Search Results (2,600)

Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Düzce, Türkiye. A multidimensional suite of environmental predictors—including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)—was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (~0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R² ≈ 0.937474; Richness R² ≈ 0.855305; Evenness R² ≈ 0.631796). Full article

To avoid continuous cropping problems with Bupleurum, we screened suitable preceding crops for rotation with Bupleurum through different crop rotations. Therefore, the objective of this study was to find out the relationships between microbial community characteristics, soil properties, and enzyme activities under four different rotation patterns, including fallow-Bupleurum (CK), maize-Bupleurum (M), soybean-Bupleurum (So), and sunflower-Bupleurum (Su). Results indicated that under all four rotation patterns, So treatment significantly enhanced soil nutrients and enzyme activities compared to CK. So not only optimized the composition of soil bacterial and fungal communities but markedly enhanced microbial α diversity. Additionally, So exhibited high similarity in bacterial and fungal community composition with M, and featured complex symbiotic relationships within the soil microbial network. While no clear discrepancies were detected in the abundance of the top twenty metabolic pathways in the predictive functions of bacterial and fungal communities across four rotation patterns, the metabolic pathway function MET-SAM-PWY (methionine synthesis pathway) in bacterial communities and the metabolic pathway function VALSYN-PWY (valine synthesis pathway) in fungal communities were particularly prominent under the So rotation pattern. RDA suggested that soil properties (available phosphorus and pH) and enzyme activities (sucrase and alkaline phosphatase activities) were the driving forces for bacterial community composition, while soil properties (soil organic matter and available potassium) and enzyme activities (sucrase and catalase activities) regulated fungal community composition. Hence, the soybean-Bupleurum rotation pattern represents a cultivation practice more beneficial for the sustainable development of the bupleurum industry, which can significantly improve soil fertility and the micro-ecological environment. Full article

Leptospirosis is a zoonotic disease caused by pathogenic Leptospira, prevalent in tropical/sub-tropical regions. This study aimed to clarify the prevailing leptospiral species, clinical features, and risk factors of leptospirosis in north-central Bangladesh in 2024. Venous blood and urine samples were collected from 117 patients with clinically suspected leptospirosis. Among these cases, 75 (64%) tested positive for Leptospira infection by IgM ELISA test and/or PCR. By phylogenetic analysis of the 16S rRNA gene, all the samples tested were classified into L. wolffii (pathogenic group P2), showing high sequence identity to those of the type strain Khorat-H2 (97–99%) and L. wolffii reported in Bangladesh previously. Confirmed leptospirosis patients were mostly male (93%), aged 15–60 years (93%), living in rural areas in low socioeconomic conditions. Variable symptoms were presented by patients, with jaundice (84%), nausea/vomiting (84%), and myalgia (67%) being common. Some patients showed severe symptoms involving the nervous system (disorientation and neck stiffness) and the respiratory tract (cough, shortness of breath, and hemoptysis). Major risk factors for leptospirosis were exposures to mud/wet soil, sanding water, heavy rain, working in a paddy field, and cattle. In conclusion, L. wolffii was revealed to be circulating endemically in north-central Bangladesh, since its first detection in 2018, associated with variable and severe clinical symptoms in humans. Full article

Platycodonis radix is a commonly used traditional Chinese medicine (TCM) material. Its bioactive compounds and medicinal value are closely related to its geographical origin. The internal components of Platycodonis radix from different origins are different due to the influence of environmental factors such as soil and climate. These differences can affect the medicinal value. Therefore, accurate identification of Platycodonis radix origin is crucial for drug safety and scientific research. Traditional methods of identification of TCM materials, such as morphological identification and physicochemical analysis, cannot meet the efficiency requirements. Although emerging technologies such as computer vision and spectroscopy can achieve rapid detection, their accuracy in identifying the origin of Platycodonis radix is limited when relying solely on RGB images or spectral features. To solve this problem, we aim to develop a rapid, non-destructive, and accurate method for origin identification of Platycodonis radix using hyperspectral imaging (HSI) combined with deep learning. We captured hyperspectral images of Platycodonis radix slices in 400–1000 nm range, and proposed a deep learning classification model based on these images. Our model uses one-dimensional (1D) convolution kernels to extract spectral features and two-dimensional (2D) convolution kernels to extract spatial features, fully utilizing the hyperspectral data. The average accuracy has reached 96.2%, significantly better than that of 49.0% based on RGB images and 81.8% based on spectral features in 400–1000 nm range. Furthermore, based on hyperspectral images, our model’s accuracy is 14.6%, 8.4%, and 9.6% higher than the variants of VGG, ResNet, and GoogLeNet, respectively. These results not only demonstrate the advantages of HSI in identifying the origin of Platycodonis radix, but also demonstrate the advantages of combining 1D convolution and 2D convolution in hyperspectral image classification. Full article

This study develops a novel analytical solution for three-dimensional axisymmetric consolidation of unsaturated soils incorporating radial–vertical composite seepage mechanisms and anisotropic permeability characteristics. A groundbreaking dual orthogonal expansion framework is established, utilizing innovative Bessel–trigonometric function coupling to solve the inherently complex spatiotemporal coupled partial differential equations in cylindrical coordinate systems. The mathematical approach synergistically combines modal expansion theory with Laplace transform methodology, achieving simultaneous spatial expansion of gas–liquid two-phase pressure fields through orthogonal function series, thereby transforming the three-dimensional problem into solvable ordinary differential equations. Rigorous validation demonstrates exceptional accuracy with coefficient of determination R² exceeding 0.999 and relative errors below 2% compared to numerical simulations, confirming theoretical correctness and practical applicability. The analytical solutions reveal four critical findings with quantitative engineering implications: (1) dual-directional drainage achieves 28% higher pressure dissipation efficiency than unidirectional drainage, providing design optimization criteria for vertical drainage systems; (2) normalized matric suction variation exhibits characteristic three-stage evolution featuring rapid decline, plateau stabilization, and slow recovery phases, while water phase follows bidirectional inverted S-curve patterns, enabling accurate consolidation behavior prediction under varying saturation conditions; (3) gas-water permeability ratio k_a/k_w spanning 0.1 to 1000 produces two orders of magnitude time compression effect from 10⁻² s to 10⁻⁴ s, offering parametric design methods for construction sequence control; (4) initial pressure gradient parameters λ_a and λ_w demonstrate opposite regulatory mechanisms, where increasing λ_a retards consolidation while λ_w promotes the process, providing differentiated treatment strategies for various geological conditions. The unified framework accommodates both uniform and gradient initial pore pressure distributions, delivering theoretical support for refined embankment engineering design and construction control. Full article

Artificial neural networks (ANNs) offer considerable advantages in predicting evaporation (EVAP), particularly in handling nonlinear relationships and complex interactions among factors like soil surface temperature (SST) and wind speed (WS). In Al Medina, Saudi Arabia, the connections among WS, SST at 5 cm, SST at 10 cm, and EVAP have been modeled using an ANN. This study demonstrates the practical effectiveness and applicability of the approach in simulating complex nonlinear dynamics in real-life systems. The modeling process employs time series data for WS, SST at both 5 cm and 10 cm, and EVAP, gathered from January to December (2002–2010). Four ANNs labeled T1–T4 were developed and trained with the feedforward backpropagation (FFBP) algorithm using MATLAB routines, each featuring a distinct configuration. The networks were further refined through the enumeration technique, ultimately selecting the most efficient network for forecasting EVAP values. The results from the ANN model are compared with the actual measured EVAP values. The mean square error (MSE) values for the optimal network topology are 0.00343, 0.00394, 0.00309, and 0.00306 for T1, T2, T3, and T4, respectively. Full article

This paper presents a novel hybrid and hierarchical architecture of a Convolutional Neural Network (CNN), based on MobileNetV2 and Support Vector Machines (SVM) for the classification of crop diseases (SVMobileNetV2). The system feeds from multispectral images captured by Unmanned Aerial Vehicles (UAVs) alongside data from IoT nodes. The primary objective is to improve classification performance in terms of both accuracy and precision. This is achieved by integrating contemporary Deep Learning techniques, specifically different CNN models, a prevalent type of artificial neural network composed of multiple interconnected layers, tailored for the analysis of agricultural imagery. The initial layers are responsible for identifying basic visual features such as edges and contours, while deeper layers progressively extract more abstract and complex patterns, enabling the recognition of intricate shapes. In this study, different datasets of tropical crop images, in this case banana crops, were constructed to evaluate the performance and accuracy of CNNs in detecting diseases in the crops, supported by transfer learning. For this, multispectral images are used to create false-color images to discriminate disease through spectra related to the blue, green and red colors in addition to red edge and near-infrared. Moreover, we used IoT nodes to include environmental data related to the temperature and humidity of the environment and the soil. Machine Learning models were evaluated and fine-tuned using standard evaluation metrics. For classification, we used fundamental metrics such as accuracy, precision, and the confusion matrix; in this study was obtained a performance of up to 86.5% using current deep learning models and up to 98.5% accuracy using the proposed hybrid and hierarchical architecture (SVMobileNetV2). This represents a new paradigm to significantly improve classification using the proposed hybrid CNN-SVM architecture and UAV-based multispectral images. Full article

Grazing systems’ production efficiency is a dynamic interaction between soil, pasture, livestock, and climate. The magnitude of the changes is related to the mechanical stress applied by the livestock and their feeding behaviour. In Southern Chile, dairy cattle present a high heterogeneity in breeds, size, live weight, and milk production. This study investigated whether cows of contrasting size/live weight can improve degraded pasture and positively modify soil (Andosol-Duric Hapludand) physical features. Three pasture types were used as follows: (i) cultivated fertilised Lolium perenne L. (perennial ryegrass) and Trifolium repens L. (white clover) mixture (BM); (ii) cultivated fertilised L. perenne, T. repens, Bromus valdivianus Phil. (pasture brome), Holcus lanatus L. (Yorkshire fog), and Dactylis glomerata L. (cocksfoot) mixture (MSM); and (iii) naturalised fertilised pasture Agrostis capillaris L. (browntop), B. valdivianus, and T. repens (NFP). Pastures were grazed with two groups of dairy cows of contrasting size and live weight: light cows (LC) [live weight: 464 ± 5.4 kg; height at the withers: 132 ± 0.6 cm (average ± s.e.m.)] and heavy cows (HC) [live weight: 600 ± 8.7 kg; height at the withers: 141 ± 0.9 cm (average ± s.e.m.)]. Hoof area was measured, and the pressure applied by cows on the soil was calculated. Soil differences in penetration resistance (PR) and macro-porosity (wCP > 50 μm) between pastures were explained by tillage and seeding, rather than as a result of livestock presence and movement (animal trampling). The PR variation during the year was associated with the soil water content (SWC). Grazing dairy cows of contrasting live weight caused changes in soil and pasture attributes, and they behaved differently during grazing. Light cows were linked to more intense grazing, a stable soil structure, and pastures with competitive species and greater tiller density. In MSM, pasture consumption increased, and the soil was more resilient to hoof compression. In general, grazing with heavy cows in these three different pasture systems did not negatively impact soil physical properties. These findings indicate that volcanic soils are resilient and that during renovation, the choice of pasture type has a greater initial impact on soil structure than the selection of cow size, but incorporating lighter cows can be a strategy to promote denser pasture swards in these grazing systems. Full article

This study investigates the air filtration capabilities of fibrous membranes fabricated via electrospinning, with a focus on optimizing processing parameters. Specifically, Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), a well-characterized biodegradable polyester, was electrospun to produce membranes exhibiting precisely controlled surface microstructures. The optimal fiber morphology was attained under conditions of a 20 kV applied electric field, a solution flow rate of 0.5 mL·h⁻¹, a polymer concentration of 13 wt.%, and a needle inner diameter of 0.21 mm. The microstructural features of the electrospun PHBV membranes were characterized using scanning electron microscopy (SEM). Complementary analysis via ¹³C nuclear magnetic resonance (NMR) spectroscopy confirmed that the membranes comprised pure 3-hydroxyvalerate (3HV) copolymerized with 3-hydroxybutyrate (3HB) terminal units, with 3HV mole fractions ranging from 17% to 50%. The incorporation of different molar percentages of 3HV in PHBV membrane significantly enhances its durability, as evidenced by Ball Burst Strength (BBS) measurements, with an elongation at burst that is 65–86% greater than that of ASTM F2100 level 3 mask. The nanofibrous membranes exhibited a controlled pore size distribution, indicating their potential suitability for air filtration applications. Particle filtration efficiency (PFE) assessments under standard atmospheric pressure conditions showed that the optimized electrospun PHBV membranes achieved filtration efficiencies exceeding 98%. Additionally, the influence of 3HV content on biodegradation behavior was evaluated through soil burial tests conducted over 90 days. Results indicated that membranes with lower 3HV content (17 mol.%) experienced the greatest weight loss, suggesting accelerated degradation in natural soil environments. Full article

Soil organic carbon (SOC) is a crucial indicator of soil quality and carbon cycling. While remote sensing and machine learning enable regional scale SOC prediction, most studies rely on vegetation indices (VIs) derived from bare-soil periods, potentially neglecting vegetation–soil interactions during crop growth. Given the bidirectional relationship between SOC and crop growth, we hypothesized that using crop-lush period VIs (VIs_lush) instead of bare-soil period VIs (VIs_bare) would increase the inversion accuracy. To test this hypothesis, we chose the cropland area in Liaoning Province as the study area and developed three modeling strategies (MS-1: VIs_lush + other features; MS-2: VIs_bare + other features; and MS-3: without VIs) using Landsat 8 imagery, topographic and precipitation data, and ensemble learning models (XGBoost, RF, and AdaBoost), with SHapley Additive exPlanations (SHAP) analysis for variable interpretation. We found that (1) all models achieved their highest performance under MS-1, with XGBoost outperforming the others across all modeling strategies; (2) for XGBoost, MS-1 yielded the highest inversion accuracy (R² = 0.84, RMSE = 2.22 g·kg⁻¹, RPD = 2.49, and RPIQ = 3.25); compared with MS-2, MS-1 reduced the RMSE by 0.31 g·kg⁻¹, increased R² from 0.77 to 0.84, and reduced the RPD by 0.31 and the RPIQ by 0.40, and compared with MS-3, MS-1 reduced the RMSE by 0.41 g·kg⁻¹, increased R² from 0.79 to 0.84, and reduced the RPD by 0.39 and the RPIQ by 0.51; (3) based on the SHAP analysis of the three modeling strategies, it is considered that precipitation, terrain and terrain analysis results are important indicators for SOC content inversion, and it is confirmed that VIs_lush contributed more than VIs_bare, supporting the rationale of using lush-period imagery; and (4) Liaoning Province exhibited distinct SOC spatial patterns (mean: 13.08 g·kg⁻¹), with values ranging from 2.19 g·kg⁻¹ (sandy central–western area) to 33.86 g·kg⁻¹ (eastern mountains/coast). This study demonstrates that integrating growth stage-specific VIs with ensemble learning can significantly enhance regional-scale SOC prediction. Full article

Photovoltaic (PV) fault detection faces significant challenges in distinguishing subtle defects from complex backgrounds while maintaining reliability across diverse environmental conditions. Traditional approaches struggle with scalability and accuracy limitations, particularly when detecting electrical damage, physical defects, and environmental soiling in thermal imagery. This paper presents SolarFaultAttentionNet, a novel dual-attention deep learning framework that integrates channel-wise and spatial attention mechanisms within a multi-path CNN architecture for enhanced PV fault classification. The approach combines comprehensive data augmentation strategies with targeted attention modules to improve feature discrimination across six fault categories: Electrical-Damage, Physical-Damage, Snow-Covered, Dusty, Bird-Drop, and Clean. Experimental validation on a dataset of 885 images demonstrates that SolarFaultAttentionNet achieves 99.14% classification accuracy, outperforming state-of-the-art models by 5.14%. The framework exhibits perfect detection for dust accumulation (100% across all metrics) and robust electrical damage detection (99.12% F1 score) while maintaining an optimal sensitivity (98.24%) and specificity (99.91%) balance. The computational efficiency (0.0160 s inference time) and systematic performance improvements establish SolarFaultAttentionNet as a practical solution for automated PV monitoring systems, enabling reliable fault detection critical for maximizing energy production and minimizing maintenance costs in large-scale solar installations. Full article

The compressive, tensile, and shear strength properties of two cement-stabilized soils (CSS) treated with 2% to 4% of cement are investigated for several different curing times at several densities. The measured Mohr–Coulomb (MC) shear strength features, cohesion (c), and friction angle (φ) are compared with values reported in the literature for similar materials and are subject to debate depending on the estimation methods used. In addition, an alternative geometric criterion based on indirect tensile strength (ITS) and unconfined compressive strength (UCS) is evaluated. The results show that the value of c determined using the alternative criterion is slightly higher than the value of c measured using the direct shear (DS) test. A relationship between mixture variables and c is established and validated by combining numerical and experimental approaches. The friction angle appears to be constant, independent of mixture parameters. This parameter is underestimated using the geometric approach. Full article

Soil erosion is an increasingly severe problem and a global focus. As one of the countries facing relatively serious soil erosion, China encounters significant ecological challenges. This study focuses on the carbon sink benefits of comprehensive soil and water conservation management in the red soil erosion area of southern China, conducting an in-depth analysis using the Ziyang small watershed in Shangyou County, Jiangxi Province, as a typical case. Research methods involved constructing an integrated monitoring approach combining basic data, measured data, and remote sensing data. Changes in soil and vegetation carbon storage in the Ziyang small watershed across different years were determined by establishing a baseline scenario and applying inverse distance spatial interpolation, quadrat calculation, feature extraction, and screening. The results indicate that from 2002 to 2023, after 21 years of continuous implementation of various soil and water conservation measures under comprehensive watershed management, the carbon storage of the Ziyang small watershed increased significantly, yielding a net carbon sink of 54,537.28 tC. Tending and Management of Coniferous and Broad-leaved Mixed Forest, Low-efficiency Forest Improvement, and Thinning and Tending contributed substantially to the carbon sink, accounting for 72.72% collectively. Furthermore, the carbon sink capacity of the small watershed exhibited spatial variation influenced by management measures: areas with high carbon density were primarily concentrated within zones of Tending and Management of Coniferous and Broad-leaved Mixed Forest, while areas with low carbon density were mainly found within zones of Bamboo Forest Tending and Reclamation. The increase in watershed carbon storage was attributed to contributions from both vegetation and soil carbon pools. Comprehensive management of soil erosion demonstrates a significant carbon accumulation effect. The annual growth rate of vegetation carbon storage was higher than that of soil carbon storage, yet the proportion of soil carbon storage increased yearly. This study provides a theoretical basis and data foundation for the comprehensive management of soil and water conservation in small watersheds in the southern red soil erosion region of China and can offer technical and methodological support for other soil and water conservation carbon sink projects in this area. Full article

In recent years, China’s hilly and mountainous areas have faced widespread farmland abandonment. However, research on farmland abandonment and its driving mechanisms in hilly and mountainous regions is limited. This study proposes a transferable methodological framework that integrates Landsat data, Google Earth Engine, a time sliding-window algorithm, and the interpretable XGBoost–Shapley Additive explanation (SHAP) model. The time sliding-window algorithm is used to robustly detect long-term land cover changes across the entire study period. The SHAP quantifies the contributions of key drivers to farmland abandonment, providing transparent insights into the driving mechanisms. Applying this framework, we systematically analyzed the spatiotemporal evolution patterns and driving factors of farmland abandonment in Ji’an City, a typical city located in the hilly and mountainous areas of southern China and ultimately developed a farmland abandonment probability distribution map. The findings demonstrate the following. (1) Methodological validation showed that the random forest classifier achieved a mean overall accuracy (OA) of 91.05% (Kappa = 0.88) and the abandonment maps achieved OA of 91.58% (Kappa = 0.83). (2) Spatiotemporal analysis revealed that farmland area increased by 13.26% over 1990–2023, evolving through three stages: fluctuation (1990–2005), growth (2006–2015), and stability (2016–2023). The abandonment rate showed a long-term decreasing trend, peaking in 1998, whereas the abandoned area reached its minimum in 2007. From a spatial perspective, abandonment was more pronounced in mountainous and hilly regions of the study areas. (3) The XGBoost–SHAP model (R² > 0.85) identified key driving factors, including the potential crop yield, soil properties, mean annual precipitation, population density, and terrain features. By offering an interpretable and transferable monitoring framework, this study not only advances farmland abandonment research in complex terrains but also provides concrete policy implications. The results can guide targeted protection of high-risk abandonment zones, promote sustainable land-use planning, and support adaptive agricultural policies in hilly and mountainous regions. Full article

The study of faults in seismic gap areas is essential for assessing the potential for future seismic activity and developing strategies to mitigate its impact. In this research, we employed a combination of geomorphological analysis, aerophotogrammetry, high-resolution topography, and soil analysis to estimate the age of tectonically exposed fault surfaces in a seismic gap area. Our focus was on the Piano delle Rose Fault in the northern Calabria region, (southern Italy), which is a significant regional tectonic structure associated with seismic hazards. We conducted a field survey to carry out structural and pedological observations and collect soil samples from the fault surface. These samples were analyzed to estimate the fault’s age based on their features and degree of pedogenic development. Additionally, we used high-resolution topography and aerophotogrammetry to create a detailed 3D model of the fault surface, allowing us to identify features such as fault scarps and offsets. Our results indicate recent activity on the fault surface, suggesting that the Piano delle Rose Fault may pose a significant seismic hazard. Soil analysis suggests that the onset of the fault surface is relatively young, estimated in an interval time from 450,000 to ~ 300,000 years old. Considering these age constraints, the long-term slip rates are estimated to range between ~0.12 mm/yr and ~0.33 mm/yr, which are values comparable with those of many other well-known active faults of the Apennines extensional belt. Analyses of key fault exposures document cumulative displacements up to 21 m. These values yield long-term slip rates ranging from ~0.2 mm/yr (100,000 years) to ~1.0 mm/yr (~20,000 years LGM), indicating persistent Late Quaternary activity. A second exposure records ~0.6 m of displacement in very young soils, confirming surface faulting during recent times and suggesting that the fault is potentially capable of generating ground-rupturing earthquakes. High-resolution topography and aerophotogrammetry analyses show evidence of ongoing tectonic deformation, indicating that the area is susceptible to future seismic activity and corresponding risk. Our study highlights the importance of integrating multiple techniques for examining fault surfaces in seismic gap areas. By combining geomorphological analysis, aerophotogrammetry, high-resolution topography, and soil analysis, we gain a comprehensive understanding of the structure and behavior of faults. This approach can help assess the potential for future seismic activity and develop strategies for mitigating its impact. Full article