Search Results (2,404)

Changes in vegetation type shape the soil microenvironment, thereby regulating the changes in the organic carbon pool by influencing microbial communities and the accumulation of microbial necromass carbon (MNC). This study investigated microbial biomass—via phospholipid fatty acids (PLFAs) analysis—and MNC accumulation across three cold–temperate forest types: Larix gmelinii forest (L), Larix gmelinii–Betula platyphylla Sukaczev mixed forest (LB), and Betula platyphylla Sukaczev forest (B). The results showed that the L had the lowest contents of pH, water content (WC), soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), and total phosphorus (TP), but the highest contents of dissolved organic carbon (DOC), available phosphorus (AP), and carbon to nitrogen ratio (C/N) (p < 0.05). LB had the lowest PLFAs content and the highest ratio of Gram-positive bacteria/Gram-negative bacteria (G+/G−), and total fungi/total bacteriai (F/B) of L was the highest. B had the highest alpha diversity index, and significantly positively correlated with pH, SOC, TN, AN, and TP. TP and C/N were the primary elements for significant differences in microbial community structure. The order of MNC content and its contribution to SOC was B > LB > L. MNC was significantly negatively correlated with PLFAs, DOC, and AP, and significantly positively correlated with pH, SOC, TN, AN, TP, Shannon–Wiener and Pielou indices. In conclusion, this study demonstrates that Betula platyphylla Sukaczev forest retains more carbon, nitrogen, and phosphorus, microbial alpha diversity, and acquires more MNC, which can provide a basis for subsequent forest management and carbon sequestration projects. Full article

In the ecologically fragile western Shanxi Loess region, stand density regulation of artificial Robinia pseudoacacia L. forests plays a crucial role in sustaining the water regulation functions of the litter-soil system, yet multi-scale mechanistic analyses remain scarce. To address this gap, we established six stand density classes (ranging from 1200 to 3200 stems/ha) and quantified litter water-holding traits and soil physicochemical properties. We then applied principal component analysis (PCA) and structural equation modeling (SEM) to examine density-litter-soil relationships. Low-density stands (≤2000 stems/ha) exhibited significantly higher litter accumulation (6.08–6.37 t/ha) and greater litter water-holding capacity (maximum 20.58 t/ha) than the high-density stands (p < 0.05). Soil capillary water-holding capacity decreased with increasing density (4702.63–4863.28 t/ha overall), while non-capillary porosity (5.26–6.21%) and soil organic carbon (~12.5 g/kg) were higher in high-density stands (≥2800 stems/ha), reflecting a structural-carbon optimization trade-off. PCA revealed a primary hydrological function axis with low-density stands clustering in the positive quadrant, while high-density stands shifted toward nutrient-conservation traits. SEM confirmed that stand density affected soil capillary water-holding capacity indirectly through litter accumulation (significant indirect path; non-significant direct path), highlighting the central role of litter quantity. When density exceeded ~2400 stems/ha, litter decomposition rate decreased by ~56%, coinciding with capillary porosity falling below ~47%, a threshold linked to impaired balance between water storage and infiltration. These findings identify 1200–1600 stems/ha as the optimal density range; in this range, soil capillary water-holding capacity reached 4788–4863 t/ha, and available phosphorus remained ≥2.1 mg/kg, providing a density-centered, near-natural management paradigm for constructing “water-conservation vegetation” on the Loess Plateau. Full article

Early and accurate detection of distributed bearing faults is essential to prevent equipment failures and reduce downtime in industrial environments. This study explores the optimal selection of input signal sources for high-accuracy distributed fault classification, employing wavelet transform and machine learning algorithms. The primary contribution of this work is to demonstrate that robust distributed bearing fault diagnosis can be achieved through optimal sensor fusion and wavelet-based feature engineering, without the need for deep learning or high-dimensional inputs. This approach provides interpretable, computationally efficient, and generalizable fault classification, setting it apart from most existing studies that rely on larger models or more extensive data. All experiments were conducted in a controlled laboratory environment across multiple loads and speeds. A comprehensive dataset, including three-axis vibration, stray magnetic flux, and two-phase current signals, was used to diagnose six distinct bearing fault conditions. The wavelet transform is applied to extract frequency-domain features, capturing intricate fault signatures. To identify the most effective input signal combinations, we systematically evaluated Random Forest, XGBoost, and Support Vector Machine (SVM) models. The analysis reveals that specific signal pairs significantly enhance classification accuracy. Notably, combining vibration signals with stray magnetic flux consistently achieved the highest performance across models, with Random Forest reaching perfect test accuracy (100%) and SVM showing robust results. These findings underscore the importance of optimal source selection and wavelet-transformed features for improving machine learning model performance in bearing fault classification tasks. While the results are promising, validation in real-world industrial settings is needed to fully assess the method’s practical reliability and impact on predictive maintenance systems. Full article

Assessing the quality of groundwater, a primary source of water in many sectors, is of paramount importance. To this end, modeling the geospatial distribution of chemical contaminants in groundwater can be of great utility. Machine learning (ML) models are being increasingly used to overcome the shortcomings of conventional predictive techniques. We report here a systematic review of the nature and utility of various supervised and unsupervised ML models during the past two decades of machine learning groundwater hazard mapping (MLGHM). We identified and reviewed 284 relevant MLGHM journal articles that met our inclusion criteria. Firstly, trend analysis showed (i) an exponential increase in the number of MLGHM studies published between 2004 and 2025, with geographical distribution outlining Iran, India, the US, and China as the countries with the most extensively studied areas; (ii) nitrate as the most studied target, and groundwater chemicals as the most frequently considered category of predictive variables; (iii) that tree-based ML was the most popular model for feature selection; (iv) that supervised ML was far more favored than unsupervised ML (94% vs. 6% of models) with tree-based category—mostly random forest (RF)—as the most popular supervised ML. Secondly, compiling accuracy-based comparisons of ML models from the explored literature revealed that RF, deep learning, and ensembles (mostly meta-model ensembles and boosting ensembles) were frequently reported as the most accurate models. Thirdly, a critical evaluation of MLGHM models in terms of predictive accuracy, along with several other factors such as models’ computational efficiency and predictive power—which have often been overlooked in earlier review studies—resulted in considering the relative merits of commonly used MLGHM models. Accordingly, a flowchart was designed by integrating several MLGHM key criteria (i.e., accuracy, transparency, training speed, number of hyperparameters, intended scale of modeling, and required user’s expertise) to assist in informed model selection, recognising that the weighting of criteria for model selection may vary from problem to problem. Lastly, potential challenges that may arise during different stages of MLGHM efforts are discussed along with ideas for optimizing MLGHM models. Full article

Net primary productivity (NPP) is a vital indicator of carbon sequestration and ecosystem resilience. However, the dynamics of NPP across different land use types and especially the interactive function of natural drivers remain insufficiently quantified in regions with significant land use change. Therefore, this study selected Heilongjiang Province in China as the research area. Utilizing multi-source data from 2001 to 2022, it identified the primary land use types, analyzed the mean values and trends of vegetation NPP for each type, and quantified the driving effects of natural factors on NPP across these land types. Results show that forests had the highest mean NPP (514.01 gC m⁻²·a⁻¹) and shrub–grass–wetland composites the lowest (269.2 gC m⁻²·a⁻¹); cropland-to-forest transitions boosted NPP most notably. Critically, precipitation–temperature interactions dominated NPP variation, while elevation acted mainly through modulating other factors. This study offers a strategic framework for spatial planning and ecosystem management, supporting climate mitigation and carbon sequestration policies. Full article

The species of the Bufonidae family exhibit a great diversity of habitats, diurnal or nocturnal habits, a complex evolutionary history, and a wide distribution, which makes this group suitable for morphological studies. In this work, we aimed to identify the existence of morphological patterns related to the habitat use and diurnal or nocturnal habits of Bufonidae in the Brazilian Amazon. To achieve this, we studied the morphological measurements of 210 specimens from three zoological collections and characterized the type of habitat and diurnality/nocturnality of the species. The morphological patterns and habitat use were investigated through principal component analysis (PCA) and multiple correspondence analysis (MCA), respectively. The evaluation of the relationships between morphological variation, habitat use, and diurnality/nocturnality was performed via redundancy analysis (RDA). Accordingly, Amazonian bufonids were divided into three morphological groups associated with different vegetation types and environments, demonstrating that body size is closely linked to diurnal or nocturnal life habits and habitat. Species with large body sizes are associated to anthropized areas, while intermediate and smaller species are associated with primary forests. Full article

Artificial water replenishment has improved the ecological environment of West Lake by introducing external clean water, but pollution issues still persist in some local regions. However, whether enhancing water exchange through internal water diversion within the lake can improve local water quality remains unverified. This study employed the Delft3D hydrodynamic model to simulate the spatiotemporal distribution of local water renewal time in West Lake, revealing that regions with prolonged water renewal times exhibited diminished resilience to water quality disturbances. This study utilized the Random Forest algorithm to determine the responsiveness of West Lake’s water transparency to parameters such as local water renewal time, and further discussed the impact of reducing local water renewal time on transparency under different water quality conditions. The results indicate that the sensitivity of West Lake’s transparency to water quality parameters closely resembles that of lakes with rainwater storage. The primary mechanism by which external water diversion improves transparency is through pollutant dilution, whereas enhanced local water exchange capacity contributes minimally to this effect. This conclusion demonstrates that localized internal water diversion within the lake is only suitable for preventing ecological issues such as regional eutrophication and algal blooms, but cannot effectively improve the overall lake ecosystem. Furthermore, this study identifies key factors affecting water transparency in artificially managed waters, highlighting priority monitoring indicators for similar water bodies. It also provides evidence to support research on aquatic optics and the development of remote sensing algorithms for such waters. Full article

Norway spruce (Picea abies (L.) H. Karst.) is a primary forest-forming species in the European part of Russia, both in terms of its distribution and economic importance. A number of studies indicate that one of the reasons for the disturbance of spruce forests is linked to rising temperatures, particularly the detrimental effects of extreme droughts. The aim of our research is to identify changes in the structural and functional organization of mature spruce forests at the center of the East European Plain. The study was conducted in intact spruce forests using resurveyed vegetation relevés within the Smolensk–Moscow Upland, with relevés repeated after 40 years (in 1985 and 2025). Changes in structural and functional parameters of spruce communities were analyzed. The results showed that significant disturbances of the tree layer led to changes in the vegetation of subordinate layers, as well as the successional dynamics of spruce forests. It was found that following the collapse of old-growth spruce stands, two types of secondary succession developed: (1) with the renewal of spruce and (2) with active development of shrubs (hazel and rowan) and undergrowth of broadleaved species. It was also demonstrated that the typological diversity of the studied communities changed over 40 years not only due to the loss of the tree layer and the formation of new “non-forest” types but also because several mixed spruce-broadleaved communities transitioned into broadleaved ones, and pine–spruce communities of boreal origin shifted to nemoral types. An analysis of the complete species composition of spruce forests based on Ellenberg’s scales scoring revealed changes in habitat conditions over the 40-year period. A noticeable trend was an increase in the proportion of thermophilic and alkaliphilic species, indicating a shift toward a nemoral vegetation spectrum. It is expected that under the current forest management regime, the next 40 to 60 years will see a decline in the proportion of spruce within mixed stands, potentially culminating in the complete collapse of monospecific spruce forests in the center of the East European Plain. Full article

This study aimed to develop a non-contact, marker-less machine learning model to estimate forearm pronation–supination angles using 2D hand landmarks derived from MediaPipe Hands, with inertial measurement unit sensor angles used as reference values. Twenty healthy adults were recorded under two camera conditions: medial (in-camera) and lateral (out-camera) viewpoints. Five regression models were trained and evaluated: Linear Regression, ElasticNet, Support Vector machine (SVM), Random Forest, and Light Gradient Boosting Machine (LightGBM). Among them, LightGBM achieved the highest accuracy, with a mean absolute error of 5.61° in the in-camera setting and 4.65° in the out-camera setting. The corresponding R² values were 0.973 and 0.976, respectively. The SHAP analysis identified geometric variations in the palmar triangle as the primary contributors, whereas elbow joint landmarks had a limited effect on model predictions. These results suggest that forearm rotational angles can be reliably estimated from 2D images, with an accuracy comparable to that of conventional goniometers. This technique offers a promising alternative for functional evaluation in clinical settings without requiring physical contact or markers and may facilitate real-time assessment in remote rehabilitation or outpatient care. Full article

Soil microbial communities are important for nutrient cycling regulation in forest ecosystems. However, limited knowledge exists regarding the characteristics of these microbial communities in Masson pine (Pinus massoniana Lamb.) plantations of different stand ages. In this study, four planted Masson pine stands (8-year-old, 12-year-old, 22-year-old, and 38-year-old stands) and one natural broadleaved forest stand (as a control) with three replications, were selected in the Laoshan Forest Farm, Qiandao Lake Town, Zhejiang Province, China. Soil physicochemical properties were measured and their effects on soil microbial communities were studied. Amplicon-based high-throughput sequencing was employed to process raw sequence data for soil microbes. It is worth noting that significant differences (p < 0.05) in soil bacterial genera were observed among different stand age groups. Total nitrogen (TN), total phosphorus (TP), total potassium (TK), available potassium (AK), soil organic carbon (SOC), and soil bulk density (BD) were identified as the primary factors influencing bacterial community distribution (p < 0.05). Available nitrogen (AN), SOC, TN, and TK showed significant correlations with soil fungal communities (p < 0.05). These findings underscore the crucial role of soil physicochemical properties in shaping soil microbial community composition in Masson pine plantations. Full article

The Yellow River Basin (YRB), a pivotal ecoregion in China, has long been plagued by a range of ecological problems, including water loss, soil erosion, and ecological degradation. Despite previous reports on the ecological environment of YRB, systematic studies on the multi-factor driving mechanism and the coupling between the ecological and hydrological systems remain scarce. In this study, with multi-source remote-sensing imagery and measured hydrological data, the random forest (RF) model and the geographical detector (GD) technique were employed to quantify the dynamic spatiotemporal changes in the ecological environment of YRB in 2000–2024 and identify the driving factors. The variables analyzed in this study included gross primary productivity (GPP), fractional vegetation cover (FVC), land use and cover change (LUCC), meteorological statistics, as well as runoff and sediment data measured at hydrological stations in YRB. The main findings are as follows: first, the GPP and FVC increased significantly by 37.9% and 18.0%, respectively, in YRB in 2000–2024; second, LUCC was the strongest driver of spatiotemporal changes in the ecological environment of YRB; third, precipitation and runoff contributed positively to vegetation growth, whereas the sediment played a contrary role, and the response of ecological variables to the hydrological processes exhibited a time lag of 1–2 years. This study is expected to provide scientific insights into ecological conservation and water resources management in YRB, and offer a decision-making basis for the design of sustainability policies and eco-restoration initiatives. Full article

Non-point source pollution (NPS) from agriculture is a primary driver of water eutrophication, necessitating effective control for regional water ecological security and sustainable agricultural development. This study focuses on the Chenzhuang village watershed, a typical green agricultural demonstration area in Jiangsu Province, using the HYPE model to analyze hydrological processes and Total Nitrogen (TN) and Total Phosphorus (TP) migration patterns. The model achieved robust performance, with Nash–Sutcliffe Efficiency (NSE) values exceeding 0.7 for daily runoff and 0.35 for monthly TN and TP simulations, ensuring reliable predictions. A multi-scenario simulation framework evaluated the synergistic control effectiveness of Best Management Practices (BMPs), including agricultural production management, nutrient management, and landscape configuration, on TN and TP pollution. The results showed that crop rotation reduced annual average TN and TP concentrations by 11.8% and 13.6%, respectively, by shortening the fallow period. Substituting 50% of chemical fertilizers with organic fertilizers decreased TN by 50.5% (from 1.92 mg/L to 0.95 mg/L) and TP by 68.2% (from 0.22 mg/L to 0.07 mg/L). Converting 3% of farmland to forest enhanced pollutant interception, reducing TN by 4.14% and TP by 2.78%. The integrated BMP scenario (S13), combining these measures, achieved TN and TP concentrations of 0.63 mg/L and 0.046 mg/L, respectively, meeting Class II surface water standards since 2020. Economic analysis revealed an annual net income increase of approximately 15,000 CNY for a 50-acre plot. This was achieved through cost savings, increased crop value, and policy compensation. These findings validate a “source reduction–process interception” approach, providing a scalable management solution for NPS control in small rural watersheds while balancing environmental and economic benefits. Full article

Our study focused on the quantification of aboveground biomass stock and aboveground net primary productivity (ANPP) in young, planted beech (Fagus sylvatica L.). We selected 15 young even-aged stands targeting moderately fertile sites. Three rectangular plots were established within each stand, and all trees were annually measured for height and stem basal diameter from 2020 to 2024. For biomass modeling, we conducted destructive sampling of 111 beech trees. Each tree was separated into foliage and woody components, oven-dried, and weighed to determine dry mass. Allometric models were developed using these predictors: tree height, stem basal diameter, and their combination. Biomass accumulation was closely correlated with stand age, allowing us to scale tree-level models to stand-level predictions using age as a common predictor. Biomass stocks of both woody parts and foliage increased with stand age, reaching 48 Mg ha⁻¹ and 6 Mg ha⁻¹, respectively, at the age of 15 years. A comparative analysis indicated generally higher biomass in naturally regenerated stands, except for foliage at age 16, where planted stands caught up with the naturally regenerated ones. Our findings contribute to a better understanding of forest productivity dynamics and offer practical models for estimating carbon sequestration potential in managed forest ecosystems. Full article

Although numerous studies have used ML to predict gear transmission error, few have provided a normalized, interpretable risk metric for early tolerance assessment. This work fills that gap by proposing the Tonal Risk Index (TRI). Kinematic Transmission Error (KTE) is a well-established primary excitation source of tonal gear noise in electric vehicle drivetrains. This study introduces the TRI, a novel, dimensionless indicator that quantifies relative tonal noise risk directly from predicted KTE values. We employ a large-scale dataset of 39,984 Monte Carlo simulations comprising 15 manufacturing tolerance and process-shift variables, with KTE values as the target. Baseline linear regression failed to capture the strongly non-linear relationships between tolerances and KTE (R² ≈ 0), whereas non-linear models—Random Forest and XGBoost—achieved high predictive accuracy (R² ≈ 0.82). Feature importance analysis revealed that pitch error, radial run-out, and misalignment are consistently the most influential parameters, with notable interaction effects such as pitch error × run-out and misalignment × form-defect shift. The TRI normalises predicted KTE values to a 0–1 scale, enabling rapid comparison of tolerance configurations in terms of tonal excitation risk. This approach supports early-stage design decision-making, reduces reliance on high-fidelity simulations and physical prototypes, and aligns with sustainability objectives by lowering material usage and energy consumption. The results demonstrate that data-driven surrogate models, combined with the TRI metric, can effectively bridge the gap between manufacturing tolerances and NVH performance assessment. Full article

This study examined the spatiotemporal dynamics and driving mechanisms of ecosystem service value (ESV) in Wuhan from 1985 to 2020. Using multi-temporal land-use data, remotely sensed vegetation indices, and socioeconomic statistics, we estimated the ESV with an improved equivalent-factor method and analyzed its drivers using a Geodetector and geographically weighted regression (GWR). Over the 35-year period, total ESV for Wuhan showed a mildly declining trend, decreasing from CNY 37.464 billion in 1985 to CNY 36.439 billion in 2020. Waterbodies contributed the largest share of ESV, followed by croplands and forests. In the urban core, ESV declined significantly, with low-value zones expanding outward from the city center. Spatial autocorrelation analysis revealed significant “high–high” and “low–low” clustering. Geodetector results indicated slope, elevation, and normalized difference vegetation index (NDVI) as the primary natural drivers, with human footprint, gross domestic product (GDP), and population density acting as important socioeconomic auxiliaries. Interactions between natural and socioeconomic factors substantially increased the explanatory power. Furthermore, GWR revealed pronounced spatial heterogeneity in the sign and magnitude of the factor effects across the study area, underscoring the complexity of ESV drivers. These findings provide quantitative evidence to support spatially differentiated ecological planning and conservation strategies during urbanization in Wuhan and the broader mid-Yangtze region. Full article