Search Results (4,041)

Tracking ecosystem productivity in fast-evolving estuarine wetlands is often constrained by the trade-off between spatial detail and temporal continuity in satellite observations. To address this, we developed a reproducible fusion–VPM framework that integrates multi-sensor data to map Gross Primary Production (GPP) at a high spatiotemporal resolution. By combining the Flexible Spatiotemporal Data Fusion (FSDAF) method with a Time-Series Linear Fitting Model (TSLFM), we constructed a continuous 30 m, 8-day vegetation index record for China’s Yellow River Delta (YRD) from 2000 to 2021. This record was propagated through the Vegetation Photosynthesis Model (VPM) to simulate GPP and quantify the relative contributions of land-use/land-cover change (LUCC) versus environmental factors. The results show a marginally significant increase in total GPP (9.74 Gg C a⁻¹, p = 0.074) over the last two decades. Deconvolution of driving factors reveals that 87.45% of the GPP increase occurred in stable land-cover areas, where the Enhanced Vegetation Index (EVI) was the dominant driver (explaining 79.97% of the variability). In areas undergoing LUCC, the net effect on GPP primarily reflected the combined influences of artificial saline–alkali wetland expansion and cropland expansion: water-to-vegetation conversions enhanced GPP, whereas vegetation-to-water conversions fully offset these gains. This study demonstrates the efficacy of spatiotemporal data fusion in overcoming observational gaps and provides a transferable analytical framework for diagnosing carbon dynamics in complex, dynamic deltaic ecosystems. This study not only provides a critical, high-resolution assessment of carbon dynamics for the YRD but also delivers a generalizable analytical framework for mapping and attributing GPP trends in complex deltaic ecosystems worldwide. Full article

Background and Objectives: The effect of growth hormone (GH) on body composition is well recognized, and recombinant human GH (rGH) therapy may improve lean mass and related parameters. The aim of this study was to analyze changes in body composition parameters and lipid profile under rGH treatment in children diagnosed with short stature and to explore potential influencing factors. Materials and Methods: A secondary data analysis was conducted in the Endocrinology Department of the Mures County Hospital, Romania, approved by the local Ethics Committee. All children diagnosed with short stature and receiving rGH treatment were eligible for inclusion if they had four body composition analyses at least 6 months apart. Analyzed variables included age, gender, environment, mean rGH dose, height and body mass index (BMI) SDS, body composition parameters assessed by bioimpedance, and family-related variables. Statistical analysis was performed using SPSS v.25 with a level of significance α = 0.05. Results: There was no statistically significant trend in body composition parameters taken during serial measurements, except for the sarcopenic index and height (p < 0.001). Environment, pubertal development, and family-related variables other than maternal BMI had no significant influence on body composition or lipid profile. Gender differences in body composition revealed that the change in muscle mass (p = 0.009) and skeletal muscle mass (p = 0.013) was statistically significantly higher for boys, and body fat (p = 0.013) for girls. In linear regression analysis, mother’s BMI emerged as a significant predictor for changes in high-density lipoprotein cholesterol (HDL-C) levels (p = 0.032, β = −0.691) during rGH therapy. Body composition changes did not differ by treatment indication. Conclusions: Gender may be associated with treatment-related changes in body composition during pediatric rGH therapy, while maternal BMI may predict HDL-C variation. rGH treatment appears to improve the sarcopenic index and has minimal and variable effects on the lipid profile. Full article

Fissured rock masses are widespread in deep underground mining engineering, and they are prone to inducing instability and failure during excavation activities. Borehole pressure relief is one of the most effective measures with which to control dynamic disaster in high-stress roadways. After pressure relief, redistribution of stress leads to stress concentration in the far-field surrounding rock (far away from working face), which can be represented by true triaxial compression state. However, current research on the energy evolution behavior of fissured rock masses under far-field conditions remains relatively limited. This study analyzes the energy evolution process, peak energy characteristics, and laws of energy storage and dissipation in fractured sandstone under different fissure dip angles (θ, 30°, 45°, 60°, 90°), with intermediate principal stresses (σ₂, 10, 20, … 120 MPa) and minimum principal stresses (σ₃, 10, 20, … 50 MPa). The results indicate that the curve of dissipated energy ratio versus maximum principal strain becomes more distinctly concave as θ increases under true triaxial compression. The growth rate of the dissipated energy ratio and dissipated energy with maximum principal strain gradually decreases when σ₃ is high, and the fissured sandstone is prone to exhibiting ductile failure, leading to a reduced energy dissipation rate. The peak elastic strain energy of fissured sandstone increases gradually with increasing σ₂ and shows a linear characteristic. The energy storage and dissipation law is nonlinear with increasing peak total energy for the fissured sandstone with different values of θ. However, the law exhibits a linear trend under varying σ₂ and σ₃. This study provides a new approach and insight into the failure characteristics of deep fissured sandstone and aims to offer theoretical guidance for the layout and construction safety of roadways or mining panels in far-field surrounding rock in future engineering practices. Full article

Floodplain vegetation is an essential part of riverine wetland ecosystems. Hydrological fluctuations significantly influence its survival and distribution. This study examines the floodplain vegetation of the Jingjiang section of the Yangtze River. This study uses annual mean NDVI data over six time periods from 2000 to 2023 to represent the changes in floodplain vegetation. The driving factors include inundation frequency, annual mean temperature, annual mean precipitation, elevation, and slope gradient. To analyze the data, this study employs multiple analytical methods, including threshold segmentation, pixel-by-pixel linear regression (using the least squares method), Geodetector, and Pearson’s correlation analysis. This study clarifies the spatiotemporal evolution of the NDVI and the distribution of vegetation in these floodplain. It also quantitatively assesses the influence of multiple drivers and reveals the areas and extent of vegetation distribution affected by different inundation frequencies. The findings indicate: (1) Over six time periods from 2000 to 2023, NDVI values and the area covered by vegetation in the Jingjiang section of the Yangtze River floodplain exhibited fluctuating growth trends. The area covered by vegetation increased by 66.94 km² in 2023 compared with that in 2000. (2) NDVI values were influenced by multiple interacting drivers, with inundation frequency being the dominant factor affecting vegetation change in the Jingjiang section (q-value: 0.79–0.86), followed by slope (q-value: 0.46–0.56). Interactions between different drivers amplify their impact on the annual average NDVI value. (3) Areas with inundation frequencies of 20%–40% exhibit positive spatial correlation with NDVI values. The maximum area of positive correlation is 112.51 km², which is predominantly distributed across the central and marginal bars of the Jingjiang section. Within this range, inundation frequency has the strongest positive effect on vegetation growth. Full article

This study presents a validated framework to quantify regional sea-level risk on U.S. coasts by (i) extracting trends and seasonality from satellite altimetry (ADT, GMSL), (ii) learning regional dynamical regimes via PCA-embedded KMeans on gridded ADT time series, and (iii) coupling these regimes with socioeconomic exposure (population, income, ocean-sector employment/GDP) and wetland submersion scoring. Relative to linear and ARIMA/SARIMA baselines, a sinusoid+trend fit and an LSTM forecaster reduce out-of-sample error (MAE/RMSE) across the North Atlantic, North Pacific, and Gulf of Mexico. The clustering separates high-variability coastal segments, and an interpretable submersion score integrates elevation quantiles and land cover to produce ranked adaptation priorities. Overall, the framework converts heterogeneous physical signals into decision-ready coastal risk tiers to support targeted defenses, zoning, and conservation planning. Full article

This study provides a systematic analysis of the spatiotemporal distribution and trends in the frequency of significant wave height (SWH) exceeding level 5 (SWH > 2.5 m) and level 7 (SWH > 6 m) in the Northwest Pacific (NWP) for 1993–2024, which are defined as f₅ and f₇, respectively, as well as their correlations with major climate indexes. Our results indicate that (1) the high-value zones for the annual mean f₅ and f₇ are both located in the south waters of the Aleutian Islands, with maximum values of 58.0% and 6.4%, respectively. Winter’s contribution is greatest (maximum values of 96.9% and 16.8% per year), while summer’s is the smallest. (2) f₅ exhibits a significant decline trend across the entire NWP basin (of −0.15 to −0.30%/yr), with the steepest decline occurring in autumn (−0.69%/yr) and the shallowest in summer. f₇ exhibits a significant linear decrease in the open ocean east of Japan (−0.08%/yr) while showing a significant linear increase in the waters east of the Kamchatka Peninsula (0.08%/yr). Both variations peak in winter (maximum values of −0.27% and 0.30% per year) and are smallest in summer. (3) Seasonal and regional variations in climate index–f₅ and f₇ relationships reflect large-scale atmospheric modulation of waves. For example, the Oceanic Niño Index shows a predominantly negative correlation with f₅ in winter (maximum correlation coefficient r_m = −0.70) around the Luzon Strait, shifting to a significant positive correlation in summer (r_m = 0.70) across the extensive region east of Taiwan Island and the Philippines. The Pacific Decadal Oscillation index shows a significant positive correlation with f₇ in summer and autumn (r_m = 0.69) east of Taiwan Island and a strong negative correlation in winter (r_m = −0.77) to the east of Kamchatka Peninsula. Full article

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F₂ population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation. Full article

This study provides a practice-oriented sensitivity analysis of DeePC for pressure management in water distribution systems. Two public benchmark systems were used, Fossolo (simpler) and Modena (more complex). Each run fixed a monitored node and pressure reference, applied the same randomized identification phase followed by closed-loop control, and quantified performance by the mean absolute error (MAE) of the node pressure relative to the reference value. To better characterize closed-loop behavior beyond MAE, we additionally report (i) the maximum deviation from the reference over the control window and (ii) a valve actuation effort metric, normalized to enable fair comparison across different numbers of valves and, where relevant, different control update rates. Motivated by the need for practical guidance on how hydraulic boundary conditions and algorithmic choices shape DeePC performance in complex water networks, we examined four factors: (1) placement of an additional internal PRV, supplementing the reservoir-outlet PRVs; (2) the control time step $(\Delta t)$; (3) a uniform reservoir-head offset $(\Delta h)$; and (4) DeePC regularization weights ${(\lambda }_g,{\lambda }_u,{\lambda }_y)$. Results show strong location sensitivity, in Fossolo, topologically closer placements tended to lower MAE, with exceptions; the baseline MAE with only the inlet PRV was 3.35 [m], defined as a DeePC run with no additions, no extra valve, and no changes to reservoir head, time step, or regularization weights. Several added-valve locations improved the MAE (i.e., reduced it) below this level, whereas poor choices increased the error up to ~8.5 [m]. In Modena, 54 candidate pipes were tested, the baseline MAE was 2.19 [m], and the best candidate (Pipe 312) achieved 2.02 [m], while pipes adjacent to the monitored node did not outperform the baseline. Decreasing $\Delta t$ across nine tested values consistently reduced MAE, with an approximately linear trend over the tested range, maximum deviation was unchanged (7.8 [m]) across all $\Delta t$ cases, and actuation effort decreased with shorter steps after normalization. Changing reservoir head had a pronounced effect: positive offsets improved tracking toward a floor of ≈0.49 [m] around $\Delta h$ ≈ +30 [m], whereas negative offsets (below the reference) degraded performance. Tuning of regularization weights produced a modest spread (≈0.1 [m]) relative to other factors, and the best tested combination (λy, λg, λu) = (10², 10⁻³, 10⁻²) yielded MAE ≈ 2.11 [m], while actuation effort was more sensitive to the regularization choice than MAE/max deviation. We conclude that baseline system calibration, especially reservoir heads, is essential before running DeePC to avoid biased or artificially bounded outcomes, and that for large systems an external optimization (e.g., a genetic-algorithm search) is advisable to identify beneficial PRV locations. Full article

Macroalgae plays a significant role in the formulation of innovative and environmentally sustainable approaches to address food challenges. Specifically, green macroalgae serve as dietary supplements aimed at improving the health, growth, and feeding efficiency of various species of marine and freshwater fishes, as well as mollusks. The effects of Chaetomorpha linum extract (CLE) on growth performance, physiological responses, and disease resistance are studied in Bellamya bengalensis against Aeromonas hydrophila. In this experiment, adult B. bengalensis (4412 ± 165.25 mg) were randomly divided into 15 rectangular glass aquariums (35 snail/aquaria; 45 L capacity) and their basal diet was supplemented with different levels of CLE, including 0 (CLE0), 1 (CLE1), 2 (CLE2), 3 (CLE3), and 4 (CLE4) g/kg for 60 days. The growth performance in the CLE3 dietary group was significantly higher that of the CLE0 group, exhibiting both linear and quadratic trends in relation to dietary CLE levels (p < 0.05). The activities of pepsin, amylase, and lipase were found to be highest in CLE3 and lowest in CLE0. Both linear and quadratic responses to dietary CLE levels in digestive enzymes were observed (p < 0.05). The activities of superoxide dismutase and catalase in the hepatopancreas were found to be elevated in snails due to the synergistic effect of the supplemented CLE diet. Among different levels of diet given, CLE2-supplemented snails showed an increase in their enzyme activity (p < 0.05). Interestingly, all the CLE-treated snails expressed elevated levels of mucus lysozyme and mucus protein when compared to control (p < 0.05). Additionally, hepatopancreatic acid phosphatase and alkaline phosphatase activity were elevated in snails consuming CLE3 (p < 0.05). The transcription levels of immune-related genes, including mucin-5ac and cytochrome, were significantly elevated in snails that were fed a diet supplemented with 2–4 g of CLE/kg. Furthermore, the transcription level of the acid phosphatase-like 7 protein gene also increased in snails receiving CLE-supplemented diets. After a 14-day period of infection, snails that consumed a diet supplemented with 3–4 g/kg of CLE exhibited a notable increase in survival rates against virulent A. hydrophila. Based on the above findings, it is suggested that a diet supplemented with 3 g/kg of CLE may enhance growth, antioxidant and immune defense, and disease resistance in the freshwater snail B. bengalensis. Full article

Determining long-term changes in global ice and water storage from satellite gravimetry remains challenging due to the limited temporal coverage of high-resolution missions. Here, we combine Satellite Laser Ranging (SLR) and Gravity Recovery and Climate Experiment (GRACE) data to reconstruct large-scale, non-linear mass variations from 1995 to 2024, extending gravity-based observations into the pre-GRACE era while preserving spatial detail through backward extrapolation. The combined model reveals widespread and statistically significant accelerations in global water and ice mass changes and enables the identification of key turning points in their temporal evolution. Results indicate that in Svalbard, a non-linear transition in ice mass balance occurred in late 2004, followed by a pronounced acceleration of mass loss due to climate warming. Glaciers in the Gulf of Alaska exhibit persistent mass loss with a marked intensification after 2012, while in the Antarctic Peninsula, ice mass loss substantially slowed and a potential trend reversal emerged around 2021. The reconstructed mass anomalies show strong consistency with independent satellite altimetry and climate indicators, including a clear response to the 1997/1998 El Niño event prior to the GRACE mission. These findings demonstrate that integrating SLR with GRACE enables robust detection of non-linear, climate-driven mass redistribution on a global scale and provides a physically consistent extension of satellite gravimetry records beyond the GRACE era. Full article

Pipeline leakage is a common issue in many pressurized pipeline systems, with significant hazards, making it a current research hotspot. To reveal the fundamental characteristics of leakage in straight pipelines and 90° elbows transporting different media and thereby predict leakage locations, this paper conducts numerical calculations of the internal flow, while also predicting the pipeline leakage location monitoring model. The study finds that under air medium conditions, the nonlinear function model demonstrates excellent prediction accuracy, with R² > 0.99 for the water₃ condition. Under water medium conditions, the model’s fitting performance gradually weakens with increasing inlet pressure, with R² dropping to 0.77. For a bent pipe, when air is used as the medium, the pressure peak at the large bend angle increases significantly under high inlet pressure. In contrast, when water is the medium, the local pressure reconstruction effect in the bent pipe exhibits a linear strengthening trend as the inlet pressure increases. Full article

The optimization of grinding is a multi-objective problem characterized by high dimensionality, non-linearity, and complexity. Solving this multi-objective optimization (MOO) problem is one of the most challenging tasks in the field of mechanical engineering. In-depth research on multi-objective parameter optimization technology for grinding is of great significance for improving processing efficiency, optimizing product quality, and reducing energy consumption. This paper takes the multi-objective optimization problem of grinding as its starting point. First, it introduces the basic theory of multi-objective optimization and two primary methods for solving such problems: optimization target dimension reduction and multi-objective optimization. Second, the key technologies of the two methods are reviewed, including the modeling method of the optimization problem, the multi-objective optimization algorithm for solving the optimization model, and the prior and posterior trade-off methods used to obtain the compromised optimal solutions. Finally, the existing problems of the multi-objective optimization methods in grinding processing are summarized and the future development trends are predicted. This paper aims to provide researchers with a comprehensive understanding of the multi-objective optimization technology in grinding processing, enabling them to make more reasonable decisions when dealing with actual multi-objective optimization problems. Full article

The photovoltaic field has seen significant development in recent years, with continuously expanding installation capacity and increasing grid integration. However, due to the intermittency of solar energy and meteorological variability, PV output power poses serious challenges to grid security and dispatch reliability. Traditional forecasting methods largely rely on modeling historical power and meteorological data, often neglecting the consideration of cloud movement, which constrains further improvement in prediction accuracy. To enhance prediction accuracy and model interpretability, this paper proposes a dual-branch attention-based PV power prediction model that integrates physical features from ground-based cloud images. Regarding input features, a cloud segmentation model is constructed based on the vision foundation model DINO encoder and an improved U-Net decoder to obtain cloud cover information. Based on deep feature point detection and an attention matching mechanism, cloud motion vectors are calculated to extract cloud motion speed and direction features. For feature processing, feature attention and temporal attention mechanisms are introduced, enabling the model to learn key meteorological factors and critical historical time steps. Structurally, a parallel architecture consisting of a linear branch and a nonlinear branch is adopted. A context-aware fusion module adaptively combines the prediction results from both branches, achieving collaborative modeling of linear trends and nonlinear fluctuations. Comparative experiments were conducted using two years of engineering data. Experimental results demonstrate that the proposed model outperforms the benchmarks across multiple metrics, validating the predictive advantages of the dual-branch structure that integrates physical features under complex weather conditions. Full article

Agriculture in the European Union operates in a context where productivity, output growth, and sustainability increasingly shape policy priorities and economic choices. This research explores how these elements have interacted and influenced one another from 2000 to 2024, focusing on the dynamic relationships among economic performance, sustainability, labor productivity, and agricultural output across EU member states. The methodology is straightforward: it starts with factor analysis to uncover the fundamental structures linking key variables and to clarify connections that are often hidden in aggregated data. Building on these insights, a General Linear Model provides a clearer picture of how economic performance and sustainability affect changes in labor productivity and agricultural output, revealing the mechanisms through which these factors promote or hinder agricultural progress. To enhance understanding, cluster analysis groups EU countries according to shared patterns, enabling interpretation of national differences within broader structural trends rather than as isolated cases. The findings show that countries with stronger economies and more consistent sustainability initiatives tend to achieve higher productivity and output, while the clusters identified demonstrate significant differences that explain the diverse development paths within the Union. Full article

Vegetation indices (VIs) enable rapid, non-destructive biochemical monitoring in olive fruits, yet their performance across diverse germplasm and ripening stages remains systematically uncharacterized. This exploratory screening systematically evaluated 87 VIs for predicting chlorophyll and polyphenol content across 31 cultivars at four ripening stages, prioritizing genetic diversity to establish species-level biochemical–spectral relationships through integration of hyperspectral data (380–1080 nm) with biochemical analyses. Modified Chlorophyll Absorption Ratio Index 3 (MCARI 3) and Transformed Chlorophyll Absorption Ratio Index (TCARI) achieved 91 strong correlations (|r| ≥ 0.9) across 124 cultivar-stage combinations. High-performing indices incorporated 550 nm with red/red-edge bands (670–710 nm) and non-linear formulations. Moderate inter-cultivar variability indicated that cultivar-specific calibrations may be necessary. Principal component analysis captured the totality of variance, revealing three biochemical clusters, high-chlorophyll cultivars (n = 5; 91.8 and 7385.6 mg kg⁻¹ chlorophyll/polyphenols, respectively), typical-range cultivars (n = 22; 126.6 and 4016.8 mg kg⁻¹), and elite cultivars (n = 5; 790.4 and 5799.8 mg kg⁻¹), demonstrating VIs’ capacity for cultivar discrimination. Chlorophyll degradation exhibited conserved patterns, supporting universal tracking models. Conversely, polyphenol dynamics displayed marked genotype-dependency, with cultivars showing positive, negative, or minimal variation, yielding non-significant population-level effects, despite robust cultivar-specific trends. Full article