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Introduction: Zanthoxylum bungeanum Maxim is an economically significant crop in Asia, but large-scale cultivation is often threatened by frequent diseases, leading to significant yield declines. Deep learning-based methods for crop disease recognition have emerged as a vital research area in agriculture. Methods: This paper presents a novel model, LT-DeepLab, for the semantic segmentation of leaf spot (folium macula), rust, frost damage (gelu damnum), and diseased leaves and trunks in complex field environments. The proposed model enhances DeepLabV3+ with an innovative Fission Depth Separable with CRCC Atrous Spatial Pyramid Pooling module, which reduces the structural parameters of Atrous Spatial Pyramid Pooling module and improves cross-scale extraction capability. Incorporating Criss-Cross Attention with the Convolutional Block Attention Module provides a complementary boost to channel feature extraction. Additionally, deformable convolution enhances low-dimensional features, and a Fully Convolutional Network auxiliary header is integrated to optimize the network and enhance model accuracy without increasing parameter count. Results: LT-DeepLab improves the mean Intersection over Union (mIoU) by 3.59%, the mean Pixel Accuracy (mPA) by 2.16%, and the Overall Accuracy (OA) by 0.94% compared to the baseline DeepLabV3+. It also reduces computational demands by 11.11% and decreases the parameter count by 16.82%. Discussion: These results indicate that LT-DeepLab demonstrates excellent disease segmentation capabilities in complex field environments while maintaining high computational efficiency, offering a promising solution for improving crop disease management efficiency. [ABSTRACT FROM AUTHOR]

This paper presents the first effort to unravel and quantify the strengthening of a shale induced by nanoparticle injection from the perspectives of cross-scale and multi-constituent mechanical properties. After being subject to injection of pure water and an aqueous suspension of carbon black nanoparticle of ~ 50 nm in diameter under a differential pressure of 850 kPa, the shale specimens were characterized by big data nanoindentation (BDNi) to probe the mechanical properties of both individual constituents at the microscale and the bulk rock at the macroscale, leading to comparatively assessing the effects of injecting pure water and aqueous nanoparticle suspension on the mechanical properties. Microstructural characterization by electron microscopy and X-ray computed tomography validates the successful injection of nanoparticles into the microcracks and micropores of the rock. While the nanoparticles can infiltrate to depths of up to 100 s μm in zones with densely populated microcracks, the maximum depths of injection in crack-free zones are only 2–5 μm. Moreover, the injected nanoparticles mostly act as inert fillers in the interconnected micropores and microcracks but can seldom enter the isolated micropores. Comparison of the BDNi results from pure water versus nanoparticle-injected specimens shows that the Young's modulus of the clay matrix experiences the highest increase by 23.1%, while the counterpart of non-porous quartz the lowest by 12.8%. Overall, the bulk shale's Young's modulus increases by 21.5%. Such data are consistent with the microcharacterization results that the injected nanoparticles mainly remain in the micropores and microcracks within the clay matrix. Owing to their hydrophobic nature, the carbon black nanoparticles have little effect on the rock's hardness. The findings can shed light on the practical applications of nanoparticle injection for improved wellbore stability in shale formations. [ABSTRACT FROM AUTHOR]

Effective climate change adaptation requires cohesive inter-institutional networks across different scales, facilitating the sharing of data, information, knowledge, and practices. However, the impact of adaptation attributes across scales is poorly understood due to limited focus on these networks. Based on interviews with 26 institutions operating at the national level (ION) in India and 26 institutions operating within a state (Himachal Pradesh) (IOS), this study analysed adaptation attributes and the inter-institutional networks across the two scales to understand its implications at different scales. IONs have a greater capacity (compared to IOS) to frame guidelines, standards and regulations for practitioners along with better accessibility to resources and information. When coupled with bridging institutions, this can enhance adaptive capacities at other scales. Conversely, learnings from low regret adaptive measures being implemented by IOS are opportunities for informing national policy strategies. While national adaptation strategies and goals can inspire adaptation at lower scales, the currently fragmented inter-institutional network in India reduces the passage and accessibility of data and information, creating a bottleneck for the smooth devolution of adaptation attributes. Recruitment and deployment practices for water officials further entrench silo attitudes, impeding essential data accessibility. Adaptation needs comprehensive networks across vertical, horizontal, and diagonal institutional connections to improve climate risk perception and strategy implementation. Policy measures should consider socio-institutional factors beyond legislative prescriptions. [ABSTRACT FROM AUTHOR]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A shallow water equations-based model with an improved local time-stepping (LTS) scheme is developed for modeling coastal hydrodynamics across multiple scales, from large areas to detailed local regions. To enhance the stability of the shallow water model for long-duration simulations and at larger LTS gradings, a prediction-correction method using a single-layer interface that couples coarse and fine time discretizations is adopted. The proposed scheme improves computational efficiency with an acceptable additional computational burden and ensures accurate conservation of time truncation errors in a discrete sense. The model performance is verified with respect to conservation and computational efficiency through two idealized tests: the spreading of a drop of shallow water and a tidal flat/channel system. The results of both tests demonstrate that the improved LTS scheme maintains precision as the LTS grading increases, preserves conservation properties, and significantly improves computational efficiency with a speedup ratio of up to 2.615. Furthermore, we applied the LTS scheme to simulate tides at grid scales of 40,000 m to 200 m for a portion of the Northwest Pacific. The proposed model shows promise for modeling cross-scale hydrodynamics in complex coastal and ocean engineering problems. [ABSTRACT FROM AUTHOR]

The behaviors of biomaterials are influenced by a multitude of factors including temperature, mechanical load, and biochemical environments. The dynamic equilibrium of organism within a multiphysical field is crucial for its physiological functions. Therefore, a comprehensive investigation into the bio-thermo-mechano- electrophysiological behaviors is of great significance for the elucidation of pathologies and the development of effective diagnostic and therapeutic strategies. Such inquiries inherently require a multidisciplinary approach, necessitating the integration of diverse disciplinary knowledge and the pioneering of innovative cross-disciplinary research. The core idea is to discover key scientific questions, identify interdisciplinary disciplines, integrate disciplinary advantages, promote theoretical and technological innovations, and bring new breakthroughs in the biomedical field. With the discovery of DNA double helix structure in Cambridge University as the background, and the tissues of brain, skin, and teeth, etc. as examples, this paper introduces the evolution and intension of bio-thermo-mechano-electrophysiology, a new interdisciplinary subject [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

IntroductionZanthoxylum bungeanum Maxim is an economically significant crop in Asia, but large-scale cultivation is often threatened by frequent diseases, leading to significant yield declines. Deep learning-based methods for crop disease recognition have emerged as a vital research area in agriculture.MethodsThis paper presents a novel model, LT-DeepLab, for the semantic segmentation of leaf spot (folium macula), rust, frost damage (gelu damnum), and diseased leaves and trunks in complex field environments. The proposed model enhances DeepLabV3+ with an innovative Fission Depth Separable with CRCC Atrous Spatial Pyramid Pooling module, which reduces the structural parameters of Atrous Spatial Pyramid Pooling module and improves cross-scale extraction capability. Incorporating Criss-Cross Attention with the Convolutional Block Attention Module provides a complementary boost to channel feature extraction. Additionally, deformable convolution enhances low-dimensional features, and a Fully Convolutional Network auxiliary header is integrated to optimize the network and enhance model accuracy without increasing parameter count.ResultsLT-DeepLab improves the mean Intersection over Union (mIoU) by 3.59%, the mean Pixel Accuracy (mPA) by 2.16%, and the Overall Accuracy (OA) by 0.94% compared to the baseline DeepLabV3+. It also reduces computational demands by 11.11% and decreases the parameter count by 16.82%.DiscussionThese results indicate that LT-DeepLab demonstrates excellent disease segmentation capabilities in complex field environments while maintaining high computational efficiency, offering a promising solution for improving crop disease management efficiency.

The intricate woven structure of two-dimensional woven carbon fiber-reinforced silicon carbide (2D C/SiC) poses challenges to the investigation of damage failure mechanisms and the prediction of material performance, thereby limiting its further application in aerospace engineering. In this work, static/dynamic short beam shear tests with different densities of 2D C/SiC were carried out. It was observed that high-density specimens with fewer microporous defects exhibited better strength characteristics than those with lower density. Specifically, the shear strength of 2D C/SiC with a density of 2.07 g/cm³ increased by 18.6% compared to that of the specimen with a density of 1.94 g/cm³. The transient hysteresis strengthening effect of materials at high strain rates was discovered through cross scale research methods. It was found that deformation coordination ability of 2D C/SiC under dynamic loading was weaker than that under quasi-static conditions. Moreover, the deformation hysteresis in temporal and spatial distributions induced significant fiber plucking, further accelerating dislocation movement within the internal lattice and thereby enhancing strength of the material. The failure path of materials often follows the direction of pore defects, and stress concentration leads to cracking behavior of fiber bundles. Furthermore, microcracks within the matrix, caused by residual stress, resulted in localized regions of reduced strength. This induced the matrix cracking failure with further crack expansion within the material structure under external loads. To provide technical references for practical engineering applications and design, a realistic and effective finite element prediction model was developed for 2D C/SiC composites based on their structural characteristics. • Exploring the mapping relationship between material micropore defects and density, as well as the impact mechanism on material failure forms. • The transient hysteresis strengthening effect of the specimens under dynamic loads was proposed. • A real and effective 2D C/SiC finite element performance prediction model was constructed. [ABSTRACT FROM AUTHOR]

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM. [ABSTRACT FROM AUTHOR]

Bolted joints structures are critical fastening components widely used in mechanical equipment. Under long-term loading conditions, the bolted joints interface generates strong nonlinearities within the system. The nonlinear stiffness inside the bolt leads to changes in the stiffness of the whole system. This affects the dynamic characteristics of the whole system. It brings challenges and difficulties to the performance prediction and reliability assessment of the equipment. A cross-scale theoretical model study based on the microscopic contact mechanism can provide a more comprehensive understanding and cognition of the degradation behavior of bolted joints interfaces. The current development status and deformation process of asperity models are summarized. The research progress of statistical summation model and contact fractal model based on microscopic contact mechanism is analyzed. The experimental methods for parameter identification of connection interfaces are reviewed. The study of numerical modelling of bolted joints structures from the surface contact mechanism is briefly described. Future research directions for cross-scale modelling of bolted joints structures are outlined. [ABSTRACT FROM AUTHOR]

The coal's mechanical properties have a significant influence on mining safety and the mine environment. Preparing a standard sample and conducting repeat mechanical testing are challenging because the coal is primarily soft, fragmented, and rich in developed fractures. This study used nanoindentation technology, combined with X-ray diffraction, small-angle X-ray, a high magnification microscope, and mechanical parameter scale-up analysis, to study the micromechanical of three coals being dominated by heterogeneous components and pores. The results show that load–displacement curves with different maximum loads (50 mN, 100 mN, and 200 mN) all appear the pop-in events, and coal heterogeneity affects the frequency of their occurrence. As the maximum load is increased, pop-in event of DSC appears once each, YW increases from zero to three times and HM decreases from four to two times. The heterogeneity of pore structure has little effect on residual displacement, which is mainly affected by hard minerals, and hard minerals reduce the law that residual displacement increases with the increase in maximum load. The micromechanical parameters of soft coals are mainly affected by large pores, while hard coals are mainly affected by hard minerals. The coal's heterogeneity does not affect the linear relationship between hardness and elastic modulus, but stronger heterogeneity will weaken the linear relationship between fracture toughness and elastic modulus. Compared to the mechanical parameters after scale-up, the values obtained based on nanoindentation are less than 15.588% larger, and the increase in the heterogeneity and hard minerals can make the predicted parameters more accurate. The nanoindentation technique can not only provide an efficient and accurate method for studying the mechanical properties of heterogeneous coal at the nanoscale, an important guide for large-scale coal. [ABSTRACT FROM AUTHOR]

• The GDH-PD is proposed to address size-dependent effects and fracture problems. • The lower- and higher-order micro-modulus states of GDH-PD are firstly derived. • GDH-PD eliminates zero-energy modes of traditional higher-order peridynamics. • HOBCs are applied by constructing quadratic functional on the boundary points. • Nonlocal effects, size effects, strain gradient effects and damage are considered. In this paper, we propose the implicit stabilized dual-horizon peridynamics-based strain gradient damage model (GDH-PD) to describe the cross-scale fracture behavior of materials. To this end, firstly, the strain energy density function of GDH-PD is reformulated by considering the energy compensation to eliminate zero-energy modes of the traditional higher-order peridynamics. And then, the constitutive force state of GDH-PD is derived and explicitly expressed with the help of the proposed special dimension reduction of the nonlocal higher-order tensors. To solve the steady-state crack propagation problems, the implicit GDH-PD is developed by deriving the lower- and higher-order micro-modulus double state, such that the linearization of the equilibrium equation of GDH-PD is established. At last, the bond length-dependent energy-based failure criterion is used to characterize the cross-scale fracture in the form of bond breakage. The effectiveness of GDH-PD to characterize microstructure size effects and macrostructure strain gradient effects are investigated by numerical simulations. The numerical results are in good agreement with the analytical solutions or the available experimental results. We believe that the proposed GDH-PD may pave the way to an increased application of peridynamics to be used in the cross-scale fracture predictions for the advanced material. [ABSTRACT FROM AUTHOR]

Many academics are interested in content‐based image retrieval techniques like image segmentation. In computer vision, the most popular method for segmenting a digital image into different parts is known as image segmentation. We assigned the artificially intelligent algorithm to the image's critical areas by modeling human features in specific regions. In order to detect the object and identify the key parts in the 'RGB' photographs, we combined scenes based on a colour and depth map, or 'RGB‐D', and used cosine modulated filter bank (CMFB), which conducts cross‐scale extraction of joint features from the images during feature extraction. The proposed 'CMFB' combines the discovered collaborative elements with the discovered supplementary data. The features in multi‐scale images is combined using fusion blocks with the goal of producing additional features (FB). Then, a saliency mapping calculation is made for the loss linked to two blocks. The suggested 'CMFB' is tested with the aid of five data sets, and it is shown that, the proposed 'CMFB' outperforms other conventional techniques. [ABSTRACT FROM AUTHOR]

Processes that drive variability in catchment solute sourcing, transformation, and transport can be investigated using concentration–discharge (C–Q) relationships. These relationships reflect catchment and in-stream processes operating across nested temporal scales, incorporating both short and long-term patterns. Scientists can therefore leverage catchment-scale C–Q datasets to identify and distinguish among the underlying meteorological, biological, and geological processes that drive solute export patterns from catchments and influence the shape of their respective C–Q relationships. We have synthesized current knowledge regarding the influence of biological, geological, and meteorological processes on C–Q patterns for various solute types across diel to decadal time scales. We identify cross-scale linkages and tools researchers can use to explore these interactions across time scales. Finally, we identify knowledge gaps in our understanding of C–Q temporal dynamics as reflections of catchment and in-stream processes. We also lay the foundation for developing an integrated approach to investigate cross-scale linkages in the temporal dynamics of C–Q relationships, reflecting catchment biogeochemical processes and the effects of environmental change on water quality. [ABSTRACT FROM AUTHOR]

Global and local modeling is essential for image super-resolution tasks. However, current efforts often lack explicit consideration of the cross-scale knowledge in large-scale earth observation scenarios, resulting in suboptimal single-scale representations in global and local modeling. The key motivation of this work is inspired by two observations: 1) There exists hierarchical features at the local and global regions in remote sensing images, and 2) they exhibit scale variation of similar ground objects (e.g. cross-scale similarity). In light of these, this paper presents an effective method to grasp the global and local image hierarchies by systematically exploring the cross-scale correlation. Specifically, we developed a Cross-scale Self-Attention (CSA) to model the global features, which introduces an auxiliary token space to calculate cross-scale self-attention matrices, thus exploring global dependency from diverse token scales. To extract the cross-scale localities, a Cross-scale Channel Attention (CCA) is devised, where multi-scale features are explored and progressively incorporated into an enriched feature. Moreover, by hierarchically deploying CSA and CCA into transformer groups, the proposed Cross-scale Hierarchical Transformer (CHT) can effectively explore cross-scale representations in remote sensing images, leading to a favorable reconstruction performance. Comprehensive experiments and analysis on four remote sensing datasets have demonstrated the superiority of CHT in both simulated and real-world remote sensing scenes. In particular, our CHT outperforms the state-of-the-art approach (TransENet) in terms of PSNR by 0.11 dB on average, but only accounts for 54.8% of its parameters.

Identifying the binding affinity between a drug and its target is essential in drug discovery and repurposing. Numerous computational approaches have been proposed for understanding these interactions. However, most existing methods only utilize either the molecular structure information of drugs and targets or the interaction information of drug–target bipartite networks. They may fail to combine the molecule-scale and network-scale features to obtain high-quality representations. In this study, we propose CSCo-DTA, a novel c ross- s cale graph co ntrastive learning approach for d rug- t arget binding a ffinity prediction. The proposed model combines features learned from the molecular scale and the network scale to capture information from both local and global perspectives. We conducted experiments on two benchmark datasets, and the proposed model outperformed existing state-of-art methods. The ablation experiment demonstrated the significance and efficacy of multi-scale features and cross-scale contrastive learning modules in improving the prediction performance. Moreover, we applied the CSCo-DTA to predict the novel potential targets for Erlotinib and validated the predicted targets with the molecular docking analysis. [ABSTRACT FROM AUTHOR]

RGBT tracking combines visible and thermal infrared images to achieve tracking and faces challenges due to motion blur caused by camera and target movement. In this study, we observe that the tracking in motion blur is significantly affected by both frequency and spatial aspects. And blurred targets exhibit sharp texture details that are represented as high-frequency information. But existing trackers capture low-frequency components while ignoring high-frequency information. To enhance the representation of sharp information in blurred scenes, we introduce multi-frequency and multi-spatial information in network, called FSBNet. First, we construct a modality-specific unsymmetrical architecture and integrate an adaptive soft threshold mechanism into a DCT-based multi-frequency channel attention adapter (DFDA). DFDA adaptively integrates rich multi-frequency information. Second, we propose a masked frequency-based translation adapter (MFTA) to refine drifting failure boxes caused by camera motion. Moreover, we find that small targets get more affected by motion blur compared to larger targets, and we mitigate this issue by designing a cross-scale mutual conversion adapter (CFCA) between the frequency and spatial domains. Extensive experiments on GTOT, RGBT234 and LasHeR benchmarks demonstrate the promising performance of our method in the presence of motion blur. [ABSTRACT FROM AUTHOR]

Ostrom’s principles for the effective management of common pool resources emphasize the importance of local participation by affected actors in the design of rules. Principle 3 proposes that including local knowledge will facilitate the creation of effective rules that fit local social and ecological settings. However, the validity of the design principles is challenged in situations of high actor heterogeneity. We used a dynamic, spatially explicit simulation model to test Principle 3 in a simulated peri-urban area of a fast-growing city. In the model, urban actors appropriate land in a peri-urban social-ecological system. Urban appropriation fragments peri-urban ecosystems while reducing land availability for rural activities such as agriculture. We simulated the consequences of individual rural and urban actor decisions on emerging patterns of land-use types, using game theory to quantify competition for land, and metrics of landscape composition and configuration to quantify the impacts of rural resistance on landscape patterns. Landscape metrics relevant to ecosystem service provision (urban patch area, number of urban patches, clumping of urban patches and edge density of urban patches) had a non-linear response to resistance to urbanisation. Our results suggest that a small percentage of resisting rural actors can influence emerging landscape patterns; resistance as low as 10% of the rural population to urbanisation was sufficient to influence the degree of clumping of urban areas. The non-linear and varying response of emerging landscape patterns to conflict among actors, and the presence of tipping points for ecological processes that depend on connectivity or area, can create significant opportunities and challenges for the sustainable governance of land-use change in a spatially dynamic SES. We conclude that efforts to use Ostrom’s design principles to manage complex and dynamic landscapes such as peri-urban SESs must account for actor heterogeneity and the potential influence of actor resistance on landscape patterns.

A shallow water equations-based model with an improved local time-stepping (LTS) scheme is developed for modeling coastal hydrodynamics across multiple scales, from large areas to detailed local regions. To enhance the stability of the shallow water model for long-duration simulations and at larger LTS gradings, a prediction-correction method using a single-layer interface that couples coarse and fine time discretizations is adopted. The proposed scheme improves computational efficiency with an acceptable additional computational burden and ensures accurate conservation of time truncation errors in a discrete sense. The model performance is verified with respect to conservation and computational efficiency through two idealized tests: the spreading of a drop of shallow water and a tidal flat/channel system. The results of both tests demonstrate that the improved LTS scheme maintains precision as the LTS grading increases, preserves conservation properties, and significantly improves computational efficiency with a speedup ratio of up to 2.615. Furthermore, we applied the LTS scheme to simulate tides at grid scales of 40,000 m to 200 m for a portion of the Northwest Pacific. The proposed model shows promise for modeling cross-scale hydrodynamics in complex coastal and ocean engineering problems.

Semantic segmentation of remote sensing (RS) images plays a vital role in a variety of fields, including urban planning, natural disaster monitoring, and land resource management. Due to the complexity and low resolution of RS images,many approaches have been proposed to handle the related task. However, these previously developed approaches dedicate to contextual interaction but ignore the cross-scale semantic correlation and multi-scale boundary information. Therefore, we propose a Cross-scale Graph Interaction Network (CGIN) to address semantic segmentation problems of RS images, which consists of a semantic branch and a boundary branch. In the semantic branch, we first apply atrous convolution to extract multi-scale semantic features of RS images. Particularly, based on the multi-scale semantic features, a Cross-scale Graph Interaction (CGI) module is introduced, which establishes cross-scale graph structures and performs adaptive graph reasoning to capture the cross-scale semantic correlation of RS objects. In the boundary branch, we propose a Multiscale Boundary Feature Extraction (MBFE) module that utilizes atrous convolutions with different dilation rates to extract multi-scale boundary features. Finally, to address the problem of sparse boundary pixels in the fusion process of the two branches, we propose a Multi-scale Similarity-guided Aggregation (MSA) module by calculating the similarity of semantic features and boundary features at the corresponding scale, which can emphasize the boundary information in semantic features. Our proposed CGIN outperforms state-of-the-art approaches in numerical experiments conducted on two benchmark remote sensing datasets. [ABSTRACT FROM AUTHOR]

In this paper, multiscale Al2O3 micro/nanoparticles (0–14 wt%) mixed with FeCoNiCrMn high-entropy alloy (HEA) particles (15 wt%) were used as reinforcements to prepare dual-phase-reinforced aluminum matrix composites (AMCs) through microwave sintering. The strengthening effects of doping different weight fractions of Al2O3 micro/nanoparticles on the microstructure, mechanical properties, and strengthening mechanisms of the mixed-phase particle-reinforced AMCs were investigated. The performance after 11 wt% Al2O3+HEA microparticles was similar to that obtained by adding 2 wt% and 5 wt% Al2O3+HEA nanoparticles. The hardness, yield strength, and compressive strength of the 2 wt% nano-Al2O3 composite were 109.7 HV, 286.5 MPa, and 506.7 MPa, respectively, which were 70.9%, 98.5%, and 94.7% higher than those of the single HEA-reinforced sample. In terms of the strengthening and toughening mechanisms, Orowan strengthening, dislocation strengthening, and thermal mismatch strengthening were the main strengthening mechanisms of particles species mixed in the metal matrix composites. Hall-Petch strengthening and load-transfer effects were mainly attributed to the cross-scale mixing of particles. The hybrid particles of HEA and Al2O3 synergistically delayed interfacial crack propagation. This study provides a new preparation method for high-performance metal matrix composites.

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM.

Sustainable biopolymer additives offer a promising soil stabilisation methodology, with a strong potential to be tuned to soil's specific nature, allowing the tailoring of mechanical properties for a range of geotechnical applications. However, the biopolymer chemical characteristics driving soil mechanical property modifications have yet to be fully established. Within this study we employ a cross-scale approach, utilising the differing galactose:mannose (G:M) ratios of various Galactomannan biopolymers (Guar Gum G:M 1:2, Locust Bean Gum G:M 1:4, Cassia Gum G:M 1:5) to investigate the effect of microscale chemical functionality upon macroscale soil mechanical properties. Molecular weight effects are also investigated, utilising Carboxy Methyl Cellulose (CMC). Soil systems comprising of SiO2 (100%) (SiO2) and a Mine Tailing (MT) exemplar composed of SiO2 (90%) + Fe2O3 (10%) (SiO2 + Fe) are investigated. The critical importance of biopolymer additive chemical functionality for the resultant soil mechanical properties, is demonstrated..For Galactomannan G:M 1:5 stabilised soils the 'high-affinity, high-strength', mannose-Fe interactions at the microscale (confirmed by mineral binding characterisation) are attributed to the 297% increase in the SiO2 + Fe systems Unconfined Compressive Strength (UCS), relative to SiO2 only. Conversely for SiO2 Galactomannan-stabilised soils, when increasing the G:M ratio from 1:2 to 1:5, a 85% reduction in UCS is observed, attributed to mannose's inability to interact with SiO2. UCS variations of up to a factor of 12 were observed across the biopolymer–soil mixes studied, in line with theoretically and experimentally expected values, due to the differences in the G:M ratios. The limited impact of molecular weight upon soil strength properties is also shown in CMC-stabilised soils. When considering a soil's stiffness and energy absorbance, the importance of biopolymer–biopolymer interaction strength and quantity is discussed, further deciphering biopolymer characteristics driving soil property modifications. This study highlights the importance of biopolymer chemistry for biopolymer stabilisation studies, illustrating the use of simple low-cost, accessible chemistry-based instrumental tools and outlining key design principles for the tailoring of biopolymer–soil composites for specific geotechnical applications. [ABSTRACT FROM AUTHOR]

Aim: Recent studies highlight the importance of linking landscape ecology and macroecology for a better understanding of broad‐scale biodiversity patterns. The "landscape context effect" denotes that species responses and biodiversity in a focal area are shaped by neighbouring landscape composition and structure outside the focal area. Here, we test whether the landscape context effect could be pronounced at macroecological scales. Location: Sub‐Saharan Africa and continental China. Time period: Late 20th to early 21st centuries. Taxa studied: Terrestrial mammals (≥2 kg). Methods: We calculated species richness on the basis of grid cells of 50 km × 50 km and 100 km × 100 km. We used ordinary least square and random forest models to examine the relationships between species richness within grid cells and landscape context (defined as composition and structure of the neighbouring landscape outside the grid cells, with distances of 10–400 km to the boundary of a given grid cell). We used variation partitioning to quantify the independent and shared explanatory power of the landscape context variables, grouping species by body size and diet. Results: Landscape context alone explained ≤20% of the variation in species richness, even when controlling for correlations with macroenvironmental variables (climate, productivity and topography) and correlations with landscape attributes within the grid cells. Importantly, the explanatory power of landscape context at the scales of 100–400 km ofen outweighed grid‐cell landscape attributes or macro‐environmental variables. The independent explanatory power of landscape context was lowest for small‐sized omnivores. Furthermore, we found higher independent explanatory power for large herbivores in sub‐Saharan Africa than in continental China. Main conclusions: Landscape context plays a substantial role in shaping local biodiversity patterns at regional and continental scales, with its strength varying with organism diet and movement needs and possibilities. These findings support that conservation efforts should include effective management of landscape structure, with attention to differing space requirements among organism groups. Our work also illustrates the scope for testing landscape ecological hypotheses at macroecological scales. [ABSTRACT FROM AUTHOR]

Despite their significance, how interactions of plant diversity at multiple spatial scales and soil microbial stoichiometry alter a series of ecosystem functions (multifunctionality, EMF) in response to anthropogenic nitrogen (N) input and herbivores are poorly known. We conducted a 17‐year sheep grazing experiment with 6‐year N addition to explore the impacts of grazing (0, 2.7, 5.3 and 8.7 sheep ha−1) and N addition (N0, N5, N10 and N20, i.e., 0, 5, 10 and 20 g N m−2 yr−1, respectively) on grassland functions and EMF via changes in plant α‐and β‐diversity, and carbon to nitrogen ratio (C:N) of soil microbes in a typical steppe. The results show that grazing or N addition alone significantly affected EMF with a treatment order of 2.7 and 8.7 sheep ha−1 > 0 and 5.3 sheep ha−1 for grazing intensity or N5 > N10 and N20 > N0 for N addition, which resulted in a significant higher EMF in the combination treatment of 2.7 sheep ha−1 and 5 g N m−2 yr−1. Plant α‐and β‐diversity, and soil microbial C:N were the predominant drivers of changes in EMF. Grazing reduced EMF indirectly by decreasing the plant β‐diversity. N addition promoted EMF indirectly by decreasing plant α‐diversity. In addition, lower plant α‐diversity enhanced EMF indirectly by increasing soil microbial C:N. Our results suggest that the negative effects of herbivore on EMF were stronger at larger spatial scales compared to the smaller local communities, while N addition could maintain a higher level of EMF at smaller scales rather than at the larger ones. Our results highlight that multiple spatial scales should be considered to fully unravel the effects of herbivore and eutrophication on ecosystem functions. Our results also demonstrate the important role of soil microbe in maintaining higher grassland multifunctionality, thus we should include the soil microbial functions (i.e., C and N transformation) in further studies. Our results suggest that grazing at a low grazing intensity of 2.7 sheep ha−1 with a low N supplementation of 5 g N m−2 yr−1 could maintain the most important ecosystem functions. Our work provides important insight into grassland conservation and management, aiming to maintain the capacity of grasslands to sustainably supply ecological and productive functions. [ABSTRACT FROM AUTHOR]

Visual tracking is fundamentally the problem of conditional probability regressing of the target location in each video frame. Convolutional neural network (CNN) have been dominant in visual tracking these years, but CNN-based trackers neglect long-range dependency in likelihood representation and prior information, these destroy the spatial consistency of target. Recently emerging Transformer-based trackers mitigate these, however, they do not possess the ability to build interactions among features of cross-scale. Moreover, the sine position encoding prior in Transformer-based tracker is content-unaware and fails to reflect the relative index of different positions. To address these issues and inspired by Bayesian probabilistic formulation, we propose a cross-scale full Transformer tracker with content-based prior bias (named BTT). There are four main contributions of the method, (i) we propose a hierarchical full Transformer tracking architecture to introduce long-range dependency, which enriches the likelihood representation of model, and alleviates the destruction of spatial consistency. (ii) An expanding layer without using convolution or interpolation operation is proposed to aggregate layer information of different scales to construct cross-scale likelihood estimation. (iii) We further demonstrate the defect of sine position encoding with mathematical derivation, and introduce a content-based positional encoding bias as prior in the Transformer architecture to reflect the relative index of inputs. (iv) And extensive experiments show that the proposed tracker achieves better performance compared with CNN-based trackers in cases of illumination, low resolution, deformation on various datasets, and achieves superior performance on others attributes. The proposed tracker obtains 70.3%, 69.1%, 63.4% on OTB2015, UAV123, and LaSOT, respectively. [ABSTRACT FROM AUTHOR]

Context: Coral reef resilience is the product of multiple interacting processes that occur across various interacting scales. This complexity presents challenges for identifying solutions to the ongoing worldwide decline of coral reef ecosystems that are threatened by both local and global human stressors. Objectives: We highlight how coral reef resilience is studied at spatial, temporal, and functional scales, and explore emerging technologies that are bringing new insights to our understanding of reef resilience. We then provide a framework for integrating insights across scales by using new and existing technological and analytical tools. We also discuss the implications of scale on both the ecological processes that lead to declines of reefs, and how we study those mechanisms. Methods: To illustrate, we present a case study from Kāneʻohe Bay, Hawaiʻi, USA, linking remotely sensed hyperspectral imagery to within-colony symbiont communities that show differential responses to stress. Results: In doing so, we transform the scale at which we can study coral resilience from a few individuals to entire ecosystems. Conclusions: Together, these perspectives guide best practices for designing management solutions that scale from individuals to ecosystems by integrating multiple levels of biological organization from cellular processes to global patterns of coral degradation and resilience. [ABSTRACT FROM AUTHOR]

A three‐dimensional unstructured‐grid hydrodynamic and water quality model (Semi‐implicit Cross‐scale Hydroscience Intergrated System Model‐Integrated Compartment Model) is applied successfully for Chesapeake Bay. The model is validated with observations of salinity, chlorophyll‐a, dissolved oxygen, nutrients, and phytoplankton productions from the year 1991 to 1995 for the mainstem and some major tributaries, based on multiple model skill scores. Model experiments are conducted to test the importance of having (1) an accurate representation of bathymetry to correctly predict hypoxia and other processes and (2) a high‐resolution model grid for tributaries to correctly simulate water quality variables. Comparison with the model experiment results with bathymetry smoothing indicates that bathymetry smoothing, as commonly used for many systems, changes the stratification and lateral circulation pattern, resulting in more salt intrusion into shallow water regions, and an increase in the freshwater age. Consequently, a model with bathymetry smoothing can lead to an unrealistic prediction of the distribution of hypoxia and phytoplankton production. Local grid refinement shows significant improvement of model simulations on local stratification and water quality variables. Overall, the use of high‐resolution unstructured grid model leads to a faithful representation of the complex geometry, and thus a seamless cross‐scale capability for simulating water quality processes in the Bay including tributaries and tidal creeks. [ABSTRACT FROM AUTHOR]

Accurate segmentation of lung nodules from pulmonary computed tomography (CT) slices plays a vital role in the analysis and diagnosis of lung cancer. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in the automatic segmentation of lung nodules. However, they are still challenged by the large diversity of segmentation targets, and the small inter-class variances between the nodule and its surrounding tissues. To tackle this issue, we propose a features complementary network according to the process of clinical diagnosis, which made full use of the complementarity and facilitation among lung nodule location information, global coarse area, and edge information. Specifically, we first consider the importance of global features of nodules in segmentation and propose a cross-scale weighted high-level feature decoder module. Then, we develop a low-level feature decoder module for edge feature refinement. Finally, we construct a complementary module to make information complement and promote each other. Furthermore, we weight pixels located at the nodule edge on the loss function and add an edge supervision to the deep supervision, both of which emphasize the importance of edges in segmentation. The experimental results demonstrate that our model achieves robust pulmonary nodule segmentation and more accurate edge segmentation. [ABSTRACT FROM AUTHOR]

In the future, the extraterrestrial human activities, such as resources exploitation and base construction beyond the Earth need the aid of geotechnical engineering technology. Currently, there are only two approaches for humans to obtain the rock samples beyond the Earth: sample-return activities by spacecraft and meteorite investigation. Meteorites are rare, expensive, small and arbitrary in shape, so it is difficult to process them into standard rock samples required by MTS and other traditional macroscale rock mechanical tests. In this paper, a novel technique for measuring mechanical property of small-size meteorites was developed based on microscale rock mechanics experiments (micro-RME) and statistical probability models. Firstly, the composition, content and distribution of rock-forming minerals in NWA13618 meteorites were obtained by TIMA. Then, Gaussian mixture model was used to calculate the mechanical parameters of four main minerals in meteorite NWA13618. The elastic moduli of olivine, pyroxene, Fe-Ni and feldspar are 116.73, 101.77, 87.24 and 70.74 GPa, respectively. Lastly, the homogenization method Mori-Tanaka model isapplied to calculate the macroscale centimeter elastic modulus of NWA13618 meteorite is 90.48 GPa according to the achieved mineral content and mechanical properties. The microscale rock mechanical experiment and scale upgrading method proposed in this paper provide theoretical basis and technical means for predicting the mechanical properties of L4 parent asteroid.

• Identified the contact angle on ZrO 2 using Molecular Dynamics for both main crystallographic planes at Boiling Water Reactor in-situ operating conditions. • Connected the nanoscale Molecular Dynamics simulations to mesoscale Lattice Boltzmann simulations through the contact angle. • Modelled the nucleation of bubbles on a ZrO 2 surface at Boiling Water Reactor in-situ operating conditions taking into account the micro-roughness. • Obtained a correlation between the roughness and the shape of the surface and the nucleation site density. The mechanism of bubble nucleation in boiling water at solid surface remains poorly understood. In this study, the water boiling and bubble nucleation density under nuclear reactor operation conditions were investigated by multiphase Lattice Boltzmann and Molecular Dynamics simulations. The developed multiscale model takes into account the surface energy of ZrO 2 cladding in different crystallographic planes obtained by molecular dynamics simulations and uses it to inform the corresponding contact angle of two-phase fluid in the thermal multiphase Lattice Boltzmann model. The model describes the bubble formation on a rough surface due to boiling and predicts the number of active nucleation sites on surfaces of different roughness. Further, the effect of the different contact angles on bubble dynamics while in close contact with the surface is investigated. The obtained results are in good agreement with experimental observations and provide functional relationships between interface properties (roughness, surface energy) and the nucleation site density, necessary for macroscopic simulations of the boiling phenomena. [ABSTRACT FROM AUTHOR]

Water and chloride ions within pores of cementitious materials plays a crucial role in the damage processes of cement pastes, particularly in the binding material comprising calcium-silicate-hydrates (C-S-H). The migration mechanism of water and chloride ions restricted in C-S-H nanopores is complicated due to the presence of interfacial effects. The special mechanical properties of the solid–liquid interface determine the importance of boundary slip and Electric Double Layer (EDL) and ion diversity in pore solutions determines the difference of the EDL and the stability of water film slip. A cross-scale model covering slip effects, time-varying of EDL and ion correlation needs to be developed so that the interfacial effects concentrated at the pore scale can be extended to affect the overall diffusivity of C-S-H. The statistics of pore size distribution and fractal dimension were used to quantitatively compare the similarities between model and C-S-H structure, thus proving the reliability of cross-scale reconstructed C-S-H transmission model. The results show that the slip effect is the dominant factor affecting the diffusion ability of C-S-H, the contribution of the slip effect is up to 60% and the contribution rate of EDL time-varying only up to about 15%. Moreover, the slip effect is sensitive to both ion correlation and C-S-H inhomogeneity and EDL time-varying is almost insensitive to ion correlation changes. This quantification provides a necessary benchmark for understanding the destructiveness of cement-based materials in the salt rich environment and provides new insights into improving the durability of concrete by changing the solid–liquid interface on the micro-nanoscale. [ABSTRACT FROM AUTHOR]

The investigations on the dynamics of hierarchical oscillator chains provide basic theories for the fields of acoustic metamaterial and passive guidance of energy flow. In the present study, the characteristics of stiffness-dominated energy transfer of a reduced cross-scale oscillator chain that generates chaotic vibrations are studied. The semi-analytical solutions are obtained by the complexification-averaging method and the least-square method for thoroughly searching the branches of responses. Then, the energy transfer patterns of the oscillator chain are analyzed based on Runge–Kutta method. Moreover, chaotic responses are determined using the displacements and Lyapunov exponents of the system. The average energy and normalized energy flux are defined to evaluate the energy distribution in each oscillator and the energy transfer between oscillators, respectively. The semi-analytical results show that the oscillator chain produces complex unstable branches of response around the resonance frequency of the linear oscillator. It is found that the detached higher branches of response of the chain are similar to that of a system consisting of one linear oscillator and one cubic oscillator. When chaotic responses occur, the energy transfer patterns that reflected by average energy and normalized energy flux are similar in the same branch of response for different excitation frequencies. However, energy transfer patterns in different branches of response are quite different even at the same excitation frequency, and the higher branch generates the lower energy transfer. Furthermore, analyses of the input energy and the scale between cubic oscillators demonstrate that variations of the two parameters slightly affect the normalized energy flux. [ABSTRACT FROM AUTHOR]

A coupled bio-chemo-hydro-mechanical model (BCHM) is developed to investigate the permeability reduction and stiffness improvement in soil by microbially induced calcite precipitation (MICP). Specifically, in our model based on the geometric method a link between the micro- and macroscopic features is generated. This allows the model to capture the macroscopic material property changes caused by variations in the microstructure during MICP. The developed model was calibrated and validated with the experimental data from different literature sources. Besides, the model was applied in a scenario simulation to predict the hydro-mechanical response of MICP-soil under continuous biochemical, hydraulic and mechanical treatments. Our modelling study indicates that for a reasonable prediction of the permeability reduction and stiffness improvement by MICP in both space and time, the coupled BCHM processes and the influences from the microstructural aspects should be considered. Due to its capability to capture the dynamic BCHM interactions in flexible settings, this model could potentially be adopted as a designing tool for real MICP applications. [ABSTRACT FROM AUTHOR]

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A droplet hitting a superhydrophobic surface will undergo the Cassie to Wenzel transition when the wetting force exceeds the anti-wetting force. The critical velocity of the droplet's Cassie to Wenzel state transition can reflect the wettability of the surface. However, the critical velocity research is still at the microscale and has not been extended to the nanoscale mechanism. A cross-scale critical velocity prediction model for superhydrophobic surfaces with symmetric structures is proposed here based on a mechanical equilibrium system. The model's applicability is verified by experimental data. It demonstrates that the mechanical equilibrium system of droplet impact with capillary pressure and Laplace pressure as anti-wetting forces is more comprehensive, and the model proposed in this study predicts the critical velocity more precisely with a maximum error of 12% compared to the simulation results. Furthermore, the correlation between the simulation at the nanoscale and the evaluation of the macroscopic symmetrical protrusion surface properties is established. Combined with the model and the correlation, the relationship between the microscopic mechanism and the macroscopic examination of droplet dynamics on the superhydrophobic surface be presented, and the wettability evaluation method of macroscopic surfaces based on the molecular simulation mechanism can be realized. [ABSTRACT FROM AUTHOR]

Bio-inspired reversible adhesion has significant potential in many fields requiring flexible grasping and manipulation, such as precision manufacturing, flexible electronics, and intelligent robotics. Despite extensive efforts for adhesive synthesis with a high adhesion strength at the interface, an effective strategy to actively tune the adhesion capacity between a strong attachment and an easy detachment spanning a wide range of scales has been lagged. Herein, we report a novel soft-hard-soft sandwiched composite design to achieve a stable, repeatable, and reversible strong adhesion with an easily scalable performance for a large area ranging from ∼1.5 to 150 cm2 and a high load ranging from ∼20 to 700 N. Theoretical studies indicate that this design can enhance the uniform loading for attachment by restraining the lateral shrinkage in the natural state, while facilitate a flexible peeling for detachment by causing stress concentration in the bending state, yielding an adhesion switching ratio of ∼54 and a switching time of less than ∼0.2 s. This design is further integrated into versatile grippers, climbing robots, and human climbing grippers, demonstrating its robust scalability for a reversible strong adhesion. This biomimetic design bridges microscopic interfacial interactions with macroscopic controllable applications, providing a universal and feasible paradigm for adhesion design and control. [ABSTRACT FROM AUTHOR]

Macroecology research seeks to understand ecological phenomena with causes and consequences that accumulate, interact, and emerge across scales spanning several orders of magnitude. Broad‐extent, fine‐grain information (i.e., high spatial resolution data over large areas) is needed to adequately capture these cross‐scale phenomena, but these data have historically been costly to acquire and process. Unoccupied aerial systems (UAS or drones carrying a sensor payload) and the National Ecological Observatory Network (NEON) make the broad‐extent, fine‐grain observational domain more accessible to researchers by lowering costs and reducing the need for highly specialized equipment. Integration of these tools can further democratize macroecological research, as their strengths and weaknesses are complementary. However, using these tools for macroecology can be challenging because mental models are lacking, thus requiring large up‐front investments in time, energy, and creativity to become proficient. This challenge inspired a working group of UAS‐using academic ecologists, NEON professionals, imaging scientists, remote sensing specialists, and aeronautical engineers at the 2019 NEON Science Summit in Boulder, Colorado, to synthesize current knowledge on how to use UAS with NEON in a mental model for an intended audience of ecologists new to these tools. Specifically, we provide (1) a collection of core principles for collecting high‐quality UAS data for NEON integration and (2) a case study illustrating a sample workflow for processing UAS data into meaningful ecological information and integrating it with NEON data collected on the ground—with the Terrestrial Observation System—and remotely—from the Airborne Observation Platform. With this mental model, we advance the democratization of macroecology by making a key observational domain—the broad‐extent, fine‐grain domain—more accessible via NEON/UAS integration. [ABSTRACT FROM AUTHOR]

Window self-attention based Transformer receives the advanced results in image denoising. However, the current methods still have some limitations in capturing the global dependencies and local responses. To tackle these problems, this paper proposes a novel Transformer based image denoising method, called as CSformer, which is equipped with two key blocks, including the cross-scale features fusion (CS2F) block and mixed global-local Swin (M-Swin) Transformer block. The CSformer has a specific multi-scale framework, in which the multi-scale features, extracted by M-Swin Transformer, are fused using CS2F block. Such cross-scale fusion not only enriches the features, but also yields the multi-scale self-attention. In addition, the M-Swin Transformer block consists of the Swin Transformer block and the separable convolution based convolutional local-extraction (CLE) block, which can boost the ability of Transformer in local representation. We demonstrate the superiority of CSformer on some well-known datasets at different noise levels with comparisons to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recently, embedding and metric-based few-shot learning (FSL) has been introduced into hyperspectral image classification (HSIC) and achieved impressive progress. To further enhance the performance with few labeled samples, we in this paper propose a novel FSL framework for HSIC with a class-covariance metric (CMFSL). Overall, the CMFSL learns global class representations for each training episode by interactively using training samples from the base and novel classes, and a synthesis strategy is employed on the novel classes to avoid overfitting. During the meta-training and meta-testing, the class labels are determined directly using the Mahalanobis distance measurement rather than an extra classifier. Benefiting from the task-adapted class-covariance estimations, the CMFSL can construct more flexible decision boundaries than the commonly used Euclidean metric. Additionally, a lightweight cross-scale convolutional network (LXConvNet) consisting of 3D and 2D convolutions is designed to thoroughly exploit the spectral-spatial information in the high-frequency and low-frequency scales with low computational complexity. Furthermore, we devise a spectral-prior-based refinement module (SPRM) in the initial stage of feature extraction, which cannot only force the network to emphasize the most informative bands while suppressing the useless ones, but also alleviate the effects of the domain shift between the base and novel categories to learn a collaborative embedding mapping. Extensive experiment results on four benchmark data sets demonstrate that the proposed CMFSL can outperform the state-of-the-art methods with few-shot annotated samples. [ABSTRACT FROM AUTHOR]

Multiscale object detection in Synthetic Aperture Radar (SAR) images can locate and recognize key objects in large-scene SAR images, and it is one of the key technologies in SAR image interpretation. However, for the simultaneous detection of SAR objects with large size differences, that is, cross-scale object detection, existing object detection methods are difficult to extract the features of cross-scale objects, and also difficult to realize cross-scale object simultaneous detection. In this study, we propose a multiscale object detection method based on the Feature-Transferable Pyramid Network (FTPN) for SAR images. In the feature extraction stage, the feature migration method is used to obtain an effective mosaic of the feature images of each layer and extract feature images with different scales. Simultaneously, the void convolution method is used to increase the receptive field of feature extraction and aid the network in extracting large object features. These steps can effectively preserve the features of objects of different sizes, to realize the simultaneous detection of cross-scale objects in SAR images. The experiments based on the GaoFen-3 SAR dataset, SAR Ship Detection Dataset (SSDD), and high-resolution SSDD-2.0 show that the proposed method can detect cross-scale objects, such as airports and ships in SAR images, and the mean Average Precision (mAP) can reach 96.5% on the existing dataset, which is 8.1% higher than that of the characteristic pyramid network algorithm. Moreover, the overall performance of the proposed method is better than that of the latest YOLOv4 and other object detection algorithms.