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
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
Vibrio harveyi,a Gram-negative bacterium ubiquitous in marine environments,is recognized as an opportunistic pathogen affecting various aquatic organisms such as fish,shrimp,and shellfish.To enhance its environmental ...Vibrio harveyi,a Gram-negative bacterium ubiquitous in marine environments,is recognized as an opportunistic pathogen affecting various aquatic organisms such as fish,shrimp,and shellfish.To enhance its environmental resilience and adaptive capacity,V.harveyi employs a complex quorum sensing mechanism to modulate its virulence factors,such as bioluminescence,biofilm formation,and motility.Therefore,targeting the quorum sensing of V.harveyi could be a promising strategy to develop novel approaches to ensure the microbial safety of seafood products.This study aims to evaluate the impact of curcumin-mediated photo/sonodynamic treatment on quorum sensing in V.harveyi and its regulated functions.The results indicate a significant decrease of luminescence in V.harveyi following curcumin-mediated photo/sonodynamic treatment.Correspondingly,the biofilm formation ability and bacterial motility of V.harveyi were also greatly impaired by the treatment.Notably,the production of reactive oxygen species in bacteria induced by the photo/sonodynamic treatment could be the underlying mechanism involved in the observed disruption of quorum sensing.These findings underscore the great potential of photo/sonodynamic treatment as a promising strategy to disrupt quorum sensing and mitigate the virulence of V.harveyi,thereby contributing to the development of effective control strategies against this pervasive pathogen.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
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
The development of multifunctional intelligent textiles has become an important innovation direction in the field of textile engineering, under the dual demands of intelligent health monitoring and environmental prote...The development of multifunctional intelligent textiles has become an important innovation direction in the field of textile engineering, under the dual demands of intelligent health monitoring and environmental protection. Although singlefunctional textiles with antibacterial, photochromic, and strain-sensing properties have been developed, they are unable to meet the demand for multifunctional textiles. In this respect, this study developed a poly(lactic acid) and poly(3-hydroxybutyrate)/thermoplastic polyurethane/carbon black nanoparticle composite nanofiber yarn(PPTCY) with a hollow-core-sheath structure using a simple conjugate electrospinning technology. PPTCY possessed excellent mechanical strength and could be effectively woven. More importantly, it not only enabled real-time visual monitoring of ultraviolet(UV) intensity in the environment but also possessed excellent antibacterial properties and strain-sensing performance. Its ΔE value was up to 58.24, and its antibacterial rates against Escherichia coli and Staphylococcus aureus were both 99.99%. This fabric had excellent strainsensing performance, high linearity, and durability under both pressure and stretching deformations. This research provides favorable technical support for the application of intelligent textiles in the field of UV protection and traffic safety.展开更多
Conductive hydrogel-based stretchable electronics have been extensively investigated,among which strain sensors are the most prominently studied.While the mechanical properties significantly affect the performance of ...Conductive hydrogel-based stretchable electronics have been extensively investigated,among which strain sensors are the most prominently studied.While the mechanical properties significantly affect the performance of these devices,the systematic correlation between specific mechanical parameters and sensing performance remains rarely explored.This work compares the influences of Young’s modulus and mechanical hysteresis on the sensing performance between highly entangled PAM-Li and double-network PAM-Li-Agar-3 strain sensors.Owing to the brittle agar network,which imparts a higher Young’s modulus and pronounced mechanical hysteresis to the double-network PAMLi-Agar-3 hydrogel,the corresponding sensor requires a greater driving force for deformation and yields signals with poor reproducibility.In comparison,the PAM-Li hydrogel,characterized by highly entangled polymer chains,exhibits a lower Young’s modulus and negligible mechanical hysteresis.Consequently,signals from the PAM-Li strain sensor demonstrate enhanced sensitivity and stability.Therefore,this work demonstrates that a low Young’s modulus and minimal mechanical hysteresis are critical factors for achieving superior sensing performance in strain sensors,as systematically validated through comparative analyses across diverse application scenarios.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
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
Detecting biomarkers in body fluids by optical lateral flow immune assay(LFIA) technology provides rapid access to disease information for early diagnosis.LFIA is based on an antigen-antibody reaction and is rapidly b...Detecting biomarkers in body fluids by optical lateral flow immune assay(LFIA) technology provides rapid access to disease information for early diagnosis.LFIA is based on an antigen-antibody reaction and is rapidly becoming the preferred choice of physicians and patients for point-of-care testing due to its simplicity,cost-effectiveness,and rapid detection.Observing the optical signal change from the colloidal gold of the traditional LFIA strip has been widely applied for various biomarkers detection in body fluids.Despite the significant progress,rapid real-time detection of color changes in the colloidal gold by the naked eye still faces many limitations,such as large errors and the inability to quantify and accurately detect.New optical LFIA strip technology has emerged in recent years to extend its application scenarios for achieving quantitative detection such as fluorescence,afterglow,and chemiluminescence.Herein,we summarized the development of optical LFIA technology from single to hyphenated optical signals for biomarkers detection in body fluids from invasive and non-invasive sources.Moreover,the challenge and outlook of optical LFIA strip technology are highlighted to inspire the designing of next-generation diagnostic platforms.展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contr...Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contribute to the identification of defects in underground structures,this study conducted a four-point bending test of a reinforced concrete(RC)beam and uniaxial loading tests of an RC specimen with local cavities.The experimental results revealed the disparity in DFOS strain spike profiles between these two structural anomalies.The effectiveness of DFOS in the quantification of crack opening displacement(COD)was also demonstrated,even in cases where perfect bonding was not achievable between the cable and structures.In addition,DFOS strain spikes observed in two diaphragm wall panels of a twin circular shaft were also reported.The most probable cause of those spikes was identified as the mechanical behavior associated with local concrete contamination.With the utilization of the strain profiles obtained from laboratory tests and field monitoring,three types of multi-classifiers,based on support vector machine(SVM),random forest(RF),and backpropagation neural network(BP),were employed to classify strain profiles,including crack-induced spikes,non-crack-induced spikes,and non-spike strain profiles.Among these classifiers,the SVM-based classifier exhibited superior performance in terms of accuracy and model robustness.This finding suggests that the SVM-based classifier holds promise as a potential solution for the automatic detection and classification of defects in underground structures during long-term monitoring.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,Prof.Peng Wu of the Analytical&Testing Center,Sichuan University.
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financially supported by National Natural Science Foundation of China(32272450)Science Fund for Distinguished Young Scholars of Fujian Province(2023J06020)Special Funds for Science and Technology Innovation of Fujian Agriculture and Forestry University(KFB23132A)。
文摘Vibrio harveyi,a Gram-negative bacterium ubiquitous in marine environments,is recognized as an opportunistic pathogen affecting various aquatic organisms such as fish,shrimp,and shellfish.To enhance its environmental resilience and adaptive capacity,V.harveyi employs a complex quorum sensing mechanism to modulate its virulence factors,such as bioluminescence,biofilm formation,and motility.Therefore,targeting the quorum sensing of V.harveyi could be a promising strategy to develop novel approaches to ensure the microbial safety of seafood products.This study aims to evaluate the impact of curcumin-mediated photo/sonodynamic treatment on quorum sensing in V.harveyi and its regulated functions.The results indicate a significant decrease of luminescence in V.harveyi following curcumin-mediated photo/sonodynamic treatment.Correspondingly,the biofilm formation ability and bacterial motility of V.harveyi were also greatly impaired by the treatment.Notably,the production of reactive oxygen species in bacteria induced by the photo/sonodynamic treatment could be the underlying mechanism involved in the observed disruption of quorum sensing.These findings underscore the great potential of photo/sonodynamic treatment as a promising strategy to disrupt quorum sensing and mitigate the virulence of V.harveyi,thereby contributing to the development of effective control strategies against this pervasive pathogen.
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supported by the Science and Technology Guidance Project of China National Textile and Apparel Council (Grant No.2024033)the Jiangsu Provincial Key Research and Development Program (Grant No.BE2019045)。
文摘The development of multifunctional intelligent textiles has become an important innovation direction in the field of textile engineering, under the dual demands of intelligent health monitoring and environmental protection. Although singlefunctional textiles with antibacterial, photochromic, and strain-sensing properties have been developed, they are unable to meet the demand for multifunctional textiles. In this respect, this study developed a poly(lactic acid) and poly(3-hydroxybutyrate)/thermoplastic polyurethane/carbon black nanoparticle composite nanofiber yarn(PPTCY) with a hollow-core-sheath structure using a simple conjugate electrospinning technology. PPTCY possessed excellent mechanical strength and could be effectively woven. More importantly, it not only enabled real-time visual monitoring of ultraviolet(UV) intensity in the environment but also possessed excellent antibacterial properties and strain-sensing performance. Its ΔE value was up to 58.24, and its antibacterial rates against Escherichia coli and Staphylococcus aureus were both 99.99%. This fabric had excellent strainsensing performance, high linearity, and durability under both pressure and stretching deformations. This research provides favorable technical support for the application of intelligent textiles in the field of UV protection and traffic safety.
基金supported by the National Natural Science Foundation of China(52303145,52403083)the Natural Science Foundation of Sichuan Province(2025ZNSFSC1400)supported by the start-up funding at Chengdu University(T202207)。
文摘Conductive hydrogel-based stretchable electronics have been extensively investigated,among which strain sensors are the most prominently studied.While the mechanical properties significantly affect the performance of these devices,the systematic correlation between specific mechanical parameters and sensing performance remains rarely explored.This work compares the influences of Young’s modulus and mechanical hysteresis on the sensing performance between highly entangled PAM-Li and double-network PAM-Li-Agar-3 strain sensors.Owing to the brittle agar network,which imparts a higher Young’s modulus and pronounced mechanical hysteresis to the double-network PAMLi-Agar-3 hydrogel,the corresponding sensor requires a greater driving force for deformation and yields signals with poor reproducibility.In comparison,the PAM-Li hydrogel,characterized by highly entangled polymer chains,exhibits a lower Young’s modulus and negligible mechanical hysteresis.Consequently,signals from the PAM-Li strain sensor demonstrate enhanced sensitivity and stability.Therefore,this work demonstrates that a low Young’s modulus and minimal mechanical hysteresis are critical factors for achieving superior sensing performance in strain sensors,as systematically validated through comparative analyses across diverse application scenarios.
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.
基金supported by the National Natural Science Foundation of China (Nos.22234005,22494632,22404081)the Natural Science Foundation of Jiangsu Province (Nos.BK20222015,BK20240534)。
文摘Detecting biomarkers in body fluids by optical lateral flow immune assay(LFIA) technology provides rapid access to disease information for early diagnosis.LFIA is based on an antigen-antibody reaction and is rapidly becoming the preferred choice of physicians and patients for point-of-care testing due to its simplicity,cost-effectiveness,and rapid detection.Observing the optical signal change from the colloidal gold of the traditional LFIA strip has been widely applied for various biomarkers detection in body fluids.Despite the significant progress,rapid real-time detection of color changes in the colloidal gold by the naked eye still faces many limitations,such as large errors and the inability to quantify and accurately detect.New optical LFIA strip technology has emerged in recent years to extend its application scenarios for achieving quantitative detection such as fluorescence,afterglow,and chemiluminescence.Herein,we summarized the development of optical LFIA technology from single to hyphenated optical signals for biomarkers detection in body fluids from invasive and non-invasive sources.Moreover,the challenge and outlook of optical LFIA strip technology are highlighted to inspire the designing of next-generation diagnostic platforms.
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
基金support from the Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City,University of Macao (Grant No.SKL-IoTSC (UM)-2021-2023/ORPF/A19/2022)the General Research Fund project from Research Grants Council of Hong Kong Special Administrative Region Government of China (Grant No.15214722)the Start-up Fund from The Hong Kong Polytechnic University (Grant No.BD88).
文摘Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contribute to the identification of defects in underground structures,this study conducted a four-point bending test of a reinforced concrete(RC)beam and uniaxial loading tests of an RC specimen with local cavities.The experimental results revealed the disparity in DFOS strain spike profiles between these two structural anomalies.The effectiveness of DFOS in the quantification of crack opening displacement(COD)was also demonstrated,even in cases where perfect bonding was not achievable between the cable and structures.In addition,DFOS strain spikes observed in two diaphragm wall panels of a twin circular shaft were also reported.The most probable cause of those spikes was identified as the mechanical behavior associated with local concrete contamination.With the utilization of the strain profiles obtained from laboratory tests and field monitoring,three types of multi-classifiers,based on support vector machine(SVM),random forest(RF),and backpropagation neural network(BP),were employed to classify strain profiles,including crack-induced spikes,non-crack-induced spikes,and non-spike strain profiles.Among these classifiers,the SVM-based classifier exhibited superior performance in terms of accuracy and model robustness.This finding suggests that the SVM-based classifier holds promise as a potential solution for the automatic detection and classification of defects in underground structures during long-term monitoring.
