Search Results (1,668)

Heilongjiang Province, northeastern China, occupies a key metallogenic position between the eastern Central Asian Orogenic Belt and the western Pacific continental margin. This review integrates published geological, geochronological, fluid–inclusion, and H–O–S–Pb isotope data to evaluate tectonic controls on endogenic gold mineralization. Four principal metallogenic episodes are recognized: Caledonian porphyry-related mineralization associated with Paleo–Asian Ocean subduction–accretion; Variscan skarn-forming magmatic–hydrothermal activity; Indosinian post-collisional structurally focused and epithermal hydrothermal activity; and Yanshanian mineralization characterized by Late Jurassic compressional reworking followed by a dominant Cretaceous Paleo–Pacific-related magmatic–hydrothermal pulse. Ore-forming fluids evolved non-monotonically from Paleozoic high-temperature magmatic fluids to more heterogeneous crustal, metamorphic, and meteoric–water–influenced systems. Zhengguang and Laozuoshan provide clear evidence for temporally distinct superimposed mineralization. Later hydrothermal reactivation could either remobilize and reconcentrate Au or leach, disperse, and obscure earlier mineralization. Exploration should therefore prioritize reactivated structural corridors where intrusive centers, reactive wall rocks, volcanic basins, and multi-stage alteration overlap. Full article

The Northeast black soil region is a major grain-producing area in China, where county-scale assessment of topsoil quality is essential for soil conservation and provides a potential indicator framework for regional soil quality monitoring. In this study, 500 composite cultivated-layer samples were collected from dryland croplands in Keshan County, Heilongjiang Province. Soil physical properties, basic chemical properties, and macro-, secondary, and micronutrient contents were measured to establish a total data set (TDS). Three hierarchical total data sets (TDS1, TDS2, and TDS3) were established from the original TDS according to the progressive incorporation of soil physical properties, basic chemical properties, macronutrients, secondary nutrients, and micronutrients. Within each hierarchical TDS, key indicators were selected using random forest (RF), mutual information (MI), principal component analysis (PCA), and minimum spanning tree (Tree) methods to construct algorithm-specific minimum data sets (MDSs). TDS1 included soil physical properties, basic chemical properties, and macronutrients; TDS2 further incorporated secondary nutrients; and TDS3 additionally included micronutrients to represent progressively comprehensive soil nutrient information. The soils exhibited considerable soil organic matter and cation exchange capacity, with mean values of 51.45 g kg⁻¹ and 34.85 cmolc kg⁻¹, respectively, and a mean pH of 6.04. Across the four algorithms, commonly retained indicators included SOM, TN, pH, CEC, and Silt, while nutrient-related indicators such as AP, AK, S, Zn, and Fe were additionally selected under different hierarchical MDSs, reflecting the importance of multi-nutrient information in soil quality characterization. Available phosphorus, available potassium, sulfur, and zinc showed greater spatial variability than basic physicochemical properties. The four algorithms differed in indicator selection, reflecting their distinct sensitivities to linear variation, information gain, multilevel contributions, and network structure. RF_MDS1 showed the highest agreement with the TDS (R² = 0.924), indicating its strong capability in preserving overall soil quality information using a simplified indicator set. RF_MDS3 maintained a high consistency with the TDS (R² = 0.836) while incorporating additional secondary nutrients and micronutrients. Therefore, RF_MDS3 was considered a more comprehensive MDS framework when multi-nutrient representation and potential nutrient constraint identification were prioritized, whereas RF_MDS1 remained an efficient option for simplified soil quality assessment. This framework may provide a transferable approach for soil quality assessment and nutrient management in comparable black-soil regions and dryland farming systems. Full article

Water resource conservation and intensive utilization is an important pathway for promoting sustainable regional water resource management and high-quality development. Against the backdrop of increasing constraints on water resources, existing studies have paid insufficient attention to the multidimensional comprehensive assessment of water resource conservation and intensive utilization capacity and its underlying evolutionary mechanisms. Therefore, Heilongjiang Province was selected as the study area, and an evaluation system comprising 15 indicators was established. The game-theoretic combination weighting method, TOPSIS model, obstacle degree model, and GM(1,1) grey forecasting model were employed to comprehensively evaluate, diagnose obstacle factors, and predict the trend of water resource conservation and intensive utilization capacity in Heilongjiang Province from 2004 to 2023. The results showed that the overall capacity exhibited a fluctuating upward trend, with the comprehensive evaluation value increasing from 0.44 to 0.62. The industrial water reuse rate, effective utilization coefficient of farmland irrigation water, comprehensive water consumption rate, per capita water consumption, and ecological water use rate were the indicators with relatively high obstacle contributions. The obstacle factors exhibited distinct stage-specific characteristics: the constraining effects of efficiency-related indicators gradually weakened, whereas those of the comprehensive water consumption rate and per capita water consumption generally intensified, indicating that the factors constraining water resource conservation and intensive utilization in Heilongjiang Province underwent distinct stage-specific changes. The prediction results indicated that the capacity for water resource conservation and intensive utilization in Heilongjiang Province would continue to increase steadily in the future. However, balancing ecological water use requirements with growing water demand remains an important factor affecting sustainable water resource utilization. The evaluation–diagnosis–prediction framework developed in this study can provide a reference for the assessment and optimized management of regional water resource conservation and intensive utilization. Full article

Permafrost maps are important for regional planning in cold regions, but binary products do not represent gradients in thermal conditions, seasonal thaw response, or mapping confidence. This study developed an uncertainty-aware regional permafrost-condition screening framework for the Daxing’anling region during 2003–2022 by integrating TTOP-derived mean annual ground temperature (MAGT), reconstructed permafrost-occurrence probability, and Kudryavtsev-model-derived active-layer thickness (ALT). MAGT ranged from −4.11 to 5.28 °C, with a mean of −0.28 °C, and comparison with 23 published borehole records from 17 reported locations yielded r = 0.756 and RMSE = 0.419 °C. Explicit measurement depths were available for 12 of the 23 records and ranged from 10 to 15 m. Model-derived ALT ranged from 1.480 to 1.864 m. The complete point-scale evaluation using all 12 valid maximum depth of seasonal thaw (MDST) records yielded an RMSE of 0.92 m. Adding ALT changed 21.05% of valid-pixel assignments. Cold–low-response permafrost, Cold–moderate-response permafrost, Warm–enhanced-response permafrost, Near-thaw transitional permafrost, Marginal/low-confidence permafrost, and Non-permafrost occupied 7.3%, 17.1%, 14.9%, 4.1%, 13.9%, and 42.8% of the domain, respectively. Full Monte Carlo uncertainty propagation retained 78.65% modal agreement with the deterministic baseline, and 56.65% of the domain had a maximum class-membership probability below 0.60, whereas threshold-only perturbation retained 98.92% agreement. The framework is therefore suited to regional investigation and monitoring prioritization; project-level engineering decisions require direct geotechnical and deformation-based evidence. Full article

Jingpo Lake in Northeast China is a vital aquatic ecosystem, yet it lacks baseline molecular data for its insects. Trichoptera (caddisflies) are excellent ecological bioindicators, but their accurate identification is often hindered by morphological similarities, life-stage limitations, and insufficient DNA barcode records. To address this gap, this study combined morphological identification with DNA barcoding to establish a local reference database. From 285 collected adult caddisflies, 105 representative specimens were selected for genetic analysis. The survey identified 22 species across 9 families, revealing four new records for China and six for Heilongjiang Province. Analyses confirmed the high efficacy of the COI marker, demonstrating low intraspecific genetic divergence (0.78%) and high interspecific divergence (17.80%), with a clear barcode gap separating all examined species. Species accumulation curves indicated robust sampling of the main genera, although expanded collection efforts could still uncover additional species. In conclusion, this study provides a valuable DNA barcode reference library for Trichoptera in the Jingpo Lake region. It establishes foundational data for regional biodiversity inventories and future ecological monitoring based on environmental DNA (eDNA) or metabarcoding approaches. Full article

The spatial mismatch between tourism resources and tourism vitality represents one of the key constraints on the high-quality development of regional tourism. Taking 77 county-level administrative units in Heilongjiang Province as the study area, this research utilizes panel data from 2019 to 2024 to evaluate tourism resource endowment and tourism vitality through the entropy-weighted TOPSIS method. A coupling coordination degree model, quadrant classification approach, and spatial autocorrelation analysis are employed to identify spatial mismatch patterns. Furthermore, a progressive analytical framework integrating XGBoost-based nonlinear modeling, SHAP interpretability analysis, and Geodetector-based spatial validation is constructed to identify the main explanatory factors and nonlinear associations related to these mismatch patterns. The results reveal that: (1) significant spatial mismatches exist between tourism resources and tourism vitality across counties in Heilongjiang Province. The overall coupling coordination degree remains at a low coordination level and exhibits a distinct core–periphery spatial structure; (2) four categories of mismatch units are identified, including high high matching, resource-leading mismatch, vitality-leading mismatch, and low low matching types. These categories display pronounced spatial clustering characteristics, with a sharp contrast between the high-value clusters in the Harbin metropolitan area and border regions and the low-value clusters in western Suihua; (3) the number of hotels shows the highest explanatory contribution in the model, showing a clear threshold-like pattern, while border ports demonstrate a sparse but important association pattern; (4) strong interaction relationships exist among explanatory variables, with nearly 30% of the model’s explanatory power originating from synergistic multi-factor interactions; and (5) Geodetector analysis further confirms the spatial explanatory power of the major explanatory factors and the significance of multivariate interaction effects. This study provides policy references for optimizing the allocation of tourism resources and enhancing tourism vitality in underdeveloped border regions. Full article

Bridge crack detection is a critical task in structural health monitoring, yet existing deep learning methods often suffer from parameter redundancy in backbone networks, insufficient adaptivity to complex background interference in feature enhancement modules, and feature conflicts between classification and regression subtasks in coupled detection heads. To address these challenges, this paper proposes YOLO-STCDE, an improved lightweight detection model built upon YOLOv12. The model integrates three synergistic architectural innovations: (1) ST-Net, a lightweight backbone that replaces the original R-ELAN with a star operation-based design incorporating DynamicTanh activation for implicit high-dimensional feature mapping and adaptive amplitude calibration; (2) A2C2f-CD, a dual-dynamic gated neck module that embeds DynamicTanh and Convolutional Gated Linear Units into the A2C2f architecture, enhancing crack feature discrimination under complex backgrounds; and (3) Efficient-Detect, a decoupled detection head with a shared convolution stem that eliminates task conflict while substantially compressing parameter overhead. Extensive experiments on the bridge crack dataset demonstrate that YOLO-STCDE achieves 91.6% mAP@0.5 and 69.4% mAP@0.5:0.95 with only 2.25 M parameters and 5.6 GFLOPs, representing improvements of 3.2 and 9.5 percentage points over the YOLOv12n baseline, respectively, while simultaneously reducing the parameter count by 10.4%. Compared with state-of-the-art lightweight detectors including YOLOv11n and Hyper-YOLO, YOLO-STCDE attains the highest detection accuracy with the smallest model footprint, demonstrating an optimal balance between accuracy and efficiency for real-world bridge crack inspection. Full article

Strong heterogeneity in unconventional reservoirs leads to complex fracture propagation and challenges in quantitative stimulation evaluation. This study integrates true triaxial fracturing experiments, three-dimensional CT reconstruction, multi-field coupled numerical simulation, and multiple linear regression to investigate the fracture behavior of Gulong shale (Q1, Q9) and tight sandstone under supercritical carbon dioxide (SC-CO₂) fracturing. A fracture complexity index (FCI) that incorporates fractal dimension, spatial uniformity, and aperture distribution is proposed as a quantitative metric. The results show that SC-CO₂ significantly reduces breakdown pressure and increases fracture complexity compared to water. For Q9 shale, SC-CO₂ gives a breakdown pressure of 32.91 MPa (10.46% lower than water), a fractal dimension of 2.41, and an FCI of 8.92. In tight sandstone, the SC-CO₂ breakdown pressure is 34.12 MPa, whereas water increases it to 44.50 MPa; the fractal dimension and FCI are only 2.05 and 3.40, respectively, lower than those of shale fractured with water. Multiple linear regression quantifies contribution weights: lithological weak-plane development dominates fracture complexity (41.6%), far exceeding the brittleness index. The injection rate mainly controls stimulation scale: the damage area ratio rises from 1.79% to 2.90% when the rate increases from 10 to 40 mL/min. The horizontal stress difference is key to complexity enhancement: the fractal dimension increases from 1.9230 to 1.9901 as the stress difference rises from 0 to 4 MPa. The numerical simulations further reveal the coupled thermal-hydraulic-mechanical effects. The proposed FCI-based evaluation and regression models provide a quantitative framework for optimizing SC-CO₂ fracturing design in heterogeneous unconventional reservoirs. Full article

High temperature can impair leaf water status, photosynthetic function, and membrane stability in ornamental woody seedlings. However, integrated evidence combining gas exchange, chlorophyll fluorescence, oxidative injury, antioxidant activity, and osmotic-adjustment-related responses in heat-stressed Syringa oblata remains limited. One-year-old seedlings from a single nursery batch were exposed for 7 d to 25/18 °C or 40/30 °C day/night conditions and sprayed with either a solvent solution or 0.5 mM salicylic acid. Growth, leaf water status, photosynthetic pigments, gas exchange, pulse-amplitude-modulated fluorescence, OJIP/JIP-test parameters, oxidative-injury markers, antioxidant enzyme activities, and osmotic-adjustment-related compounds were evaluated. Heat treatment reduced leaf relative water content, photosynthetic performance, and photosystem II function and increased reactive oxygen species accumulation, lipid peroxidation, and electrolyte leakage. Compared with heat treatment alone, seedlings receiving salicylic acid under heat showed an 82.2% higher net photosynthetic rate, a 12.2% higher maximum quantum efficiency of photosystem II, and a 116.4% higher performance index on an absorption basis. Hydrogen peroxide, malondialdehyde, and electrolyte leakage were 35.1%, 35.8%, and 32.5% lower, respectively. Antioxidant enzyme activities were also higher under heat plus salicylic acid than under heat alone, whereas additional increases in proline and soluble sugars were not statistically confirmed; soluble protein was partially maintained. Gas exchange was measured after treatment at a common leaf-chamber temperature of 25 °C and therefore represented retained photosynthetic capacity under standardized conditions. Overall, foliar application of 0.5 mM salicylic acid was associated with partial maintenance of photosynthetic function and lower oxidative injury during short-term heat exposure. Because one chamber was assigned to each treatment combination in a single experimental run, possible chamber-specific effects could not be statistically separated from treatment-related differences. Independent validation is therefore required. Full article

Electrostatic spraying improves droplet deposition on the undersides of leaves and within canopy-obscured regions. However, existing studies mainly rely on two-dimensional trajectory analyses or simplified computational fluid dynamics (CFD) models, limiting the mechanistic understanding of the three-dimensional transport behaviour of charged droplets. To address this limitation, an integrated analytical framework combining theoretical droplet dynamics, CFD–DPM simulations, high-speed imaging, and wind-tunnel experiments was developed. Within this framework, a three-dimensional trajectory-tracking method was established to quantitatively characterise the electrostatic envelopment effect using measurable transport parameters, including droplet trajectories and effective electrostatic envelopment distance. Numerical simulations and experimental evaluations were combined to analyse the relationships among three-dimensional droplet transport, electrostatic envelopment, and leaf deposition performance. Results showed that deposition efficiency reached 15.92% at an induction voltage of 12 kV, representing an increase of 13.94 percentage points compared with uncharged spraying. Crosswind speed was the dominant factor affecting deposition, followed by induction voltage and spray pressure. The effective electrostatic envelopment distance was approximately 2.1 cm. The proposed framework enables quantitative characterisation of electrostatic envelopment and provides a mechanistic basis for analysing the relationship between three-dimensional droplet transport and deposition performance, offering a framework for electrostatic spraying evaluation and operating parameter optimisation. Full article

Mulberry leaves are used as functional food materials, but drought can reduce leaf production and physiological stability. This study evaluated whether foliar sodium selenite application could partially alleviate drought injury while increasing total selenium and selected functional-quality indicators in mulberry (Morus alba L.) leaves. Seedlings were assigned to four treatments: normal water supply with the surfactant-containing control spray (CK), normal water supply with sodium selenite application (Se), drought stress with the control spray (D+CK), and drought stress with sodium selenite application (D+Se). Drought reduced plant growth, leaf water status, leaf pigment status, gas exchange, and PSII photochemical performance while increasing oxidative damage and membrane injury. Under drought stress, sodium selenite application partially maintained growth, water status, photosynthetic performance, and antioxidant enzyme activities and was associated with lower oxidative-damage indicators. Drought alone increased several stress-responsive secondary metabolites but reduced biomass, polysaccharides, and soluble protein, indicating a trade-off rather than a uniform improvement in leaf quality. Compared with D+CK, D+Se increased total selenium, several functional compounds, and in vitro antioxidant capacity. Overall, foliar application of 10 μM sodium selenite was associated with partial drought-stress alleviation and higher functional-quality indicators under controlled pot conditions. Further studies are required to optimize the application dose and evaluate selenium speciation, bioaccessibility, intake safety, and field performance. Full article

The spoilage of blue honeysuckle during storage is mainly caused by specific dominant microorganisms, while most antimicrobial systems are developed without considering these target spoilage species. In this study, dominant spoilage yeasts were isolated from blue honeysuckle berries, and an antimicrobial system was constructed using these yeasts, along with common foodborne bacteria as target strains. The results showed that the antimicrobial system exhibited significant synergistic antimicrobial activity. Further analysis indicated that the essential oils disrupted the cell membrane structure and increased membrane permeability, thereby promoting the interaction of ε-PL with intracellular components. Multiple physiological alterations were detected after treatment, including membrane depolarization, leakage of intracellular components, accumulation of intracellular reactive oxygen species, and suppression of cellular metabolism. These observations suggested that damage to microbial cells might be synergistically induced by the composite system. Application of the system to blue honeysuckle berries effectively inhibited the growth of endogenous spoilage microorganisms and delayed quality deterioration during storage, and significantly reduced weight loss and decay rates, as well as changes in color, anthocyanin content, and vitamin C content. These results indicate that constructing antimicrobial systems based on dominant spoilage microorganisms can improve preservation efficiency and provide a useful approach for the storage of blue honeysuckle and similar fruits. Full article

Accurate estimation of forest tree species diversity at regional scales is essential for biodiversity monitoring, forest resource management, and ecological conservation. Because tree species differ in canopy spectral responses and phenological dynamics, multi-season remote sensing observations can provide critical information for characterizing species composition and diversity patterns. However, the potential contribution of seasonal image features to improving remote-sensing-based tree species diversity estimation has often been insufficiently considered. In this study, the Yichun forest region in Heilongjiang Province, northeastern China, was selected as the study area. Sentinel-1, Sentinel-2, and topographic data were integrated to extract multi-seasonal spectral, vegetation index, texture, radar, and topographic features. The Boruta algorithm was used for feature selection, and random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector regression (SVR), Bayesian regularized neural network (BRNN), and Stacking ensemble learning were developed to estimate and map Richness, Shannon, and Gini–Simpson indices. The results showed that: (1) Sentinel-2 optical features were the primary information source for tree species diversity estimation, topographic factors further improved model performance, and Sentinel-1 radar features mainly provided complementary structural information; (2) seasonal remote sensing features differed in their predictive ability, with Richness performing better in spring, while Shannon and Gini–Simpson achieved higher accuracy in winter. The four-season fusion scenario produced the highest accuracy for all three indices, with optimal R² values of 0.51, 0.63, and 0.57, respectively; (3) the Stacking ensemble generally improved estimation accuracy and model stability, although the optimal model differed among diversity indices, with Stacking, SVR, and RF performing best for Richness, Shannon, and Gini–Simpson, respectively; and (4) summer Sentinel-2 NDVI, GNDVI, and NDWI contributed strongly to all three indices, elevation was particularly important for Richness, and winter vegetation indices and autumn red-edge bands and texture features were also informative for Shannon and Gini–Simpson. These findings indicate that integrating multi-seasonal remote sensing features and multi-source data using machine learning models can effectively improve forest tree species diversity estimation, providing technical support for regional forest biodiversity monitoring and precision forest management. Full article

Research on Urban Comprehensive Carrying Capacity (UCCC) from the perspective of Production–Living–Ecological Spaces (PLES) can provide new insights for enhancing carrying capacity and offer decision-making references for sustainable urban development. Taking China’s three northeastern provinces as an example, this study, based on the PLES perspective, constructs an evaluation indicator system for UCCC. Combining the MEREC method and Grey Clustering Analysis, it assesses the UCCCs of the three northeastern provinces and analyzes the spatiotemporal evolution characteristics and coupling coordination patterns. On this basis, a Two-Way Fixed-Effects model (TW-FE) and a threshold panel model are employed to identify the key factors influencing the UCCCs and analyze the mechanism through which these factors affect the UCCCs. The research findings indicate the following: (1) From 2014 to 2023, the UCCCs in the three northeastern provinces showed an overall steady increase. The UCCC of Liaoning Province consistently remained higher than those of Jilin and Heilongjiang Provinces. In terms of the internal structure of the PLES, the Living Space Carrying Capacity (LSCC) was higher than the Production Space Carrying Capacity (PSCC) and Ecological Space Carrying Capacity (ESCC), serving as the primary driving force for the UCCC. (2) The UCCCs in the three northeastern provinces exhibited significant spatial heterogeneity. The UCCCs of Shenyang, Dalian, Changchun, and Harbin were higher than those of surrounding cities. The global Moran’s I indicated that strong spatial dependence had not yet formed, and regional synergy mechanisms remained weak. The local Moran’s I revealed that the spatial aggregation pattern displayed dynamic changes. (3) The coupling coordination degrees of the carrying capacities of the PLES in the three northeastern provinces and each of their prefecture-level cities still did not reach the “Barely Coordinated” stage during the study period. (4) The key influencing factors for the overall UCCCs in the three northeastern provinces were the Degree of Openness to the Outside World (DOOW), Financial Development Level (FDL), and Economic Development Level (EDL). The key influencing factors for the UCCCs differed among Liaoning, Jilin, and Heilongjiang Provinces, and these key factors exhibited a nonlinear effect on the UCCCs. Full article