Multi-scale Heterogeneity Research Articles

Synergistic Effect of Surface Thermal Heterogeneity in Phase With Topography on Deep Moist Convection

AbstractUsing large eddy simulation, we investigate the combined effect of terrain and surface sensible heat flux (SHF) heterogeneity on the development of afternoon deep moist convection (DMC). We implement an analytically derived, two‐dimensional terrain and SHF variations transformed from a κ−3 (where κ is the wavenumber) spectrum spanning wavelengths from 32 to 0.2 km. By separately coupling multiscale terrain with a homogeneous SHF field and the multiscale SHF field with flat terrain, we discern the individual impacts of these κ−3‐spectrum forcings on DMC. Our specific forcing configuration demonstrates that the multiscale terrain had a greater influence on DMC development compared to the multiscale SHF field. While the solely surface SHF heterogeneity forcing results in a wider pool of high relative humidity above the boundary layer, its significance is relatively lower in the mountainous terrain cases due to the shorter interaction time between highly buoyant thermals and the surrounding environment. However, when the multiscale terrain and SHF field are synchronized, DMC develops rapidly within a time frame of 4.5 hr, which is facilitated by enhanced surface buoyancy fluxes, the presence of highly buoyant thermals, and the persistence of mesoscale structures such as near‐surface convergence and mesoscale updrafts. Our study highlights the importance of the synergistic effects between multiscale terrain and surface SHF heterogeneity in DMC development. Additionally, our multiscale analyses of atmospheric variables reveal distinct atmospheric regimes between the pre‐storm and DMC periods. These findings contribute to a better understanding of the complex dynamics involved in the formation of afternoon DMC.

K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis

Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell–cell communication, cell differentiation, and differential gene expression. However, analyzing scRNA-seq data is a challenge due to sparsity and the large number of genes involved. Therefore, dimensionality reduction and feature selection are important for removing spurious signals and enhancing downstream analysis. Traditional PCA, a main workhorse in dimensionality reduction, lacks the ability to capture geometrical structure information embedded in the data, and previous graph Laplacian regularizations are limited by the analysis of only a single scale. We propose a topological Principal Components Analysis (tPCA) method by the combination of persistent Laplacian (PL) technique and L2,1 norm regularization to address multiscale and multiclass heterogeneity issues in data. We further introduce a k-Nearest-Neighbor (kNN) persistent Laplacian technique to improve the robustness of our persistent Laplacian method. The proposed kNN-PL is a new algebraic topology technique which addresses the many limitations of the traditional persistent homology. Rather than inducing filtration via the varying of a distance threshold, we introduced kNN-tPCA, where filtrations are achieved by varying the number of neighbors in a kNN network at each step, and find that this framework has significant implications for hyper-parameter tuning. We validate the efficacy of our proposed tPCA and kNN-tPCA methods on 11 diverse benchmark scRNA-seq datasets, and showcase that our methods outperform other unsupervised PCA enhancements from the literature, as well as popular Uniform Manifold Approximation (UMAP), t-Distributed Stochastic Neighbor Embedding (tSNE), and Projection Non-Negative Matrix Factorization (NMF) by significant margins. For example, tPCA provides up to 628%, 78%, and 149% improvements to UMAP, tSNE, and NMF, respectively on classification in the F1 metric, and kNN-tPCA offers 53%, 63%, and 32% improvements to UMAP, tSNE, and NMF, respectively on clustering in the ARI metric.

Probabilistic assessment of scalar transport under hydrodynamically unstable flows in heterogeneous porous media

Quantitative predictions of scalar transport in natural porous media is a nontrivial task given the presence of multi-scale spatial heterogeneity in the permeability field. Due to data scarcity, the structural map of the permeability field is subject to uncertainty and therefore, model predictions are uncertain. For such reasons, probabilistic models of flow and transport in natural porous media are required in risk assessment and to provide reliable decision making under uncertainty. Further complexities arise when the viscosity of the injected solute differs from that of the ambient fluid. Under the presence of viscosity contrast, hydrodynamic instabilities give rise to viscous fingering, which induces additional disorder in both velocity and solute concentration fields. This work examines the combined role of viscous fingering and permeability heterogeneity in the probabilistic description of transport predictions. In particular, we focus on metrics that are important for risk analysis, such as the solute plume’s early arrival times and the maximum concentration observed at a given location. We propose to use the Projection Pursuit Adaptation (PPA) method in the Polynomial Chaos Expansion (PCE) framework to quantify uncertainty in transport model predictions. The PPA method is a data-driven approach that optimally represents a given quantity of interest in a low-dimensional manifold. Unlike other dimension reduction techniques in uncertainty quantification, the PPA method utilizes non-linear information of the quantity of interest to identify the low-dimensional manifold, thereby increasing the likelihood of finding a more accurate lower-dimensional space. Moreover, the PPA model converges to the physical solution in a mean squared sense with respect to the polynomial order, enabling the construction of a converged model even with limited available data. Then, the PPA results are compared to Monte Carlo simulations using the same amount of data. This comparison illustrates that while Monte Carlo simulations are able to capture low-order statistics, they struggle to represent more detailed probability density functions. Our results show how the combined effect of permeability heterogeneity and viscosity contrast can enhance the mobility of the solute plume.

Multi-Scale Pore Structure Heterogeneity in Tuff Reservoirs Investigated with Multi-Experimental Method and Fractal Dimensions in Chang 7 Formation, Southern Ordos Basin

Multi-Scale Pore Structure Heterogeneity in Tuff Reservoirs Investigated with Multi-Experimental Method and Fractal Dimensions in Chang 7 Formation, Southern Ordos Basin

Multi-Scale Heterogeneity of Electrode Reaction for 18650-Type Lithium-Ion Batteries during Initial Charging Process

The improvement of fast-charging capabilities for lithium-ion batteries significantly influences the widespread application of electric vehicles. Fast-charging performance depends not only on materials but also on the battery’s inherent structure and the heterogeneity of the electrode reaction. Herein, we utilized advanced imaging techniques to explore how the internal structure of cylindrical batteries impacts macroscopic electrochemical performance. Our research unveiled the natural 3D structural non-uniformity of the electrodes, causing heterogeneity of electrode reaction. This non-uniformity of reaction exhibited a macro–meso–micro-scale feature in four dimensions: the exterior versus the interior of the electrode, the middle versus the sides of the cell, the inside versus the outside of the cell, and the surface versus the body of the electrode. Furthermore, the single-coated side of the anode demonstrated notably faster reaction than the double-coated sides, leading to the deposition of island-like lithium during fast charging. These discoveries offer novel insights into multi-scale fast-charging mechanisms for commercial batteries, inspiring innovative approaches to battery design.

Multiscale Heterogeneities-Based Piezoresistive Interfaces with Ultralow Detection Limitation and Adaptively Switchable Pressure Detectability.

Mechanical compliance and electrical enhancement are crucial for pressure sensors to promote performances when perceiving external stimuli. Here we propose a bioinspired multiscale heterogeneity-based interface to adaptively regulate its structure layout and switch to desirable piezoresistive behaviors with ultralow detection limitation. In such a multiscale heterogeneities system, the micro-/nanoscale spiny Ag-MnO2 heterostructure contributes to an ultralow detection limitation of 0.008 Pa and can perceive minor pressure increments under preloads with high resolution (0.0083%). The macroscale heterogeneous orientation of the cellular backbone enables anisotropic deformation, allowing the sensor to switch to rational sensitivity and working range (e.g., 580 kPa-1 for 0-20 kPa/54 kPa-1 for 60-140 kPa) as required. The sensor's stepwise activation progresses from the micro-/nanoscale heterostructure to the macroscale heterogeneous orientation, which can adaptively match diverse sensing tasks in complex applications scenarios. This multiscale heterogeneous and switchable design holds immense potential in the development of intelligent electromechanical devices, including wearable sensors, soft robotics, and smart actuators.

Modifying the electrical, optical, and magnetic properties of cancer cells: A comprehensive approach for cancer management

AbstractDespite the recent successes of cancer therapy, numerous obstacles remain. The medical community of the 16th century believed that magnets might be used to cure or prevent illness. However, this concept has only recently been put into practice in the treatment of cancer. Cancers vary greatly not only at the patient, tissue, and cellular levels but also at the molecular level. Because of this multiscale heterogeneity, effective treatments that not only differentiate between cancerous and healthy tissues but also target a wide variety of tumor subclones are difficult to develop. Most treatments either take advantage of a specific biological characteristic shared by cancer cells (e.g., their propensity for rapid division) or indiscriminately eradicate every cell in an area of interest. In this article, we review the physical, chemical, electrical, optical, and magnetic properties of cancer cells, before discussing how these properties may be modulated by current and future cancer therapies.

Multi-scale heterogeneity and habitat preference in the distribution of characteristic protection villages in China

Multi-scale heterogeneity and habitat preference in the distribution of characteristic protection villages in China

Spatial heterogeneity evolution and driving mechanism analysis of land use change in major river basins: a case study of urban agglomerations in the Yellow River Basin

Exploring the dynamics of land resource distribution within major river basins is pivotal for informing ecological conservation efforts and fostering sustainable development. The Yellow River Basin is a crucial region for human activities and economic development in China. This study meticulously examines the evolution of land use patterns across the seven principal urban agglomerations within the Yellow River Basin from 1990 to 2020. Employing the Multiscale Geographically Weighted Regression (MGWR) model, the research rigorously scrutinizes the multi-scale spatial heterogeneity of cultivated land dynamics within urban agglomerations during the years 2010 and 2020. The results show pronounced disparities in land utilization among urban agglomerations, with upstream areas predominantly characterized by grass and downstream regions dominated by cultivated land. Noteworthy is the significant expansion of construction land, particularly manifesting dynamic shifts over time. Furthermore, the driving mechanisms exhibit discernible scale variations, with evident spatial heterogeneity in their impact on cultivated land changes.

Exploring Multiscale Spatial Interactions: Multiscale Geographically Weighted Negative Binomial Regression

In this article, I develop and implement the multiscale geographically weighted negative binomial (MGWNB) model, extending the spatially weighted interaction models by integrating a multiscale framework. This model effectively tackles the multiscale nonstationarity and overdispersion issues found in spatial interaction models. By comparing it with multiscale geographically weighted Poisson regression using simulated data, I demonstrate its superior performance in several aspects, including its capability to estimate the scale of processes, its effectiveness in capturing the spatial heterogeneity, and its ability to produce a better goodness of fit. The application of MGWNB in interprovincial population migration in China, using 2020 Chinese census data, also demonstrates its effectiveness and efficiency, revealing strong multiscale spatial heterogeneity in the migration patterns.

Research on the crack resistance of semi-flexible pavement mixture based on meso-heterogeneous model

Semi-Flexible Pavement (SFP) material, prepared by pouring cement-based grouting material into the voids of a porous asphalt mixture, has good load bearing capacity and excellent resistance to rutting. It is gaining increased applications in areas where permanent deformation of asphalt mixtures is a concern. When applied as the pavement surface course, the major distress observed in SFP is cracking. Currently, there is a lack of comprehensive understanding of the cracking mechanism of SFP. To improve such understanding, this study investigated the effects of fracture parameters of asphalt mortar and asphalt-aggregate interface on the crack resistance of SFP materials through laboratory experiments and numerical simulation using the finite element method. Firstly, a three-point bending test was performed on a beam SFP specimen. A multi-scale mesoscopic heterogeneity model was then established based on the mesoscopic structure of the specimen and calibrated and validated using the load-displacement curves and the cracking process features. With the validated model, the effects of different fracture parameters on the beam cracking process were investigated. It was found that the load-bearing capacity of SFP increased with the increase of the tensile strength of asphalt mortar and asphalt-aggregate interface in a certain range, while the crack propagation rate after crack initiation also increased. An increase in the fracture energy of asphalt mortar and asphalt-aggregate interface delayed the crack propagation rate, but with an attenuative effect with the increase of fracture energy. Based on the actual test conditions, the desirable range of key cracking parameters of SFP materials were recommended.

Multiscale and diverse spatial heterogeneity analysis of void structures in reef carbonate reservoirs

The complex structure of carbonate reservoirs significantly impacts the development of oil reservoirs. Most published studies focus on void systems in sandstones and shales, while carbonates have received little attention. In this study, we investigated five carbonate oil reservoirs located in Perm Krai, Russia. Using thin-section observation, SEM, XRD, XRF spectroscopy, and computed microtomography (μ-CT), we evaluated the microscopic structure of the reservoirs’ void space and assessed its impact on the performance of acid stimulation and hydraulic fracturing. The examination of core samples shows that they are monomineral, composed predominantly of calcite (≈99%). The core samples have similar lithological compositions, but the structure of their void space differs significantly, presenting a variety of shapes, sizes and distributions in the sample volume. SEM analysis reveals the presence of nanofractures and microfractures that are open, partially filled, or completely filled with calcite. With an average of 6.5%, the porosity of the carbonate objects shows little variation; however, their permeability varies substantially, from 11.5 to 628.9 ∙103 μm2. In addition, our study established that the structure of the void space has a considerable effect on the fluid inflow into the well and on the efficiency of acid stimulation and hydraulic fracturing. During acid stimulation or hydraulic fracturing in reservoirs with nano-fractured zones, a sharp deviation in the development of wormholes or fractures is possible. Therefore, the effectiveness of these treatments cannot reliably be predicted when nanofractures are present.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study

BackgroundPost-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC.MethodsThis multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes.ResultsThe radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713–0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2–62.5% vs. 16.3–18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07–6.75, P < 0.001) and all causes of deaths (HR 1.53–2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity.ConclusionsWe present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

Pursuing ultrastrong and ductile medium entropy alloys via architecting nanoprecipitates-enhanced hierarchical heterostructure

Nanoprecipitate-strengthened medium/high entropy alloys (M/HEAs) with ultrahigh strengths and unusual physical properties represent promising candidates for structural applications. However, further strengthening these materials usually relies on increasing the volume fraction of nanoprecipitates and/or introducing harder intermetallics that inevitably sacrifice the ductility. Here, we report a strategy to break this trade-off in a Co-Cr-Ni-based MEA by architecting a hierarchical heterostructure that integrates high-volume fraction (up to ∼30%) D022-γ’’ nanoprecipitates, shearable and non-shearable intermetallic particles, with a low stacking fault energy (SFE) matrix with heterogeneous grain structure. This hierarchical heterostructure can arouse hybrid strengthening mechanisms and lead to cross-scale and dynamic strain partitioning during plastic deformation, contributing to a superior strain-hardening capability and an excellent combination of the yield / ultimate tensile strength of 1323 MPa / 1690 MPa and 29% tensile elongation. This study provides a promising paradigm to design ultrastrong and ductile metallic materials via controlling multiscale microstructural heterogeneities.

Measuring the Influence of Multiscale Geographic Space on the Heterogeneity of Crime Distribution

Urban crimes are not homogeneously distributed but exhibit spatial heterogeneity across a range of spatial scales. Meanwhile, while geographic space shapes human activities, it is also closely related to multiscale characteristics. Previous studies have explored the influence of underlying geographic space on crime occurrence from the mechanistic perspective, treating geographic space as a collection of points or lines, neglecting the multiscale nature of the spatial heterogeneity of crime and underlying geographic space. Therefore, inspired by the recent concept of “living structure” in geographic information science, this study applied a multiscale analysis method to explore the association between underlying geographic space and crime distribution. Firstly, the multiscale heterogeneity is described while simultaneously considering both the statistical and geometrical characteristics. Then, the spatial association rule mining approach is adopted to quantitatively measure the association between crime occurrence and geographic space at multiple scales. Finally, the effectiveness of the proposed methods is evaluated by crime incidents in the city of Philadelphia. Experimental results show that crime heterogeneity is indeed closely related with the spatial scales. It is also proven that the influence of underlying geographic space on crime heterogeneity varies with the spatial scales. This study may enrich the methodology in crime pattern and crime explanation analysis, and it provides useful insights for effective crime prevention.

The Multi-Scale Spatial Heterogeneity of Ecosystem Services’ Supply–Demand Matching and Its Influencing Factors on Urban Green Space in China

As population growth and urbanization continue to accelerate, city dwellers are increasingly conscious of the demand for urban green space (UGS) and the ecosystem services (ESs) it provides. Great efforts are made for the supply of certain ESs in UGS. However, less is known about the residents’ preferences and the supply–demand matching of UGS types, as well as the various ESs it provides at different spatial scales. Given this, our research establishes a research framework to reveal the heterogeneity of USG types and the supply–demand matching degree (SDM) of ESs from municipal, provincial, and national spatial scales, and examines the correlation between the influencing factors and demands of residents for UGS. This study mainly used the Gini coefficient, the Lorenz curve, Z-scores, the Jenks natural breaks classification method, Pearson correlation analysis, and spatial analysis. The main findings are that (1) the Gini coefficients are 0.433 and 0.137 at the municipal and provincial scales, respectively, indicating that the supply of UGS is more unequal at the municipal scale than provincial scale; (2) the multi-scale demand for ESs between residents has no significant difference. At the provincial scale, the area with low demand is larger than that of high demand, while at the municipal scale, the contrary is the case; (3) the SDM was in a deficit at both the provincial and municipal scales. And as the scaling-up occurred, the spatial heterogeneity of the SDM decreased; (4) the number of influencing factors that significantly affected the UGS type and ESs grew as the scale increased. Among them, the impact of age and COVID-19 on three scales deserves attention. These results identify regions with deficits and surpluses in ESs provided by UGS in China at different scales. This research also advises that attention should be paid to the distribution of UGS between cities within provinces, and future UGS planning should focus on building regional green spaces to promote the well-being of an aging society. The findings in this study would offer insights for managers to improve UGS construction and urban forestry planning in the future.

Deformation mechanisms in a novel multiscale hetero-structured Mg alloy with high strength-ductility synergy

Most of the high-strength Mg alloys exhibit poor ductility at room temperature. Architecturing appropriate heterostructure in metallic materials gives rise to the breakthrough of the inherent strength-ductility trade-off dilemma. Here, we prepared a novel multiscale hetero-structure in Mg-9Al-1 Zn (AZ91) alloy, consisting of bimodal-structured center-region and fine-grained surface-layer, providing high strength and high ductility, i.e. yield strength of ∼303 MPa, tensile strength of ∼400 MPa, and elongation of ∼14.5 %. The multiscale microstructural heterogeneities were induced via a simple rolling process that combines the advantages of both accumulative rolling bonding (ARB) and hard plate rolling (HPR). By utilizing quasi-in-situ electron backscattered diffraction (EBSD)-digital image correlation (DIC) characterizations combined with the determination of dislocation/disclination distributions, it has been found that diversified strain accommodation mechanisms are stimulated by the strain incompatibilities near hetero-interfaces in the multiscale hetero-structure. In particular, non-basal slips have been activated largely in both the bimodal-structured center-region and fine-grained surface-layer. Moreover, a unique slip trace distribution has been observed in the fine grains adjacent to the basal-oriented coarse grain. Such a strain accommodation can better coordinate local strain incompatibility among the hetero-interface. The uniaxial loading–unloading–reloading tensile loop tests demonstrate that the multiscale heterogeneities impart a pronounced hetero-deformation induced hardening during tensile deformation. Multiple deformation modes, including the non-Schmid and non-basal slips, have been activated substantially in the multiscale hetero-structured Mg alloy, effectively relaxing local stress concentration and achieving stable plastic flow at a high-stress state, which is beneficial to improving strain-hardening ability and strength-ductility synergy. Our work could shed light on designing and processing strong and ductile Mg alloys, by tailoring proper microstructural heterogeneity to activate cooperated accommodating deformation modes.

Assessing mesoscale permeability in unsaturated, fractured, and karstified carbonate reservoirs: a joint geological, petrophysical, and hydraulic approach for the interpretation of injection tests

In the study of subsurface reservoirs, permeability is a key parameter whose evaluation and extrapolation at the desired scale, such as that of the numerical model mesh, are both a necessity and a difficult task. Relating permeability measurements to the geological characteristics of the rock, at intermediate scales seldom characterized, can help understanding the heterogeneity of the medium and correctly determining the permeability at the desired scale. This concern is particularly important in karst reservoirs, which exhibit highly variable permeability at different scales of observation and from one location to another. Here, we study the petrophysical and geological properties of carbonate facies from the centimeter to the meter scale. Several boreholes a few meters apart were cored and exhaustively described. Petrophysical measurements were made on rock samples, while inter-packer injection tests were undertaken to investigate some meter intervals of the medium surrounding the wells. The results show that the investigated medium is a complex multi-medium with a multi-scale heterogeneity. The detailed geological description allowed explaining the differences between the permeability values at different scales and from one interval to another. Relationships were quantified between the texture of the limestone matrix, the density and aperture of the discontinuities, the permeability of the matrix, and the permeability contrast related to the dual medium at the meter scale. A strong correlation between this permeability contrast and the aperture of the discontinuities, itself more correlated to the texture of the rock than to the facies, is highlighted.

Hybrid-MPET: An Open-Source Simulation Software for Hybrid Electrode Batteries

As the design of single-component battery electrodes has matured, the battery industry has turned to hybrid electrodes with blends of two or more active materials to enhance battery performance. Leveraging the best properties of each material while mitigating their drawbacks, multi-component hybrid electrodes open a vast new design space that could be most efficiently explored through simulations. In this article, we introduce a mathematical modeling framework and open-source battery simulation software package for Hybrid Multiphase Porous Electrode Theory (Hybrid-MPET), capable of accounting for the parallel reactions, phase transformations and multiscale heterogeneities in hybrid porous electrodes. Hybrid-MPET models can simulate both solid solution and multiphase active materials in hybrid electrodes at intra-particle and inter-particle scales. Its modular design also allows the combination of different active materials at any capacity fraction. To illustrate the novel features of Hybrid-MPET, we present experimentally validated models of silicon-graphite (Si-Gr) anodes used in electric vehicle batteries and carbon monofluoride (CFx) - silver vanadium oxide (SVO) cathodes used in implantable medical device batteries. The results demonstrate the potential of Hybrid-MPET models to accelerate the development of hybrid electrode batteries by providing fast predictions of their performance over a wide range of design parameters and operating protocols.

Slice-to-slice context transfer and uncertain region calibration network for shadow detection in remote sensing imagery

Although current methods based on deep learning (DL) have been widely employed in shadow detection tasks, the cluttered background and complex shadow features in remote sensing images (RSIs) make shadow detection still a challenging task. In this paper, we apply a neural network combined with a distance transformation algorithm to RSI shadow detection for the first time and propose a novel slice-to-slice context transfer and uncertainty region calibration network (SCUCNet). First, an interslice context semantic transfer (SCT) module is proposed to explore the connections among spatial pixel information and extract sufficient global contextual information via interslice feature transfer. This process helps alleviate the interference of multiscale differences and complex backgrounds on the detection results. Second, to further optimize the initial output mapping, calibrate the uncertainty region, and enhance the feature representation, we propose a plug-and-play uncertainty region awareness and calibration (URAC) module combined with a distance transform algorithm, which introduces only 0.09 M parameters. The quantitative results show that the proposed model outperforms state-of-the-art methods for shadow detection in RSIs, achieving an Intersection over Union (IoU) of 88.33% on the aerial imagery dataset for shadow detection (AISD). In addition, extensive experiments and visualizations further prove the validity and interpretability of our method, and the proposed method can effectively mitigate the interference caused by multiscale differences, morphological diversity, spectral similarity and heterogeneity.