Coal mining induces changes in the nature of rock and soil bodies,as well as hydrogeological conditions,which can easily trigger the occurrence of geological disasters such as water inrush,movement of the coal seam ro...Coal mining induces changes in the nature of rock and soil bodies,as well as hydrogeological conditions,which can easily trigger the occurrence of geological disasters such as water inrush,movement of the coal seam roof and floor,and rock burst.Transparency in coal mine geological conditions provides technical support for intelligent coal mining and geological disaster prevention.In this sense,it is of great significance to address the requirements for informatizing coal mine geological conditions,dynamically adjust sensing parameters,and accurately identify disaster characteristics so as to prevent and control coal mine geological disasters.This paper examines the various action fields associated with geological disasters in mining faces and scrutinizes the types and sensing parameters of geological disasters resulting from coal seam mining.On this basis,it summarizes a distributed fiber-optic sensing technology framework for transparent geology in coal mines.Combined with the multi-field monitoring characteristics of the strain field,the temperature field,and the vibration field of distributed optical fiber sensing technology,parameters such as the strain increment ratio,the aquifer temperature gradient,and the acoustic wave amplitude are extracted as eigenvalues for identifying rock breaking,aquifer water level,and water cut range,and a multi-field sensing method is established for identifying the characteristics of mining-induced rock mass disasters.The development direction of transparent geology based on optical fiber sensing technology is proposed in terms of the aspects of sensing optical fiber structure for large deformation monitoring,identification accuracy of optical fiber acoustic signals,multi-parameter monitoring,and early warning methods.展开更多
Mountain excavation and city construction(MECC)projects being launched in the Loess Plateau in China involve the creation of large-scale artificial land.Understanding the subsurface evolution characteristics of the ar...Mountain excavation and city construction(MECC)projects being launched in the Loess Plateau in China involve the creation of large-scale artificial land.Understanding the subsurface evolution characteristics of the artificial land is essential,yet challenging.Here,we use an improved fiber-optic monitoring system for its subsurface multi-physical characterization.The system enables us to gather spatiotemporal distribution of various parameters,including strata deformation,temperature,and moisture.Yan’an New District was selected as a case study to conduct refined in-situ monitoring through a 77 m-deep borehole and a 30 m-long trench.Findings reveal that the ground settlement involves both the deformation of the filling loess and the underlying intact loess.Notably,the filling loess exhibits a stronger creep capability compared to underlying intact loess.The deformation along the profile is unevenly distributed,with a positive correlation with soil moisture.Water accumulation has been observed at the interface between the filling loess and the underlying intact loess,leading to a significant deformation.Moreover,the temperature and moisture in the filling loess have reached a new equilibrium state,with their depths influenced by atmospheric conditions measuring at 31 m and 26 m,respectively.The refined investigation allows us to identify critical layers that matter the sustainable development of newly created urban areas,and provide improved insights into the evolution mechanisms of land creation.展开更多
Distributed fiber-optic sensing has become an indispensable tool for large-scale structural and environmentalmonitoring, where spectral interrogation of backscattering light enables high-precision quantitative measure...Distributed fiber-optic sensing has become an indispensable tool for large-scale structural and environmentalmonitoring, where spectral interrogation of backscattering light enables high-precision quantitative measurement ofexternal perturbations. Conventional spectral analysis methods, typically based on frequency-domain serialinterrogation or time-to-frequency mapping, face inherent trade-offs between measurement speed, dynamic strainmeasurement range, and system complexity. Here, we present a distributed frequency comb enabled spectrumcorrelationreflectometry as a universal spectral analysis framework that leverages optical frequency comb for parallelmulti-frequency interrogation, which is experimentally demonstrated in a phase-sensitive optical time-domainreflectometry (φ-OTDR) system. This method eliminates the need for large frequency scans, achieving more thantenfold improvement in measurement speed over the state-of-the-art spectral analysis methods. Compared to existingphase-demodulated φ-OTDR systems, this method enables vibration amplitude monitoring with a dynamic strainmeasurement range expanded by more than an order of magnitude, while intrinsically circumventing phaseunwrapping issues and interference fading. This work establishes a new paradigm for distributed spectral analysis,providing a flexible and robust platform for a wide range of sensing technologies, including Rayleigh and Brillouinbasedschemes, which may have significant impact for geophysics, seismology, civil engineering, and other fields.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
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
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
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
Desert shrubs are indispensable in maintaining ecological stability by reducing soil erosion,enhancing water retention,and boosting soil fertility,which are critical factors in mitigating desertification processes.Due...Desert shrubs are indispensable in maintaining ecological stability by reducing soil erosion,enhancing water retention,and boosting soil fertility,which are critical factors in mitigating desertification processes.Due to the complex topography,variable climate,and challenges in field surveys in desert regions,this paper proposes YOLO-Desert-Shrub(YOLO-DS),a detection method for identifying desert shrubs in UAV remote sensing images based on an enhanced YOLOv8n framework.This method accurately identifying shrub species,locations,and coverage.To address the issue of small individual plants dominating the dataset,the SPDconv convolution module is introduced in the Backbone and Neck layers of the YOLOv8n model,replacing conventional convolutions.This structural optimization mitigates information degradation in fine-grained data while strengthening discriminative feature capture across spatial scales within desert shrub datasets.Furthermore,a structured state-space model is integrated into the main network,and the MambaLayer is designed to dynamically extract and refine shrub-specific features from remote sensing images,effectively filtering out background noise and irrelevant interference to enhance feature representation.Benchmark evaluations reveal the YOLO-DS framework attains 79.56%mAP40weight,demonstrating 2.2%absolute gain versus the baseline YOLOv8n architecture,with statistically significant advantages over contemporary detectors in cross-validation trials.The predicted plant coverage exhibits strong consistency with manually measured coverage,with a coefficient of determination(R^(2))of 0.9148 and a Root Mean Square Error(RMSE)of1.8266%.The proposed UAV-based remote sensing method utilizing the YOLO-DS effectively identify and locate desert shrubs,monitor canopy sizes and distribution,and provide technical support for automated desert shrub monitoring.展开更多
High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleim...High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.展开更多
Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisiti...Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas.展开更多
With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I...With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.展开更多
Intermittent rivers and ephemeral streams(IRES),also known as non-perennial river segments(NPRs),have garnered attention due to their significant roles in watershed hydrology and ecosystem services,especially in the c...Intermittent rivers and ephemeral streams(IRES),also known as non-perennial river segments(NPRs),have garnered attention due to their significant roles in watershed hydrology and ecosystem services,especially in the context of climate change and escalating human activities.Recent advances in machine learning(ML)techniques have significantly improved the analysis of dynamic changes in IRES.Various ML models,including random forest(RF),long short-term memory(LSTM),and U-Net,demonstrate clear advantages in processing complex hydrological data,enhancing the efficiency and accuracy of IRES extraction from remote sensing data.Furthermore,hybrid ML approaches enhance predictive performance in complex hydrological scenarios by integrating multiple algorithms.However,ML methods still face challenges,including high data dependence,computational complexity,and scalability issues with models.This review proposes an IRES monitoring framework that combines satellite data with ML algorithms,integrating remote sensing technologies such as optical imaging and synthetic aperture radar,and evaluates the advantages and limitations of different ML methods.It further highlights the potential of integrating multiple ML techniques and high-resolution remote sensing data to monitor IRES dynamics,conduct ecological assessments,and support sustainable water management,offering a scientific foundation for addressing environmental and anthropogenic pressures.展开更多
Integrated sensing and communication(ISAC)is an appealing approach to address spectrum congestion and beamforming is an effective method to realize ISAC.In this paper,we investigate the beamforming design problem for ...Integrated sensing and communication(ISAC)is an appealing approach to address spectrum congestion and beamforming is an effective method to realize ISAC.In this paper,we investigate the beamforming design problem for multiple-input multipleoutput(MIMO)ISAC systems and propose to maximize the radar beampattern gain of the target direction while ensuring the signal-to-interference-plus-noise ratio(SINR)constraints of communication users.Particularly,we discuss two cases of ISAC transmit beamforming,i.e.,Case-Ⅰand Case-Ⅱ,which do not have and do have the dedicated probing signal,respectively.For these two cases of transmit beamforming design problems,we start from the single-user scenario and provide the closed-form solutions for MIMO ISAC beamforming vectors.Then,we consider the multiuser scenario and utilize the semidefinite relaxation technique to convert the beamforming design problems into convex semidefinite programming problems.Furthermore,we investigate the impact of the channel correlation between radar and communication on the performance gain of MIMO ISAC systems and characterize the performance tradeoff.Numerical results validate that the dedicated probing signal is unnecessary in the single-user scenario,whereas it has a slight improvement in target detection performance at low SINR thresholds in the multi-user scenario.It is also shown that the stronger the correlation between radar and communication channels,the greater the performance gain of the system.展开更多
Foodborne bacteria produce biofilms and their viable but non-culturable(VBNC)formation,can affect food quality and safety.Studies have shown that these characteristics are regulated by the bacterial quorum sensing(QS)...Foodborne bacteria produce biofilms and their viable but non-culturable(VBNC)formation,can affect food quality and safety.Studies have shown that these characteristics are regulated by the bacterial quorum sensing(QS)system.Quenching the QS system of foodborne bacteria and blocking the expression of the corresponding genes may be an effective way to improve food quality and safety.Therefore,this article reviews the QS systems for foodborne bacteria,the regulatory mechanisms of QS systems in biofilm and VBNC formation and resuscitation,the research progress on quorum sensing inhibitors(QSIs)for Gram-negative and Gram-positive bacteria,and introduces QSIs from various sources.In addition,we have also summarized the current research issues on QS regulation of biofilms and VBNC formation.The systematic study of the QS phenomenon of foodborne bacteria in practical situations,the mechanism of bacterial QS cooperation-cheating,the screening of novel and highly active QSIs,the combination of QSIs and other technologies to improve their bioavailability,and the regulatory network between biofilm and VBNC formation and resuscitation are research directions that need to be paid attention to in the future.展开更多
To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-r...To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.展开更多
The growing demand for personalized health care,smart wearables,and advanced environmental monitoring has spurred the development of multifunctional materials that combine flexibility,environmental adaptability,and di...The growing demand for personalized health care,smart wearables,and advanced environmental monitoring has spurred the development of multifunctional materials that combine flexibility,environmental adaptability,and diverse functionalities.However,conventional materials often failed to integrate these attributes simultaneously,hindering their applicability in next-generation technologies.Here,we present an organic-inorganic hybrid crystalline material with a unique sandwich-like architecture,in which a flexible organic crystal core is encased by reduced graphene oxide(rGO)and thermoplastic polyurethane(TPU).This strategic integration endows the material with fluorescence,cryogenic flexibility,and electrical conductivity,while also enabling dual sensing and actuation capabilities.The rGO layer facilitates real-time humidity(25-90%RH)and temperature(25-180℃)sensing through environmental interactions,whereas the differential thermal expansion between TPU and the flexible crystal core drives efficient photothermal actuation at-150℃ for advanced thermal regulation.The hybrid material exhibits stable performance under extreme conditions,making it a promising candidate for biomedical monitoring,flexible electronics,and energy applications.This work establishes hybrid crystalline materials as versatile and scalable platforms for addressing complex technological demands,paving the way for their application in next-generation multifunctional devices.展开更多
基金National Natural Science Foundation of China,Grant/Award Number:42130706。
文摘Coal mining induces changes in the nature of rock and soil bodies,as well as hydrogeological conditions,which can easily trigger the occurrence of geological disasters such as water inrush,movement of the coal seam roof and floor,and rock burst.Transparency in coal mine geological conditions provides technical support for intelligent coal mining and geological disaster prevention.In this sense,it is of great significance to address the requirements for informatizing coal mine geological conditions,dynamically adjust sensing parameters,and accurately identify disaster characteristics so as to prevent and control coal mine geological disasters.This paper examines the various action fields associated with geological disasters in mining faces and scrutinizes the types and sensing parameters of geological disasters resulting from coal seam mining.On this basis,it summarizes a distributed fiber-optic sensing technology framework for transparent geology in coal mines.Combined with the multi-field monitoring characteristics of the strain field,the temperature field,and the vibration field of distributed optical fiber sensing technology,parameters such as the strain increment ratio,the aquifer temperature gradient,and the acoustic wave amplitude are extracted as eigenvalues for identifying rock breaking,aquifer water level,and water cut range,and a multi-field sensing method is established for identifying the characteristics of mining-induced rock mass disasters.The development direction of transparent geology based on optical fiber sensing technology is proposed in terms of the aspects of sensing optical fiber structure for large deformation monitoring,identification accuracy of optical fiber acoustic signals,multi-parameter monitoring,and early warning methods.
基金supported by National Natural Science Foundation of China(Grant Nos.4203070 and 41977217)the Key Research&Development Program of Shaanxi Province(Grant No.2020ZDLSF06-03).
文摘Mountain excavation and city construction(MECC)projects being launched in the Loess Plateau in China involve the creation of large-scale artificial land.Understanding the subsurface evolution characteristics of the artificial land is essential,yet challenging.Here,we use an improved fiber-optic monitoring system for its subsurface multi-physical characterization.The system enables us to gather spatiotemporal distribution of various parameters,including strata deformation,temperature,and moisture.Yan’an New District was selected as a case study to conduct refined in-situ monitoring through a 77 m-deep borehole and a 30 m-long trench.Findings reveal that the ground settlement involves both the deformation of the filling loess and the underlying intact loess.Notably,the filling loess exhibits a stronger creep capability compared to underlying intact loess.The deformation along the profile is unevenly distributed,with a positive correlation with soil moisture.Water accumulation has been observed at the interface between the filling loess and the underlying intact loess,leading to a significant deformation.Moreover,the temperature and moisture in the filling loess have reached a new equilibrium state,with their depths influenced by atmospheric conditions measuring at 31 m and 26 m,respectively.The refined investigation allows us to identify critical layers that matter the sustainable development of newly created urban areas,and provide improved insights into the evolution mechanisms of land creation.
基金supported by the National Key R&D Program of China(2023YFB2906303)Major Program(JD)of Hubei Province(2023BAA013)+1 种基金the National Natural Science Foundation of China(62105111,62225110)the Chilean National Agency for Research and Development(Fondecyt Regular 1241085,Fondequip EQM220113 and Basal AFB240002).
文摘Distributed fiber-optic sensing has become an indispensable tool for large-scale structural and environmentalmonitoring, where spectral interrogation of backscattering light enables high-precision quantitative measurement ofexternal perturbations. Conventional spectral analysis methods, typically based on frequency-domain serialinterrogation or time-to-frequency mapping, face inherent trade-offs between measurement speed, dynamic strainmeasurement range, and system complexity. Here, we present a distributed frequency comb enabled spectrumcorrelationreflectometry as a universal spectral analysis framework that leverages optical frequency comb for parallelmulti-frequency interrogation, which is experimentally demonstrated in a phase-sensitive optical time-domainreflectometry (φ-OTDR) system. This method eliminates the need for large frequency scans, achieving more thantenfold improvement in measurement speed over the state-of-the-art spectral analysis methods. Compared to existingphase-demodulated φ-OTDR systems, this method enables vibration amplitude monitoring with a dynamic strainmeasurement range expanded by more than an order of magnitude, while intrinsically circumventing phaseunwrapping issues and interference fading. This work establishes a new paradigm for distributed spectral analysis,providing a flexible and robust platform for a wide range of sensing technologies, including Rayleigh and Brillouinbasedschemes, which may have significant impact for geophysics, seismology, civil engineering, and other fields.
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.
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supported by the National Public Welfare Forest Desert Shrubbery Monitoring Project。
文摘Desert shrubs are indispensable in maintaining ecological stability by reducing soil erosion,enhancing water retention,and boosting soil fertility,which are critical factors in mitigating desertification processes.Due to the complex topography,variable climate,and challenges in field surveys in desert regions,this paper proposes YOLO-Desert-Shrub(YOLO-DS),a detection method for identifying desert shrubs in UAV remote sensing images based on an enhanced YOLOv8n framework.This method accurately identifying shrub species,locations,and coverage.To address the issue of small individual plants dominating the dataset,the SPDconv convolution module is introduced in the Backbone and Neck layers of the YOLOv8n model,replacing conventional convolutions.This structural optimization mitigates information degradation in fine-grained data while strengthening discriminative feature capture across spatial scales within desert shrub datasets.Furthermore,a structured state-space model is integrated into the main network,and the MambaLayer is designed to dynamically extract and refine shrub-specific features from remote sensing images,effectively filtering out background noise and irrelevant interference to enhance feature representation.Benchmark evaluations reveal the YOLO-DS framework attains 79.56%mAP40weight,demonstrating 2.2%absolute gain versus the baseline YOLOv8n architecture,with statistically significant advantages over contemporary detectors in cross-validation trials.The predicted plant coverage exhibits strong consistency with manually measured coverage,with a coefficient of determination(R^(2))of 0.9148 and a Root Mean Square Error(RMSE)of1.8266%.The proposed UAV-based remote sensing method utilizing the YOLO-DS effectively identify and locate desert shrubs,monitor canopy sizes and distribution,and provide technical support for automated desert shrub monitoring.
基金funded by the Henan Province Key R&D Program Project,“Research and Application Demonstration of Class Ⅱ Superlattice Medium Wave High Temperature Infrared Detector Technology”,grant number 231111210400.
文摘High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.
基金Under the auspices of the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28050400)Jilin Province Key Research and Development Project(No.20230202040NC)Common Application Support Platform for National Civil Space Infrastructure Land Observation Satellites(No.2017-000052-73-01-001735)。
文摘Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas.
基金supported by National Natural Science Foundation of China(NSFC)under grant U23A20310.
文摘With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.
基金National Natural Science Foundation of China,No.41671026。
文摘Intermittent rivers and ephemeral streams(IRES),also known as non-perennial river segments(NPRs),have garnered attention due to their significant roles in watershed hydrology and ecosystem services,especially in the context of climate change and escalating human activities.Recent advances in machine learning(ML)techniques have significantly improved the analysis of dynamic changes in IRES.Various ML models,including random forest(RF),long short-term memory(LSTM),and U-Net,demonstrate clear advantages in processing complex hydrological data,enhancing the efficiency and accuracy of IRES extraction from remote sensing data.Furthermore,hybrid ML approaches enhance predictive performance in complex hydrological scenarios by integrating multiple algorithms.However,ML methods still face challenges,including high data dependence,computational complexity,and scalability issues with models.This review proposes an IRES monitoring framework that combines satellite data with ML algorithms,integrating remote sensing technologies such as optical imaging and synthetic aperture radar,and evaluates the advantages and limitations of different ML methods.It further highlights the potential of integrating multiple ML techniques and high-resolution remote sensing data to monitor IRES dynamics,conduct ecological assessments,and support sustainable water management,offering a scientific foundation for addressing environmental and anthropogenic pressures.
基金National Natural Science Foundation of China under Grant 62571248 and Grant 62201266Key Laboratory of Intelligent Space TTC&O(Space Engineering University),Ministry of Education under Grant CYK2025-01-12。
文摘Integrated sensing and communication(ISAC)is an appealing approach to address spectrum congestion and beamforming is an effective method to realize ISAC.In this paper,we investigate the beamforming design problem for multiple-input multipleoutput(MIMO)ISAC systems and propose to maximize the radar beampattern gain of the target direction while ensuring the signal-to-interference-plus-noise ratio(SINR)constraints of communication users.Particularly,we discuss two cases of ISAC transmit beamforming,i.e.,Case-Ⅰand Case-Ⅱ,which do not have and do have the dedicated probing signal,respectively.For these two cases of transmit beamforming design problems,we start from the single-user scenario and provide the closed-form solutions for MIMO ISAC beamforming vectors.Then,we consider the multiuser scenario and utilize the semidefinite relaxation technique to convert the beamforming design problems into convex semidefinite programming problems.Furthermore,we investigate the impact of the channel correlation between radar and communication on the performance gain of MIMO ISAC systems and characterize the performance tradeoff.Numerical results validate that the dedicated probing signal is unnecessary in the single-user scenario,whereas it has a slight improvement in target detection performance at low SINR thresholds in the multi-user scenario.It is also shown that the stronger the correlation between radar and communication channels,the greater the performance gain of the system.
基金financially supported by the National Natural Science Foundation of China(32202191)and(32272279)the Key R&D Project of Shandong Province(2023CXPT007 and 2024CXPT014)the Key R&D Project of Qingdao Science and Technology Plan(24-2-3-4-zyyd-jch).
文摘Foodborne bacteria produce biofilms and their viable but non-culturable(VBNC)formation,can affect food quality and safety.Studies have shown that these characteristics are regulated by the bacterial quorum sensing(QS)system.Quenching the QS system of foodborne bacteria and blocking the expression of the corresponding genes may be an effective way to improve food quality and safety.Therefore,this article reviews the QS systems for foodborne bacteria,the regulatory mechanisms of QS systems in biofilm and VBNC formation and resuscitation,the research progress on quorum sensing inhibitors(QSIs)for Gram-negative and Gram-positive bacteria,and introduces QSIs from various sources.In addition,we have also summarized the current research issues on QS regulation of biofilms and VBNC formation.The systematic study of the QS phenomenon of foodborne bacteria in practical situations,the mechanism of bacterial QS cooperation-cheating,the screening of novel and highly active QSIs,the combination of QSIs and other technologies to improve their bioavailability,and the regulatory network between biofilm and VBNC formation and resuscitation are research directions that need to be paid attention to in the future.
基金supported by the National Natural Science Foundation of China(Grant No.52339001).
文摘To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.
基金support from the National Natural Science Foundation of China(52373181 and 52173164)the Natural Science Foundation of Jilin Province(20250102120JC and 20230101038JC)+1 种基金the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZB20240259)the Project funded by China Postdoctoral Science Foundation(2024M761121 and 2025T180139).
文摘The growing demand for personalized health care,smart wearables,and advanced environmental monitoring has spurred the development of multifunctional materials that combine flexibility,environmental adaptability,and diverse functionalities.However,conventional materials often failed to integrate these attributes simultaneously,hindering their applicability in next-generation technologies.Here,we present an organic-inorganic hybrid crystalline material with a unique sandwich-like architecture,in which a flexible organic crystal core is encased by reduced graphene oxide(rGO)and thermoplastic polyurethane(TPU).This strategic integration endows the material with fluorescence,cryogenic flexibility,and electrical conductivity,while also enabling dual sensing and actuation capabilities.The rGO layer facilitates real-time humidity(25-90%RH)and temperature(25-180℃)sensing through environmental interactions,whereas the differential thermal expansion between TPU and the flexible crystal core drives efficient photothermal actuation at-150℃ for advanced thermal regulation.The hybrid material exhibits stable performance under extreme conditions,making it a promising candidate for biomedical monitoring,flexible electronics,and energy applications.This work establishes hybrid crystalline materials as versatile and scalable platforms for addressing complex technological demands,paving the way for their application in next-generation multifunctional devices.
