The sixth generation(6G)of mobile communication system is witnessing a new paradigm shift,i.e.,integrated sensing-communication system.A comprehensive dataset is a prerequisite for 6G integrated sensing-communication ...The sixth generation(6G)of mobile communication system is witnessing a new paradigm shift,i.e.,integrated sensing-communication system.A comprehensive dataset is a prerequisite for 6G integrated sensing-communication research.This paper develops a novel simulation dataset,named M3SC,for mixed multi-modal(MMM)sensing-communication integration,and the generation framework of the M3SC dataset is further given.To obtain multimodal sensory data in physical space and communication data in electromagnetic space,we utilize Air-Sim and WaveFarer to collect multi-modal sensory data and exploit Wireless InSite to collect communication data.Furthermore,the in-depth integration and precise alignment of AirSim,WaveFarer,andWireless InSite are achieved.The M3SC dataset covers various weather conditions,multiplex frequency bands,and different times of the day.Currently,the M3SC dataset contains 1500 snapshots,including 80 RGB images,160 depth maps,80 LiDAR point clouds,256 sets of mmWave waveforms with 8 radar point clouds,and 72 channel impulse response(CIR)matrices per snapshot,thus totaling 120,000 RGB images,240,000 depth maps,120,000 LiDAR point clouds,384,000 sets of mmWave waveforms with 12,000 radar point clouds,and 108,000 CIR matrices.The data processing result presents the multi-modal sensory information and communication channel statistical properties.Finally,the MMM sensing-communication application,which can be supported by the M3SC dataset,is discussed.展开更多
Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combini...Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.展开更多
Autism spectrum disorder(AsD)is a highly heterogeneous neurodevelopmental disorder.Early diagnosis and intervention are crucial for improving outcomes.Traditional single-modality diagnostic methods are subjective,limi...Autism spectrum disorder(AsD)is a highly heterogeneous neurodevelopmental disorder.Early diagnosis and intervention are crucial for improving outcomes.Traditional single-modality diagnostic methods are subjective,limited,and struggle to reveal the underlying pathological mechanisms.In contrast,multimodal data analysis integrates behavioral,physiological,and neuroimaging information with advanced machine-learning and deeplearning algorithms to overcome these limitations.In this review,we surveyed the recent pediatric AsD literature,highlighting artificial intelligence-driven diagnostic techniques,multimodal data fusion strategies,and emerging trends in ASD assessment.We surveyed studies that integrated two or more modalities and summarized the fusion levels,learning paradigms,tasks,datasets,and metrics.Multimodal approaches outperform singlemodality baselines in classification,severity estimation,and subtyping by leveraging complementary information and reducing modality-specific biases.Multimodal approaches significantly enhance diagnostic accuracy and comprehensiveness,enabling early screening of AsD,symptom subtyping,severity assessment,and personalized interventions.Advances in multimodal fusion techniques have promoted progress in precision medicine for the treatment of ASD.展开更多
Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to instal...Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.展开更多
In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing method...In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions.To address these issues,we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition.A dynamic gating mechanism is applied across unimodal encoding,cross-modal alignment,and emotion transfer modeling,substantially improving noise robustness and feature alignment.First,we construct a unimodal encoder based on gated recurrent units and feature-selection gating to suppress intra-modal noise and enhance contextual representation.Second,we design a gated-attention crossmodal encoder that dynamically calibrates the complementary contributions of visual and audio modalities to the dominant textual features and eliminates redundant information.Finally,we introduce a gated enhanced emotion transfer module that explicitly models the temporal dependence of emotional evolution in dialogues via transfer gating and optimizes continuity modeling with a comparative learning loss.Experimental results demonstrate that the proposed method outperforms state-of-the-art models on the public MELD and IEMOCAP datasets.展开更多
Azobenzene-winged phenanthrolines(L1 and L2)were designed,synthesized,and fully characterized.Ligand L1 forms an in-situ cobalt complex,which has been effectively employed as a circular dichroism(CD)-active chiral sen...Azobenzene-winged phenanthrolines(L1 and L2)were designed,synthesized,and fully characterized.Ligand L1 forms an in-situ cobalt complex,which has been effectively employed as a circular dichroism(CD)-active chiral sensor.The resulting ternary complex(L1-Co^(2+)-amino alcohol)exhibits pronounced exciton-coupled circular dichroism(ECCD)signals at the characteristic azobenzene absorption bands.These signals arise from efficient chirality transfer from the chiral amino alcohol to the azobenzene chromophores,enabling the determination of the absolute configuration of chiral amino alcohols.Accordingly,the L1-Co^(2+)coordination system demonstrates considerably potential in chirality sensing applications.Remarkably,the induced ECCD signals are highly responsive to multiple external stimuli,including photoirradiation,solvent polarity,temperature,and redox conditions.In particular,temperature and redox changes can induce a reversible inversion of the ECCD signal,thereby establishing this system as a multifunctional,stimuli-responsive chiroptical molecular switch.展开更多
The rapid advancement of flexible electronics technology has placed higher demands on the structural design and performance regulation of elastic materials.Cellulosic elastomers,with their biodegradability,renewabilit...The rapid advancement of flexible electronics technology has placed higher demands on the structural design and performance regulation of elastic materials.Cellulosic elastomers,with their biodegradability,renewability,and tunability,emerge as ideal candidate materials.Entropy-driven self-as sembly promotes the spontaneous formation of ordered structures,serving as a crucial pathway for optimizing cellulose elastomer properties.However,the structure-property relationship between the self-assembled ordered structures of cellulose elastomers and their mechanical and electrical properties remains insufficiently explored.It hinders the expansion of their applications in electronic devices.This paper systematically reviews the structure-property regulation mechanisms of self-assembled cellulosic elastomers from an entropy-driven perspective.It elucidates the application principles and performance optimization strategies for mechanical energy harvesting and self-powered sensing,while also exploring the challenges and prospects for performance enhancement.This work provides a reference for the development of self-assembled cellulosic elastomers in the field of energy devices.展开更多
The demand for sensors capable of operating in extreme environment of the fields,such as aerospace vehicles,aeroengines and fire protection,is rapidly increasing.However,developing flexible ceramic fibrous pressure se...The demand for sensors capable of operating in extreme environment of the fields,such as aerospace vehicles,aeroengines and fire protection,is rapidly increasing.However,developing flexible ceramic fibrous pressure sensors that combine high temperature stability with robust mechanical properties remains a significant challenge.Herein,through precise multi-scale process control,high-strength(2.1 MPa)TiC-SiC flexible fibrous membrane is successfully fabricated.The membrane exhibits exceptional thermal resistance(2000℃)and long–term thermal stability(1800℃ for 5 h)in the inert atmosphere.Meanwhile,the TiC-SiC fibrous membrane shows excellent oxidation resistance and still achieves strength of 1.8 MPa after being oxidized at 1200℃ for 1 h in air.Remarkably,TiC-SiC fibrous membrane withstands a load of approximately 1400 times its own weight and the ablation of butane flame(~1300℃)for at least 1 h without breaking.Notably,after heat treatment at 1800℃ for 5 h in an argon atmosphere,the TiC-SiC fibrous membrane even sustains pressure–sensing performance for up to 300 cycles.The membrane exhibits stable resistivity up to 900℃ and shows sensing stability under butane flame.The results of this work provide an effective and feasible solution to fill the research gap of flexible fibrous sensors for extreme environments.展开更多
Background Zearalenone(ZEN),a common mycotoxin in ruminant diets,could disturb the rumen ecosystem and impair rumen fermentation.Noticeably,ZEN has been shown to reduce the relative abundances of specific bacterial ta...Background Zearalenone(ZEN),a common mycotoxin in ruminant diets,could disturb the rumen ecosystem and impair rumen fermentation.Noticeably,ZEN has been shown to reduce the relative abundances of specific bacterial taxa that potentially possess quorum sensing(QS)functions,which are deemed essential for the microbial interactions and adaptations during rumen fermentation.Nonetheless,whether QS communications participate in the responses of rumen microbial fermentation to ZEN remains unknown.Therefore,the present trial was performed to explore the potential roles of QS during the alterations of rumen microbial fermentation by ZEN through a rumen simulation technique(RUSITEC)system,in a replicated 4×4 Latin square design.Results ZEN significantly(P<0.05)reduced QS signal autoinducer-2(AI-2),and tended to(P=0.051)downregulate QS signal C4-homoserine lactone(HSL).ZEN also significantly(P<0.05)decreased total volatile fatty acid(TVFA),acetate,propionate,isobutyrate,isovalerate,organic matter disappearance(OMD),neutral detergent fiber disappearance(NDFD),and acid detergent fiber disappearance(ADFD)in different manners.The linear discriminant analysis effect size(LEf Se)analysis indicated significantly(P<0.05)differential enrichments of a series of bacterial taxa such as Butyrivibrio_sp_X503,Rhizobium daejeonense,Hoylesella buccalis,Ezakiella coagulans,Enterococcus cecorum,Ruminococcus_sp_zg-924,Polystyrenella longa,and Methylacidimicrobium fagopyrum across different treatments.The phylogenetic investigation of communities by reconstruction of unobserved states 2(PICRUSt2)analysis suggested that QS were predicted to be significantly(P<0.05)affected by ZEN.The metabolomics analysis detected considerable significantly(P<0.05)differing metabolites and implied that ZEN challenge significantly(P<0.05)influenced the indole alkaloid biosynthesis,biosynthesis of alkaloids derived from shikimate pathway,and sesquiterpenoid and triterpenoid biosynthesis.Significant(P<0.05)interconnections of QS molecules with the differential rumen fermentation traits,differential bacterial taxa,and differential metabolites were exhibited by Spearman analysis.Conclusions ZEN negatively affected the QS signals of AI-2 and C4-HSL,which was found to correlate with the fluctuations in specific rumen fermentation characteristics,ruminal bacterial populations,and ruminal metabolisms.These interrelationships implied the potential involvement of QS in the reactions of rumen microbiota to ZEN contamination,and probably contributed to the inhibition of rumen fermentation.展开更多
Interferometry is a crucial investigative technique used across diverse fields to achieve highprecision measurements.It works by analyzing the phase difference between two interfering waves,which results from variatio...Interferometry is a crucial investigative technique used across diverse fields to achieve highprecision measurements.It works by analyzing the phase difference between two interfering waves,which results from variations in optical path lengths within an interferometer.We introduce a novel method for directly measuring changes in the phase difference within an optical interferometer,importantly,with the added advantage of a controllable enhancement factor.This approach is achieved through a two-step process:first,the optical phase difference is encoded into a sub-GHz radiofrequency(RF)signal using microwave-photonic manipulation;then,RF interferometry-assisted phase amplification is implemented at the destructive interference point.In our experiments,we demonstrate a phase sensitivity of 2.14 rad∕nm operating at 140 MHz using a miniature in-fiber Fabry-Pérot interferometer for sub-nanometer displacement sensing,which reveals a sensitivity magnification factor of 258.6.With further refinement,we anticipate that even higher enhancement factors can be achieved,paving the way for the development of cost-effective,ultrasensitive interferometry-based instruments for high-precision optical measurements.展开更多
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
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
Point-of-care diagnostics and inline quantitative phase imaging(QPI)drive the demand for portable,ultra-miniaturized,and robust optical imaging and metrology systems.We propose and demonstrate a wavefront sensor integ...Point-of-care diagnostics and inline quantitative phase imaging(QPI)drive the demand for portable,ultra-miniaturized,and robust optical imaging and metrology systems.We propose and demonstrate a wavefront sensor integrated into a photonic integrated circuit,enabling single-shot optical phase retrieval.We implemented an integrated wavefront sensor array with a spatial resolution of 17μm and a numerical aperture of 0.1.Furthermore,we experimentally demonstrated the reconstruction of wavefronts defined by Zernike polynomials,specifically the first 14 terms(Z_(1)to Z_(14)),achieving an average root mean square error below 0.07.This advancement paves the way for fully integrated,portable,and robust optical imaging systems,facilitating integrated wavefront sensors in demanding applications such as point-of-care diagnostics,endoscopy,in situ QPI,and inline surface profile measurement.展开更多
As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy...As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.展开更多
Shallow landslides are strongly controlled by near-surface lithological variability,yet conventional geological maps are often too generalized to support accurate susceptibility assessment in complex terrains.This rev...Shallow landslides are strongly controlled by near-surface lithological variability,yet conventional geological maps are often too generalized to support accurate susceptibility assessment in complex terrains.This review synthesizes recent advances in remote sensing–based lithological mapping and evaluates their integration into landslide susceptibility modeling.Evidence from the literature indicates that remote sensing-derived lithological products,particularly those incorporating mineralogical information and higher spatial resolution,consistently outperform traditional geological maps in improving model accuracy and spatial detail,especially in heterogeneous environments.However,key challenges remain,including scale mismatches between surface observations and subsurface controls,limited ground validation,uncertainty propagation,and restricted model transferability across regions.The review identifies multi-sensor data fusion and explainable machine learning as the most promising directions for advancing lithological discrimination and model reliability.Future progress depends on integrating remote sensing with process-based understanding,improving validation strategies,and standardizing uncertainty reporting.These developments are essential for enabling more robust,scalable,and operationally relevant landslide susceptibility assessments in complex terrains.Lastly,we describe the directions of research that focus on multi-sensor fusion,explainable machine learning,UAV(Unmanned Aerial Vehicle)-enabled validation,and standardized uncertainty reporting that can help articulate landslide susceptibility assessment,making them even more robust and operationally significant.展开更多
Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targ...Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016).展开更多
A multi-stage stress relaxation test was performed on a granodiorite sample to understand the deformation process prior to the macroscopic failure of brittle rocks,as well as the transient response during stress relax...A multi-stage stress relaxation test was performed on a granodiorite sample to understand the deformation process prior to the macroscopic failure of brittle rocks,as well as the transient response during stress relaxation.Distributed optical fiber sensing was used to measure strains across the sample surface by helically wrapping the single-mode fiber around the cylindrical sample.Close agreement was observed between the circumferential strains obtained from the optical fibers and the extensometer.The reconstructed full-field strain contours show strain heterogeneity from the crack closure phase,and the strains in the later deformation phase are dominantly localized within the former high-strain zone.The Gini coefficient was used to quantify the degree of strain localization and shows an initial increase during the crack closure phase,a decrease during the linear elastic phase,and a subsequent increase during the post-yielding phase.This behavior corresponds to a process of initial localization from an imperfect boundary condition,homogenization,and eventual relocalization prior to the macroscopic failure of the sample.The transient strain rate decay during the stress relaxation phase was quantified using the p-value in the“Omori-like"power law function.A higher initial stress at the onset of relaxation results in a lower p-value,indicating a slower strain rate decay.As the sample approaches macroscopic failure,the lowest p-value shifts from the most damaged zone to adjacent areas,suggesting stress redistribution or crack propagation in deformed crystalline rocks under stress relaxation conditions.展开更多
Climate change is rapidly altering hydrological systems through changes in precipitation patterns,increase the rate of glacier retreat rates,altered snow dynamics,and groundwater stress.Although remote sensing has bee...Climate change is rapidly altering hydrological systems through changes in precipitation patterns,increase the rate of glacier retreat rates,altered snow dynamics,and groundwater stress.Although remote sensing has been extensively deployed in hydrological research,existing reviews typically focus on a single hydrological variable or on particular satellite missions.The review synthesizes remote sensing technologies to monitor climate-related hydrological variations across various components of the water cycle.It is a systematic examination of major satellite missions,sensor technologies,and analytical methods used to monitor precipitation,soil moisture,snow cover,surface water processes,and groundwater variability.The review will employ a structured literature review methodology,focusing on recent peer-reviewed articles that apply optical,microwave,radar,and gravimetric remote sensing methods for hydrological monitoring under changing climatic conditions.It has paid specific attention to the provision of the comparative capabilities,spatial-temporal resolutions,and practical applications of key satellite missions,such as Landsat,Sentinel,MODIS(Moderate Resolution Imaging Spectroradiometer),GPM(Global Precipitation Measurement),and GRACE(Gravity Recovery and Climate Experiment).Moreover,to illustrate the use of remote sensing in detecting glacier retreat,drought formation,and coastal groundwater salinization,regional case studies are selected and analyzed.The review identifies new opportunities to use multi-sensor data,machine learning,and high-resolution monitoring to enhance hydrological analyses.This study is useful in practice by synthesizing existing technological opportunities and research trends to enhance climate-responsive water resource monitoring and by outlining future research directions in remote sensing-based hydrological analysis.展开更多
Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable meas...Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable measures of the Earth system across scales.This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning,remote sensing,and sensor data fusion to generate decision-ready environmental insights.We use the application-first approach,which considers remote sensing,in situ and Internet of Things(IoT)sensing,and physics-based models as complementary streams of evidence with similar strengths and failures.We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas:atmosphere and air quality,water–land–ecosystem dynamics,and hazards.Rapid-response situational awareness,ecosystem condition metrics,drought and flood indicators,exposure maps,and hazard/extreme indicators are key products.The integrated systems to environment interface in three high impact application areas:atmosphere and air quality,water-land-ecosystem dynamics,and hazard Examine Our operational requirements can often determine real-life value such as latency,time stability,smooth degradation in the presence of missing or degraded inputs,and calibrated uncertainty usable in thresholdbased decisions.These pitfalls are common across fields:mismatch in the scale between a point sensor and a gridded product,objectives on proxies in remotely sensed measurements,domain shift in the extremes and changing baselines,and evaluation aspects,which overestimate generalization because of spatiotemporal autocorrelation.Based on these lessons,we present cross-domain proposals for strong validation,uncertainty quantification,provenance,and versioning,as well as fair performance evaluation.We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness,transparency,and operational responsibility,thus allowing the integrated environmental intelligence system to be deployed,which may be relied on to monitor human health,resource allocation,and survival in a more climate-adapted world.展开更多
Also known as imaging spectroscopy,hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability.Hyperspectral observations can be used to measure tens to hundr...Also known as imaging spectroscopy,hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability.Hyperspectral observations can be used to measure tens to hundreds of narrow bands of reflected radiation to resolve diagnostic absorption bands and spectral shape variations associated with vegetation pigments,water status of the canopy,biochemical composition,mineralogies,and organic matter of the soil,and water quality constituents of aquatic water.These abilities allow one to make a transition between the descriptive mapping and the functional monitoring,the anticipation of stress and disturbance early,and the more accurate attribution of environmental change.This summary encompasses improvements on the entire sensor-to-product pipeline,including field and UAV(Unmanned Aerial Vehicle)system platform developments,airborne campaign and spaceborne mission developments,calibration and analysis-ready preprocessing improvements,empirical learning methodology improvements,radiative transfer-based inversion method,spectral unmixing,deep learning,and hybrid physics-machine learning.We underline the increased importance of the combination of data with LiDAR(Light Detection and Ranging),SAR(Synthetic Aperture Radar),and thermal features aimed at decreasing the level of ambiguity and enhancing operational resilience.Applications based on decision are evaluated in terms of biodiversity and habitat evaluation,vegetation functionality and restoration,stress and disturbance,sustainable agricultural production,inland water quality and coastal water quality,land degradation and soil status,and environmental impact assessment.Inhibiting factors to operational adoption have always been perceived to be domain shift by region,season,and sensor,ground truth and validation,mixed pixels and scale mismatch,preprocessing sensitivities,and desirable uncertainty quantification and product output that is interpretable.We conclude with the scalability,sustainability,service priorities,such as harmonization standards,representative benchmarking,uncertainty-aware delivery,and co-design of stakeholders.展开更多
基金This work was supported in part by the Ministry National Key Research and Development Project(Grant No.2020AAA0108101)the National Natural Science Foundation of China(Grants No.62125101,62341101,62001018,and 62301011)+1 种基金Shandong Natural Science Foundation(Grant No.ZR2023YQ058)the New Cornerstone Science Foundation through the XPLORER PRIZE.The authors would like to thank Mengyuan Lu and Zengrui Han for their help in the construction of electromagnetic space in Wireless InSite simulation platform and Weibo Wen,Qi Duan,and Yong Yu for their help in the construction of phys ical space in AirSim simulation platform.
文摘The sixth generation(6G)of mobile communication system is witnessing a new paradigm shift,i.e.,integrated sensing-communication system.A comprehensive dataset is a prerequisite for 6G integrated sensing-communication research.This paper develops a novel simulation dataset,named M3SC,for mixed multi-modal(MMM)sensing-communication integration,and the generation framework of the M3SC dataset is further given.To obtain multimodal sensory data in physical space and communication data in electromagnetic space,we utilize Air-Sim and WaveFarer to collect multi-modal sensory data and exploit Wireless InSite to collect communication data.Furthermore,the in-depth integration and precise alignment of AirSim,WaveFarer,andWireless InSite are achieved.The M3SC dataset covers various weather conditions,multiplex frequency bands,and different times of the day.Currently,the M3SC dataset contains 1500 snapshots,including 80 RGB images,160 depth maps,80 LiDAR point clouds,256 sets of mmWave waveforms with 8 radar point clouds,and 72 channel impulse response(CIR)matrices per snapshot,thus totaling 120,000 RGB images,240,000 depth maps,120,000 LiDAR point clouds,384,000 sets of mmWave waveforms with 12,000 radar point clouds,and 108,000 CIR matrices.The data processing result presents the multi-modal sensory information and communication channel statistical properties.Finally,the MMM sensing-communication application,which can be supported by the M3SC dataset,is discussed.
基金funded by the Natural Science Foundation of Chongqing Municipality,grant number CSTB2022NSCQ-MSX0503.
文摘Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.
基金supported by the National Key Research and Development Program of China(Research Grant Number:2023YFC3603600).
文摘Autism spectrum disorder(AsD)is a highly heterogeneous neurodevelopmental disorder.Early diagnosis and intervention are crucial for improving outcomes.Traditional single-modality diagnostic methods are subjective,limited,and struggle to reveal the underlying pathological mechanisms.In contrast,multimodal data analysis integrates behavioral,physiological,and neuroimaging information with advanced machine-learning and deeplearning algorithms to overcome these limitations.In this review,we surveyed the recent pediatric AsD literature,highlighting artificial intelligence-driven diagnostic techniques,multimodal data fusion strategies,and emerging trends in ASD assessment.We surveyed studies that integrated two or more modalities and summarized the fusion levels,learning paradigms,tasks,datasets,and metrics.Multimodal approaches outperform singlemodality baselines in classification,severity estimation,and subtyping by leveraging complementary information and reducing modality-specific biases.Multimodal approaches significantly enhance diagnostic accuracy and comprehensiveness,enabling early screening of AsD,symptom subtyping,severity assessment,and personalized interventions.Advances in multimodal fusion techniques have promoted progress in precision medicine for the treatment of ASD.
基金funded by Princess Nourah bint Abdulrahman University Researchers Support-ing Project number(PNURSP2026R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.
基金funded by“the Fanying Special Program of the National Natural Science Foundation of China,grant number 62341307”“the Scientific research project of Jiangxi Provincial Department of Education,grant number GJJ200839”“theDoctoral startup fund of JiangxiUniversity of Technology,grant number 205200100402”.
文摘In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions.To address these issues,we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition.A dynamic gating mechanism is applied across unimodal encoding,cross-modal alignment,and emotion transfer modeling,substantially improving noise robustness and feature alignment.First,we construct a unimodal encoder based on gated recurrent units and feature-selection gating to suppress intra-modal noise and enhance contextual representation.Second,we design a gated-attention crossmodal encoder that dynamically calibrates the complementary contributions of visual and audio modalities to the dominant textual features and eliminates redundant information.Finally,we introduce a gated enhanced emotion transfer module that explicitly models the temporal dependence of emotional evolution in dialogues via transfer gating and optimizes continuity modeling with a comparative learning loss.Experimental results demonstrate that the proposed method outperforms state-of-the-art models on the public MELD and IEMOCAP datasets.
基金the support of this work by the National Natural Science Foundation of China(Nos.22471182,22271201,22422108,22171194)the Science&Technology Department of Sichuan Province(No.2025ZNSFSC0125)+1 种基金the Fundamental Research Funds for the Central Universities(No.20826041D4117)the Comprehensive Training Platform of Specialized Laboratory,College of Chemistry.
文摘Azobenzene-winged phenanthrolines(L1 and L2)were designed,synthesized,and fully characterized.Ligand L1 forms an in-situ cobalt complex,which has been effectively employed as a circular dichroism(CD)-active chiral sensor.The resulting ternary complex(L1-Co^(2+)-amino alcohol)exhibits pronounced exciton-coupled circular dichroism(ECCD)signals at the characteristic azobenzene absorption bands.These signals arise from efficient chirality transfer from the chiral amino alcohol to the azobenzene chromophores,enabling the determination of the absolute configuration of chiral amino alcohols.Accordingly,the L1-Co^(2+)coordination system demonstrates considerably potential in chirality sensing applications.Remarkably,the induced ECCD signals are highly responsive to multiple external stimuli,including photoirradiation,solvent polarity,temperature,and redox conditions.In particular,temperature and redox changes can induce a reversible inversion of the ECCD signal,thereby establishing this system as a multifunctional,stimuli-responsive chiroptical molecular switch.
基金supported by the National Natural Science Foundation of China(32571991)Guangxi Natural Science Foundation of China(2023GXNSFGA026001&2025GXNSFAA069870)the Foundation of State Key Laboratory of Biobased Material and Green Papermaking.(No.GZKF202323)。
文摘The rapid advancement of flexible electronics technology has placed higher demands on the structural design and performance regulation of elastic materials.Cellulosic elastomers,with their biodegradability,renewability,and tunability,emerge as ideal candidate materials.Entropy-driven self-as sembly promotes the spontaneous formation of ordered structures,serving as a crucial pathway for optimizing cellulose elastomer properties.However,the structure-property relationship between the self-assembled ordered structures of cellulose elastomers and their mechanical and electrical properties remains insufficiently explored.It hinders the expansion of their applications in electronic devices.This paper systematically reviews the structure-property regulation mechanisms of self-assembled cellulosic elastomers from an entropy-driven perspective.It elucidates the application principles and performance optimization strategies for mechanical energy harvesting and self-powered sensing,while also exploring the challenges and prospects for performance enhancement.This work provides a reference for the development of self-assembled cellulosic elastomers in the field of energy devices.
基金supported by National Natural Science Foundation of China(Grant No.52272100)the Fund of Science and Technology on Advanced Ceramic Fibers and Composites Laboratory(Grant No.WDZC20215250507)the Fund of National Key Laboratory of Nuclear Reactor Technology of Nuclear Power Institute of China(KGSW-0324-0301-08)。
文摘The demand for sensors capable of operating in extreme environment of the fields,such as aerospace vehicles,aeroengines and fire protection,is rapidly increasing.However,developing flexible ceramic fibrous pressure sensors that combine high temperature stability with robust mechanical properties remains a significant challenge.Herein,through precise multi-scale process control,high-strength(2.1 MPa)TiC-SiC flexible fibrous membrane is successfully fabricated.The membrane exhibits exceptional thermal resistance(2000℃)and long–term thermal stability(1800℃ for 5 h)in the inert atmosphere.Meanwhile,the TiC-SiC fibrous membrane shows excellent oxidation resistance and still achieves strength of 1.8 MPa after being oxidized at 1200℃ for 1 h in air.Remarkably,TiC-SiC fibrous membrane withstands a load of approximately 1400 times its own weight and the ablation of butane flame(~1300℃)for at least 1 h without breaking.Notably,after heat treatment at 1800℃ for 5 h in an argon atmosphere,the TiC-SiC fibrous membrane even sustains pressure–sensing performance for up to 300 cycles.The membrane exhibits stable resistivity up to 900℃ and shows sensing stability under butane flame.The results of this work provide an effective and feasible solution to fill the research gap of flexible fibrous sensors for extreme environments.
基金financially supported by the National Natural Science Foundation of China(Grant No.32302764)Hunan Provincial Natural Science Foundation(Grant No.2024JJ5179)+1 种基金Key laboratory for the feed and biology technique of Xinjiang Uygur Autonomous Region(Grant No.XJSLSW-2023001)Hunan Herbivores Industry Technological System(Grant No.HARS-08)。
文摘Background Zearalenone(ZEN),a common mycotoxin in ruminant diets,could disturb the rumen ecosystem and impair rumen fermentation.Noticeably,ZEN has been shown to reduce the relative abundances of specific bacterial taxa that potentially possess quorum sensing(QS)functions,which are deemed essential for the microbial interactions and adaptations during rumen fermentation.Nonetheless,whether QS communications participate in the responses of rumen microbial fermentation to ZEN remains unknown.Therefore,the present trial was performed to explore the potential roles of QS during the alterations of rumen microbial fermentation by ZEN through a rumen simulation technique(RUSITEC)system,in a replicated 4×4 Latin square design.Results ZEN significantly(P<0.05)reduced QS signal autoinducer-2(AI-2),and tended to(P=0.051)downregulate QS signal C4-homoserine lactone(HSL).ZEN also significantly(P<0.05)decreased total volatile fatty acid(TVFA),acetate,propionate,isobutyrate,isovalerate,organic matter disappearance(OMD),neutral detergent fiber disappearance(NDFD),and acid detergent fiber disappearance(ADFD)in different manners.The linear discriminant analysis effect size(LEf Se)analysis indicated significantly(P<0.05)differential enrichments of a series of bacterial taxa such as Butyrivibrio_sp_X503,Rhizobium daejeonense,Hoylesella buccalis,Ezakiella coagulans,Enterococcus cecorum,Ruminococcus_sp_zg-924,Polystyrenella longa,and Methylacidimicrobium fagopyrum across different treatments.The phylogenetic investigation of communities by reconstruction of unobserved states 2(PICRUSt2)analysis suggested that QS were predicted to be significantly(P<0.05)affected by ZEN.The metabolomics analysis detected considerable significantly(P<0.05)differing metabolites and implied that ZEN challenge significantly(P<0.05)influenced the indole alkaloid biosynthesis,biosynthesis of alkaloids derived from shikimate pathway,and sesquiterpenoid and triterpenoid biosynthesis.Significant(P<0.05)interconnections of QS molecules with the differential rumen fermentation traits,differential bacterial taxa,and differential metabolites were exhibited by Spearman analysis.Conclusions ZEN negatively affected the QS signals of AI-2 and C4-HSL,which was found to correlate with the fluctuations in specific rumen fermentation characteristics,ruminal bacterial populations,and ruminal metabolisms.These interrelationships implied the potential involvement of QS in the reactions of rumen microbiota to ZEN contamination,and probably contributed to the inhibition of rumen fermentation.
基金support from the Roy A.Wilkens Professorship Endowment。
文摘Interferometry is a crucial investigative technique used across diverse fields to achieve highprecision measurements.It works by analyzing the phase difference between two interfering waves,which results from variations in optical path lengths within an interferometer.We introduce a novel method for directly measuring changes in the phase difference within an optical interferometer,importantly,with the added advantage of a controllable enhancement factor.This approach is achieved through a two-step process:first,the optical phase difference is encoded into a sub-GHz radiofrequency(RF)signal using microwave-photonic manipulation;then,RF interferometry-assisted phase amplification is implemented at the destructive interference point.In our experiments,we demonstrate a phase sensitivity of 2.14 rad∕nm operating at 140 MHz using a miniature in-fiber Fabry-Pérot interferometer for sub-nanometer displacement sensing,which reveals a sensitivity magnification factor of 258.6.With further refinement,we anticipate that even higher enhancement factors can be achieved,paving the way for the development of cost-effective,ultrasensitive interferometry-based instruments for high-precision optical measurements.
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provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘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.
基金supported by the National Natural Science Foundation of China(Grant Nos.52175509 and 52450158)the National Key Research and Development Program of China(Grant No.2023YFF1500900)+2 种基金the Shenzhen Fundamental Research Program(Grant No.JCYJ20220818100412027)the Guangdong-Hong Kong Technology Cooperation Funding Scheme Category C Platform(Grant No.SGDX20230116093543005)the Innovation Project of Optics Valley Laboratory(Grant No.OVL2023PY003)。
文摘Point-of-care diagnostics and inline quantitative phase imaging(QPI)drive the demand for portable,ultra-miniaturized,and robust optical imaging and metrology systems.We propose and demonstrate a wavefront sensor integrated into a photonic integrated circuit,enabling single-shot optical phase retrieval.We implemented an integrated wavefront sensor array with a spatial resolution of 17μm and a numerical aperture of 0.1.Furthermore,we experimentally demonstrated the reconstruction of wavefronts defined by Zernike polynomials,specifically the first 14 terms(Z_(1)to Z_(14)),achieving an average root mean square error below 0.07.This advancement paves the way for fully integrated,portable,and robust optical imaging systems,facilitating integrated wavefront sensors in demanding applications such as point-of-care diagnostics,endoscopy,in situ QPI,and inline surface profile measurement.
基金Supported by Natural Science Foundation General Project of Heilongjiang Province(C2018050).
文摘As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.
文摘Shallow landslides are strongly controlled by near-surface lithological variability,yet conventional geological maps are often too generalized to support accurate susceptibility assessment in complex terrains.This review synthesizes recent advances in remote sensing–based lithological mapping and evaluates their integration into landslide susceptibility modeling.Evidence from the literature indicates that remote sensing-derived lithological products,particularly those incorporating mineralogical information and higher spatial resolution,consistently outperform traditional geological maps in improving model accuracy and spatial detail,especially in heterogeneous environments.However,key challenges remain,including scale mismatches between surface observations and subsurface controls,limited ground validation,uncertainty propagation,and restricted model transferability across regions.The review identifies multi-sensor data fusion and explainable machine learning as the most promising directions for advancing lithological discrimination and model reliability.Future progress depends on integrating remote sensing with process-based understanding,improving validation strategies,and standardizing uncertainty reporting.These developments are essential for enabling more robust,scalable,and operationally relevant landslide susceptibility assessments in complex terrains.Lastly,we describe the directions of research that focus on multi-sensor fusion,explainable machine learning,UAV(Unmanned Aerial Vehicle)-enabled validation,and standardized uncertainty reporting that can help articulate landslide susceptibility assessment,making them even more robust and operationally significant.
基金funded by the Hainan Province Science and Technology Special Fund under Grant ZDYF2024GXJS292.
文摘Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016).
基金support of her postdoctoral research at the GFZ Helmholtz Centre for Geosciences.P.Pan acknowledges the financial support of the National Natural Science Foundation of China(Grant No.52339001)H.Hofmann and Y.Ji acknowledge the financial support of the Helmholtz Association's Initiative and Networking Fund for the Helmholtz Young Investigator Group ARES(contract number VH-NG-1516).
文摘A multi-stage stress relaxation test was performed on a granodiorite sample to understand the deformation process prior to the macroscopic failure of brittle rocks,as well as the transient response during stress relaxation.Distributed optical fiber sensing was used to measure strains across the sample surface by helically wrapping the single-mode fiber around the cylindrical sample.Close agreement was observed between the circumferential strains obtained from the optical fibers and the extensometer.The reconstructed full-field strain contours show strain heterogeneity from the crack closure phase,and the strains in the later deformation phase are dominantly localized within the former high-strain zone.The Gini coefficient was used to quantify the degree of strain localization and shows an initial increase during the crack closure phase,a decrease during the linear elastic phase,and a subsequent increase during the post-yielding phase.This behavior corresponds to a process of initial localization from an imperfect boundary condition,homogenization,and eventual relocalization prior to the macroscopic failure of the sample.The transient strain rate decay during the stress relaxation phase was quantified using the p-value in the“Omori-like"power law function.A higher initial stress at the onset of relaxation results in a lower p-value,indicating a slower strain rate decay.As the sample approaches macroscopic failure,the lowest p-value shifts from the most damaged zone to adjacent areas,suggesting stress redistribution or crack propagation in deformed crystalline rocks under stress relaxation conditions.
基金funded by the Inner Mongolia Autonomous Region Science and Technology Plan Project(No 2025YFHH0250).
文摘Climate change is rapidly altering hydrological systems through changes in precipitation patterns,increase the rate of glacier retreat rates,altered snow dynamics,and groundwater stress.Although remote sensing has been extensively deployed in hydrological research,existing reviews typically focus on a single hydrological variable or on particular satellite missions.The review synthesizes remote sensing technologies to monitor climate-related hydrological variations across various components of the water cycle.It is a systematic examination of major satellite missions,sensor technologies,and analytical methods used to monitor precipitation,soil moisture,snow cover,surface water processes,and groundwater variability.The review will employ a structured literature review methodology,focusing on recent peer-reviewed articles that apply optical,microwave,radar,and gravimetric remote sensing methods for hydrological monitoring under changing climatic conditions.It has paid specific attention to the provision of the comparative capabilities,spatial-temporal resolutions,and practical applications of key satellite missions,such as Landsat,Sentinel,MODIS(Moderate Resolution Imaging Spectroradiometer),GPM(Global Precipitation Measurement),and GRACE(Gravity Recovery and Climate Experiment).Moreover,to illustrate the use of remote sensing in detecting glacier retreat,drought formation,and coastal groundwater salinization,regional case studies are selected and analyzed.The review identifies new opportunities to use multi-sensor data,machine learning,and high-resolution monitoring to enhance hydrological analyses.This study is useful in practice by synthesizing existing technological opportunities and research trends to enhance climate-responsive water resource monitoring and by outlining future research directions in remote sensing-based hydrological analysis.
文摘Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable measures of the Earth system across scales.This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning,remote sensing,and sensor data fusion to generate decision-ready environmental insights.We use the application-first approach,which considers remote sensing,in situ and Internet of Things(IoT)sensing,and physics-based models as complementary streams of evidence with similar strengths and failures.We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas:atmosphere and air quality,water–land–ecosystem dynamics,and hazards.Rapid-response situational awareness,ecosystem condition metrics,drought and flood indicators,exposure maps,and hazard/extreme indicators are key products.The integrated systems to environment interface in three high impact application areas:atmosphere and air quality,water-land-ecosystem dynamics,and hazard Examine Our operational requirements can often determine real-life value such as latency,time stability,smooth degradation in the presence of missing or degraded inputs,and calibrated uncertainty usable in thresholdbased decisions.These pitfalls are common across fields:mismatch in the scale between a point sensor and a gridded product,objectives on proxies in remotely sensed measurements,domain shift in the extremes and changing baselines,and evaluation aspects,which overestimate generalization because of spatiotemporal autocorrelation.Based on these lessons,we present cross-domain proposals for strong validation,uncertainty quantification,provenance,and versioning,as well as fair performance evaluation.We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness,transparency,and operational responsibility,thus allowing the integrated environmental intelligence system to be deployed,which may be relied on to monitor human health,resource allocation,and survival in a more climate-adapted world.
文摘Also known as imaging spectroscopy,hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability.Hyperspectral observations can be used to measure tens to hundreds of narrow bands of reflected radiation to resolve diagnostic absorption bands and spectral shape variations associated with vegetation pigments,water status of the canopy,biochemical composition,mineralogies,and organic matter of the soil,and water quality constituents of aquatic water.These abilities allow one to make a transition between the descriptive mapping and the functional monitoring,the anticipation of stress and disturbance early,and the more accurate attribution of environmental change.This summary encompasses improvements on the entire sensor-to-product pipeline,including field and UAV(Unmanned Aerial Vehicle)system platform developments,airborne campaign and spaceborne mission developments,calibration and analysis-ready preprocessing improvements,empirical learning methodology improvements,radiative transfer-based inversion method,spectral unmixing,deep learning,and hybrid physics-machine learning.We underline the increased importance of the combination of data with LiDAR(Light Detection and Ranging),SAR(Synthetic Aperture Radar),and thermal features aimed at decreasing the level of ambiguity and enhancing operational resilience.Applications based on decision are evaluated in terms of biodiversity and habitat evaluation,vegetation functionality and restoration,stress and disturbance,sustainable agricultural production,inland water quality and coastal water quality,land degradation and soil status,and environmental impact assessment.Inhibiting factors to operational adoption have always been perceived to be domain shift by region,season,and sensor,ground truth and validation,mixed pixels and scale mismatch,preprocessing sensitivities,and desirable uncertainty quantification and product output that is interpretable.We conclude with the scalability,sustainability,service priorities,such as harmonization standards,representative benchmarking,uncertainty-aware delivery,and co-design of stakeholders.
