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Fault diagnosis of wind turbine bearings is crucial for ensuring operational safety and reliability. However, traditional serial-structured deep learning models often fail to simultaneously extract spatio- temporal features from fault signals in noisy environments, leading to critical information loss. To address this limitation, this paper proposes a Wind Turbine Bearing Fault Diagnosis Model Based on Efficient Cross Space Multiscale CNN Transformer Parallelism (ECMCTP). The model first transforms one-dimensional vibration signals into two-dimensional time-frequency images using Continuous Wavelet Transform (CWT). Subsequently, parallel branches are employed to extract spatio-temporal features: the Convolutional Neural Network (CNN) branch integrates a multiscale feature extraction module, a Reversed Residual Structure (RRS), and an Efficient Multiscale Attention (EMA) mechanism to enhance local and global feature extraction capabilities; the Transformer branch combines Bidirectional Gated Recurrent Units (BiGRU) and Transformer to capture both local temporal dynamics and long-term dependencies. Finally, the features from both branches are concatenated along the channel dimension and classified using a softmax classifier. Experimental results on two publicly available bearing datasets demonstrate that the proposed model achieves 100% accuracy under noise-free conditions and maintains superior noise robustness under low signal-to-noise ratio (SNR) conditions, showcasing excellent robustness and generalization capabilities.

1. Forest productivity may be determined not only by biodiversity but also by environmental factors and stand structure attributes. However, the relative importance of these factors in determining productivity is still controversial for subtropical forests. 2. Based on a large dataset from 600 permanent forest inventory plots across subtropical China, we examined the relationship between biodiversity and forest productivity and tested whether stand structural attributes (stand density in terms of trees per ha, age and tree size) and environmental factors (climate and site conditions) had larger effects on productivity. Furthermore, we quantified the relative importance of environmental factors, stand structure and diversity in determining forest productivity. 3. Diversity, together with stand structure and site conditions, regulated the variability in forest productivity. The relationship between diversity and forest productivity did not vary along environmental gradients. Stand density and age were more important modulators of forest productivity than diversity. 4. Synthesis. Diversity had significant and positive effects on productivity in species rich subtropical forests, but the effects of stand density and age were also important. Our work highlights that while biodiversity conservation is often important, the regulation of stand structure can be even more important to maintain high productivity in subtropical forests.

Phosphorus (P) is a limiting nutrient for plant growth in most forest ecosystems. In response to P deficiency, plants alter root exudates (organic acids, phosphatases, and protons) to increase P bioavailability in soils. However, little is known about how bioavailable P pools (soluble-P, exchangeable-P, hydrolysable-P, and ligand-P extracted by CaCl₂, citric acid, enzyme mixture, and HCl solution, respectively) change with stand age, especially for plantation forests. We selected a chronosequence of second-generation Chinese fir [Cunninghamia lanceolata (Lamb.) Hook., Taxodiaceae] plantations with increasing age including 3, 8–11, 16, 20, 25, 29, and 32 years. We measured total P and four bioavailable P pools in organic (O) and mineral horizons, and rhizosphere soil, as well as root exudates in the rhizosphere, litter biomass on the forest floor, and annual P uptake. Soluble-P, exchangeable-P, and ligand-P in the O horizon increased with stand age due to litter accumulation. Exchangeable-P and ligand-P in mineral soil decreased with stand age because of the increasing annual P uptake that depleted inorganic P. Exchangeable-P and ligand-P in the rhizosphere increased with stand age because the decrease in pH and citric acid concentration led to phosphate being more strongly bound to Fe and Al oxyhydroxides. Consequently, the trees’ ability for P mobilization decreased with stand age, but the P recycling within the tree increased. Continuous mineralization of hydrolysable-P by acid phosphatase replenished inorganic P pools, especially in solution. The progressive incorporation of P in the biological cycle with increasing tree age indicates that extending rotation periods might be an appropriate measure to increase P supply.

The process of rapid urbanization has been causing non-negligible disturbances to our ecosystems, which has aggravated the mismatch between ecosystem service (ES) supply and demand. A clear understanding of the relationship between the ES supply–demand mismatch and urbanization is crucial as it could have a lot of significance for implementing ecological compensation and conservation action. Although a large number of studies have explored this problem, previous studies have focused primarily on the spatial mismatching of the ESs, and only a few studies have considered the spatial relationship between the ES supply–demand mismatch and urbanization at the watershed scale. Taking the Yangtze River Economic Belt (YREB) as an example, this study quantitatively assesses the supply and demand of five ESs, including carbon sequestration, water retention, soil conservation, food production, and recreational opportunity. The bivariate Moran’s I method was used to analyze and visualize the spatial correlation between the ES supply–demand mismatch and urbanization. The results indicate that both the total supply and the total demand of the five ESs increased, while the increasing rate of total demand was higher than the total supply of the ESs; this resulted in a significant spatial mismatch between the supply and demand of the ESs from 2000 to 2020. There is also a negative spatial correlation between the ES supply–demand and urbanization, while the results of local spatial clustering have obvious spatial heterogeneity. The metropolis and its surrounding counties are mostly the ES supply and demand deficit area, but some surrounding counties have managed to transform a deficit into a surplus. These results indicate that urbanization has a certain interference on the mismatch of the ES supply and demand, and this interference is not irreversible. Moreover, this study provides a reliable reference for government management in the context of balancing urbanization and the ecosystem.

Although stand age affects biomass partitioning and allometric equations, the size of these effects and whether it is worth incorporating stand age into allometric equations, requires further attention. We sampled a total of 90 trees for 10 Chinese fir (Cunninghamia lanceolata) plantations at seven stand age classes to obtain the data of tree component biomass using destructive harvesting. A multilevel modeling approach was applied to examine how stand age effects differ among tree components and predictor variables (diameter at breast height, DBH and tree height, H). Age class-specific allometric equations and the best fitting generalized equation that included stand age as a complementary variable were developed for each tree component. Large differences in both the intercept and slope for different stand age classes indicated that stand age affected allometric models. Branch and leaves were more sensitive to the environment and were the tree components most affected by stand age. Age class-specific allometric equations fitted well (R2 > 0.65, p < 0.001) using DBH and the combined form DBH2H as predictor variables. Including stand age as a complementary variable improved the fit of generalized allometric equations. Stem, aboveground and total tree biomass predicted by the multilevel model and generalized equation were comparable to the observed data. However, the multilevel model and generalized equations had a relatively low predictive capacity for branch, leaf and root biomass. These results could improve our capacity to evaluate carbon sequestration and other ecosystem functions in plantations.

As a key component of terrestrial ecosystems, soil interacts directly with aboveground vegetation. Evaluating soil quality is therefore of great significance to comprehensively explore the interaction mechanism of this association. The purpose of this study was to fully understand the characteristics of aboveground vegetation, soil quality, and their potential coupling relationship among different forest types in Hunan Province, and to provide a theoretical basis for further exploring the mechanisms underlying soil–vegetation interactions in central China. We have set up sample plots of five kinds of forests (namely broad-leaved forest, coniferous forest, coniferous broad-leaved mixed forest, bamboo forest, and shrub forest) in Hunan Province. To explore the differences of vegetation characteristics and soil physical and chemical properties among the five stand types, variance analysis, principal component analysis, and regression analysis were used. Finally, we explored the coupling relationship between soil quality and aboveground vegetation characteristics of each forest. We found that there were significant differences in soil quality among the forest types, ranked as follows: shrub forest > bamboo forest > broad-leaved forest > mixed coniferous and broad-leaved forest > coniferous forest. In general, there was a negative correlation between vegetation richness and soil quality in the broad-leaved forest and the shrub forest, but they showed a positive correlation in the coniferous forest, the mixed coniferous and broad-leaved forest, and the bamboo forest. As a necessary habitat condition for aboveground vegetation, soil directly determines the survival and prosperity of plant species. These results indicated that for vegetation–soil dynamics in a strong competitive environment, as one aspect wanes the other waxes. However, in a weak competitive environment, the adverse relationship between vegetation and soil is less pronounced and their aspects can promote.

In this study, we have considered the relationship between the spatial configuration of land use and water quality in the Three Gorges Reservoir Area. Using land use types, landscape metrics, and long-term water quality data, as well as statistical and spatial analysis, we determined that most water quality parameters were negatively correlated with non-wood forest and urban areas but were strongly positively correlated with the proportion of forest area. Landscape indices such as patch density, contagion, and the Shannon diversity index were able to predict some water quality indicators, but the mean shape index was not significantly related to the proportions of farmland and water in the study area. Regression relationships were stronger in spring and fall than in summer, and relationships with nitrogen were stronger than those of the other water quality parameters (R ² > 0.80) in all three seasons. Redundancy analysis showed that declining stream water quality was closely associated with configurations of urban, agricultural, and forest areas and with landscape fragmentation (PD) caused by urbanization and agricultural activities. Thus, a rational land use plan of adjusting the land use type, controlling landscape fragmentation, and increasing the proportion of forest area would help to achieve a healthier river ecosystem in the Three Gorges Reservoir Area (TGRA).

To better understand the soil carbon dynamics and cycling in terrestrial ecosystems in response to environmental changes, we studied soil respiration, litter decomposition, and their relations to soil temperature and soil water content for 18-months (Aug. 2010-Jan. 2012) in three different-aged Pinus massoniana forests in the Three Gorges Reservoir Area, China. Across the experimental period, the mean total soil respiration and litter respiration were 1.94 and 0.81, 2.00 and 0.60, 2.19 and 0.71 µmol CO2 m(-2) s(-1), and the litter dry mass remaining was 57.6%, 56.2% and 61.3% in the 20-, 30-, and 46-year-old forests, respectively. We found that the temporal variations of soil respiration and litter decomposition rates can be well explained by soil temperature at 5 cm depth. Both the total soil respiration and litter respiration were significantly positively correlated with the litter decomposition rates. The mean contribution of the litter respiration to the total soil respiration was 31.0%-45.9% for the three different-aged forests. The present study found that the total soil respiration was not significantly affected by forest age when P. masonniana stands exceed a certain age (e.g. >20 years old), but it increased significantly with increased soil temperature. Hence, forest management strategies need to protect the understory vegetation to limit soil warming, in order to reduce the CO2 emission under the currently rapid global warming. The contribution of litter decomposition to the total soil respiration varies across spatial and temporal scales. This indicates the need for separate consideration of soil and litter respiration when assessing the climate impacts on forest carbon cycling.

Sepsis is a condition resulting from the uncontrolled immune response to infection, leading to widespread inflammatory damage and potentially fatal organ dysfunction. Currently, there is a lack of specific prevention and treatment strategies for sepsis across different age groups. Programmed Cell Death (PCD) can regulate the enrichment of effector immune cells or regulatory immune cells, providing a new perspective for immunotherapy. Within the framework of computational biology and machine learning strategies, and against the backdrop of global multicenter sepsis cohort data, this study aims to deeply mine and screen specific biomarkers related to the immune microenvironment and programmed cell death in populations across different life stages (neonates, children, and adults). This will provide foundational data for precision treatment and drug development in artificial intelligence-assisted sepsis diagnosis and treatment management. Gene expression data from sepsis patients across global multicenter populations, including China, Europe, and the United States, were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. A literature review was conducted to obtain 18 PCD-related genes, which were intersected with DEGs to identify DEGs associated with specific types of PCD. Nine machine learning algorithms (Logistic Regression LR, Decision Tree DT, Gradient Boosting Machine GBM, K-Nearest Neighbors KNN, LASSO, Principal Component Analysis PCA, Random Forest RF, Support Vector Machine SVM, and XGBoost) were applied to training and testing datasets with 10-fold cross-validation to select three optimized algorithm models. The SHAP algorithm was further used to quantify the contribution of each gene based on cell death features to the prediction of sepsis. Key PCD patterns were identified based on model evaluation metrics (Accuracy, Precision, Recall, F1 score, and Receiver Operating Characteristic Curve ROC), and their associated DEGs were obtained through intersection, followed by immune-related analysis of DEGs. The study included a total of 1507 sepsis cases and 484 controls globally, with 90 neonatal cases and 95 controls, 527 children cases and 101 controls, and 890 adult cases and 288 controls. The best model for predicting sepsis across different populations was GBM.The key PCD patterns selected by machine learning for different age groups were Pyroptosis (neonates), Ferroptosis (children), and Autophagy (adults). (1) In neonatal sepsis, the models constructed by GBM, XGBoost, and RF algorithms performed the best, and identified 5 key DEGs associated with Pyroptosis (CHMP7, NLRC4, AIM2, GZMB, PRKACA), with NLRC4 showing the best predictive ability (AUC = 0.902, P  < 0.05), significantly positively correlated with neutrophils and negatively correlated with CD8 + T cells. (2) In the children sepsis population, models constructed using the Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms demonstrated the best performance. Six key DEGs associated with Ferroptosis were identified (AKR1C3, GCLM, PEBP1, CARS, MAP1LC3B, SCL11A2), among which MAP1LC3B, playing a role in mitochondrial reactive oxygen species energy metabolism, showed the strongest predictive ability (AUC = 0.883, P  < 0.05). It was significantly positively correlated with M0-type macrophages and significantly negatively correlated with activated CD4 + memory T cells. (3) In the adult sepsis population, models constructed using GBM, SVM, and LASSO algorithms showed the best performance. Three key DEGs associated with Autophagy were identified (TSPO, HTRA2, USP10), with TSPO, which mediates oxidative stress regulation, iron homeostasis, and cholesterol transport, showing the strongest predictive ability (AUC = 0.825, P  < 0.05). It was significantly positively correlated with M1-type macrophages and significantly negatively correlated with CD8 + T cells. This study, through the integrated application of computational biology and machine learning algorithms, discovered biomarkers of PCD patterns that affect cytokine storm-mediated inflammation and immunosuppressive effects in sepsis populations across different age groups (neonates, children, and adults). These findings have specific clinical application and drug development value, providing a scientific basis for the global application of artificial intelligence-assisted sepsis diagnosis and treatment management.

IntroductionMicrovascular obstruction (MVO) is a common complication following primary percutaneous coronary intervention (PPCI) for ST-segment elevation myocardial infarction (STEMI) and is strongly associated with adverse clinical outcomes. MVO is a dynamic, multifactorial process shaped by factors spanning the myocardial infarction–reperfusion continuum and by PPCI-related microcirculatory injury, which leaves current early risk stratification—often a static snapshot—with limited power to anticipate its evolution. Renalase, a cardioprotective enzyme, exhibits a post-reperfusion surge that parallels MVO development; periprocedural renalase release may likewise be driven by overlapping mechanisms along the ischaemia–reperfusion pathway. This hypothesis-generating observation supports evaluating the delta-Renalase (periprocedural change in serum renalase) as a candidate association-based biomarker. Accordingly, this study aims to assess whether delta-Renalase is independently associated with MVO in patients with STEMI after PPCI and to evaluate its incremental predictive value, without causal inference.Methods and analysisThe Renalase and MicroVascular Obstruction Study (ReMVOS) is a prospective, single-centre, observational cohort study conducted at a nationally accredited chest pain centre in China. We will enrol 266 patients with consecutive STEMI with symptom onset within 12 hours who undergo PPCI. The exposure variable is delta-Renalase, calculated as the increase in serum renalase levels at 24 hours post-PPCI relative to the preprocedural baseline. The primary outcome is the presence of MVO, assessed by cardiovascular magnetic resonance (CMR) performed 2–5 days post-PPCI. Secondary outcomes include infarct size and peak global longitudinal strain quantified by CMR, major adverse cardiovascular events within 90 days and peak oxygen pulse from cardiopulmonary exercise testing (CPET) at the 90-day visit. The independent association and predictive value of delta-Renalase will be evaluated using a prespecified multivariable logistic regression model.Ethics and disseminationThis protocol has been approved by the Ethics Committee of the Third Xiangya Hospital of Central South University (approval No. K24655). All patients will provide written informed consent prior to enrolment. The findings of this study will be disseminated through publications in peer-reviewed international medical journals and presentations at relevant academic conferences.Trial registration numberNCT06669520.