Search Results (190)

Bottle-brush polymers with aggrecan-like side chains represent a class of biomimetic macromolecules that replicate key structural and functional features of natural complexes of aggrecans with hyaluronic acid (HA) which are the major components of articular cartilage. In this study, we employ numerical self-consistent field (SCF) modeling combined with analytical theory to investigate the conformational properties of cylindrical molecular bottle-brushes composed of aggrecan-like double-comb side chains tethered to the main chain (the backbone of the bottle-brush). We demonstrate that the architecture of the brush-forming double-comb chains and, in particular, the distribution of polymer mass between the root and peripheral domains significantly influences the spatial distribution of primary side chain ends, leading to formation of a “dead” zone near the backbone of the bottle-brush and non-uniform density profiles. The axial stretching force imposed by grafted double-combs in the main chain, as well as normal force acting at the junction point between the bottle-brush backbone and the double-comb side chain are shown to depend strongly on the side-chain architecture. Furthermore, we analyze the induced bending rigidity and persistence length of the bottle-brush, revealing that while the overall scaling behavior follows established power laws, the internal structure can be finely tuned without altering the backbone stiffness. These theoretical findings provide valuable insights into relations between architecture and properties of bottle-brush-like supra-biomolecular structures, such as aggrecan-hyaluronan complexes. Full article

The root causes forcing the differential pore-throat performances and crude oil recoverability in heterogeneous shale lithofacies of saline-lacustrine fine-grained mixed sedimentary sequences are still debated. Especially application cases of fractal theory in characterizing pore-throat heterogeneity are still lacking and the significance of differential multifractal distribution patterns on reservoir assessment remains controversial. This present study focuses on the shale-oil reservoirs in saline-lacustrine fine-grained mixed depositional sequences of the Middle Permian Lucaogou Formation (southern Junggar Basin, NW China), and presents a set of new results from petrographical investigation, field-emission scanning electron microscopy (FE-SEM) imaging, fluid injection experiments (low-pressure N₂ adsorption and high-pressure mercury intrusion porosimetry (HMIP)), nuclear magnetic resonance (NMR) spectroscopy and T₁-T₂ mapping, directional spontaneous imbibition, as well as contact angle measurements. Our results demonstrated that the investigated lithofacies are mainly divided into a total of five lithofacies categories: felsic siltstones, sandy dolomitic sandstones, dolarenites, micritic dolomites, and dolomitic mudstones, respectively. More importantly, the felsic siltstone and sandy dolomitic siltstones can be identified as the most advantageous lithofacies categories exhibiting the strongest movable oil-bearing capacity owing to an acceptable complexity and heterogeneity of mesopore-throat structures, as evidenced by the corresponding moderate fractal dimension of mesopores (D₂) from HMIP and apparently lower fractal dimension of movable fluids’ pores (D₂) from NMR results. Particularly noteworthy is the relatively poor shale-oil movability recognized in the dolarenites, micritic dolomites, and dolomitic mudstones due to heterogeneous and unfavorable pore-throat systems, even though an acceptable micro-connectivity and a more oleophilic interfacial wettability prevails in crucial dolomitic components. Finally, a comprehensive and conceptual model is established for an effective and characteristic parameter system for assessing differential reservoir petrophysical properties, interfacial wettability, and shale-oil movability concerning heterogeneous lithofacies categories. Our achievements can serve as an analog for investigating saline-lacustrine mixed shale-oil reservoirs to gain a more comprehensive understanding of differential recoverability of dessert reservoir intervals, and to guide the assessment of “sweet spots” distribution and optimization of engineering technique schemes for commercial exploitation. Full article

Automated crop monitoring in controlled environments is imperative for enhancing crop productivity. The availability of small unmanned aerial systems (sUAS) and cost-effective LiDAR sensors present an opportunity to conveniently gather high-quality data for crop monitoring. The LiDAR-collected point cloud data, however, often encounter challenges such as occlusions and low point density that can be addressed by acquiring additional data from multiple flight paths. This study evaluated the performance of using an Iterative Closest Point (ICP)-based algorithm for registering sUAS-based LiDAR point clouds collected in the greenhouse environment. To address the issue of objects that may cause ICP or local feature-based registration to mismatch correspondences, this study developed a robust registration pipeline. First, the geometric centroid of the ground floor boundary was leveraged to improve the initial alignment, and then piecewise ICP was implemented to achieve fine registration. The evaluation of point cloud registration performance included visualization, root mean square error (RMSE), volume estimation of reference objects, and the distribution of point cloud density. The best RMSE dropped from 20.4 cm to 2.4 cm, and point cloud density improved after registration, and the volume-estimation error for reference objects dropped from 72% (single view) to 6% (post-registration). This study presents a promising approach to point cloud registration that outperforms conventional ICP in greenhouse layouts while eliminating the need for artificial reference objects. Full article

Tropical cyclones (TCs) rank among the most destructive natural hazards globally, with core damaging potential originating from regions of intense wind shear and steep wind speed gradients within the eyewall and spiral rainbands. Accurately characterizing these fine-scale structural features is therefore critical for understanding TC intensity evolution, wind hazard distribution, and disaster mitigation. Recently, the deep learning-based downscaling methods have shown significant advantages in efficiently obtaining high-resolution wind field distributions. However, existing methods are mainly used to downscale general wind fields, and research on downscaling extreme wind field events remains limited. There are two main difficulties in downscaling TC wind fields. The first one is that high-quality datasets for TC wind fields are scarce; the other is that general deep learning frameworks lack the ability to capture the dynamic characteristics of TCs. Consequently, this study proposes a novel deep learning framework, CycloneWind, for downscaling TC surface wind fields: (1) a high-quality dataset is constructed by integrating Cyclobs satellite observations with ERA5 reanalysis data, incorporating auxiliary variables like low cloud cover, surface pressure, and top-of-atmosphere incident solar radiation; (2) we propose CycloneWind, a dynamically constrained Transformer-based architecture incorporating three wind field dynamical operators, along with a wind dynamics-constrained loss function formulated to enforce consistency in wind divergence and vorticity; (3) an Adaptive Dynamics-Guided Block (ADGB) is designed to explicitly encode TC rotational dynamics using wind shear detection and wind vortex diffusion operators; (4) Filtering Transformer Layers (FTLs) with high-frequency filtering operators are used for modeling wind field small-scale details. Experimental results demonstrate that CycloneWind successfully achieves an 8-fold spatial resolution reconstruction in TC regions. Compared to the best-performing baseline model, CycloneWind reduces the Root Mean Square Error (RMSE) for the U and V wind components by 9.6% and 4.9%, respectively. More significantly, it achieves substantial improvements of 23.0%, 22.6%, and 20.5% in key dynamical metrics such as divergence difference, vorticity difference, and direction cosine dissimilarity. Full article

Drought is a critical factor influencing the distribution of forest species in both present and future global terrestrial ecosystems. Therefore, to investigate the sensitivity of typical afforestation tree species on the Loess Plateau to drought and its influencing factors, we conducted field experiments to measure the sap flow, soil moisture content, fine root density, leaf water potential, and xylem hydraulic characteristics of three deciduous trees: apple (Malus domestica), black locust (Robinia pseudoacacia), and jujube (Ziziphus jujube). We found that the canopy conductance (Gc) of black locust and apple trees was highly sensitive to VPD variations. Their transpiration (T) was also sensitive to soil moisture variation, especially for black locust. In contrast, the Gc and T sensitivity of jujube trees was low. The differences in their drought sensitivities can primarily be attributed to variations in xylem hydraulic conductivity and embolism vulnerability. Our results demonstrate that both mature black locust and apple trees on the Loess Plateau have strong drought sensitivity, especially black locust. Therefore, alterations in precipitation patterns driven by climate change may significantly influence the community distribution of black locusts trees on the Loess Plateau. Full article

Phosphorus (P) availability is a major constraint on plant growth in many forest ecosystems, yet the strategies by which different tree species acquire and utilize various forms of soil phosphorus remain poorly understood. This study investigated how coexisting tree species with contrasting mycorrhizal types, specifically arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) associations, respond to different phosphorus forms under field conditions. An in situ root bag experiment was conducted using four phosphorus treatments (control, inorganic, organic, and mixed phosphorus) across four subtropical tree species. A comprehensive set of fine root traits, including morphological, physiological, and mycorrhizal characteristics, was measured to evaluate species-specific phosphorus foraging strategies. The results showed that AM species were more responsive to phosphorus form variation than ECM species, particularly under inorganic and mixed phosphorus treatments. Significant changes in root diameter (RD), root tissue density (RTD), and acid phosphatase activity (RAP) were observed in AM species, often accompanied by higher phosphorus accumulation in fine roots. For example, RD in AM species significantly decreased under the Na₃PO₄ treatment (0.94 mm) compared to the control (1.18 mm), while ECM species showed no significant changes in RD across treatments (1.12–1.18 mm, p > 0.05). RTD in AM species significantly increased under Na₃PO₄ (0.030 g/cm³) and Mixture (0.021 g/cm³) compared to the control (0.012 g/cm³, p < 0.05), whereas ECM species exhibited consistently low RTD values across treatments (0.017–0.020 g/cm³, p > 0.05). RAP in AM species increased significantly under Na₃PO₄ (1812 nmol/g/h) and Mixture (1596 nmol/g/h) relative to the control (1348 nmol/g/h), while ECM species showed limited variation (1286–1550 nmol/g/h, p > 0.05). In contrast, ECM species displayed limited trait variation across treatments, reflecting a more conservative acquisition strategy. In addition, trait correlation analysis revealed stronger coordination among root traits in AM species. And AM species exhibited high variability across treatments, while ECM species maintained consistent trait distributions with limited plasticity. These findings suggest that AM and ECM species adopt fundamentally different phosphorus acquisition strategies. AM species rely on integrated morphological and physiological responses to variable phosphorus conditions, while ECM species maintain stable trait configurations, potentially supported by fungal symbiosis. Such divergence may contribute to functional complementarity and species coexistence in phosphorus-limited subtropical forests. Full article

Blasting is an effective technique for fragmenting rock in open-pit mining operations. Blasting operations produce either boulders or fine fragments, both of which increase costs and pose environmental risks. As a result, predicting the mean fragmentation size (MFS) distribution of rock is critical for assessing blasting operations’ quality and mitigating risks. Due to the limitations of empirical and statistical models, several researchers are turning to artificial intelligence (AI)-based techniques to predict the MFS distribution of rock. Thus, this study uses three AI tree-based algorithms—extra trees (ET), gradient boosting (GB), and random forest (RF)—to predict the MFS distribution of rock. The prediction accuracy of the models is optimized utilizing the tree-structured Parzen estimators (TPEs) algorithm, which results in three models: TPE-ET, TPE-GB, and TPE-RF. The dataset used in this study was collected from the published literature and through the data augmentation of a large-scale dataset of 3740 blast samples. Among the evaluated models, the TPE-ET model exhibits the best performance with a coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and max error of 0.93, 0.04, 0.03, and 0.25 during the testing phase. Moreover, the block size (XB, m) and modulus of elasticity (E, GPa) parameters are identified as the most influential parameters for predicting the MFS distribution of rock. Lastly, an interactive web application has been developed to assist engineers with the timely prediction of MFS. The predictive model developed in this study is a reliable intelligent model because it combines high accuracy with a strong, explainable AI tool for predicting MFS. Full article

The environmental heterogeneity caused by altitude can lead to trade-offs in nutrient utilization and allocation strategies among plant organs; however, there is still a lack of research on the nutrient variation in the “flower–leaf–branch–fine root–soil” systems of native shrubs along altitude gradients in China’s unique karst regions. Therefore, we analyzed the carbon (C), nitrogen (N), and phosphorus (P) contents and their ratios in flowers, leaves, branches, fine roots, and surface soil of Hypericum kouytchense shrubs across 2200–2700 m altitudinal range in southwestern China’s karst areas, where this species is widely distributed and grows well. The results show that H. kouytchense organs had higher N content than both global and Chinese plant averages. The order of C:N:P value across plant organs was branches > fine roots > flowers > leaves. Altitude significantly affected the nutrient dynamics in plant organs and soil. With increasing altitude, P content in plant organs exhibited a significant concave pattern, leading to unimodal trends in the C:P of plant organs, as well as the N:P of leaves and fine roots. Meanwhile, plant organs except branches displayed significant homeostasis coefficients in C:P and fine root P, indicating a shift in H. kouytchense’s P utilization strategy from acquisitive-type to conservative-type. Strong positive relationships between plant organs and soil P and available P revealed that P was the key driver of nutrient cycling in H. kouytchense shrubs, enhancing plant organ–soil coupling relationships. In conclusion, H. kouytchense demonstrates flexible adaptability, suggesting that future vegetation restoration and conservation management projects in karst ecosystems should consider the nutrient adaptation strategies of different species, paying particular attention to P utilization. Full article

The sustainable integration of Distributed Energy Resources (DER) with the next-generation distribution networks requires robust, adaptive, and accurate hosting capacity (HC) forecasting. Dynamic Operating Envelopes (DOE) provide real-time constraints for power import/export to the grid, ensuring dynamic DER integration and efficient network operation. However, conventional HC analysis and forecasting approaches struggle to capture temporal dependencies, the impact of DOE constraints on network operation, and uncertainty in DER output. This study introduces a dynamic optimization framework that leverages the benefits of the sensitivity gate of the Sensitivity-Enhanced Recurrent Neural Network (SERNN) forecasting model, Particle Swarm Optimization (PSO), and Bayesian Optimization (BO) for HC forecasting. The PSO determines the optimal weights and biases, and BO fine-tunes hyperparameters of the SERNN forecasting model to minimize the prediction error. This approach dynamically adjusts the import/export of the DER output to the grid by integrating the DOE constraints into the SG-PSO-BO architecture. Performance evaluation on the IEEE-123 test network and a real Australian distribution network demonstrates superior HC forecasting accuracy, with an $R^2$ score of 0.97 and 0.98, Mean Absolute Error (MAE) of 0.21 and 0.16, and Root Mean Square Error (RMSE) of 0.38 and 0.31, respectively. The study shows that the model effectively captures the non-linear and time-sensitive interactions between network parameters, DER variables, and weather information. This study offers valuable insights into advancing dynamic HC forecasting under real-time DOE constraints in sustainable DER integration, contributing to the global transition towards net-zero emissions. Full article

As a critical link connecting groundwater and vegetation, the vadose zone’s lithological structural heterogeneity directly influences soil water distribution and vegetation growth. A comprehensive understanding of the ecological effects of the vadose zone can provide scientific evidence for groundwater ecological protection and natural vegetation conservation in arid regions. This study, taking the Minqin Basin in the lower reaches of China’s Shiyang River as a case, reveals the constraining effects of vadose zone lithological structures on vegetation water supply, root development, and water use strategies through integrated analysis, field investigations, and numerical simulations. The findings highlight the critical ecological role of the vadose zone. This role primarily manifests through two mechanisms: regulating capillary water rise and controlling water-holding capacity. They directly impact soil water supply efficiency, alter the spatiotemporal distribution of water deficit in the root zone, and drive vegetation to develop adaptive root growth patterns and stratified water use strategies, ultimately leading to different growth statuses of natural vegetation. During groundwater level fluctuations, fine-grained lithologies in the vadose zone exhibit stronger capillary water response rates, while multi-layered lithological structures (e.g., “fine-over-coarse” configurations) demonstrate pronounced delayed water release effects. Their effective water-holding capacities continue to exert ecological effects, significantly enhancing vegetation drought resilience. Full article

Plant roots influence various soil physical properties by altering the soil structure and pore configuration; however, a detailed understanding of these effects remains limited. In this study, we applied a relatively simple approach for segmenting plant roots and soil constituents using X-ray computed tomography (CT) images to evaluate root-induced changes in soil structure. The method combines manual initialization with a layer-wise automated region-growing approach, enabling the extraction of the root systems of soybean, Italian ryegrass, and Guinea grass. The method utilizes freely available software with a simple interface and does not require advanced image analysis skills, making it accessible to a wide range of researchers. The soil particles, pore water, and pore air were segmented using a Kriging-based thresholding technique. The segmented four-phase images allowed for the quantification of the volume fractions of soil constituents, pore size distributions, and coordination numbers. Furthermore, by separating the rhizosphere and bulk soil, we found that the root presence significantly reduced solid fractions and increased water content, particularly in the upper soil layers. Macropores and fine pores were observed near the roots, highlighting the complex structural impacts of root growth. While further validation is needed to assess the method’s applicability across different soil types and imaging conditions, it provides a practical basis for visualizing and quantifying root–soil interactions, and could contribute to advancing our understanding of how plant roots influence key soil hydraulic and thermal properties. Full article

Water quality monitoring is crucial for ecological protection and water resource management. However, traditional monitoring methods suffer from limitations in temporal, spatial, and spectral resolution, which constrain the effective evaluation of urban rivers and multi-scale aquatic systems. To address challenges such as ecological heterogeneity, multi-scale complexity, and data noise, this paper proposes a deep learning framework, TL-Net, based on unmanned aerial vehicle (UAV) hyperspectral imagery, to estimate four water quality parameters: total nitrogen (TN), dissolved oxygen (DO), total suspended solids (TSS), and chlorophyll a (Chla); and to produce their spatial distribution maps. This framework integrates Transformer and long short-term memory (LSTM) networks, introduces a cross-temporal attention mechanism to enhance feature correlation, and incorporates an adaptive feature fusion module for dynamically weighted integration of local and global information. The experimental results demonstrate that TL-Net markedly outperforms conventional machine learning approaches, delivering consistently high predictive accuracy across all evaluated water quality parameters. Specifically, the model achieves an R² of 0.9938 for TN, a mean absolute error (MAE) of 0.0728 for DO, a root mean square error (RMSE) of 0.3881 for total TSS, and a mean absolute percentage error (MAPE) as low as 0.2568% for Chla. A spatial analysis reveals significant heterogeneity in water quality distribution across the study area, with natural water bodies exhibiting relatively uniform conditions, while the concentrations of TN and TSS are substantially elevated in aquaculture areas due to aquaculture activities. Overall, TL-Net significantly improves multi-parameter water quality prediction, captures fine-scale spatial variability, and offers a robust and scalable solution for inland aquatic ecosystem monitoring. Full article

This study develops a comprehensive, data-driven framework for predicting satellite imagery prices using four state-of-the-art ensemble learning algorithms: XGBoost, LightGBM, AdaBoost, and CatBoost. Two distinct datasets—optical and Synthetic Aperture Radar (SAR) imagery—were assembled, each characterized by nine technical and economic features (e.g., imaging mode, spatial resolution, satellite manufacturing cost, and acquisition timeliness). Bayesian optimization is employed to systematically tune hyperparameters, thereby minimizing overfitting and maximizing generalization. Models are evaluated on held-out test sets (20% of data) using Pearson’s correlation coefficient (R), mean bias error (MBE), root mean square error (RMSE), unbiased RMSE (ubRMSE), Nash–Sutcliffe Efficiency (NSE), and Kling–Gupta Efficiency (KGE). For optical imagery, the Bayesian-optimized XGBoost model achieves the best performance ($R=0.9870$, $\mathrm{RMSE}=\$3.44/{\mathrm{km}}^2$, $\mathrm{NSE}=0.9651$, $\mathrm{KGE}=0.8950$), followed closely by CatBoost ($R=0.9826$, $\mathrm{RMSE}=\$3.83/{\mathrm{km}}^2$). For SAR imagery, CatBoost outperforms all others after optimization ($R=0.9278$, $\mathrm{RMSE}=\$9.94/{\mathrm{km}}^2$, $\mathrm{NSE}=0.8575$, $\mathrm{KGE}=0.8443$), reflecting its robustness to heavy-tailed price distributions. AdaBoost also demonstrates competitive accuracy, while LightGBM and XGBoost exhibit larger errors in high-value regimes. SHapley Additive exPlanations (SHAP) analysis reveals that imaging mode and spatial resolution are the primary drivers of price variance across both domains, followed by satellite manufacturing cost and acquisition recency. These insights demonstrate how ensemble models capture nonlinear, high-dimensional interactions that traditional rule-based pricing schemes overlook. Compared to static, experience-driven price brackets, our machine learning approach provides a scalable, transparent, and economically rational pricing engine—adaptable to rapidly changing market conditions and capable of supporting fine-grained, application-specific pricing strategies. Full article

Biodegradable mulch films not only provide similar field benefits to conventional mulch films but also degrade naturally, rendering them an effective alternative to traditional polyethylene mulch films for mitigating “white pollution”. However, recent studies have focused on the material selection and soil ecological impacts of biodegradable mulch films, while their effects on soil water temperature regulation and root architecture in drip-irrigated rice cultivation remain unclear. To address this research gap, in this study, various treatments including no mulch (NM), conventional plastic mulch (PM), and four types of biodegradable mulch films (BM-W₁, BM-B₁, BM-B₂, and BM-B₃) were established, and their effects on the soil hydrothermal flux, root architecture, biomass accumulation, and resource use efficiency of drip-irrigated rice were analyzed at different growth stages. The results indicated the following: (1) Compared with the NM treatment, film mulching increased the soil hydrothermal fluxes and water retention capacity, thereby promoting root growth and biomass accumulation, ultimately increasing the effective panicle number and grain yield. (2) Among the biodegradable film treatments, BM-B₃ (with a degradation period of 105 days) maintained relatively higher soil temperature for a longer duration, which increased surface root distribution in the mid-to-late growth stages, further improving fine root growth and biomass accumulation, consequently enhancing both yield and water use efficiency. In contrast, BM-B₁ and BM-B₂ exhibited excessively rapid degradation rates, leading to significant fluctuations in soil moisture and temperature, thereby negatively affecting water supply and nutrient uptake and ultimately restricting root growth and development. (3) The entropy weight (EW) technique for order of preference by similarity to ideal solution (TOPSIS) model results revealed that although the PM treatment was more advantageous in terms of soil temperature, root dry weight, and soil moisture content, BM-B₃ provided a slightly higher yield than the PM treatment did and offered the advantage of biodegradability, making it a preferred alternative to conventional mulch film. In summary, this study revealed the mechanism by which biodegradable mulch films enhanced biomass accumulation and yield formation in drip-irrigated rice production by optimizing soil hydrothermal dynamics and root architecture, thereby exploring their potential as replacements for conventional mulch films. These findings provide a theoretical basis for the efficient and sustainable production of drip-irrigated rice in arid regions. Full article

Forest soil respiration plays a crucial role in the global carbon cycle. However, accurately estimating regional soil carbon fluxes is challenging due to the spatial heterogeneity of soil respiration at the stand level. This study examines the spatial variation of soil respiration and its driving factors in subtropical coniferous and broad-leaved mixed forests in southern China, aiming to provide insights into accurately estimating regional carbon fluxes. The findings reveal that the coefficient of variation (CV) of soil respiration at a scale of 50 m × 50 m is 18.82%, indicating a moderate degree of spatial variation. Furthermore, 52% of the spatial variation in soil respiration can be explained by the variables under investigation. The standardized total effects of the main influencing factors are as follows: soil organic carbon (0.71), diameter at breast height within a radius of 5 m (0.31), soil temperature (0.27), and soil bulk density (−0.25). These results imply that even in relatively homogeneous areas with flat terrain, fine-scale soil respiration exhibits significant spatial heterogeneity. The spatial distribution of woody plant resources predominantly regulates this variation, with root distribution, shading effects, and changes in soil physical and chemical properties being the main influencing mechanisms. The study emphasizes the importance of simulations at different microscales to unravel the potential mechanisms governing macroscopic phenomena. Additionally, it highlights the need for incorporating a more comprehensive range of variables to provide more meaningful references for regional soil carbon flux assessment. Full article