The mechanistic target of rapamycin(m TOR) is a serine/threonine kinase that plays a pivotal role in cellular growth, proliferation, survival, and metabolism. In the central nervous system(CNS), the mTOR pathway regul...The mechanistic target of rapamycin(m TOR) is a serine/threonine kinase that plays a pivotal role in cellular growth, proliferation, survival, and metabolism. In the central nervous system(CNS), the mTOR pathway regulates diverse aspects of neural development and function. Genetic mutations within the m TOR pathway lead to severe neurodevelopmental disorders, collectively known as “mTORopathies”(Crino, 2020). Dysfunctions of m TOR, including both its hyperactivation and hypoactivation, have also been implicated in a wide spectrum of other neurodevelopmental and neurodegenerative conditions, highlighting its importance in CNS health.展开更多
First,statistics on the operational lines and mileage of urban rail transit in China are conducted.The results show that,as of Dec.31,2025,there were 60 cities with urban rail transit in operation nationwide,with a to...First,statistics on the operational lines and mileage of urban rail transit in China are conducted.The results show that,as of Dec.31,2025,there were 60 cities with urban rail transit in operation nationwide,with a total operational mileage of approximately 12837.8 km(excluding the electronic guideway rubber-tired system,there were 57 cities,with a total operational mileage of 12651.6 km).The metro system dominates,while low-capacity systems exhibit a multi-modal development pattern.Subsequently,the characteristics of China′s urban rail transit industry development are analyzed,indicating that:(1)It should closely align with the theme of urban intensive development,promote quality improvement and efficiency enhancement of existing lines,and focus on the supporting role of initial passenger flow for new line construction,multi-network integration,and economic and financial sustainability.(2)Significant innovative achievements have been made in safety resilience,green and low-carbon development,intelligent construction,and digital transformation.Finally,development recommendations for the"15th Five-Year Plan"period are proposed:promoting cost reduction and efficiency improvement in the rail transit industry,enhancing the operational efficiency of existing networks,continuously exploring railway services for urban commuting,strengthening external exchanges,and driving the"going global"strategy of the urban rail transit industry.展开更多
The United Nations Sustainable Development Goal(SDG) 2 aims to achieve Zero Hunger by 2030.However,global hunger and food insecurity have continued to rise at an alarming rate(UN 2023).Subtropical regions are home to ...The United Nations Sustainable Development Goal(SDG) 2 aims to achieve Zero Hunger by 2030.However,global hunger and food insecurity have continued to rise at an alarming rate(UN 2023).Subtropical regions are home to more than 30% of the world's population,predominantly in developing countries where per capita farmland and food supply are only 40% of those in developed nations(FAO 2018).Meeting the Zero Hunger target amid ongoing population growth in these regions requires a substantial increase in agricultural production while minimizing soil degradation and adverse ecological impacts.This challenge is shared by many countries across South Asia,Africa,and Central and South America.展开更多
Innovation and Development Policy,简称“IDP”,中文名:《创新与发展政策(英文)》,是由中国科学院主管,中国科学院科技战略咨询研究院、中国科学学与科技政策研究会共同主办,国内外发行(ISSN:2096-5141,CN:10-1513/D)的英文国际期刊...Innovation and Development Policy,简称“IDP”,中文名:《创新与发展政策(英文)》,是由中国科学院主管,中国科学院科技战略咨询研究院、中国科学学与科技政策研究会共同主办,国内外发行(ISSN:2096-5141,CN:10-1513/D)的英文国际期刊。目前期刊每年出版两期。已发表论文在期刊网站可以自由获取,http://idp-journal.casisd.cn/browse/al/al2019/。展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the backgroun...Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.展开更多
Standards are the common language that consolidates global consensus and builds the most solid foundation for international partnerships.They are the cornerstone for global sustainable and high-quality development.You...Standards are the common language that consolidates global consensus and builds the most solid foundation for international partnerships.They are the cornerstone for global sustainable and high-quality development.Young students,with their active and vibrant minds,represent the future and hope of standardization.展开更多
China is carving out a distinctive development path which features urban-rural integration.This approach has not only yielded tangible results domestically but also drawn the attention of other countries.
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
Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet th...Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.展开更多
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To ...Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.展开更多
To advance intelligent construction,standards must come first.The Ministry of Housing and Urban-Rural Development has issued the List for Replicable Experience and Practices for Developing Intelligent Construction fou...To advance intelligent construction,standards must come first.The Ministry of Housing and Urban-Rural Development has issued the List for Replicable Experience and Practices for Developing Intelligent Construction four times successively and the Technical Guidelines for Intelligent Construction(Trial).展开更多
Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric n...Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.展开更多
The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and dispos...The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines.展开更多
China aims to build on its achievements of the past five years to accelerate rural revitalization during the 15th Five-Year Plan period.IN China,issues relating to agriculture,rural areas,and rural residents have alwa...China aims to build on its achievements of the past five years to accelerate rural revitalization during the 15th Five-Year Plan period.IN China,issues relating to agriculture,rural areas,and rural residents have always been a priority in national development.At its fourth plenary session held in October 2025,the 20th Central Committee of the Communist Party of China(CPC)once again emphasized the need to accelerate agricultural and rural modernization,and advance all-around rural revitalization over the next five years.展开更多
Against the backdrop of China s accelerated green development and its pursuit of the"dual carbon"goals,green finance,as a crucial instrument for the rational allocation of resources,plays a significant role ...Against the backdrop of China s accelerated green development and its pursuit of the"dual carbon"goals,green finance,as a crucial instrument for the rational allocation of resources,plays a significant role in promoting agricultural green transformation.Taking Henan Province as a case study,this research employs panel data from 18 prefecture-level cities spanning 2014 to 2023,utilizing dynamic panel models and mediating effect models for empirical analysis.The findings reveal that green finance indirectly fosters agricultural green development by driving agricultural technological progress.This promoting effect is significant,with the mediating effect accounting for 3.67%.Based on these results,policy recommendations are proposed:expanding the coverage of green finance and constructing a diversified product system;comprehensively promoting agricultural technological innovation and mechanization development;and coordinating and integrating green financial resources to facilitate agricultural green transformation tailored to local conditions.展开更多
Neurodevelopmental processes represent a finely tuned interplay between genetic and environmental factors,shaping the dynamic landscape of the developing brain.A major component of the developing brain that enables th...Neurodevelopmental processes represent a finely tuned interplay between genetic and environmental factors,shaping the dynamic landscape of the developing brain.A major component of the developing brain that enables this dynamic is the white matter(WM),known to be affected in neurodevelopmental disorders(NDDs)(Rokach et al.,2024).WM formation is mediated by myelination,a multifactorial process driven by neuro-glia interactions dependent on proper neuronal functionality(Simons and Trajkovic,2006).Another key aspect of neurodevelopmental abnormalities involves neuronal dynamics and function,with recent advances significantly enhancing our understanding of both neuronal and glial mitochondrial function(Devine and Kittler,2018;Rojas-Charry et al.,2021).Energy homeostasis in neurons,attributed largely to mitochondrial function,is critical for proper functionality and interactions with oligodendrocytes(OLs),the cells forming myelin in the brain’s WM.We herein discuss the interplay between these processes and speculate on potential dysfunction in NDDs.展开更多
Environmental DNA(eDNA)technology has revolutionized biodiversity monitoring with its non-invasive,sensitive,and cost-efficient approach.This paper systematically reviews eDNA advancements,examining its applications i...Environmental DNA(eDNA)technology has revolutionized biodiversity monitoring with its non-invasive,sensitive,and cost-efficient approach.This paper systematically reviews eDNA advancements,examining its applications in aquatic and terrestrial ecosystems and assessing China’s standardization progress.It delineates four developmental phases from single-species detection to high-throughput sequencing,and highlights China’s contribution to the development of technical standards.While significant progress has been made,challenges persist in quantitative accuracy,methodological consistency,and large-scale implementation.Future efforts should prioritize enhanced standardization,improved quantification techniques,broader applications,and international collaboration to drive innovation in eDNA technology.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
基金supported by grants from Simons Foundation (SFARI 479754),CIHR (PJT-180565)the Scottish Rite Charitable Foundation of Canada (to YL)funding from the Canada Research Chairs program。
文摘The mechanistic target of rapamycin(m TOR) is a serine/threonine kinase that plays a pivotal role in cellular growth, proliferation, survival, and metabolism. In the central nervous system(CNS), the mTOR pathway regulates diverse aspects of neural development and function. Genetic mutations within the m TOR pathway lead to severe neurodevelopmental disorders, collectively known as “mTORopathies”(Crino, 2020). Dysfunctions of m TOR, including both its hyperactivation and hypoactivation, have also been implicated in a wide spectrum of other neurodevelopmental and neurodegenerative conditions, highlighting its importance in CNS health.
文摘First,statistics on the operational lines and mileage of urban rail transit in China are conducted.The results show that,as of Dec.31,2025,there were 60 cities with urban rail transit in operation nationwide,with a total operational mileage of approximately 12837.8 km(excluding the electronic guideway rubber-tired system,there were 57 cities,with a total operational mileage of 12651.6 km).The metro system dominates,while low-capacity systems exhibit a multi-modal development pattern.Subsequently,the characteristics of China′s urban rail transit industry development are analyzed,indicating that:(1)It should closely align with the theme of urban intensive development,promote quality improvement and efficiency enhancement of existing lines,and focus on the supporting role of initial passenger flow for new line construction,multi-network integration,and economic and financial sustainability.(2)Significant innovative achievements have been made in safety resilience,green and low-carbon development,intelligent construction,and digital transformation.Finally,development recommendations for the"15th Five-Year Plan"period are proposed:promoting cost reduction and efficiency improvement in the rail transit industry,enhancing the operational efficiency of existing networks,continuously exploring railway services for urban commuting,strengthening external exchanges,and driving the"going global"strategy of the urban rail transit industry.
文摘The United Nations Sustainable Development Goal(SDG) 2 aims to achieve Zero Hunger by 2030.However,global hunger and food insecurity have continued to rise at an alarming rate(UN 2023).Subtropical regions are home to more than 30% of the world's population,predominantly in developing countries where per capita farmland and food supply are only 40% of those in developed nations(FAO 2018).Meeting the Zero Hunger target amid ongoing population growth in these regions requires a substantial increase in agricultural production while minimizing soil degradation and adverse ecological impacts.This challenge is shared by many countries across South Asia,Africa,and Central and South America.
文摘Innovation and Development Policy,简称“IDP”,中文名:《创新与发展政策(英文)》,是由中国科学院主管,中国科学院科技战略咨询研究院、中国科学学与科技政策研究会共同主办,国内外发行(ISSN:2096-5141,CN:10-1513/D)的英文国际期刊。目前期刊每年出版两期。已发表论文在期刊网站可以自由获取,http://idp-journal.casisd.cn/browse/al/al2019/。
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金financially supported byChongqingUniversity of Technology Graduate Innovation Foundation(Grant No.gzlcx20253267).
文摘Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.
文摘Standards are the common language that consolidates global consensus and builds the most solid foundation for international partnerships.They are the cornerstone for global sustainable and high-quality development.Young students,with their active and vibrant minds,represent the future and hope of standardization.
文摘China is carving out a distinctive development path which features urban-rural integration.This approach has not only yielded tangible results domestically but also drawn the attention of other countries.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘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.
基金Tianmin Tianyuan Boutique Vegetable Industry Technology Service Station(Grant No.2024120011003081)Development of Environmental Monitoring and Traceability System for Wuqing Agricultural Production Areas(Grant No.2024120011001866)。
文摘Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
文摘Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.
文摘To advance intelligent construction,standards must come first.The Ministry of Housing and Urban-Rural Development has issued the List for Replicable Experience and Practices for Developing Intelligent Construction four times successively and the Technical Guidelines for Intelligent Construction(Trial).
基金financial support from the National Key Research and Development Program of China(2022YFB3804905)National Natural Science Foundation of China(22375047,22378068,and 22378071)+1 种基金Natural Science Foundation of Fujian Province(2022J01568)111 Project(No.D17005).
文摘Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.
基金Project(2022YFC2904103)supported by the National Key Research and Development Program of ChinaProjects(52374112,52274108)supported by the National Natural Science Foundation of China+1 种基金Projects(BX20220036,BX20230041)supported by the Postdoctoral Innovation Talents Support Program,ChinaProject(2232080)supported by the Beijing Natural Science Foundation,China。
文摘The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines.
文摘China aims to build on its achievements of the past five years to accelerate rural revitalization during the 15th Five-Year Plan period.IN China,issues relating to agriculture,rural areas,and rural residents have always been a priority in national development.At its fourth plenary session held in October 2025,the 20th Central Committee of the Communist Party of China(CPC)once again emphasized the need to accelerate agricultural and rural modernization,and advance all-around rural revitalization over the next five years.
基金Supported by Zhejiang Provincial Philosophy and Social Sciences Planning Project(23FNSQ58YB).
文摘Against the backdrop of China s accelerated green development and its pursuit of the"dual carbon"goals,green finance,as a crucial instrument for the rational allocation of resources,plays a significant role in promoting agricultural green transformation.Taking Henan Province as a case study,this research employs panel data from 18 prefecture-level cities spanning 2014 to 2023,utilizing dynamic panel models and mediating effect models for empirical analysis.The findings reveal that green finance indirectly fosters agricultural green development by driving agricultural technological progress.This promoting effect is significant,with the mediating effect accounting for 3.67%.Based on these results,policy recommendations are proposed:expanding the coverage of green finance and constructing a diversified product system;comprehensively promoting agricultural technological innovation and mechanization development;and coordinating and integrating green financial resources to facilitate agricultural green transformation tailored to local conditions.
文摘Neurodevelopmental processes represent a finely tuned interplay between genetic and environmental factors,shaping the dynamic landscape of the developing brain.A major component of the developing brain that enables this dynamic is the white matter(WM),known to be affected in neurodevelopmental disorders(NDDs)(Rokach et al.,2024).WM formation is mediated by myelination,a multifactorial process driven by neuro-glia interactions dependent on proper neuronal functionality(Simons and Trajkovic,2006).Another key aspect of neurodevelopmental abnormalities involves neuronal dynamics and function,with recent advances significantly enhancing our understanding of both neuronal and glial mitochondrial function(Devine and Kittler,2018;Rojas-Charry et al.,2021).Energy homeostasis in neurons,attributed largely to mitochondrial function,is critical for proper functionality and interactions with oligodendrocytes(OLs),the cells forming myelin in the brain’s WM.We herein discuss the interplay between these processes and speculate on potential dysfunction in NDDs.
基金supported by the National Natural Science Foundation of China(Grant No.32160172)the Key Science-Technology Project of Inner Mongolia(2023KYPT0010)+1 种基金the Natural Science Foundation of Inner Mongolia Autonomous Region of China(Grant No.2025QN03006)the 2023 Inner Mongolia Public Institution High-Level Talent Introduction Scientific Research Support Project.
文摘Environmental DNA(eDNA)technology has revolutionized biodiversity monitoring with its non-invasive,sensitive,and cost-efficient approach.This paper systematically reviews eDNA advancements,examining its applications in aquatic and terrestrial ecosystems and assessing China’s standardization progress.It delineates four developmental phases from single-species detection to high-throughput sequencing,and highlights China’s contribution to the development of technical standards.While significant progress has been made,challenges persist in quantitative accuracy,methodological consistency,and large-scale implementation.Future efforts should prioritize enhanced standardization,improved quantification techniques,broader applications,and international collaboration to drive innovation in eDNA technology.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
