To prepare a conductive polymer actuator with decent performance,a self-built experimental platform for the preparation of polypyrrole film is employed.One of the essential goals is to examine the mechanical character...To prepare a conductive polymer actuator with decent performance,a self-built experimental platform for the preparation of polypyrrole film is employed.One of the essential goals is to examine the mechanical characteristics of the actuator in the presence of various combinations of process parameters,combined with the orthogonal test method of"four factors and three levels".The bending and sensing characteristics of actuators of various sizes are methodically examined using a self-made bending polypyrrole actuator.The functional relationship between the bending displacement and the output voltage signal is established by studying the characteristics of the actuator sensor subjected to various degrees of bending.The experimental results reveal that the bending displacement of the actuator tip almost exhibits a linear variation as a function of length and width.When the voltage reaches 0.8 V,the bending speed of the actuator tends to be stable.Finally,the mechanical properties of the self-assembled polypyrrole actuator are verified by the design and fabrication of the microgripper.展开更多
With the rapid development of science and technology,new sensing technology has been used increasingly in mechatronics system,for the system of intelligent,automation and efficiency,provide strong support.Emerging sen...With the rapid development of science and technology,new sensing technology has been used increasingly in mechatronics system,for the system of intelligent,automation and efficiency,provide strong support.Emerging sensor technology in electromechanical integration system of innovative applications not only promote the system of intelligent upgrade,also for its wide application in the field of multiple provides a strong support,and along with the advance of technology and application scenario development,emerging sensor technology in electromechanical integration system to play a more important role.In this regard,this paper first expounds the overview of emerging sensing technology,then analyzes the innovation and integration of emerging sensing technology and mechatronics system,and finally further explores the practical application of emerging sensing technology in mechatronics system,in order to provide some reference for relevant researchers.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
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
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
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
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
Nanoscale confinement environments often affect the transport mechanisms of nanofluids.Understanding the dynamic behavior of molecules in two-dimensional(2D)confined channels is of great importance in the areas of sen...Nanoscale confinement environments often affect the transport mechanisms of nanofluids.Understanding the dynamic behavior of molecules in two-dimensional(2D)confined channels is of great importance in the areas of sensing,catalysis and energy storage.As a popular candidate for a new type of gas sensing material,MXenes have the problem of nonselectivity towards polar gases with slow responses,which severely limits their applications.Here,we report a study on regulating the confinement effect of 2D channels between MXene layers through annealing treatment and ion(Na^(+))intercalation for high-performance ammonia(NH_(3))sensing.Firstly,the annealing treatment accurately modulates the size of the 2D channels to effectively block the entry of large-size gas molecules and improve the selectivity for NH_(3).Ab initio molecular dynamics(AIMD)also confirms that the modulated channel size has a special"nano-pumping effect",which can accelerate the dynamic behavior of NH_(3) molecules in the 2D confined space.Moreover,the intercalation of Na+ions increases the adsorption capacity of NH_(3).Therefore,the"nano-pumping effect"and theintercalation of Na+ions effectively enhance the response speed and sensitivity of MXene to NH_(3),respectively.The experimental results show that the modified Ti_(3)C_(2) exhibits high sensitivity(0.17),rapid response(181 s),excellent selectivity and stability towards NH_(3).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 quorum sensing （QS） process, bacteria regulate gene expression by utilizing small signaling molecules called autoinducers in response to a variety of environmental cues. Autoinducer 2 （AI-2）, a QS signaling mol...In quorum sensing （QS） process, bacteria regulate gene expression by utilizing small signaling molecules called autoinducers in response to a variety of environmental cues. Autoinducer 2 （AI-2）, a QS signaling molecule proposed to be involved in interspecies communication, is produced by many species of gram-negative and gram-positive bacteria. In Escherichia coil and Salmonella typhimurium, the extracellular AI-2 is imported into the cell by a transporter encoded by the lsr operon. Upstream of the lsr operon, there is a divergently transcribed gene encoding LsrR, which was reported previously to repress the transcription of the lsr operon and itself. Here, we have demonstrated for the first time that LsrR represses the transcription of the lsr operon and itself by directly binding to their promoters using gel shift and DNase I footprinting assays. The β-galactosidase reporter assays further suggest that two motifs in both the lsrR and lsrA promoter regions are crucial for the LsrR binding. Furthermore, in agreement with the conclusion that phosphorylated AI-2 can relieve the repression of LsrR in previous studies, our data show that phospho- AI-2 renders LsrR unable to bind to its own promoter in vitro.展开更多
The urban thermal distribution characteristics and its variation are dynamically monitored and synthetically analyzed by using GIS technology. The meteorological satellite data serve as main information source, assist...The urban thermal distribution characteristics and its variation are dynamically monitored and synthetically analyzed by using GIS technology. The meteorological satellite data serve as main information source, assisted as auxiliary information sources by the landsat satellite TM data, land use thematic maps and meteorological observed data. A correlated pattern on the ground surface brightness temperatures and air temperatures has been studied and established with good performance of application.展开更多
Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and ...Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.展开更多
基金Funded by the National Natural Science Foundation of Hunan Province,Chinal(No.2021JJ60012)。
文摘To prepare a conductive polymer actuator with decent performance,a self-built experimental platform for the preparation of polypyrrole film is employed.One of the essential goals is to examine the mechanical characteristics of the actuator in the presence of various combinations of process parameters,combined with the orthogonal test method of"four factors and three levels".The bending and sensing characteristics of actuators of various sizes are methodically examined using a self-made bending polypyrrole actuator.The functional relationship between the bending displacement and the output voltage signal is established by studying the characteristics of the actuator sensor subjected to various degrees of bending.The experimental results reveal that the bending displacement of the actuator tip almost exhibits a linear variation as a function of length and width.When the voltage reaches 0.8 V,the bending speed of the actuator tends to be stable.Finally,the mechanical properties of the self-assembled polypyrrole actuator are verified by the design and fabrication of the microgripper.
文摘With the rapid development of science and technology,new sensing technology has been used increasingly in mechatronics system,for the system of intelligent,automation and efficiency,provide strong support.Emerging sensor technology in electromechanical integration system of innovative applications not only promote the system of intelligent upgrade,also for its wide application in the field of multiple provides a strong support,and along with the advance of technology and application scenario development,emerging sensor technology in electromechanical integration system to play a more important role.In this regard,this paper first expounds the overview of emerging sensing technology,then analyzes the innovation and integration of emerging sensing technology and mechatronics system,and finally further explores the practical application of emerging sensing technology in mechatronics system,in order to provide some reference for relevant researchers.
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provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
基金supported by the 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 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supported by the National Natural Science Foundation of China(Nos.52422505 and 12274124)the Innovative Research Group Project of the National Natural Science Foundation of China(No.52321002).
文摘Nanoscale confinement environments often affect the transport mechanisms of nanofluids.Understanding the dynamic behavior of molecules in two-dimensional(2D)confined channels is of great importance in the areas of sensing,catalysis and energy storage.As a popular candidate for a new type of gas sensing material,MXenes have the problem of nonselectivity towards polar gases with slow responses,which severely limits their applications.Here,we report a study on regulating the confinement effect of 2D channels between MXene layers through annealing treatment and ion(Na^(+))intercalation for high-performance ammonia(NH_(3))sensing.Firstly,the annealing treatment accurately modulates the size of the 2D channels to effectively block the entry of large-size gas molecules and improve the selectivity for NH_(3).Ab initio molecular dynamics(AIMD)also confirms that the modulated channel size has a special"nano-pumping effect",which can accelerate the dynamic behavior of NH_(3) molecules in the 2D confined space.Moreover,the intercalation of Na+ions increases the adsorption capacity of NH_(3).Therefore,the"nano-pumping effect"and theintercalation of Na+ions effectively enhance the response speed and sensitivity of MXene to NH_(3),respectively.The experimental results show that the modified Ti_(3)C_(2) exhibits high sensitivity(0.17),rapid response(181 s),excellent selectivity and stability towards NH_(3).
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.
基金We thank our colleagues J Zang and X Liu for their technical assistance in protein purification. This work was supported by the One Hundred Talent Project of the Chinese Academy of Sciences and the National Natural Science Foundation of China （50738006）.
文摘In quorum sensing （QS） process, bacteria regulate gene expression by utilizing small signaling molecules called autoinducers in response to a variety of environmental cues. Autoinducer 2 （AI-2）, a QS signaling molecule proposed to be involved in interspecies communication, is produced by many species of gram-negative and gram-positive bacteria. In Escherichia coil and Salmonella typhimurium, the extracellular AI-2 is imported into the cell by a transporter encoded by the lsr operon. Upstream of the lsr operon, there is a divergently transcribed gene encoding LsrR, which was reported previously to repress the transcription of the lsr operon and itself. Here, we have demonstrated for the first time that LsrR represses the transcription of the lsr operon and itself by directly binding to their promoters using gel shift and DNase I footprinting assays. The β-galactosidase reporter assays further suggest that two motifs in both the lsrR and lsrA promoter regions are crucial for the LsrR binding. Furthermore, in agreement with the conclusion that phosphorylated AI-2 can relieve the repression of LsrR in previous studies, our data show that phospho- AI-2 renders LsrR unable to bind to its own promoter in vitro.
基金The Key Project of the Ninth Five-Year Plan Period, No. 96-908-05-06
文摘The urban thermal distribution characteristics and its variation are dynamically monitored and synthetically analyzed by using GIS technology. The meteorological satellite data serve as main information source, assisted as auxiliary information sources by the landsat satellite TM data, land use thematic maps and meteorological observed data. A correlated pattern on the ground surface brightness temperatures and air temperatures has been studied and established with good performance of application.
基金supported by grants from the National Aeronautics and Space Administration Applied Science Program,USA (NNX12AQ31G,NNX14AP91G,PI:Dr.Liping Di)
文摘Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.
