Search Results (624)

Rape is one of the most widely cultivated and highest-yielding fruit crops in the world. However, research on its precise nutrient diagnosis and fertilization theory is severely lacking, significantly restricting the development of the grape industry. In this study, an L₁₆(4⁵) orthogonal experimental design was applied to determine the effects of varying ratios of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) on the fruit quality of ‘87-1’ grape (Vitis vinifera L.) cultivated in a facility, aiming to optimize nutrient application rates and improve fruit quality. Among the treatments T5 (N2P1K2Ca3Mg4), T14 (N4P2K3Ca1Mg4), and T9 (N3P1K3Ca4Mg2), treatment T9 had the most significant effect on single fruit weight, total soluble solids (TSS) content, fruit firmness (FF), and fruit quality index (FQI) and was conducive to the positive accumulation of the above quality indicators. Based on a comprehensive multi-factor analysis of variance, the optimal fertilization combination for achieving a high FQI was N3P1K2Ca1Mg2, corresponding to application rates of 375.0, 0, 168.8, 0, and 70.5 kg·hm⁻² for N, P₂O₅, K₂O, CaO, and MgO, respectively. Furthermore, to establish standards for multivariate compositional nutrient diagnosis (CND) and define the nutrient sufficiency range for ‘87-1’ grape fruit cultivated in a facility, the nutrient concentrations in various plant tissues and the soil and the FQI were measured across 80 treatments over five consecutive years. The nutritional status of the grapes cultivated under these treatments was calculated using the Technique for Order Preference by Similarity to Ideal Solution and the CND method. Based on the optimal nutrient ranges for high FQI sub-populations, a precise fertilization model was developed to facilitate economic fertilizer savings, quality improvement, and standardized grape cultivation in a facility. Full article

Sand dune migration, as a typical dynamic process of aeolian geomorphology in arid regions, directly influences regional ecological security and infrastructure development. Focusing on the western edge of the Kumtag Desert, this study uses remote sensing imagery and field investigations, combined with multi-factor meteorological observations and CMIP6 climate scenarios, to quantitatively analyze the migration characteristics and influencing factors of representative dunes, and to construct a predictive model for future migration trends. The dominant migration direction is W–WNW–NW, which closely matches the composite resultant drift potential. The average annual migration speed is 12.86 m·a⁻¹, classifying these dunes as fast-moving; small to medium dunes migrate faster (13.84 m·a⁻¹) than large dunes (11.27 m·a⁻¹). Wind speed, sand-moving wind frequency, drift potential (DP), Vegetation Fractional Cover (FVC), and precipitation significantly affect migration speeds; wind speed is the primary driver (single-factor R² = 0.41), while precipitation (R² = 0.26) and FVC (R² = 0.27) exert a suppressing effect, particularly on small to medium dunes. Based on stepwise multiple regression analysis combined with CMIP6 multi-model predictions, under the SSP8.5 scenario, characterized by significant temperature increases, drastic fluctuations in precipitation patterns, and notable increases in wind speed, the average annual sand dune migration speed is projected to reach 18.59 m·a⁻¹ by the end of this century, an increase of 5.78 m·a⁻¹ compared to the current speeds; whereas under the SSP1–2.6 and SSP2–4.5 scenarios, changes are projected to be minor and overall relatively stable. The findings of this study provide a scientific basis for regional infrastructure and engineering planning, as well as for the renovation and protection of existing oil and power transmission lines. Full article

The precise prediction of high-speed railway bridge pier settlement plays a crucial role in construction, maintenance, and long-term operation; however, current mainstream prediction methods mostly rely on independent analyses based on traditional or hybrid models, neglecting the impact of geological and environmental factors on subsidence. To address this issue, this paper proposes a multi-factor settlement prediction model for high-speed railway bridge piers named the Reversible Instance Normalization Multi-Scale Adaptive Resolution Stream CMamba, abbreviated as RMCMamba. During the data preprocessing process, the Enhanced PS-InSAR technology is adopted to obtain the time series data of land settlement in the study region. Utilizing the cubic improved Hermite interpolation method to fill the missing values of monitoring and considering the environmental parameters such as groundwater level, temperature, precipitation, etc., a multi-factor high-speed railway bridge pier settlement dataset is constructed. RMCMamba fuses the reversible instance normalization (RevIN) and the multiresolution forecasting head (MARSHead), enhancing the model’s long-range dependence capture capability and solving the time series data distribution drift problem. Experimental results demonstrate that in the multi-factor prediction scenario, RMCMamba achieves an MAE of 0.049 mm and an RMSE of 0.077 mm; in the single-factor prediction scenario, the proposed method reduces errors compared to traditional prediction approaches and other deep learning-based methods, with MAE values improving by 4.8% and 4.4% over the suboptimal method in multi-factor and single-factor scenarios, respectively. Ablation experiments further verify the collaborative advantages of combining reversible instance normalization and the multi-resolution forecasting head, as RMCMamba’s MAE values improve by 5.8% and 4.4% compared to the original model in multi-factor and single-factor scenarios. Hence, the proposed method effectively enhances the prediction accuracy of high-speed railway bridge pier settlement, and the constructed multi-source data fusion framework, along with the model improvement strategy, provides technological and experiential references for relevant fields. Full article

The soil carbon pool in saline–alkali land is a research hotspot in the field of agricultural environmental science. However, there are no systematic conclusions regarding the paddy soil carbon pool in the Yellow River Delta in China. Therefore, this study focused on the paddy soil in the Yellow River Delta; using statistical analysis methods and establishing relevant models, we explored the dynamic changes in organic carbon and its active components and their influencing factors in saline paddy fields under two planting patterns. The results showed that there was no significant difference in the dissolved organic carbon (DOC) content between the two planting patterns. However, the rice–wheat rotation pattern was more conducive to the accumulation of microbial biomass carbon (MBC). The soil organic carbon (SOC) and readily oxidizable organic carbon (ROC) contents increased under the two patterns and different salinization treatments. The results of the redundancy analysis and the random forest model indicated that SSA was the key environmental parameter affecting SOC and its active components under the single-season rice pattern. Under the rice–wheat rotation pattern, soil sucrase activity (SSA) was also a key environmental factor for predicting the SOC content, while electrical conductivity (EC) contributed the most to the active components of SOC. The PLS-PM model showed that the soil carbon sequestration capacity could be improved by enhancing soil enzyme activity under the rice–wheat rotation pattern, while the influence of the soil environment on SOC and its active components was not obvious under the single-season rice pattern. In general, the rice–wheat rotation pattern has agricultural advantages in terms of maintaining ecological balance and can be widely promoted in this region. The results of this study have important practical significance for promoting the green and low-carbon development of agriculture in the Yellow River Delta region and also lay a foundation for subsequent long-term positioning observations and studies on multi-factor interactions. Full article

Scientific assessment of sustainable development potential (SDP) and analysis of spatial heterogeneity mechanisms of traditional villages are crucial for promoting the synergy between cultural heritage conservation and rural revitalization strategies. With an emphasis on traditional villages in the Cantonese region, this study develops a thorough evaluation methodology that combines spatial analysis and multi-criteria decision-making. It aims to (1) systematically reveal the spatial differentiation characteristics of sustainable development potential; (2) develop and validate a combined weighting method that effectively integrates both subjective and objective weights; and (3) identify key driving factors and their interaction mechanisms influencing the formation of this potential. To achieve these objectives, the research sequentially conducted the following steps: First, an evaluation indicator system encompassing socioeconomic, cultural, ecological, and infrastructural dimensions was developed. Second, the Analytic Hierarchy Process and the Entropy Weight Method were employed to calculate subjective and objective weights, respectively, followed by integration of these weights using a combined weighting model. Subsequently, the potential assessment results were incorporated into a Geographic Information System, and spatial autocorrelation analysis was applied to identify agglomeration patterns. Finally, the Geographical Detector model was utilized to quantitatively analyze the explanatory power of various influencing factors and their interactions on the spatial heterogeneity of potential. The main findings are as follows: First, the sustainable development potential of traditional Cantonese villages exhibits a significant “core–periphery” spatial structure, forming a high-potential corridor in the Zhongshan–Jiangmen–Foshan border area, while peripheral areas generally display “low–low” agglomeration characteristics. Second, the combined weighting model effectively reconciled 81.0% of case discrepancies, significantly improving assessment consistency (Kappa coefficient above 0.85). Third, we identified economic income (q = 0.661) and ecological baseline (q = 0.616) were identified as key driving factors. Interaction detection revealed that the interaction between economic income and transportation accessibility had the strongest explanatory power (q = 0.742), followed by the synergistic effect between ecological baseline and architectural heritage (q = 0.716), highlighting the characteristic of multi-factor synergistic driving. The quantitative and spatially explicit evaluation framework established in this study not only provides methodological innovation for research on the sustainable development of traditional villages but also offers a scientific basis for formulating regionally differentiated revitalization strategies. The research findings hold significant theoretical and practical importance for achieving a positive interaction between the conservation and development of traditional villages. Full article

The Strait of Malacca faces persistent maritime safety challenges due to high vessel density and complex navigational conditions. Current risk assessment methods often lean towards treating static accident analysis and dynamic traffic modeling separately, although some nascent hybrid approaches exist. However, these hybrids frequently lack the capacity for comprehensive, real-time factor integration. This study proposes an integrated framework coupling accident hotspot identification with vessel traffic network analysis. The framework combines trajectory clustering using improved DBSCAN with directional filters, Kernel Density Estimation (KDE) for accident hotspots, and Fuzzy Analytic Hierarchy Process (FAHP) for multi-factor risk evaluation, acknowledging its subjective and region-specific nature. The model was trained and tuned exclusively on the 2023 dataset (47 incidents), reserving the 2024 incidents (24 incidents) exclusively for independent, zero-information-leakage validation. Results demonstrate superior performance: Area Under the ROC Curve (AUC) improved by 0.14 (0.78 vs. 0.64; +22% relative to KDE-only), and Precision–Recall AUC (PR-AUC) improved by 0.16 (0.65 vs. 0.49); both p < 0.001. Crucially, all model tuning and parameter finalization (including DBSCAN/Fréchet, FAHP weights, and adaptive thresholds) relied solely on 2023 data, with the 2024 incidents reserved exclusively for independent temporal validation. The model captures 75.2% of reported incidents within 20% of the study area. Cross-validation confirms stability across all folds. The framework reveals accidents concentrate at network bottlenecks where traffic centrality exceeds 0.15 and accident density surpasses 0.6. Model-based associations suggest amplification through three pathways: environmental-mediated (34%), traffic convergence (34%), and historical persistence (23%). The integrated approach enables identification of both where and why maritime accidents cluster, providing practical applications for vessel traffic services, risk-aware navigation, and evidence-based safety regulation in congested waterways. Full article

Rainfall-driven non-point source (NPS) pollution has become a critical issue for water environment management in urban watershed systems. However, single-model use is limited to fully represent the intricate processes of rainfall-correlated NPS pollution generation and dispersion for effective decision-making. This study develops a novel cross-scale, multi-factor coupled model framework to characterize hydrologic and NPS pollution responses to different rainfall events in Shenzhen, China, a representative worldwide metropolis facing challenges from rapid urbanization. The calibrated and validated coupled model achieved remarkable agreements with observed hydrologic (Nash–Sutcliffe efficiency, NSE > 0.81) and water quality (NSE > 0.85) data in different rainfall events and demonstrated high-resolution dynamic changes in flow and pollutant transfer within the studied watershed. In an individual rainfall event, heterogeneous spatial distributions of discharge and pollutant loads were found, highly correlated with land use types. The temporal change pattern and risk of flooding and NPS pollution differed significantly with rainfall intensity, and the increase in the pollutants (mean 322% and 596%, respectively) was much larger than the discharge (207% and 302%, respectively) under intense rainfall conditions. Based on these findings, a decision-support framework was established, featuring land-use-driven spatial prioritization of industrial hotspots, rainfall-intensity-stratified management protocols with event-triggered operational rules, and integrated source-pathway-receiving end intervention strategies. The validated model framework provides quantitative guidance for optimizing infrastructure design parameters, establishing performance-based regulatory standards, and enabling real-time operational decision-making in urban watershed management. Full article

This paper develops a nonlinear shrinkage estimation method for higher-order moment matrices within a multifactor model framework and establishes its asymptotic consistency under high-dimensional settings. The approach extends the nonlinear shrinkage methodology from covariance to higher-order moments, thereby mitigating the “curse of dimensionality” and alleviating estimation uncertainty in high-dimensional settings. Monte Carlo simulations demonstrate that, compared with linear shrinkage estimation, the proposed method substantially reduces mean squared errors (MSEs) and achieves greater Percentage Relative Improvement in Average Loss (PRIAL) for covariance and cokurtosis estimates; relative to sample estimation, it delivers significant gains in mitigating uncertainty for covariance, coskewness, and cokurtosis. An empirical portfolio analysis incorporating higher-order moments shows that, when the asset universe is large, portfolios based on the nonlinear shrinkage estimator outperform those constructed using linear shrinkage and sample estimators, achieving higher annualized return and Sharpe ratio with lower kurtosis and maximum drawdown, thus providing stronger resilience against uncertainty in complex financial systems. In smaller asset universes, nonlinear shrinkage portfolios perform on par with their linear shrinkage counterparts. These findings highlight the potential of nonlinear shrinkage techniques to reduce uncertainty in higher-order moment estimation and to improve portfolio performance across diverse and complex investment environments. Full article

Accurate spatiotemporal forecasting of electric vehicle (EV) charging load is essential for smart grid management and efficient charging service operation. This paper introduced a novel spatiotemporal graph convolutional network with cross-attention (STGCN-Attention) for multi-factor charging load prediction. The model demonstrated a strong capability to capture cross-scale spatiotemporal correlations by adaptively integrating historical charging load, charging pile occupancy, dynamic electricity prices, and meteorological data. Evaluations in real-world charging scenarios showed that the proposed model achieved superior performance in hour forecasting, reducing Mean Absolute Error (MAE) by 9% and 16% compared to traditional STGCN and LSTM models, respectively. It also attained approximately 30% higher accuracy than 24 h prediction. Furthermore, the study identified an optimal 1-2-1 multi-scale temporal window strategy (hour–day–week) and revealed key driver factors. The combined input of load, occupancy, and electricity price yielded the best results (RMSE = 37.21, MAE = 27.34), while introducing temperature and precipitation raised errors by 2–8%, highlighting challenges in fine-grained weather integration. These findings provided actionable insights for real-time and intraday charging scheduling. Full article

This paper explores the key professional and institutional factors that influence the integration of environmental, social, and governance (ESG) considerations into financial evaluation and auditing processes. The study investigates the impact of legal familiarity, ESG experience, professional qualifications, and digital competencies on ESG readiness among financial analysts, auditors, and economists. By integrating a structured review of academic literature with an in-depth analysis of European regulatory instruments, the research identifies how dual materiality principles, standardized ESG metrics, and taxonomy-aligned disclosures reshape professional practices. A structured, ethics-approved survey (10 items) was administered nationally, and 145 responses were retained for analysis across economists, analysts, and auditors. Descriptive statistics, Pearson correlations, and linear/multiple regressions were used to test three hypotheses regarding ESG experience, legislative familiarity, and multifactor effects. The results reveal that familiarity with EU legislation is the strongest predictor of ESG integration capacity, while ESG-related experience and digitalization also show moderate to strong influence. The multiple regression model confirms the multifactorial nature of ESG implementation, though not all professional predictors contribute equally. Residual analysis confirms the statistical robustness of the models. The study highlights the need for regulatory literacy, targeted training, and digital adaptation as critical components of ESG competency. Full article

The present study investigated the heat extraction behavior of the horizontal well closed-loop geothermal systems under multi-factor influences. Particularly, the numerical model was established based on the geological condition of the geothermal field in Xiong’an New Area, and the XGBoost-SHAP (eXtreme Gradient Boosting and SHapley Additive exPlanations) algorithm was employed for multivariable analysis. The results indicated that the produced water temperature and thermal power of a 3000 m-long horizontal well were 2.61 and 4.23 times higher than those of the vertical well, respectively, demonstrating tantalizing heat extraction potential. The horizontal section length (SHAP values of 8.13 and 165.18) was the primary factor influencing production temperature and thermal power, followed by the injection rate (SHAP values of 1.96 and 64.35), while injection temperature (SHAP values of 1.27 and 21.42), geothermal gradient (SHAP values of 0.95 and 19.97), and rock heat conductivity (SHAP values of 0.334 and 17.054) had relatively limited effects. The optimal horizontal section length was 2375 m. Under this condition, the produced water temperature can be maintained higher than 40 °C, thereby meeting the heating demand. These findings provide important insights and guidance for the application of horizontal wells in hydrothermal reservoirs. Full article

The traditional continuous, quantitative spraying technology ignores the severity of pests, diseases and grasses, spatial distribution and other differences, resulting in low effective utilization of pesticides, environmental pollution and other problems. Variable-rate spray technology has become an important development direction in the field of precision agriculture by dynamically sensing crop canopy morphology, pest and disease distribution, and environmental parameters, adjusting the application amount in real time, and significantly improving pesticide utilization. In this study, we systematically review the core progress of variable-rate spray technology; focus on the technical system of information detection, spray volume model, and control system; analyze the current bottlenecks; and propose an optimization path to adapt to the complex agricultural conditions. At the level of information perception, LiDAR, machine vision, and multi-source sensor fusion technology constitute the main perception architecture, and infrared and ultrasonic sensors assist target recognition in complex scenes. In the construction of the spray volume model, models based on canopy volume, leaf area density, etc., are used to realize dynamic application decision by fusing equipment operating parameters, pest and disease levels, meteorological conditions, and so on. The control system takes the solenoid valve + PID control as the core program, and improves the response speed through PWM regulation and closed-loop feedback. The current technical bottlenecks are mainly concentrated in the sensor dynamic detection accuracy, model environmental adaptability, and the reliability of the execution parts. In the future, it is necessary to further promote anti-jamming multi-source heterogeneous sensor data fusion, multi-factor adaptive spray model development, lightweight edge computing deployment, and solenoid valve structural parameter optimization and other technical research, with a view to promoting the application of variable-rate spray technology to the field on a large scale and providing a theoretical reference and technological support for the green transformation of agriculture. Full article

This study systematically investigates the variation in the ceiling flame tilt angle in an immersed tube tunnel under the combined effect of longitudinal ventilation and sidewall smoke extraction. The experimental program considers different longitudinal velocities, various sidewall smoke exhaust rates and multiple relative distances between the fire source and the sidewall exhaust outlet, aiming to comprehensively reveal the flame tilt angle under multi-factor coupling conditions. Experiments were carried out in a reduced-scale tunnel model (6.64 m long, 0.96 m wide and 0.5 m high). A porous gas burner supplied a steady heat release, with its distance from the sidewall exhaust outlet systematically varied. Results indicate that the flame tilt angle decreases as the distance between the fire source and the sidewall exhaust outlet increases. A theoretical model was developed to predict the flame tilt angle by incorporating both the sidewall smoke exhaust rate and the relative fire source–exhaust distance. The model accounts for mass loss due to smoke extraction, estimated from the local longitudinal velocity distribution. Predictions from the proposed model agree well with the experimental data. Full article

In recent years, frequent wildfires have posed significant threats to both the ecological environment and socioeconomic development. Investigating the mechanisms underlying the influencing factors of forest fires and accurately predicting the likelihood of such events are crucial for effective prevention strategies. However, the field currently faces challenges, including the unclear characterization of influencing factors, limited accuracy in forest fire predictions, and the absence of models for mountain fire scenarios. To address these issues, this study proposes a research framework of “decoupling analysis-model prediction-scenario validation” and employs Principal Component Analysis (PCA) and Shapley Additive Explanations (SHAP) value quantification to elucidate the significant roles of meteorological as well as combustible state indicators through multifactor coupling. Furthermore, the Attention-based Long Short-Term Memory (ALSTM) network trained on PCA-decoupled data achieved mean accuracy, recall, and area under the precision-recall curve (PR-AUC) values of 97.82%, 94.61%, and 99.45%, respectively, through 10-time cross-validation, significantly outperforming traditional LSTM neural networks and logistic regression (LR) methods. Based on digital twin technology, a three-dimensional mountain fire scenario evolution model is constructed to validate the accuracy of the ALSTM network’s predictions and to quantify the impact of key factors on fire evolution. This approach offers an interpretable solution for predicting forest fires in complex environments and provides theoretical and technical support for the digital transformation of forest fire prevention and management. Full article

This article presents an integrated assessment of pasture ecosystem degradation under conditions of extreme aridity in the Atyrau Region, where high livestock density, limited grazing capacity, and institutional fragmentation of land tenure exacerbate degradation risks. The study aimed to conduct a spatio-temporal analysis of pasture conditions and identify critical load zones to support sustainable management strategies. The methodology was based on a multi-factor Anthropogenic Load (AL) model integrating (1) calculation of pasture load (PL) using 2023 agricultural statistics with livestock numbers converted into livestock units; (2) spatial analysis of grazing concentration through Kernel Density Estimation in ArcGIS 10.8; (3) assessment of infrastructural accessibility (Accessibility Index, A_i); and (4) quantitative evaluation of institutional land use organization (Institutional Index, I_i). This integrative approach enabled the identification of stable, transitional, and critically overloaded zones and provided a cartographic basis for sustainable management. Results revealed persistent degradation hotspots within 3–5 km of water sources and settlements, while up to 40% of productive pastures remain excluded from use. The proposed AL model demonstrated high reproducibility and applicability for environmental monitoring and regional land use planning in arid regions of Central Asia. Full article