In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-sp...In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-spectral imagery(MSI)data delineate gold-sulfide mineralization in altered rocks.Gold(Au)anomalies in hydrothermal breccias and quartz veins are associated with NE-oriented felsite dykes and silicified granitic rocks.Two main alteration types are identified:a pyrite-sericite-quartz and a sulfide-chlorite-carbonate assemblage,locally with dispersed free-milling Au specks.Dimensionality reduction techniques,including principal component analysis(PCA)and independent component analysis(ICA),enabled mapping of alteration types.Sentinel-2 PC125 composite images offered efficient lithological differentiation,while supervised classifications,i.e.,the support vector machine(SVM)of Landsat-8 yielded an accuracy of 88.55%and a Kappa value of 0.86.ASTER mineral indices contributed to map hydrothermal alteration mineral phases,including sericite,muscovite,kaolinite,and iron oxides.Results indicate that post-magmatic epigenetic hydrothermal activity significantly contributed to the Au-sulfide mineralization in the Fatira area,distinguishing it from the more prevalent orogenic gold deposits in the region.展开更多
Coal mining induces changes in the nature of rock and soil bodies,as well as hydrogeological conditions,which can easily trigger the occurrence of geological disasters such as water inrush,movement of the coal seam ro...Coal mining induces changes in the nature of rock and soil bodies,as well as hydrogeological conditions,which can easily trigger the occurrence of geological disasters such as water inrush,movement of the coal seam roof and floor,and rock burst.Transparency in coal mine geological conditions provides technical support for intelligent coal mining and geological disaster prevention.In this sense,it is of great significance to address the requirements for informatizing coal mine geological conditions,dynamically adjust sensing parameters,and accurately identify disaster characteristics so as to prevent and control coal mine geological disasters.This paper examines the various action fields associated with geological disasters in mining faces and scrutinizes the types and sensing parameters of geological disasters resulting from coal seam mining.On this basis,it summarizes a distributed fiber-optic sensing technology framework for transparent geology in coal mines.Combined with the multi-field monitoring characteristics of the strain field,the temperature field,and the vibration field of distributed optical fiber sensing technology,parameters such as the strain increment ratio,the aquifer temperature gradient,and the acoustic wave amplitude are extracted as eigenvalues for identifying rock breaking,aquifer water level,and water cut range,and a multi-field sensing method is established for identifying the characteristics of mining-induced rock mass disasters.The development direction of transparent geology based on optical fiber sensing technology is proposed in terms of the aspects of sensing optical fiber structure for large deformation monitoring,identification accuracy of optical fiber acoustic signals,multi-parameter monitoring,and early warning methods.展开更多
Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric featur...Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.展开更多
The growing demand for the miniaturization and multifunctionality of optoelectronic devices has promoted the development of transparent ferroelectrics.However,it is difficult for the superior multiple optical properti...The growing demand for the miniaturization and multifunctionality of optoelectronic devices has promoted the development of transparent ferroelectrics.However,it is difficult for the superior multiple optical properties of these materials to be compatible with the excellent ferroelectricity and piezoelectricity in transparent ceramics.Here,we successfully synthesized Bi/Eu codoped eco-friendly K0.5Na0.5NbO3transparent-ferroelectric ceramics with photo luminescence(PL)behavior,photochromic(PC)reactions and temperature-responsive PL.Based on the distinct optical properties of ceramics at different temperature ranges(room temperature and ultralow temperature),high utilization of multiple optical functions was realized.At room temperature,the PC behavior induced PL modulation contrast reaches 75.2%(at 592 nm),which can be applied in the optical information storage field.In the ultralow temperature range,the ceramics exhibit excellent sensitivity(with a maximum relative sensitivity of26.32%/K)via fluorescence intensity ratio technology and exhibit great application potential in noncontact optical temperature measurements.Additionally,the change in the PL intensity at different wavelengths(I_(614)/I_(592))can serve as a reliable indicator for detecting the occurrence of the phase transition from rhombohedral to orthorhombic at low temperature.This work provides a feasible paradigm for realizing the integration of ferroelectricity and multifarious optical properties in a single optoelectronic material.展开更多
A set of germanate garnet phosphors containing Tb^(3+)and Eu^(3+)were adequately synthesized using the high-temperature solid-state technique.The structural properties,photoluminescence characteristics,fluorescence li...A set of germanate garnet phosphors containing Tb^(3+)and Eu^(3+)were adequately synthesized using the high-temperature solid-state technique.The structural properties,photoluminescence characteristics,fluorescence lifetimes,and temperature-sensing capabilities of the phosphors were thoroughly investigated.X-ray diffraction confirms the crystalline structure of the phosphors,while photoluminescence spectra reveal a colour shift attributed to the trans fer of energy from Tb^(3+)to Eu^(3+)as the concentration of Eu^(3+)increases.The phosphors excited by UV light display a transition in colour from green to yellow,and subsequently to red,which can be used as a colour tunable phosphor in white light-emitting diode(w-LED) applications.As a novel temperature sensing material,the maximum relative sensitivity of Ca_(3)Sc_(2)Ge_(3)O_(12):Tb^(3+),Eu^(3+)phosphor is 0.1044 K-1(298 K),highlighting its potential for applications in temperature sensing.展开更多
Er^(3+)-and Tm^(3+)-doped Ca_(x)Sr_(2-x)Nb_(2)O_(7)(C_(x)S_(2-x)N,x=0.6,0.8,1,0,1.2,1,4) phosphors with layered pe rovskite structure were designed.These phosphors exhibit a dominant emission peak at 549 nm under980 n...Er^(3+)-and Tm^(3+)-doped Ca_(x)Sr_(2-x)Nb_(2)O_(7)(C_(x)S_(2-x)N,x=0.6,0.8,1,0,1.2,1,4) phosphors with layered pe rovskite structure were designed.These phosphors exhibit a dominant emission peak at 549 nm under980 nm laser excitation,attributed to the^(4)S_(3/2)→^(4)I_(15/2)transition.By increasing the content of Ca^(2+),the crystal field regulation of rare earth ions is realized and the luminescence enhancement is induced,which is manifested by the increase of^(2)H_(11/2),^(4)S_(3/2)→^(4)I_(15/2)emission.Furthermore,the temperature sensing sensitivities of C_(0.6)S_(1.4)N:Er,Tm and C_(0.6)S_(1.4)N:Er,Tm based on non-thermally coupled energy levels were studied.Finally,an anti-counterfeiting imprint was prepared using phosphors,which have high brightness and excellent photothermal stability.This work not only confirms that closer ionic radii substitution enables to increase the electronic density of states,improve the crystal field symmetry and enhance the luminescence,but also provides a promising phosphor system for temperature sensing and anti-counterfeiting applications,opening up new prospects in the optimization of the optical properties of phosphors.展开更多
Upconversion luminescent(UCL)materials have broad application prospects in the field of temperature sensing;thus,improving the luminescence performance and temperature measurement sensitivity of upconversion phosphors...Upconversion luminescent(UCL)materials have broad application prospects in the field of temperature sensing;thus,improving the luminescence performance and temperature measurement sensitivity of upconversion phosphors is highly important.In this study,SrAl_(2)Si_(2)O_(8)with good thermal stability was doped with Ho^(3+)and Yb^(3+),and the optimal concentration was determined to be S rAl_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)(in mole fraction).A series of(Sr_(0.87-x)Ba_(x))Al_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)phosphor samples was prepared by using a cationic substitution strategy and further doping Ba^(2+)to replace the Sr^(2+)lattice in the matrix.The re sults show that the introduction of Ba^(2+)effectively replaces Sr^(2+)and significantly increases the upconversion fluorescence emission intensity of SrAl_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)by approximately 2.9times.The temperature sensing properties of SrAl_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)and Sr_(0.3)7Ba_(0.5)0Al_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)were investigated.The Ho^(3+)-based5F5and5S2/5F4nonthermal coupled energy level fluorescence intensity ratio(FIR)techniques in the Ba_(0.3)7S r_(0.50)Al_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)phosphors show a maximum temperature measurement absolute sensitivity of 4.32%/K at 573 K and a maximum relative sensitivity of 1.08%/K at 373 K;these values are 5.8 and 3.2 times greater,respectively,than that of the non-Ba^(2+)-doped SrAl_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)phosphor.These results not only confirm the effectiveness of the cation substitution strategy in enhancing the upconversion luminescence performance and temperature sensing characteristics but also provide a scientific basis for the design of high-performance optical temperature sensors.展开更多
In this study,using 3,5‑di(3′,5′‑dicarboxylphenyl)‑1H‑1,2,4‑triazole(H4L)as ligands,a gadolinia‑based organic framework complex{[GdNa(L)(H_(2)O)3]·2H_(2)O}_(n)(Gd‑Na‑MOF)was successfully designed and synthesize...In this study,using 3,5‑di(3′,5′‑dicarboxylphenyl)‑1H‑1,2,4‑triazole(H4L)as ligands,a gadolinia‑based organic framework complex{[GdNa(L)(H_(2)O)3]·2H_(2)O}_(n)(Gd‑Na‑MOF)was successfully designed and synthesized by hydrothermal method.The structure and properties were systematically characterized and tested by techniques such as single‑crystal X‑ray diffraction,powder X‑ray diffraction,thermogravimetric analysis,infrared spectroscopy,and fluorescence spectroscopy.The results indicate that this complex has a unique 3D structure,excellent thermal stability,and outstanding luminescent performance.Based on its luminescent properties,a polymer‑embedding method was employed to fabricate the Gd‑Na‑MOF into a flexible,washable composite fluorescent film,Gd‑Na‑MOF@PMMA/BMA(PMMA=polymethyl methacrylate,BMA=butyl methacrylate).This fluorescent film exhibited highly sensitive recognition capability for tyramine,with a low detection limit of 1.66μmol·L^(-1).It was used for the detection of tyramine in bananas,with a recovery rate of 96.92%‑100.26%.CCDC:2466949.展开更多
Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to instal...Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.展开更多
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
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
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 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
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
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
基金the National Science Foundation of China (Grant No. NSFC: 92162213)the Geology Department Faculty of Science of Al-Azhar University (Assiut Branch)+2 种基金the China Scholarship CouncilChang'an UniversityIstanbul Technical University's Scientific Research Project (BAP Project ID: 45396, code: FHD-2024-45396)
文摘In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-spectral imagery(MSI)data delineate gold-sulfide mineralization in altered rocks.Gold(Au)anomalies in hydrothermal breccias and quartz veins are associated with NE-oriented felsite dykes and silicified granitic rocks.Two main alteration types are identified:a pyrite-sericite-quartz and a sulfide-chlorite-carbonate assemblage,locally with dispersed free-milling Au specks.Dimensionality reduction techniques,including principal component analysis(PCA)and independent component analysis(ICA),enabled mapping of alteration types.Sentinel-2 PC125 composite images offered efficient lithological differentiation,while supervised classifications,i.e.,the support vector machine(SVM)of Landsat-8 yielded an accuracy of 88.55%and a Kappa value of 0.86.ASTER mineral indices contributed to map hydrothermal alteration mineral phases,including sericite,muscovite,kaolinite,and iron oxides.Results indicate that post-magmatic epigenetic hydrothermal activity significantly contributed to the Au-sulfide mineralization in the Fatira area,distinguishing it from the more prevalent orogenic gold deposits in the region.
基金National Natural Science Foundation of China,Grant/Award Number:42130706。
文摘Coal mining induces changes in the nature of rock and soil bodies,as well as hydrogeological conditions,which can easily trigger the occurrence of geological disasters such as water inrush,movement of the coal seam roof and floor,and rock burst.Transparency in coal mine geological conditions provides technical support for intelligent coal mining and geological disaster prevention.In this sense,it is of great significance to address the requirements for informatizing coal mine geological conditions,dynamically adjust sensing parameters,and accurately identify disaster characteristics so as to prevent and control coal mine geological disasters.This paper examines the various action fields associated with geological disasters in mining faces and scrutinizes the types and sensing parameters of geological disasters resulting from coal seam mining.On this basis,it summarizes a distributed fiber-optic sensing technology framework for transparent geology in coal mines.Combined with the multi-field monitoring characteristics of the strain field,the temperature field,and the vibration field of distributed optical fiber sensing technology,parameters such as the strain increment ratio,the aquifer temperature gradient,and the acoustic wave amplitude are extracted as eigenvalues for identifying rock breaking,aquifer water level,and water cut range,and a multi-field sensing method is established for identifying the characteristics of mining-induced rock mass disasters.The development direction of transparent geology based on optical fiber sensing technology is proposed in terms of the aspects of sensing optical fiber structure for large deformation monitoring,identification accuracy of optical fiber acoustic signals,multi-parameter monitoring,and early warning methods.
基金financially supported by the Youth Innovation Promotion Association CAS(No.2021325)the National Natural Science Foundation of China(Nos.52179117,U21A20159)the Research project of Panzhihua Iron and Steel Group Mining Co.,Ltd.(No.2021-P6-D2-05)。
文摘Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.
基金Project supported by the National Natural Science Foundation of China(52072075,52102126,12104093)the Natural Science Foundation of Fujian Province(2021J05122,2021J05123,2022J01087,2022J01552,2023J01259)。
文摘The growing demand for the miniaturization and multifunctionality of optoelectronic devices has promoted the development of transparent ferroelectrics.However,it is difficult for the superior multiple optical properties of these materials to be compatible with the excellent ferroelectricity and piezoelectricity in transparent ceramics.Here,we successfully synthesized Bi/Eu codoped eco-friendly K0.5Na0.5NbO3transparent-ferroelectric ceramics with photo luminescence(PL)behavior,photochromic(PC)reactions and temperature-responsive PL.Based on the distinct optical properties of ceramics at different temperature ranges(room temperature and ultralow temperature),high utilization of multiple optical functions was realized.At room temperature,the PC behavior induced PL modulation contrast reaches 75.2%(at 592 nm),which can be applied in the optical information storage field.In the ultralow temperature range,the ceramics exhibit excellent sensitivity(with a maximum relative sensitivity of26.32%/K)via fluorescence intensity ratio technology and exhibit great application potential in noncontact optical temperature measurements.Additionally,the change in the PL intensity at different wavelengths(I_(614)/I_(592))can serve as a reliable indicator for detecting the occurrence of the phase transition from rhombohedral to orthorhombic at low temperature.This work provides a feasible paradigm for realizing the integration of ferroelectricity and multifarious optical properties in a single optoelectronic material.
基金Project supported by the National Natural Science Foundation of China (52274273)。
文摘A set of germanate garnet phosphors containing Tb^(3+)and Eu^(3+)were adequately synthesized using the high-temperature solid-state technique.The structural properties,photoluminescence characteristics,fluorescence lifetimes,and temperature-sensing capabilities of the phosphors were thoroughly investigated.X-ray diffraction confirms the crystalline structure of the phosphors,while photoluminescence spectra reveal a colour shift attributed to the trans fer of energy from Tb^(3+)to Eu^(3+)as the concentration of Eu^(3+)increases.The phosphors excited by UV light display a transition in colour from green to yellow,and subsequently to red,which can be used as a colour tunable phosphor in white light-emitting diode(w-LED) applications.As a novel temperature sensing material,the maximum relative sensitivity of Ca_(3)Sc_(2)Ge_(3)O_(12):Tb^(3+),Eu^(3+)phosphor is 0.1044 K-1(298 K),highlighting its potential for applications in temperature sensing.
基金Project supported by the Science and Technology International Cooperation Project of Qinghai Province (2022-HZ-807)the Open Project Salt Lake Chemical Engineering Research Complex,Qinghai University (2023-DXSSZZ-04)。
文摘Er^(3+)-and Tm^(3+)-doped Ca_(x)Sr_(2-x)Nb_(2)O_(7)(C_(x)S_(2-x)N,x=0.6,0.8,1,0,1.2,1,4) phosphors with layered pe rovskite structure were designed.These phosphors exhibit a dominant emission peak at 549 nm under980 nm laser excitation,attributed to the^(4)S_(3/2)→^(4)I_(15/2)transition.By increasing the content of Ca^(2+),the crystal field regulation of rare earth ions is realized and the luminescence enhancement is induced,which is manifested by the increase of^(2)H_(11/2),^(4)S_(3/2)→^(4)I_(15/2)emission.Furthermore,the temperature sensing sensitivities of C_(0.6)S_(1.4)N:Er,Tm and C_(0.6)S_(1.4)N:Er,Tm based on non-thermally coupled energy levels were studied.Finally,an anti-counterfeiting imprint was prepared using phosphors,which have high brightness and excellent photothermal stability.This work not only confirms that closer ionic radii substitution enables to increase the electronic density of states,improve the crystal field symmetry and enhance the luminescence,but also provides a promising phosphor system for temperature sensing and anti-counterfeiting applications,opening up new prospects in the optimization of the optical properties of phosphors.
基金Project supported by the National Natural Science Foundation of China(12264050)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01C727)Talent Project of Tianchi Doctoral Program in Xinjiang Uygur Autonomous Region(0301050903)。
文摘Upconversion luminescent(UCL)materials have broad application prospects in the field of temperature sensing;thus,improving the luminescence performance and temperature measurement sensitivity of upconversion phosphors is highly important.In this study,SrAl_(2)Si_(2)O_(8)with good thermal stability was doped with Ho^(3+)and Yb^(3+),and the optimal concentration was determined to be S rAl_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)(in mole fraction).A series of(Sr_(0.87-x)Ba_(x))Al_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)phosphor samples was prepared by using a cationic substitution strategy and further doping Ba^(2+)to replace the Sr^(2+)lattice in the matrix.The re sults show that the introduction of Ba^(2+)effectively replaces Sr^(2+)and significantly increases the upconversion fluorescence emission intensity of SrAl_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)by approximately 2.9times.The temperature sensing properties of SrAl_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)and Sr_(0.3)7Ba_(0.5)0Al_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)were investigated.The Ho^(3+)-based5F5and5S2/5F4nonthermal coupled energy level fluorescence intensity ratio(FIR)techniques in the Ba_(0.3)7S r_(0.50)Al_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)phosphors show a maximum temperature measurement absolute sensitivity of 4.32%/K at 573 K and a maximum relative sensitivity of 1.08%/K at 373 K;these values are 5.8 and 3.2 times greater,respectively,than that of the non-Ba^(2+)-doped SrAl_(2)Si_(2)O_(8):1%Ho^(3+),12%Yb^(3+)phosphor.These results not only confirm the effectiveness of the cation substitution strategy in enhancing the upconversion luminescence performance and temperature sensing characteristics but also provide a scientific basis for the design of high-performance optical temperature sensors.
文摘In this study,using 3,5‑di(3′,5′‑dicarboxylphenyl)‑1H‑1,2,4‑triazole(H4L)as ligands,a gadolinia‑based organic framework complex{[GdNa(L)(H_(2)O)3]·2H_(2)O}_(n)(Gd‑Na‑MOF)was successfully designed and synthesized by hydrothermal method.The structure and properties were systematically characterized and tested by techniques such as single‑crystal X‑ray diffraction,powder X‑ray diffraction,thermogravimetric analysis,infrared spectroscopy,and fluorescence spectroscopy.The results indicate that this complex has a unique 3D structure,excellent thermal stability,and outstanding luminescent performance.Based on its luminescent properties,a polymer‑embedding method was employed to fabricate the Gd‑Na‑MOF into a flexible,washable composite fluorescent film,Gd‑Na‑MOF@PMMA/BMA(PMMA=polymethyl methacrylate,BMA=butyl methacrylate).This fluorescent film exhibited highly sensitive recognition capability for tyramine,with a low detection limit of 1.66μmol·L^(-1).It was used for the detection of tyramine in bananas,with a recovery rate of 96.92%‑100.26%.CCDC:2466949.
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 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 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.
