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
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 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
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
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
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
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
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
Research on tourism climate comfort is undergoing a paradigm shift from classic static assessment to intelligent dynamic sensing.Early models(such as temperature-humidity index and tourism climate index)established ba...Research on tourism climate comfort is undergoing a paradigm shift from classic static assessment to intelligent dynamic sensing.Early models(such as temperature-humidity index and tourism climate index)established based on data of meteorological stations laid the foundation for the discipline but were unable to meet the dynamic demands of climate change,spatial heterogeneity,and individual experience.Global climate change is reshaping the landscape of tourism comfort and driving the assessment to shift towards future risk prediction.Downscaling technology becomes the key to connecting global scenarios and local assessments.Remote sensing and Internet of Things technologies have constructed a"sky-ground"collaborative sensing network,achieving a revolution in data acquisition.Artificial intelligence and big data analysis serve as the intelligent core to drive research from description to prediction.The new paradigm has significant potential in improving assessment accuracy and timeliness,but also faces challenges such as data integration,model interpretability,interdisciplinary integration,and ethical privacy.In the future,it is needed to develop interpretable AI,construct climate digital twins,and promote full-chain coupling research.This transformation is not merely an upgrade of methods,but a fundamental shift in the study of philosophy from an"environment-centered"perspective to an"experience-centered"one,providing key scientific support for sustainable tourism.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
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
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
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
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
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
Accurate assessment of site quality in coastal Casuarina equisetifolia(C.equisetifolia)plantations is essential for enhancing the protective function of shelterbelts and implementing site-specific afforestation strate...Accurate assessment of site quality in coastal Casuarina equisetifolia(C.equisetifolia)plantations is essential for enhancing the protective function of shelterbelts and implementing site-specific afforestation strategies.However,traditional ground-based surveys are limited in spatial coverage and efficiency,hindering effective forest management.To overcome these limitations,this study developed an integrated assessment framework that couples ground-based modeling with remote sensing inversion to achieve large-scale site quality mapping.Field investigations on Pingtan Island,Fujian Province,China,were used to establish a ground-based evaluation model.Soil fertility was quantified using Principal Component Analysis(PCA),and principal components were classified into discrete fertility grades through K-means clustering.These grades,together with topographic variables,were incorporated into a site quality classification model constructed using Quantification Theory I.The point-based model was subsequently extrapolated using Landsat 9 imagery to generate a spatially continuous site quality map.Spatial autocorrelation(Moran’s Ⅰ)and LISA clustering were further employed to interpret spatial patterns.Results indicate that coastal sandy soils in the study area are generally nutrient-poor,with tree growth primarily constrained by total nitrogen,organic matter,available phosphorus,and total phosphorus.The five most influential site factors,ranked by importance,are soil fertility,distance from the coastline,aspect,slope gradient,and elevation.Optimal conditions for C.equisetifolia growth include fertile soil,location>1000 m from the coastline,south-facing or semi-sunny slopes,slope gradients<15°,and elevations between 10-100 m.Only 11.94%of the area was classified as high-quality(Grade I),while 61.74%fell into moderate or poor grades(Grades Ⅲ and Ⅳ),indicating that most plantations are located on suboptimal sites.This study provides scientific support for improving the precision and sustainability of coastal shelterbelt planning and management,offering practical insights for afforestation strategies,forest restoration,and ecological forestry development in coastal zones.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
Addressing the critical detection range limitation in active electrosensing(AES)for underwater sensing,this study proposes an enhanced AES system via novel array optimization.While AES offers advantages like interfere...Addressing the critical detection range limitation in active electrosensing(AES)for underwater sensing,this study proposes an enhanced AES system via novel array optimization.While AES offers advantages like interference immunity,acoustic stealth detection,and low cost,its short range restricts applicability.A target perturbation model under differential signal acquisition reveals that signal strength increases with local electric field intensity,target size,differential channel spacing,and conductivity contrast,but decreases with target-electrode distance.To extend detection,novel array configurations were explored.Simulations demonstrate that both rectangular and offset arrays significantly outperform the traditional collinear layout.Specifically,an offset array(with 8 m transmitting–receiving spacing)achieved an effective detection range enhancement exceeding 83%under the same distortion threshold while maintaining simplified electrode structure.Experimental validation confirmed a 100%increase in maximum detection distance to 5 m under identical noise thresholds compared to the collinear array.Furthermore,a fully connected neural network-based localization model achieved a mean positioning error of 14.12 cm at 3.15 m in static scenarios.In dynamic scenarios within 1–3 m,mean errors were controlled between 13.19 cm and 27.56 cm.Mechanistic analysis indicates that increasing the array baseline enhances the signal-to-noise ratio by simultaneously suppressing near-field environmental noise and amplifying far-field signal reception.Structural innovations in array design enabled this study to significantly expand the detection range of AES systems without compromising cost efficiency.These advancements directly promote the engineering application of AES technology,offering critical technical support for underwater defense security monitoring,long-range early warning systems,and maritime rights protection.展开更多
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 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Supported by the School-level Project of Sichuan Minzu College(XYZB2017ZB).
文摘Research on tourism climate comfort is undergoing a paradigm shift from classic static assessment to intelligent dynamic sensing.Early models(such as temperature-humidity index and tourism climate index)established based on data of meteorological stations laid the foundation for the discipline but were unable to meet the dynamic demands of climate change,spatial heterogeneity,and individual experience.Global climate change is reshaping the landscape of tourism comfort and driving the assessment to shift towards future risk prediction.Downscaling technology becomes the key to connecting global scenarios and local assessments.Remote sensing and Internet of Things technologies have constructed a"sky-ground"collaborative sensing network,achieving a revolution in data acquisition.Artificial intelligence and big data analysis serve as the intelligent core to drive research from description to prediction.The new paradigm has significant potential in improving assessment accuracy and timeliness,but also faces challenges such as data integration,model interpretability,interdisciplinary integration,and ethical privacy.In the future,it is needed to develop interpretable AI,construct climate digital twins,and promote full-chain coupling research.This transformation is not merely an upgrade of methods,but a fundamental shift in the study of philosophy from an"environment-centered"perspective to an"experience-centered"one,providing key scientific support for sustainable tourism.
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 University Key Lab for Geomatics Technology&Optimize Resources Utilization in Fujian Province(KJG20104A)Fujian Forestry Science and Technology project(2023FKJ15)Fuzhou Forestry Science and Technology Research Project(2130206).
文摘Accurate assessment of site quality in coastal Casuarina equisetifolia(C.equisetifolia)plantations is essential for enhancing the protective function of shelterbelts and implementing site-specific afforestation strategies.However,traditional ground-based surveys are limited in spatial coverage and efficiency,hindering effective forest management.To overcome these limitations,this study developed an integrated assessment framework that couples ground-based modeling with remote sensing inversion to achieve large-scale site quality mapping.Field investigations on Pingtan Island,Fujian Province,China,were used to establish a ground-based evaluation model.Soil fertility was quantified using Principal Component Analysis(PCA),and principal components were classified into discrete fertility grades through K-means clustering.These grades,together with topographic variables,were incorporated into a site quality classification model constructed using Quantification Theory I.The point-based model was subsequently extrapolated using Landsat 9 imagery to generate a spatially continuous site quality map.Spatial autocorrelation(Moran’s Ⅰ)and LISA clustering were further employed to interpret spatial patterns.Results indicate that coastal sandy soils in the study area are generally nutrient-poor,with tree growth primarily constrained by total nitrogen,organic matter,available phosphorus,and total phosphorus.The five most influential site factors,ranked by importance,are soil fertility,distance from the coastline,aspect,slope gradient,and elevation.Optimal conditions for C.equisetifolia growth include fertile soil,location>1000 m from the coastline,south-facing or semi-sunny slopes,slope gradients<15°,and elevations between 10-100 m.Only 11.94%of the area was classified as high-quality(Grade I),while 61.74%fell into moderate or poor grades(Grades Ⅲ and Ⅳ),indicating that most plantations are located on suboptimal sites.This study provides scientific support for improving the precision and sustainability of coastal shelterbelt planning and management,offering practical insights for afforestation strategies,forest restoration,and ecological forestry development in coastal zones.
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supported in part by National Natural Science Foundation of China(Grant No.62273075).
文摘Addressing the critical detection range limitation in active electrosensing(AES)for underwater sensing,this study proposes an enhanced AES system via novel array optimization.While AES offers advantages like interference immunity,acoustic stealth detection,and low cost,its short range restricts applicability.A target perturbation model under differential signal acquisition reveals that signal strength increases with local electric field intensity,target size,differential channel spacing,and conductivity contrast,but decreases with target-electrode distance.To extend detection,novel array configurations were explored.Simulations demonstrate that both rectangular and offset arrays significantly outperform the traditional collinear layout.Specifically,an offset array(with 8 m transmitting–receiving spacing)achieved an effective detection range enhancement exceeding 83%under the same distortion threshold while maintaining simplified electrode structure.Experimental validation confirmed a 100%increase in maximum detection distance to 5 m under identical noise thresholds compared to the collinear array.Furthermore,a fully connected neural network-based localization model achieved a mean positioning error of 14.12 cm at 3.15 m in static scenarios.In dynamic scenarios within 1–3 m,mean errors were controlled between 13.19 cm and 27.56 cm.Mechanistic analysis indicates that increasing the array baseline enhances the signal-to-noise ratio by simultaneously suppressing near-field environmental noise and amplifying far-field signal reception.Structural innovations in array design enabled this study to significantly expand the detection range of AES systems without compromising cost efficiency.These advancements directly promote the engineering application of AES technology,offering critical technical support for underwater defense security monitoring,long-range early warning systems,and maritime rights protection.
