprehensive benefits in the reservoir operation period. In this study, six large-scale cascade reservoirs or mega hydropower systems constructed and operated by the China Yangtze Three Gorges Corporation were selected for this case study. The current joint scheduling plans of cascade reservoirs (Scheme B) were introduced, and a joint scheduling and multi-objective coordinating operation model (Scheme C) was proposed for this mega hydropower system. The Gaussian radial basis functions (GRBFs) were used to fit operation policies of each reservoir, and the Borg multi-objective evolutionary algorithm was selected to optimize three-objective functions for Scheme C. The observed daily flow data series at main hydrometric stations from 2003 to 2025 were used to simulate and compare different operation scheduling schemes. The results show that the performance of joint scheduling of cascade reservoirs (both Schemes B and C) is much better than the single-reservoir scheduling (Schemes A) with overall benefit; Scheme C-best achieves a comprehensive target of decreasing average annual spillway wastewater by 12.82 billion m³ (or a decrease of 28.5%), increasing average annual power generation by 31.02 billion kWh (or an increase of 10.7%), and improving average annual impoundment efficiency rate by 5.0%. The GRBFs can fit reservoir operation policies well, while the Borg multi-objective evolutionary algorithm can quickly converge with high-precision non-dominated solution sets. The proposed joint scheduling and multi-objective coordinating operation model will provide a scientific basis for achieving maximum benefits in flood protection and hydropower generation for the mega hydropower system. Full article

Cover crop in orchards is recognized as a sustainable practice that enhances multiple ecosystem services, yet systematic evaluations of different cover crop management models in citrus orchards remain limited. This study investigated the effects of cover crop management models (natural cover crop: T1, Lolium perenne L.: T2, Trifolium repens L.: T3, Vicia villosa Roth: T4, and mixed cover crops: T5) on soil properties, soil CO₂ flux, leaf physiological traits, fruit quality, and yield in a citrus orchard, using clean tillage as a control. Results showed that cover crop management models significantly influenced soil water content, available nitrogen (AN), available phosphorus (AP), and available potassium (AK). The V. villosa model (T4) reduced AN and AP but enhanced leaf chlorophyll (Cl) and nitrogen (N) content. Soil CO₂ flux was significantly higher under T4, and it showed the lowest soil moisture. The results of mantel tests revealed that AP and soil moisture were key drivers of leaf traits, though no significant treatment effects on fruit quality or yield were detected within the two-year experimental period. These findings indicate that cover crop management models rapidly alter soil properties and CO₂ emissions, but longer-term observations are needed to evaluate cascading effects on fruit. This study offers evidence-based soil management solutions and a framework for enhancing multiple ecosystem services in orchards worldwide. Full article

Due to the destruction of natural aquatic ecosystems, developing comprehensive biological index evaluation methods is critical for river restoration and regeneration. However, research on spatial multiple-scale biological assessments remains lacking. This study used the biological integrity index methodology to examine the effectiveness of fish and macrobenthos in ecological assessments, as well as to analyze environmental factors impacting aquatic ecosystem health assessments. The Daning River basin in Chongqing was selected as the study area, and aquatic ecosystem health assessments were conducted at both regional and local scales. The results indicated that benthos were more abundant than fish, but there were no significant differences in species richness between sub-basins (p > 0.05). The assessment results for F-IBI and B-IBI showed partial discrepancies at the local fine-scale level but were nearly consistent at the regional broad-scale sub-basin level, with only small differences between the F-IBI and B-IBI ratings in one sub-basin. This discrepancy may be due to the diverse water environment. Woodland areas have significant negative relationships with F-IBI, while water areas have significant positive relationships with it. Comprehensively, the assessment findings of F-IBI beat those of B-IBI, implying that F-IBI may be better suited for regional assessments. However, the impact of local water quality issues remains unclear. To enhance ecological restoration, it is vital to conduct additional research into the degree of interference caused by water quality variables. Full article

Groundwater level prediction is essential for sustainable water resource management. Although machine learning models are widely applied, input variable selection critically affects predictive performance, and existing studies rarely evaluate model performance comprehensively, considering accuracy, stability, physical interpretability, and computational efficiency. To address this issue, this study develops a hybrid framework integrating grid search-optimized long short-term memory (GS-LSTM) with the technique for order preference by similarity to ideal solution (TOPSIS). Using the Houston area as a case study, the framework evaluates 30 input combinations derived from precipitation (P), air temperature (T), relative humidity (H), wind speed (W), and reference evapotranspiration (E) across 22 monitoring wells to identify optimal and minimal input variable combinations sets. Key findings include: (1) optimal input combinations vary substantially among wells, highlighting spatial heterogeneity; (2) P and E are dominant drivers; (3) compared to daily input data, monthly averaged data increases the prediction success rate (proportion of successful runs across 27 hyperparameter configurations) by >40% and improves R² by >0.3; (4) the minimal set comprises eight representative combinations that collectively cover the top-three ranked variable combinations for all 22 wells, maintaining high accuracy (e.g., Well 12# daily data: MAE = 0.13 m, RMSE = 0.16 m, R² = 0.92) while reducing computational cost by 92.1% relative to testing all 30 combinations. The proposed optimal and minimal input sets offer a stable, accurate, and computationally efficient solution for groundwater resource management that accounts for spatial heterogeneity. Full article

Pathological cardiac hypertrophy is an important risk factor for cardiovascular disease. Ganoderic acid A (GAA), the primary bioactive constituent of Ganoderma lucidum (G. lucidum), is known for its stable chemical properties and diverse biological activities. It has been shown to confer protection against myocardial ischemia–reperfusion injury in rat models, potentially through modulating inflammatory responses and inhibiting protein expression linked to both NF-κB and apoptosis pathways. Nevertheless, the role of GAA in cardiac hypertrophy has not yet been fully elucidated. Using transverse aortic constriction (TAC)-induced cardiac hypertrophy in mice, we analyzed the degree of hypertrophy using echocardiography and at the pathology and molecular levels. Our results demonstrate that GAA effectively attenuates Ang II-induced cardiomyocyte hypertrophy in vitro and reduces pressure overload-induced cardiac hypertrophy in vivo. Further investigation revealed that GAA exerts its anti-hypertrophic effects by downregulating the mRNA expression of hypertrophic and fibrotic markers and attenuating inflammatory responses, and that the protective effects of GAA may involve NF-κB signaling. This study provides valuable theoretical support for the potential therapeutic application of GAA in treating pathological myocardial hypertrophy and heart failure. Full article

Environmental DNA (eDNA) metabarcoding has emerged as a powerful tool for biodiversity monitoring, yet its accuracy is fundamentally constrained by the completeness and taxonomic reliability of reference sequence databases. For the Three Gorges Reservoir (TGR), no integrated multi-marker eDNA reference library exists, hampering standardized fish conservation monitoring under the Yangtze River Ten-Year Fishing Ban. Here, we constructed a comprehensive, multi-marker eDNA reference database for the fish fauna of the TGR, encompassing mitochondrial 12S rRNA, 16S rRNA, and cytochrome c oxidase subunit I (COI) gene sequences from 173 specimens (120 species) collected between 2021 and 2024. After integrating publicly available sequences, the final database comprised 161 species. Then, we quantitatively compared species annotation performance between this local database and public repositories. Results showed that while public databases achieved higher nominal species coverage (94.67%), they exhibited critical deficiencies in annotation accuracy, correctly annotating only 77.97% (12S rRNA), 75.00% (16S rRNA), and 38.14% (COI) of sequences from shared species under controlled conditions. In contrast, the local database exhibited 92.37%, 93.10% and 100% annotation accuracy for the respective markers. Optimal interspecific Kimura 2-parameter (K2P) thresholds for species delimitation were 0.00448 (12S rRNA), 0.00531 (16S rRNA), and 0.00734 (COI). In addition, 15, 0, and 4 species pairs exhibited zero interspecific distance for 12S rRNA, 16S rRNA, and COI, respectively. These limitations reinforce the need for cautious interpretation of eDNA metabarcoding results and the integration of multiple markers or complementary nuclear loci. This study provides preliminary evidence that regionally curated, multi-marker reference libraries could improve taxonomic assignment reliability in eDNA metabarcoding compared to uncurated public repositories, providing a foundational resource for biodiversity conservation. Full article