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Remote neuromuscular electrical stimulation upregulates MDK to enhance macrophage efferocytosis via LRP1 in wound healing 认领 引用 被引量：1
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作者 Lijuan Zong Chong Liu +11 位作者 Li Zhang Xueyou Tao Qingyan Tian Xiaokai Zhou Yu Wang Na Shen Jiaming Gong Qingyuan Zhuang Tong Wang Wentao Liu Ying Shen Liang Hu 《Journal of Biomedical Research》 CAS CSCD 2026年第2期120-133,共14页
Neuromuscular electrical stimulation(NMES)is a well-established therapeutic approach for chronic wounds.Conventionally,NMES involves direct electrode contact with wounds or adjacent healthy skin;however,it is limited ... Neuromuscular electrical stimulation(NMES)is a well-established therapeutic approach for chronic wounds.Conventionally,NMES involves direct electrode contact with wounds or adjacent healthy skin;however,it is limited by the need for wound exposure and by increased pain.Our preliminary study demonstrated the innovative application of remote NMES(rNMES)to the skeletal muscle of the distal calf,which showed the potential to accelerate wound healing in remote areas.rNMES was effective in human clinical trials in our previous work,although the underlying mechanisms remain unclear.As rNMES is often used to stimulate muscle contraction in long-term bedridden patients,we analyzed data from the Gene Expression Omnibus(GEO)database and found that exercise promotes midkine(MDK)expression in muscle.MDK is a small secreted heparin-binding protein that interacts with multiple cell surface receptors to promote growth.In the present study,we found that MDK significantly enhanced macrophage efferocytosis in a low-density lipoprotein receptor-related protein 1(LRP1)-dependent manner.Our findings demonstrate that rNMES upregulates MDK expression in skeletal muscles through the AMPK-ERK axis,facilitating its delivery to wounds through the circulatory system and promoting LRP1-mediated efferocytosis of apoptotic cells,thereby expediting wound healing. 展开更多
关键词 wound healing remote neuromuscular electrical stimulation efferocytosis MDK LRP1
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Protective effect of ischemic preconditioning against hepatic ischemia-reperfusion injury and associated remote organ damage 认领 引用 被引量：1
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作者 Christina Mouratidou Efstathios T Pavlidis +7 位作者 Georgios Katsanos Serafeim-Chrysovalantis Kotoulas Athanasios Kofinas Alexandra G Marneri Eleni Massa Georgios Tsoulfas Ioannis N Galanis Theodoros E Pavlidis 《World Journal of Gastrointestinal Surgery》 SCIE 2026年第2期36-55,共20页
Hepatic ischemia-reperfusion injury is a significant complication of liver surgery,including major hepatectomy,trauma surgery and liver transplantation.It is a key factor in postoperative organ failure,which negativel... Hepatic ischemia-reperfusion injury is a significant complication of liver surgery,including major hepatectomy,trauma surgery and liver transplantation.It is a key factor in postoperative organ failure,which negatively affects prognosis and overall patient survival.Beyond its localized hepatic effects,ischemia-reperfusion injury is increasingly recognized as a potent trigger of the systemic inflammatory response and remote organ damage.The cellular and molecular mechanisms involved are highly complicated and have yet to be entirely elucidated.The core pathophysiological mechanisms of hepatic ischemia-reperfusion injury include a transition to anaerobic metabolism and adenosine triphosphate depletion;the development of intracellular acidosis and calcium overload;the impairment of mitochondrial function;oxidative stress;the activation and accumulation of distinct cell populations,notably Kupffer cells,neutrophils and platelets;the upregulation and downregulation of microRNAs;increased nitric oxide production;and the triggering of an immune system response with the activation of the complement system and excessive cytokine release.Ischemic preconditioning(IP)is a surgical technique in which brief cycles of controlled ischemia followed by reperfusion are applied directly to an organ,aiming to enhance its tolerance to subsequent prolonged ischemia.Hepatic IP has been demonstrated to reduce ischemia-reperfusion injury by decreasing the release of proinflammatory cytokines and damage-associated molecular patterns;suppressing reactive oxygen species production;activating the antioxidant enzyme heme-oxygenase 1,caspase,heat shock proteins and protein kinase cascades;modulating energy supplies and electrolyte homeostasis;and intervening in cell death pathways.In addition to its local effects on the liver,growing evidence indicates that IP also provides systemic advantages by reducing the inflammatory response and limiting injury to distant organs following major hepatic injury.This review integrates current data on IP,highlighting its role in hepatic protection and prevention of remote organ damage,while exploring the underlying mechanisms and translational potential of this approach in hepatic surgery and transplantation. 展开更多
关键词 Hepatic ischemia-reperfusion injury Remote organ damage Systemic inflammatory response Ischemic preconditioning Liver transplantation Protective method Pathophysiological mechanisms
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A Study on the Coastal Water Environment Health Assessment of the Yellow River Estuary Based on Remote Sensing Inversion Data 认领 引用
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作者 LI Lianwei ZHENG Zhi +3 位作者 MA Yingying XUE Cunjin SUN Baojian WANG Yu 《Journal of Ocean University of China》 SCIE CAS CSCD 2026年第2期553-567,共15页
The assessment of aquatic environmental health plays a vital role in the sustainable protection and management of coastal ecosystems,particularly in the Yellow River Estuary-one of China's most representative estu... The assessment of aquatic environmental health plays a vital role in the sustainable protection and management of coastal ecosystems,particularly in the Yellow River Estuary-one of China's most representative estuarine systems.To address the limitations of existing health assessment studies,which are often constrained by point-based observations lacking spatial continuity and comprehensiveness,this study integrates multiple remotely sensed surface data to perform a comprehensive health assessment of the nearshore waters of the Yellow River Estuary.An evaluation index system was first developed based on the National Seawater Quality Standards and the specific water quality characteristics of the region.Subsequently,long-term retrievals of key water quality parameters were conducted using Sentinel-2 imagery and in situ measurements from 2016 to 2023,employing the QAA-RF(Quasi-Analytical Algorithm based on Random Forest)algorithm.The Analytic Hierarchy Process(AHP)was used to determine the relative weights of each water quality factor.Through weighted integration,spatially continuous water environmental health assessment datasets were generated,enabling seasonal and annual evaluations and spatiotemporal analyses over the study period.The results indicate that the aquatic environmental health of the nearshore waters exhibits a clear spatial gradient,with poorer water quality in areas closer to the estuary and gradual improvement farther offshore.Seasonal variations are also evident,with poorer water quality observed in spring and winter-reflected by higher proportions of Inferior to Category IV and Category IV water quality(4.07% and 4.65% in spring;1.12% and 3.71% in winter)-and better conditions in summer and autumn(0.51%and 1.42% in summer;0.81% and 2.38% in autumn).On an annual scale,the overall aquatic environmental health of the Yellow River Estuary's nearshore waters remains relatively stable.This study provides a novel,spatially explicit framework for evaluating coastal water environmental health using remote sensing and machine learning approaches.By overcoming the limitations of traditional point-based assessments,it offers valuable insights and a scalable methodology for the continuous monitoring and sustainable management of estuarine and coastal ecosystems. 展开更多
关键词 Yellow River Estuary remote sensing inversion AHP health assessment
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MSFFNet:Multiscale Feature Fusion Network for Small Target Detection in Remote Sensing Images 认领 引用
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作者 Hui Zong Chenbin Wang +3 位作者 Hao Wang Xin Cheng Shangbing Gao Yunyang Yan 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期592-609,共18页
With the advancement of satellite remote sensing technology,object detection based on high-resolution remote sensing imagery has emerged as a prominent research focus in the field of computer vision.Although numerous ... With the advancement of satellite remote sensing technology,object detection based on high-resolution remote sensing imagery has emerged as a prominent research focus in the field of computer vision.Although numerous algorithms have been developed for remote sensing image object detection,they still suffer from challenges such as low detection accuracy and high false positive rates.To address these issues,we propose a novel architecture,the multiscale feature fusion network(MSFFNet).MSFFNet is composed of three key components:the Large Selective Kernel Block(LSKBlock),the Space-to-Depth ADown(SPDA)module and the Double Feature Aggregation Neck(DFAN).Specifically,the LSKBlock adaptively captures salient target features by dynamically adjusting the receptive field size,thereby enhancing detection precision.The SPDA module converts spatial correlations into channel-wise dependencies by segmenting and reordering the feature maps,which helps preserve finegrained information,suppress background interference and reduce false detections.Furthermore,the DFAN integrates shallow and deep features through a multiscale feature fusion module(MSFFM),enabling the extraction of multiscale target representations and improving overall detection performance.Extensive experiments on public datasets,SIMD,VisDrone2019 and DIOR,demonstrate the effectiveness of our approach.Compared with the YOLOv9s baseline model,MSFFNet achieves improvements in mAP50%of 0.6%,1.9%and 3.5%,respectively. 展开更多
关键词 computer vision object recognition remote sensing images
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MewCDNet: A Wavelet-Based Multi-Scale Interaction Network for Efficient Remote Sensing Building Change Detection 认领 引用
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作者 Jia Liu Hao Chen +5 位作者 Hang Gu Yushan Pan Haoran Chen Erlin Tian Min Huang Zuhe Li 《Computers, Materials & Continua》 SCIE EI 2026年第1期687-710,共24页
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra... Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability. 展开更多
关键词 Remote sensing change detection deep learning wavelet transform multi-scale
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GLMCNet: A Global-Local Multiscale Context Network for High-Resolution Remote Sensing Image Semantic Segmentation 认领 引用
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作者 Yanting Zhang Qiyue Liu +4 位作者 Chuanzhao Tian Xuewen Li Na Yang Feng Zhang Hongyue Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第1期2086-2110,共25页
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an... High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet. 展开更多
关键词 Multiscale context attention mechanism remote sensing images semantic segmentation
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Combined local knowledge with remote sensing and machine learning for seismic vulnerability assessment in rural areas:A case study of Weinan City,China 认领 引用
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作者 Zhang Xinyu Zhou Jie Liu Yaohui 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2026年第3期853-868,共16页
Seismic vulnerability assessment in rural areas is crucial for mitigating earthquake-induced losses and promoting sustainable development.In China’s rural regions,most buildings are self-constructed with limited seis... Seismic vulnerability assessment in rural areas is crucial for mitigating earthquake-induced losses and promoting sustainable development.In China’s rural regions,most buildings are self-constructed with limited seismic resilience.While traditional field surveys accurately assess individual building vulnerability,they are time-consuming and expensive,making them unsuitable for extensive rural vulnerability evaluations.To address this issue,this study presents a rapid and accurate method for estimating the seismic vulnerability of rural buildings,using the rural areas of Weinan City,China,as a case study.First,several villages were randomly selected for field investigation.The seismic vulnerability of rural buildings was assessed according to EMS-98 standards.A proxy model linking building attributes to vulnerability was established by using machine learning(ML)and applied to the whole study area.Finally,the vulnerability index method of RISK-UE was employed to evaluate the seismic risk and the possible damage under different seismic intensities.The results show that the vulnerability class of rural buildings in the rural Weinan area is dominated by C,with an average VIM index of 0.67,indicating that the overall seismic performance needs to be strengthened.With increasing seismic intensity(fromⅦtoⅩ),the number of potentially displaced households increases from 7446(3.39%)to 185459(84.39%).This method can be used to obtain reliable vulnerability estimates based on the number of building structure types in each village for low-cost seismic hazard assessment work and has strong potential for generalization. 展开更多
关键词 seismic vulnerability assessment remote sensing EMS-98 machine learning RISK-UE
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Performance assessment and recommendations for motor vehicle emission remote sensing equipment in megacity conditions:Insights from Beijing 认领 引用
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作者 Yangyang Cui Dongdong Guo +10 位作者 Huawei Yi Lijun Zhu Jianglong Yang Lijun Hao Mingliang Fu Aijun Shi Lei Nie Honglin Cao Yuanrong Yu Gen Zhang Yifeng Xue 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第7期615-622,共8页
Remote sensing equipment(RSE)plays an essential role in monitoring vehicle emissions but requires comprehensive evaluation to verify reliability.In this study,the performance of seven different RSE models in Beijing w... Remote sensing equipment(RSE)plays an essential role in monitoring vehicle emissions but requires comprehensive evaluation to verify reliability.In this study,the performance of seven different RSE models in Beijing was assessed by comparing their measurements with those of portable emission measurement systems(PEMS)and steady-state condition testing(SCT).Large variations were observed among the RSE models for CO(16%–363%),NO(2%–354%),and HC(10%–725%)emissions.Comparisons between RSE and PEMS revealed even more significant discrepancies for CO(20%–6773%),NO(15%–2818%),and HC(13%–5477%),with maximum deviations occurring at vehicle speeds between 20 and 60 km/h,reflecting the impacts of vehicle operation on emission measurements.The correlation between RSE and SCT was satisfactory for CO and NO(R² values of 0.64–0.76)but poor for HC(R²=0.16–0.22),primarily due to differences in measurement principles.Strengthening management,enhancing certification and accreditation,and regular consistency checks of RSE with SCT and PEMS are recommended for improved reliability in law enforcement applications. 展开更多
关键词 Remote sensing equipment(RSE) Air pollution Portable emission measurement systems(PEMS)
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Pre-Trained RS-VGG19 Model for Remote Sensing Natural Scene Image Classification Using Spark Distributed Framework 认领 引用
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作者 LI Zhaofei ZHANG Yijie +2 位作者 ZHAO Na LIU Guoquan ZHENG Ruiyu 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期843-857,I0003,共15页
Remote sensing image classification using deep learning methods faces challenges such as high complexity,significant computational demands,and inefficiency on resource-constrained devices,while also being affected by ... Remote sensing image classification using deep learning methods faces challenges such as high complexity,significant computational demands,and inefficiency on resource-constrained devices,while also being affected by issues like class similarity and spatial distribution.Current convolutional neural networks rely on stacking small convolutional kernels for feature learning,which results in relatively low classification accuracy,while their dependence on centralized learning architectures with high-performance GPUs/CPUs incurs substantial training costs.Therefore,this paper proposes a distributed rapid classification method for high-similarity natural scene remote sensing images using an improved VGG19 model(RS-VGG19)that combines residual connections and attention mechanisms.By introducing residual connections,the method improves training convergence speed and high-level feature learning ability,effectively preventing gradient vanishing during training.Embedding the SENet visual attention module in the tenth convolution layer allows the model to more specifically extract similar and significant features in remote sensing images.By employing a combination of cross-entropy and center loss functions,the model is able to learn features with reduced intra-class variance and increased inter-class variance,further enhancing classification accuracy.The distributed inference framework Spark is employed for decentralized model training,storing large-scale remote sensing images in the distributed file system HDFS,and accessing the pre-trained RS-VGG19 model in Docker containers on cluster nodes for distributed inference and classification using PySpark.Experimental results show that on two commonly used high-similarity remote sensing image datasets,NWPU-RESISC45 and UCMerced Land-Use,the RS-VGG19 model improves classification accuracy by 6.57%and 8.76%respectively compared to the original VGG19 model,and significantly enhances accuracy compared to other related classification models.This demonstrates the superior performance of the proposed structure and loss function fusion strategy in remote sensing image classification tasks.On the large-scale remote sensing image inference dataset NWPU-RESISC45,while maintaining classification accuracy,the distributed inference framework achieved a speedup of 11.9 when using six nodes,an improvement of 98.33%over theoretical linear speedup(6.00),reducing dependency on high-end hardware resources and significantly improving the classification speed of high-similarity natural scene remote sensing images. 展开更多
关键词 remote sensing image VGG19 residual connection SENet visual attention Spark Docker distributed classification
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A Super-Resolution Generative Adversarial Network for Remote Sensing Images Based on Improved Residual Module and Attention Mechanism 认领 引用
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作者 Yifan Zhang Yong Gan +1 位作者 Mengke Tang Xinxin Gan 《Computers, Materials & Continua》 SCIE EI 2026年第2期689-707,共19页
High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleim... High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft. 展开更多
关键词 Remote sensing imagery generative adversarial networks super-resolution enhanced residual unit selfattention mechanism
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Multi-source remote sensing unveils hydrological dynamics and drought escalation in Central Asia's arid regions 认领 引用
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作者 WANG Yuhao ZHENG Zhijie +13 位作者 ZHU Guofeng HUANG Enwei MENG Gaojia LU Siyu QIU Dongdong CHEN Longhu LI Rui JIAO Yinying ZHAO Ling QI Xiaoyu WANG Qinqin LI Wenmin MIAO Yuxin WANG Qingyang 《Journal of Geographical Sciences》 SCIE CSCD 2026年第5期1105-1129,共25页
Despite the reported“warming-wetting”trend,Central Asia faces severe water insecurity due to climate shifts and anthropogenic activities.This study integrates multi-source remote sensing data(GRACE,TRMM,MODIS)with m... Despite the reported“warming-wetting”trend,Central Asia faces severe water insecurity due to climate shifts and anthropogenic activities.This study integrates multi-source remote sensing data(GRACE,TRMM,MODIS)with machine learning to analyze drought dynamics from 2003 to 2022 using the Water Storage Deficit Index(WSDI).Results reveal significant declines in terrestrial water storage(TWS)particularly in the Tianshan Mountains(–10.20 mm/yr)and the Central Desert(–6.19 mm/yr).Drought severity has intensified since 2014,with 67%of subregions transitioning to moderate drought.Random Forest modeling indicates that drought is no longer solely climate-driven but is increasingly dominated by anthropogenic factors(GDP,urbanization,cropland),which explain over 85%of the variability.Furthermore,the WSDI outperformed traditional indices by identifying 12 major drought events linked to deep aquifer depletion—a“hidden”structural deficit often overlooked by surface-based metrics.These findings challenge the optimistic“warming-wetting”narrative,highlighting the urgent need for storage-based management strategies to address anthropogenic groundwater depletion. 展开更多
关键词 Central Asia’s arid regions drought dynamics multi-source remote sensing GRACE hydrometeorological drivers Random Forest
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DGRDet: Dynamic Gaussian Receptive Field Encoding-Based Spiking Neural Networks for Remote Sensing Object Detection 认领 引用
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作者 Li Chen Fan Zhang +3 位作者 Guangwei Xie Yanzhao Gao Xiaofeng Qi Mingqian Sun 《Computers, Materials & Continua》 SCIE EI 2026年第8期1247-1271,共25页
Remote sensing object detection aims to identify and localize specific targets in satellite or aerial imagery.Spiking Neural Networks(SNNs),benefiting from their implicit feedback-based and event-driven brain-inspired... Remote sensing object detection aims to identify and localize specific targets in satellite or aerial imagery.Spiking Neural Networks(SNNs),benefiting from their implicit feedback-based and event-driven brain-inspired dynamics,offer a promising solution to alleviate the high energy consumption of conventional ANN-based detection models.However,existing SNN-based approaches for remote sensing object detection—particularly for small,arbitrarily rotated objects—are still in their infancy and suffer from a substantial performance gap compared with ANN counterparts.In this work,we draw inspiration from the hierarchical sparse perception mechanisms of biological vision and integrate dynamic receptive field modulation into the encoding stage,proposing a high-precision spiking object detection framework tailored for remote sensing image.Specifically,we design a Hierarchical Feedback-based Gaussian Encoding(HFG)scheme,in which the parameters of Gaussian kernels are dynamically adjusted through spike-triggered top-down feedback connections.This mechanism enables the encoding process to adaptively respond to complex geometric variations of remote sensing objects,including rotation and scale changes.Based on the proposed encoding strategy,we develop DGRDet(Dynamic Gaussian Receptive Field Encoding-based Spiking Neural Networks for Remote Sensing Object Detection),a directly trained deep SNN detector for remote sensing image.Extensive evaluations on the large-scale public DOTA dataset demonstrate that DGRDet achieves competitive detection accuracy,outperforming existing SNN-based object detection methods.Moreover,compared with ANN models of comparable detection performance,DGRDet reduces spike activity by 81.31%and requires only 0.12%of the inference energy consumption,achieving a favorable balance between detection accuracy,efficiency,and energy efficiency. 展开更多
关键词 Remote sensing image object detection spiking neural networks(SNNs) hierarchical sparse dynamic gaussian encoding
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CXCL8/SDC1 axis mediates tumor stem cell interactions to drive remote transfer in thyroid cancer 认领 引用
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作者 Wenjuan Wang Jian Zhou +2 位作者 Baorui Tao Ning Kong Jie Shao 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2026年第1期142-159,共18页
This study explores the molecular mechanisms behind the remote transfer of thyroid cancer(THCA)by investigating the interaction network of C-X-C motif chemokine ligand 8+(CXCL8+monocytes and syndecan-1+(SDC1+)tumor st... This study explores the molecular mechanisms behind the remote transfer of thyroid cancer(THCA)by investigating the interaction network of C-X-C motif chemokine ligand 8+(CXCL8+monocytes and syndecan-1+(SDC1+)tumor stem cells using single-cell and spatial transcriptome sequencing.Tumor samples from THCA patients were analyzed using single-cell RNA sequencing(scRNA-seq),spatial transcriptome sequencing,and tumor tissue transcriptome analysis.Data were processed with Seurat and CellChat R packages,integrated via the SPOTlight package,and correlated with clinical data from the UCSC Xena database.Functional pathway enrichment analyses were performed using Gene Set Enrichment Analysis(GSEA),Gene Ontology(GO),and Kyoto Encyclopedia of Genes and Genome(KEGG).In vitro,a co-culture system of monocytes and THCA stem cells was developed,and protein levels were measured via enzyme-linked immunosorbent assay(ELISA)and Western blotting.The self-renewal and migration of follicular thyroid carcinoma(FTC)238-S cells were assessed through sphere formation,colony formation,Cell Counting Kit-8(CCK-8),and Transwell assays.In vivo,a subcutaneous tumor xenograft model and a lung metastasis model were established in nude mice.Transcriptomic analyses identified the CXCL8/SDC1 axis as a key mediator of Janus kinase-signal transducer and activator of transcription(JAK-STAT)signaling activation,promoting THCA stem cell self-renewal,invasion,and metastasis.CXCL8/SDC1 expression was significantly higher in the high-risk C1 subtype of THCA patients and correlated with a worse prognosis.In vitro and animal studies confirmed that the CXCL8/SDC1 axis drives tumor progression and metastasis.The interaction between CXCL8+monocytes and SDC1+tumor stem cells activates the JAK-STAT pathway,facilitating the remote transfer of THCA.Targeting the CXCL8/SDC1 axis may provide novel therapeutic strategies for improving THCA patient outcomes. 展开更多
关键词 Thyroid cancer Spatial transcriptome sequencing Single-cell transcriptome sequencing Cellular communication Tumor classification Tumor remote transfer CXCL8 Syndecan-1
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Improving adversarial robustness in remote sensing scene classification via non-local feature decoupling and soft self-attention masking recalibration 认领 引用
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作者 Shihao WU Quan PAN +2 位作者 Mingxin FU Yuan GAO Yang LI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期580-599,共20页
Despite the tremendous success of Deep Neural Networks(DNNs)in Remote Sensing(RS)scene classification,their vulnerability to adversarial examples leads to significant performance degradation,which can severely impact ... Despite the tremendous success of Deep Neural Networks(DNNs)in Remote Sensing(RS)scene classification,their vulnerability to adversarial examples leads to significant performance degradation,which can severely impact the accuracy of tasks such as RS scene classification.Therefore,it is essential to conduct a comprehensive study of the impact of adversarial attacks on RS scene classification and to develop effective defense methods to ensure the security of these tasks.In this article,we proposed a novel defense framework,named Non-local feature Decoupling and Soft-thresholding Masking(NDSM),to purify adversarial perturbations in adversarial samples.The Non-local feature Decoupling(ND)module decouples the intermediate feature map into robust and non-robust features by using denoising blocks containing non-local means operations as feature decoupling modules.The Soft-threshold Masking(SM)module further suppresses features with insignificant correlation by soft-thresholding the output of the self-attention matrix,selectively extracting valuable information for classification from non-robust features,and combining it with robust features to obtain a reconstructed robust feature map.The proposed NDSM has demonstrated its effectiveness in purifying various adversarial attacks and achieving superior defense performance on three RS classification benchmark datasets,namely UC Merced(UCM),Aerial Image Dataset(AID),and NWPU-RESISC45,when confronted with both known and unknown attacks. 展开更多
关键词 Adversarial defense Non-local feature Decoupling(ND) Remote Sensing(RS)scene classification Soft threshold Masking(SM)
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A novel method for automatic river channel morphology extraction from remote sensing satellite images:A case study of the Golmud fluvial fan 认领 引用
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作者 Shao-hua Zhao Chang-min Zhang +1 位作者 Xiang-hui Zhang Jia-le Liu 《China Geology》 CAS CSCD 2026年第2期333-348,共16页
Distributive Fluvial Systems(DFS)are critical sedimentary systems governing fluvial dynamics,sediment transport,and ecosystem sustainability in modern and ancient basins.Accurate quantification of DFS channel morpholo... Distributive Fluvial Systems(DFS)are critical sedimentary systems governing fluvial dynamics,sediment transport,and ecosystem sustainability in modern and ancient basins.Accurate quantification of DFS channel morphology is essential for advancing sedimentary modeling,optimizing water resource management,and mitigating fluvial hazards.Here,the authors present a novel automated framework that extracts DFS channel networks from remote sensing imagery by integrating multiscale image segmentation,fractal network evolution,and region-merging algorithms.Through hierarchically multiresolution feature processing,this method overcomes limitations of traditional single-scale analysis,enabling adaptive extraction while reducing segmentation heterogeneity.Specifically,the workflow consists of three stages:Image segmentation,feature extraction,and image classification.When applied to the Golmud fluvial fan(Qinghai,China),this approach achieves 90.2%overall channel extraction accuracy using 0.5 m resolution imagery,significantly outperforming traditional DEM-based(81.7%)and water spectral methods(85.4%)in resolving fine-scale channel networks.Crucially,the framework demonstrates robust adaptability to complex sedimentary environments with variable vegetation cover(<30%density)and spectral noise,providing a time-efficient,data-agnostic solution for DFS characterization. 展开更多
关键词 Channel morphology Distributive fluvial system Golmud fluvial fan Image segmentation Remote sensing imagery Ecosystem sustainability Fluvial hazards Water resource management Qinghai-Xizang Plateau
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Dual-Strategy Improvement of YOLOv11n for Multi-Scale Object Detection in Remote Sensing Images 认领 引用
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作者 Shuaiyu Zhu Sergey Ablameyko Ji Li 《Computers, Materials & Continua》 SCIE EI 2026年第8期1382-1398,共17页
Satellite remote sensing images pose significant challenges for object detection due to their high resolution,complex scenes,and large variations in target scales.To address the insufficient detection accuracy of the ... Satellite remote sensing images pose significant challenges for object detection due to their high resolution,complex scenes,and large variations in target scales.To address the insufficient detection accuracy of the YOLOv11n model in remote sensing imagery,this paper proposes two improvement strategies.Method 1:(a)a Large Separable Kernel Attention(LSKA)mechanism is introduced into the backbone network to enhance feature extraction for small objects;(b)a Gold-YOLO structure is incorporated into the neck network to achieve multi-scale feature fusion,thereby improving the detection performance of objects at different scales.Method 2:(a)the Gold-YOLO structure is also integrated into the neck network;(b)a MultiSEAMHead detection head is combined to further strengthen the representation and detection capability for small and multi-scale objects.To verify the effectiveness of the proposed improvements,experiments are conducted on the DOTAv1 dataset.The results show that,while maintaining the lightweight advantage of the model,the proposed methods improve detection accuracy(mAP@0.5)by 1.3%and 1.8%,respectively,compared with the baseline YOLOv11n,demonstrating the effectiveness and practical value of the proposed approaches for object detection in remote sensing images. 展开更多
关键词 Remote sensing imagery YOLOv11n multi-scale object detection lightweight deep learning attention mechanism feature fusion
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An Overview of Remote Sensing of Agricultural Greenhouses:Advances and Perspectives 认领 引用
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作者 GAO Yuan ZHU Bingxue SONG Kaishan 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第2期171-190,共20页
Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisiti... Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas. 展开更多
关键词 agricultural greenhouse(AGH) remote sensing deep learning precision agriculture time-series analysis
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High Resolution Remote Sensing Image Segmentation Method with Improved DeepLabv3+ 认领 引用 被引量：1
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作者 TAO Hongjie LI Zhaofei +2 位作者 QI Fei CHEN Jingjue ZHOU Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期348-358,共11页
In order to address the challenges associated with poor semantic segmentation results of classical semantic segmentation networks in high-resolution remote sensing images,limited performance in complex scenes,a large ... In order to address the challenges associated with poor semantic segmentation results of classical semantic segmentation networks in high-resolution remote sensing images,limited performance in complex scenes,a large number of network parameters,and high training costs,this study proposes an efficient segmentation method for high-resolution remote sensing images based on an improved DeepLabv3+approach.The method focuses on three key aspects:reducing the number of network parameters,minimizing computation volume,and enhancing performance.First,the proposed method replaces the original DeepLabv3+backbone network Xception,which is computationally heavy,with the lighter MobileNetV2 network for feature extraction.This substitution helps reduce the number of network parameters while maintaining effective feature extraction.Second,a lightweight convolutional block attention module(CBAM)is added after the feature extraction module to enhance the network’s feature extraction capability.The inclusion of CBAM further reduces the number of network parameters.Last,coordinate attention is introduced after the shallow features obtained from the feature extraction module.This addition allows the network to focus more on relevant features in the image,while disregarding irrelevant background information.Experimental results demonstrate the effectiveness of the proposed method.In the segmentation task of the high-resolution image dataset,the method achieves a mean intersection over union(mIoU)of 75.33%.This result surpasses mainstream semantic segmentation networks such as SegNet,PSPNet,and U-Net by 12.49%,3.16%,and 1.62%respectively.Furthermore,the proposed model has a relatively low number of network parameters,with only 6.02×106 parameters,and a computation volume of 26.45 GFLOPs.This balance between computational efficiency and segmentation accuracy makes the model highly valuable for edge computing applications. 展开更多
关键词 remote sensing image DeepLabv3+ MobileNetV2 attention mechanism semantic segmentation
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Using historical remote sensing images for detailed tectonic geomorphological interpretation in the study of active faults:Application to the Xiaojiang fault case study 认领 引用
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作者 Xingao Li Zhongtai He +1 位作者 Long Guo Linlin Li 《Earthquake Research Advances》 CAS CSCD 2026年第1期38-50,共13页
The northern section of the Xiaojiang fault is the most active section in the Xiaojiang Fault Zone,and a detailed interpretation of this fault is highly important.In this work,KeyHole-4B images and Landsat 8 images of... The northern section of the Xiaojiang fault is the most active section in the Xiaojiang Fault Zone,and a detailed interpretation of this fault is highly important.In this work,KeyHole-4B images and Landsat 8 images of the northern section of the Xiaojiang fault were collected,and remote sensing interpretation and tectonic geomorphological analysis of the northern section of the Xiaojiang fault were carried out to obtain a more detailed fault distribution.The results reveal that the northern section of the Xiaojiang fault is a group of faults that are subparallel to each other with a space of 2–4 km.The fault is located along the Jinshajiang Valley and the Xiaojiang Valley.At the same time,we counted the large-scale left-lateral dislocations of the gullies and ridges.Combined with the results of previous studies,the long-term average slip rate of the northern section of the Xiaojiang fault is 6.2±1.1 mm/a since the late Middle Pleistocene,11.4±2.8 mm/a since the middle of the late Pleistocene,and 8.0±2.0 mm/a since the middle and late Pleistocene.The high slip rate in the northern section of the Xiaojiang fault represents the response of the local strain of the central Yunnan subblock,which rotates clockwise along the boundary fault.This finding is consistent with the pattern of northwards and north-eastwards thrusting of the Indian plate,leading to eastwards extrusion and the escape of material from the Qinghai-Xizang Plateau. 展开更多
关键词 Historical remote sensing image Remote sensing interpretation Xiaojiang fault Active fault
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MKS-YOLO:A Multi-kernel Dynamic Selection Network for Object Detection in Remote Sensing Images 认领 引用
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作者 Shengfeng Guo Xiaowei He +3 位作者 Zhonglong Zheng Dawei Zhang Xuedong He Wenpeng Xia 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1918-1931,共14页
Object detection in remote sensing images presents numerous challenges,such as significant variations in object scale and pronounced differences in aspect ratios.Current mainstream approaches typically employ large co... Object detection in remote sensing images presents numerous challenges,such as significant variations in object scale and pronounced differences in aspect ratios.Current mainstream approaches typically employ large convolutional kernels or multi-scale convolutional architectures to address these challenges.However,large convolutional kernels tend to introduce substantial background noise,while the use of kernels with varying scales may lead to feature information redundancy.To tackle these issues,we propose a robust and efficient rotation-aware object detection method-MKS-YOLO.MKS-YOLO explicitly decouples high-and low-informative features through an information-aware threshold gating mechanism,effectively suppressing background noise interference.Furthermore,to reduce noise and enhance the model’s ability to perceive objects at multiple scales,we design a Multi-Kernel Dynamic Selection Module(MKSM).This module integrates multi-scale convolutional kernels and introduces a spatial attention mechanism to achieve dynamic weighted feature fusion.In addition,we incorporate a Contextual Large Kernel Attention(CLKA)mechanism to enhance feature representation capabilities for objects with high aspect ratios.Moreover,during the feature extraction stage,we introduce an Attention-based Internal Feature Interaction module(AIFI),which further strengthens the correlations among deep semantic features.To validate the effectiveness of the proposed method,we conduct extensive experiments on two widely used remote sensing image datasets:DOTA-v1.0 and DIOR-R.Experimental results demonstrate that MKS-YOLO achieves state-of-the-art detection accuracy,with Mean Average Precision(mAP)reaching 77.71%and 82.50%,respectively,fully validating the superior performance of the proposed method. 展开更多
关键词 Object detection Detectors Remote sensing Dynamic selection network
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