Search Results (200)

Soil nutrients and water are often distributed heterogeneously in space, yet how plant roots forage in response to such heterogeneity and how their strategies relate to functional traits remain poorly understood. Here, we conducted an indoor pot experiment manipulating water and nutrient supply in both homogeneous and heterogeneous patch patterns using seedlings of four tree species, focusing on root functional traits and foraging strategies. The results indicate that root foraging behavior exhibits both resource specificity and species specificity: roots tend to proliferate toward nutrient-rich and low-water patches as an adaptive strategy. Although no strict dichotomy was observed between high foraging scale (low precision) and low foraging scale (high precision) strategies under heterogeneous conditions, fine-rooted species (Acer truncatum and Koelreuteria paniculata) exhibited traits leaning toward “precise foraging”, whereas coarse-rooted species (Prunus davidiana and Quercus variabilis) tended toward a conservative “random walk” pattern, with no trade-off between root foraging scale and precision. Root morphological traits exerted significant nonlinear regulation on foraging scale: root biomass foraging scale (FS_RB) correlated positively with root diameter (RD) but negatively with specific root length (SRL) and specific root area (SRA); root length foraging scale (FS_RL) correlated positively with root length (RL), root tip number (RTN), SRL, and SRA. In contrast, root morphological traits could not explain the variation in foraging precision, suggesting that foraging precision constitutes another distinct dimension in root-trait space. In summary, this study provides key insights into the foraging strategies of plant roots in heterogeneous environments, expanding our understanding of the multidimensionality of root functional traits. Full article

The high energy consumption and spatiotemporal thermal asymmetry of data center cooling systems have become critical bottlenecks constraining their green and sustainable development. Traditional point-type temperature sensors suffer from insufficient spatial coverage, while conventional feedback control strategies exhibit delayed responses and limited adaptability under dynamic workloads. To address these challenges, this study proposes a real-time thermal symmetry management framework for data centers based on distributed fiber optic temperature sensing and model predictive control (MPC). The proposed system employs Brillouin scattering-based distributed sensing to continuously acquire high-density temperature measurements from thousands of points along a single optical fiber, enabling fine-grained perception of the three-dimensional thermal field. On this basis, a hybrid prediction model integrating thermodynamic physical equations with a Temporal Convolutional Network–Bidirectional Gated Recurrent Unit (TCN–BiGRU) deep neural network is developed to achieve accurate and stable spatiotemporal temperature forecasting. Furthermore, a symmetry-aware MPC controller is designed with the dual objectives of minimizing cooling energy consumption and suppressing thermal field deviations, thereby restoring temperature uniformity through rolling-horizon optimization. Experimental validation in a production data center demonstrates that the distributed sensing system achieves a measurement deviation of 0.12 °C, while the hybrid prediction model attains a root mean square error of 0.41 °C, representing a 26.8% improvement over baseline methods. The MPC-based control strategy reduces daily cooling energy consumption by 14.4%, improves the power usage effectiveness (PUE) from 1.58 to 1.47, and significantly enhances both thermal symmetry and operational safety. The Thermal Symmetry Index (TSI) decreased from 0.060 to 0.035, indicating a 41.7% improvement in spatial temperature distribution uniformity. The TSI is defined as the ratio of spatial temperature standard deviation to mean temperature, where lower values indicate better thermal uniformity; TSI < 0.03 represents excellent symmetry, 0.03–0.05 indicates good symmetry, and TSI > 0.08 suggests significant asymmetry requiring intervention. These results provide an effective and practical solution for intelligent operation, energy-efficient control, and low-carbon transformation of next-generation green data centers. Full article

Plant-parasitic nematodes (PPNs) cause big crop losses globally. Safe/reliable methods for their durable management strategies can harness various beneficial relationships among the plant immune system and related microbiomes. Molecular mechanisms basic to these relations reveal wide arrays of significant roles for plant-healthy growth. This review focuses on such relations of microbiomes to prime and immunize plants against PPNs. It also highlights molecular issues facing PPN-resistant varieties with possible solutions such as genetic breeding/engineering, grafting, PPN-antagonistic root exudates, and novel resistant cultivars. These issues call for optimal uses of various widespread groups of microbiomes. Related plant signaling hormones and transcription factors that regulate gene expression and modulate nematode-responsive genes to ease positive/negative adaptation are presented. Exploring PPN-resistance genes, their activation mechanisms, and signaling networks offers a holistic grasp of plant defense related to biotic/abiotic factors. Such factors relevant to systemic acquired resistance (SAR) via plant–microbe interactions to manage PPNs are stressed. The microbiomes can be added as inoculants and/or steering the indigenous rhizosphere ones. Consequently, SAR is mediated by the accumulation of salicylic acid and the subsequent expression of pathogenesis-related genes. To activate SAR, adequate priming and induction of plant defense against PPNs would rely on closely linked factors. They mainly include the engaged microbiome species/strains, plant genotypes, existing fauna/flora, compatibility with other involved biologicals, and methods/rates of the inoculants. To operationalize improved plant resistance and the microbiome’s usage, novel actionable insights for research and field applications are necessary. Synthesis of adequate screening techniques in plant breeding would better use multiple parameters (molecular and classical ones)-based ratings for PPN-host suitability designation. Sound statistical analyses and interpretation approaches can better identify genotypes with high-level, stable resistance to PPNs than the commonly used ones. Linking molecular mechanisms to consistent field relevance can be progressed via dissemination of many advanced techniques. The CRISPR/Cas9 system has been effective in knocking out both the OsHPP04 gene in rice to confer resistance against Meloidogyne graminicola and the GhiMLO3 gene in cotton to minimize the Rotylenchulus reniformis reproduction. Its genetic modifications in crops synthesized “transgene-free” PPN-resistant plants without decreased growth/yield. Characterizing microbiome species/strains needed to prime and immunize plants requires better molecular tools for fine-scale taxonomic resolution than the common ones used. The former can distinguish closely related ones that exhibit divergent phenotypes for key attributes like stability and production of enzymes and secondary metabolites. As PPN-control strategies via tritrophic interactions are more sensitive to the relevant settings than chemical nematicides, it is suggested herein to test these settings on a case-by-case basis to avoid erratic/contradictory results. Moreover, expanding the use of automated systems to expedite detection/count processes of PPN and related microbes with objectivity/accuracy is discussed. When PPNs and their related microbial distribution patterns were modeled, more aspects of their field distributions were discovered in order to optimize their integrated management. Hence, the feasibility of site-specific microbiome application in PPN–hotspot infections can be evaluated. The main technical challenges and controversies in the field are also addressed herein. Their conceptual revision based on harnessing novel techniques/tools is direly needed for future clear trends. This review also engages raising growers’ awareness to leverage such strategies for enhancing plant resistance and advancing the microbiome role. Microbiomes enjoy wide spectrum efficacy, low fitness cost, and inheritance to next generations in durable agriculture. Full article

Understanding the drivers of soil carbon efflux is critical for predicting forest carbon cycles under climate change. This study investigates how plant roots and phenology govern the spatio-temporal variability of boreal forest floor respiration (Rf) in an ectomycorrhizal-dominated forest. By analyzing stabilized soil carbon fluxes (NEE, Ra, and Rh) one year after root exclusion in northern Sweden, we challenge the passive physicochemical paradigm. Results show that the spatial distribution and magnitude of Rf are primarily driven by plant roots, with Ra accounting for >60% of total efflux. The collapse of respiration in trenched plots confirms the mycorrhizal bridge as the essential conduit for these spatial patterns. Regarding temporal variability, we identified a biological pulse driven by plant phenology. After temperature-normalization, Ra maintained a strong seasonal peak in July and August. Notably, static drivers like fine root biomass failed to explain spatial variation (R < 0.3, p > 0.05), whereas dynamic NEE showed significant positive correlations (R = 0.52, p < 0.0001). This holistic perspective suggests that the forest floor operates as an integrated metabolic continuum, where root activity and phenological pump are the main regulating factors on carbon release. Future models should reposition plant–fungal phenology as the primary engine of soil metabolism. Full article

Gross Domestic Product (GDP) serves as a core indicator for measuring the sustainable economic development of countries and regions. Accurate understanding of its spatio-temporal distribution is crucial for achieving the United Nations Sustainable Development Goals (SDGs). However, current grid-based GDP data for China’s regions predominantly consists of data from specific years, making it difficult to capture fine-grained changes in economic development. To address this, this study proposes a spatial GDP framework integrating cross-scale feature extraction (CSFs) with stacked ensemble learning. Based on China’s county-level GDP statistics and multi-source auxiliary data, it first generates a density-weighted estimation layer. This is then processed through dasymetric mapping to produce China’s Annual High-Resolution Gridded GDP Dataset (CA_GDP) from 2000 to 2021. Evaluation demonstrates the framework’s superior performance in density weight estimation, achieving an R² of 0.82 against statistical data. Compared to traditional single models like Random Forests (RF), it improves R² by 13–54%, reduces mean absolute error (MAE) by 2–26%, and lowers root mean square error (RMSE) by 19–39%, with these advantages remaining stable across time series. The dasymetric mapping of the CA_GDP dataset clearly depicts the economic development patterns and urban agglomeration effects in the southeastern coastal regions, as well as the relatively lagging economic development in western areas. Compared to existing public datasets, CA_GDP offers significant advantages in reflecting the fine-grained economic spatial structure within county-level units, providing a more reliable data foundation for identifying regional economic disparities, policy formulation and evaluation, and related research. Full article

The generalization capability of the decision-making modules in unmanned ground vehicles (UGVs) is critical for their safe deployment in unseen environments. Prevailing evaluation methods, which rely on aggregated performance over static benchmark sets, lack the granularity to diagnose the root causes of model failure, as they often conflate the distinct influences of scenario similarity and intrinsic difficulty. To overcome this limitation, we introduce a fine-grained, dynamic evaluation framework that deconstructs generalization along the dual axes of multi-level difficulty and similarity. First, scenario similarity is quantified through a four-layer hierarchical decomposition, with results aggregated into a composite similarity score. Test scenarios are independently classified into ten discrete difficulty levels via a consensus mechanism integrating large language models and task-specific proxy models. By constructing a three-dimensional (3D) performance landscape across similarity, difficulty, and task performance, we enable detailed behavioral diagnosis. The framework assesses robustness by analyzing performance within the high-similarity band (90–100%), while the full 3D landscape characterizes generalization under distribution shift. Seven interpretable metrics are derived to quantify distinct facets of both generalization and robustness. This initial validation focuses on the path-planning layer under full state observability, establishing a foundational proof-of-concept for the framework. It not only ranks algorithms but also reveals non-trivial behavioral patterns, such as the decoupling between in-distribution robustness and out-of-distribution generalization. It provides a reliable and interpretable foundation for evaluating the readiness of UGVs for safe deployment in unseen environments. Full article

This study introduces a novel imaging technology designed to address the critical challenges of high integration, wide field of view (FOV), and high-resolution detection in optoelectronic systems. The proposed approach leverages ultra-small pixels and a unique “single-eye + compound-eye” architecture, combining a concentric spherical lens for wide-FOV light collection with a distributed camera array for segmented imaging and stitching of the Petzval image plane. This design enables high-resolution imaging across a large area while maintaining compact system dimensions. The ultra-small-pixel large-format detector excels in capturing fine details, and by applying linear system theory, the technology achieves significant reductions in system size and complexity without compromising performance. Experimental validation shows that the imaging system achieves a modulation transfer function near the diffraction limit at 500 lp/mm, with a root-mean-square spot size consistently below 1 $\mathrm{\mu }$m. Additionally, the system delivers an angular resolution of 25 $\mathrm{\mu }$rad and distortion-free imaging over a ${61.5}^{\circ }\times {55}^{\circ }$ FOV. Full article

In the semi-arid Loess Plateau of China, afforestation frequently leads to soil water depletion, threatening ecosystem sustainability. Although mixed-species plantations are encouraged to enhance resource use efficiency, their effects on deep soil water and root distribution strategies remain unclear. This study compared soil water content (SWC), deep soil water deficit (SWD), and fine root distribution in pure and mixed plantations of Robinia pseudoacacia, Platycladus orientalis, and Hippophae rhamnoides to assess whether species mixing intensifies consumption for deep soil water. Soil moisture and root samples were collected with a maximum depth of 20 m across five stand types in August 2018 and during the 2019 growing season. Results showed that mixed stands exhibited shallower water depletion depth and lower SWC below 2 m than pure stands, but a more severe deep soil water deficit, with observed SWD exceeding the expected values by 12% in the R. pseudoacacia-P. orientalis mixture (MRP) and 22% in the H. rhamnoides-P. orientalis mixture (MHP), indicating intensified water consumption below 2 m. In the MRP, the maximum rooting depth was shallower than in the corresponding pure stands. Within the mixture, species-specific root plasticity was observed: the normalized fine root length density (FRLD) of P. orientalis was four times greater in mixture than in pure stand, whereas that of R. pseudoacacia was 62% lower, suggesting divergent foraging strategies. Correlation analyses indicated that SWC was differently associated with root traits between pure and mixed stands, with relationships varying by soil depth. Mixed-effects models confirmed that both plantation type and soil depth significantly influenced FRLD and Root dry weight density (RDWD), while specific root length (SRL) was mainly affected by plantation type and its interaction with depth. These findings demonstrated that mixed-species afforestation intensifies deep soil water competition. Therefore, sustainable management should prioritize the selection of species with complementary root foraging strategies and the optimization of planting densities in semi-arid regions. Full article

Urban-scale aerosol pollution monitoring is of critical importance for both climate regulation and public health. To overcome the limitations of conventional kilometer-scale satellite aerosol optical depth (AOD) products in resolving urban pollution heterogeneity, this study develops a physical-guided transfer deep neural network (PT-DNN) model based on high-resolution Landsat 8 data. The PT-DNN introduces a novel physics-guided training framework, in which radiative transfer simulations are integrated to physically constrain the AOD retrieval. Pre-training was conducted using multi-scenario radiative transfer simulations, with subsequent fine-tuning via ground-based AERONET measurements. The model architecture integrates convolutional neural network (CNN) with residual connection. Validation results over impervious surfaces indicate that the PT-DNN model outperforms conventional data-driven models, with the coefficient of determination (R²) increasing from 0.81 to 0.86 and root mean square error (RMSE) decreasing from 0.122 to 0.104. Moreover, the AOD distributions retrieved at a high spatial resolution of 30 m effectively reveal fine-scale pollution gradients within urban environments, especially in densely built-up and industrial areas. Full article

Soil salinization has become a critical constraint on agricultural productivity and eco-logical sustainability in arid regions. The accurate mapping of its spatial distribution is essential for sustainable land management. Although many studies have used satellite remote sensing combined with machine learning or convolutional neural networks (CNN) for soil salinity monitoring, most CNN approaches rely on single-scale convolution kernels. This limits their ability to simultaneously capture fine local detail and broader spatial patterns. In this study, we developed a multi-scale deep learning framework to enhance salinity prediction accuracy. We target the root-zone soil salinity in the Wei-Ku Oasis. Sentinel-2 multispectral imagery and Sentinel-1 radar backscatter data, together with topographic, climatic, soil texture, and groundwater covariates, were integrated into a unified dataset. We implemented the workflow using the Google Earth Engine (GEE; earthengine-api 0.1.419) and Python (version 3.8.18) platforms, applying the Sequential Forward Selection (SFS) algorithm to identify the optimal feature subset for each model. A multi-branch convolutional neural network (MB-CNN) with parallel 1 × 1 and 3 × 3 convolutional branches was constructed and compared against random forest (RF), 1 × 1-CNN, and 3 × 3-CNN models. On the validation set, MB-CNN achieved the best performance (R² = 0.752, MAE = 0.789, RMSE = 1.051 dS∙m⁻¹, nRMSE = 0.104), showing stronger accuracy, lower error, and better stability than the other models. The soil salinity inversion map based on MB-CNN revealed distinct spatial patterns consistent with known hydrogeological and topographic controls. This study innovatively introduces a multi-scale convolutional kernel parallel architecture to construct the multi-branch CNN model. This approach captures environmental characteristics of soil salinity across multiple spatial scales, effectively enhancing the accuracy and stability of soil salinity inversion. It provides new insights for remote sensing modeling of soil properties. Full article

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