This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the netwo...This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the network's influence on urban carbon emissions,utilizing panel data encompassing 323 city nodes.Results show network expansion and a shift in central nodes from eastern coastal areas to interior cities,with Beijing,Shenzhen,Nanjing,and Shanghai consistently acting as key innovation hubs.A core-periphery structure emerged,clustering cities into high-and low-cooperation clusters.Core cities,particularly Beijing,which gain informational advantages by bridging non-overlapping nodes and exhibit distinct characteristics in terms of the structural hole indexes,reflecting their multifaceted roles within the network.SDM analysis indicates that the green technology innovation cooperation network has a significant positive impact on urban carbon reduction efforts.Specifically,degree centrality,closeness centrality,effective size,efficiency,and hierarchy of node cities exhibit a negative correlation with carbon emissions,suggesting that higher centrality and efficiency within the network correlate with lower emissions.Conversely,betweenness centrality and constraint have a positive impact on emissions,indicating that cities that act as bridges in the network may paradoxically contribute to higher emissions.Moreover,the network's influence on carbon emissions is nuanced across different green technology sectors.Cooperation in areas such as waste management,alternative energy production,energy conservation,agriculture and forestry,and transportation is found to have a more substantial impact on carbon reduction than cooperation in nuclear power,and administrative,regulatory,and design fields.展开更多
The convergence of optical and wireless technologies is driving the evolution of intelligent indoor networks,with Fiber-to-the-Room(FTTR)emerging as a key ar⁃chitecture for delivering gigabit connectivity in both home...The convergence of optical and wireless technologies is driving the evolution of intelligent indoor networks,with Fiber-to-the-Room(FTTR)emerging as a key ar⁃chitecture for delivering gigabit connectivity in both home and enterprise environments.By deploying optical fiber directly to rooms and integrating it with advanced wireless so⁃lutions such as millimeter-wave and Wi-Fi 7,FTTR enables next-generation applications,including immersive Virtual Re⁃ality(VR)/Augmented Reality(AR)and industrial Internet of Things(IoT).Nevertheless,its large-scale deployment pres⁃ents challenges in network management,energy efficiency,in⁃terference mitigation,and intelligent root cause analysis.展开更多
With the rapid development of modern information technology,the Internet of Things(IoT)has been integrated into various fields such as social life,industrial production,education,and medical care.Through the connectio...With the rapid development of modern information technology,the Internet of Things(IoT)has been integrated into various fields such as social life,industrial production,education,and medical care.Through the connection of various physical devices,sensors,and machines,it realizes information intercommunication and remote control among devices,significantly enhancing the convenience and efficiency of work and life.However,the rapid development of the IoT has also brought serious security problems.IoT devices have limited resources and a complex network environment,making them one of the important targets of network intrusion attacks.Therefore,from the perspective of deep learning,this paper deeply analyzes the characteristics and key points of IoT intrusion detection,summarizes the application advantages of deep learning in IoT intrusion detection,and proposes application strategies of typical deep learning models in IoT intrusion detection so as to improve the security of the IoT architecture and guarantee people’s convenient lives.展开更多
In recent years,the network public opinion reversal governance events have occurred frequently.Over time,the repeated truth of the matter will not only weaken the rational judgment of the public to a certain extent,so...In recent years,the network public opinion reversal governance events have occurred frequently.Over time,the repeated truth of the matter will not only weaken the rational judgment of the public to a certain extent,so that its negative emotions accumulate,but also have a serious impact on the credibility of the media and the government,and may even further intensify social contradictions.Therefore,in the face of such a complex online public opinion space,accurately identifying the truth behind the incident and how to carry out the reversal of online public opinion governance is particularly critical.And blockchain technology,with its advantages of decentralization and immutable information,provides new technical support for the network public opinion reversal governance.Based on this,this paper gives an overview and analysis of blockchain technology and network public opinion reversal,and on this basis introduces the network public opinion reversal governance mechanism based on blockchain technology,aiming to further optimize the network public opinion reversal governance process,for reference only.展开更多
The Internet of Things technology provides a comprehensive solution for the real-time monitoring of cold chain logistics by integrating sensors,wireless communication,cloud computing,and big data analysis.Based on thi...The Internet of Things technology provides a comprehensive solution for the real-time monitoring of cold chain logistics by integrating sensors,wireless communication,cloud computing,and big data analysis.Based on this,this paper deeply explores the overview and characteristics of the Internet of Things technology,the feasibility analysis of the Internet of Things technology in the cold chain logistics monitoring,the application analysis of the Internet of Things technology in the cold chain logistics real-time monitoring to better improve the management level and operational efficiency of the cold chain logistics,to provide consumers with safer and fresh products.展开更多
Objective:To study the effect of transcranial magnetic stimulation(TMS)on improving motor symptoms in patients with Parkinson’s disease(PD).Methods:60 PD patients who visited the hospital from September 2023 to Augus...Objective:To study the effect of transcranial magnetic stimulation(TMS)on improving motor symptoms in patients with Parkinson’s disease(PD).Methods:60 PD patients who visited the hospital from September 2023 to August 2024 were selected as samples and randomly divided into two groups.Group A received conventional medication plus TMS treatment,while Group B received medication only.The efficacy of motor function improvement,neurological symptoms,mental state,sleep quality,quality of life,and adverse reactions was compared between the two groups.Results:The efficacy of Group A was higher than that of Group B(P<0.05).The scores of the Scales for Outcomes in Parkinson’s Disease-Autonomic(SCOPA-AUT),Mini-Mental State Examination(MMSE),and Pittsburgh Sleep Quality Index(PSQI)in Group A were lower than those in Group B(P<0.05).The quality of life scale(SF-36)score in Group A was higher than that in Group B(P<0.05).The adverse reaction rate in Group A was lower than that in Group B(P<0.05).Conclusion:TMS used in the treatment of PD patients can improve patients’mental state and motor function,optimize sleep quality and quality of life,and is safe and efficient.展开更多
The 2024 MRE HP Special Volume selects papers on new theoretical and experimental developments in the use of static largevolume presses(LVPs)1–3 and dynamic compression4,5 for studies under extreme high-pressure and ...The 2024 MRE HP Special Volume selects papers on new theoretical and experimental developments in the use of static largevolume presses(LVPs)1–3 and dynamic compression4,5 for studies under extreme high-pressure and high-temperature(HPHT)conditions.It also continues the previous year’s6 contemporary focus on superhydrides7–11 with extremely high superconducting temperatures Tc and addresses some controversial issues.12–14 In addition,it explores unconventional pressure-induced chemistry,particularly novel chemical stoichiometry and its impact on geochemistry and cosmochemistry in the deep interiors of Earth and other planets.18–21.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free...In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.展开更多
Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable meas...Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable measures of the Earth system across scales.This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning,remote sensing,and sensor data fusion to generate decision-ready environmental insights.We use the application-first approach,which considers remote sensing,in situ and Internet of Things(IoT)sensing,and physics-based models as complementary streams of evidence with similar strengths and failures.We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas:atmosphere and air quality,water–land–ecosystem dynamics,and hazards.Rapid-response situational awareness,ecosystem condition metrics,drought and flood indicators,exposure maps,and hazard/extreme indicators are key products.The integrated systems to environment interface in three high impact application areas:atmosphere and air quality,water-land-ecosystem dynamics,and hazard Examine Our operational requirements can often determine real-life value such as latency,time stability,smooth degradation in the presence of missing or degraded inputs,and calibrated uncertainty usable in thresholdbased decisions.These pitfalls are common across fields:mismatch in the scale between a point sensor and a gridded product,objectives on proxies in remotely sensed measurements,domain shift in the extremes and changing baselines,and evaluation aspects,which overestimate generalization because of spatiotemporal autocorrelation.Based on these lessons,we present cross-domain proposals for strong validation,uncertainty quantification,provenance,and versioning,as well as fair performance evaluation.We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness,transparency,and operational responsibility,thus allowing the integrated environmental intelligence system to be deployed,which may be relied on to monitor human health,resource allocation,and survival in a more climate-adapted world.展开更多
基金supported by the National Natural Science Foundation of China(72573020,72103022).
文摘This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the network's influence on urban carbon emissions,utilizing panel data encompassing 323 city nodes.Results show network expansion and a shift in central nodes from eastern coastal areas to interior cities,with Beijing,Shenzhen,Nanjing,and Shanghai consistently acting as key innovation hubs.A core-periphery structure emerged,clustering cities into high-and low-cooperation clusters.Core cities,particularly Beijing,which gain informational advantages by bridging non-overlapping nodes and exhibit distinct characteristics in terms of the structural hole indexes,reflecting their multifaceted roles within the network.SDM analysis indicates that the green technology innovation cooperation network has a significant positive impact on urban carbon reduction efforts.Specifically,degree centrality,closeness centrality,effective size,efficiency,and hierarchy of node cities exhibit a negative correlation with carbon emissions,suggesting that higher centrality and efficiency within the network correlate with lower emissions.Conversely,betweenness centrality and constraint have a positive impact on emissions,indicating that cities that act as bridges in the network may paradoxically contribute to higher emissions.Moreover,the network's influence on carbon emissions is nuanced across different green technology sectors.Cooperation in areas such as waste management,alternative energy production,energy conservation,agriculture and forestry,and transportation is found to have a more substantial impact on carbon reduction than cooperation in nuclear power,and administrative,regulatory,and design fields.
文摘The convergence of optical and wireless technologies is driving the evolution of intelligent indoor networks,with Fiber-to-the-Room(FTTR)emerging as a key ar⁃chitecture for delivering gigabit connectivity in both home and enterprise environments.By deploying optical fiber directly to rooms and integrating it with advanced wireless so⁃lutions such as millimeter-wave and Wi-Fi 7,FTTR enables next-generation applications,including immersive Virtual Re⁃ality(VR)/Augmented Reality(AR)and industrial Internet of Things(IoT).Nevertheless,its large-scale deployment pres⁃ents challenges in network management,energy efficiency,in⁃terference mitigation,and intelligent root cause analysis.
基金the research result of the 2022 Municipal Education Commission Science and Technology Research Plan Project“Research on the Technology of Detecting Double-Surface Cracks in Concrete Lining of Highway Tunnels Based on Image Blast”(KJQN02202403)the first batch of school-level classroom teaching reform projects“Principles Applications of Embedded Systems”(23JG2166)the school-level reform research project“Continuous Results-Oriented Practice Research Based on BOPPPS Teaching Model-Taking the‘Programming Fundamentals’Course as an Example”(22JG332).
文摘With the rapid development of modern information technology,the Internet of Things(IoT)has been integrated into various fields such as social life,industrial production,education,and medical care.Through the connection of various physical devices,sensors,and machines,it realizes information intercommunication and remote control among devices,significantly enhancing the convenience and efficiency of work and life.However,the rapid development of the IoT has also brought serious security problems.IoT devices have limited resources and a complex network environment,making them one of the important targets of network intrusion attacks.Therefore,from the perspective of deep learning,this paper deeply analyzes the characteristics and key points of IoT intrusion detection,summarizes the application advantages of deep learning in IoT intrusion detection,and proposes application strategies of typical deep learning models in IoT intrusion detection so as to improve the security of the IoT architecture and guarantee people’s convenient lives.
基金Gansu Provincial Department of Education:Innovation Fund Project for College Teachers:Research on the Governance of Network Public Opinion Reversal Based on Blockchain Technology(NO.2025A-137)Teaching Reform Research at the School Level of Gansu University of Political Science and Law in 2024:Research and Implementation of CSCW Cloud Storage Collective Lesson Preparation System Based on Blockchain Technology(NO.GZJG2024-A04)+1 种基金2024 Gansu University of Political Science and Law“Three in One Education”Research:Project Research and Implementation of CSCW Network Teaching Platform Based on Web in the Direction of Network Education(NO.GZSQYR-37)2024 Lanzhou Philosophy and Social Sciences Planning Project:Research on Cloud Governance of Network Public Opinion for Major Public Events in Lanzhou City(NO.24-B45)。
文摘In recent years,the network public opinion reversal governance events have occurred frequently.Over time,the repeated truth of the matter will not only weaken the rational judgment of the public to a certain extent,so that its negative emotions accumulate,but also have a serious impact on the credibility of the media and the government,and may even further intensify social contradictions.Therefore,in the face of such a complex online public opinion space,accurately identifying the truth behind the incident and how to carry out the reversal of online public opinion governance is particularly critical.And blockchain technology,with its advantages of decentralization and immutable information,provides new technical support for the network public opinion reversal governance.Based on this,this paper gives an overview and analysis of blockchain technology and network public opinion reversal,and on this basis introduces the network public opinion reversal governance mechanism based on blockchain technology,aiming to further optimize the network public opinion reversal governance process,for reference only.
文摘The Internet of Things technology provides a comprehensive solution for the real-time monitoring of cold chain logistics by integrating sensors,wireless communication,cloud computing,and big data analysis.Based on this,this paper deeply explores the overview and characteristics of the Internet of Things technology,the feasibility analysis of the Internet of Things technology in the cold chain logistics monitoring,the application analysis of the Internet of Things technology in the cold chain logistics real-time monitoring to better improve the management level and operational efficiency of the cold chain logistics,to provide consumers with safer and fresh products.
文摘Objective:To study the effect of transcranial magnetic stimulation(TMS)on improving motor symptoms in patients with Parkinson’s disease(PD).Methods:60 PD patients who visited the hospital from September 2023 to August 2024 were selected as samples and randomly divided into two groups.Group A received conventional medication plus TMS treatment,while Group B received medication only.The efficacy of motor function improvement,neurological symptoms,mental state,sleep quality,quality of life,and adverse reactions was compared between the two groups.Results:The efficacy of Group A was higher than that of Group B(P<0.05).The scores of the Scales for Outcomes in Parkinson’s Disease-Autonomic(SCOPA-AUT),Mini-Mental State Examination(MMSE),and Pittsburgh Sleep Quality Index(PSQI)in Group A were lower than those in Group B(P<0.05).The quality of life scale(SF-36)score in Group A was higher than that in Group B(P<0.05).The adverse reaction rate in Group A was lower than that in Group B(P<0.05).Conclusion:TMS used in the treatment of PD patients can improve patients’mental state and motor function,optimize sleep quality and quality of life,and is safe and efficient.
基金financial support from the Shanghai Key Laboratory of MFree,China(Grant No.22dz2260800)the Shanghai Science and Technology Committee,China(Grant No.22JC1410300).
文摘The 2024 MRE HP Special Volume selects papers on new theoretical and experimental developments in the use of static largevolume presses(LVPs)1–3 and dynamic compression4,5 for studies under extreme high-pressure and high-temperature(HPHT)conditions.It also continues the previous year’s6 contemporary focus on superhydrides7–11 with extremely high superconducting temperatures Tc and addresses some controversial issues.12–14 In addition,it explores unconventional pressure-induced chemistry,particularly novel chemical stoichiometry and its impact on geochemistry and cosmochemistry in the deep interiors of Earth and other planets.18–21.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
文摘In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.
文摘Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable measures of the Earth system across scales.This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning,remote sensing,and sensor data fusion to generate decision-ready environmental insights.We use the application-first approach,which considers remote sensing,in situ and Internet of Things(IoT)sensing,and physics-based models as complementary streams of evidence with similar strengths and failures.We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas:atmosphere and air quality,water–land–ecosystem dynamics,and hazards.Rapid-response situational awareness,ecosystem condition metrics,drought and flood indicators,exposure maps,and hazard/extreme indicators are key products.The integrated systems to environment interface in three high impact application areas:atmosphere and air quality,water-land-ecosystem dynamics,and hazard Examine Our operational requirements can often determine real-life value such as latency,time stability,smooth degradation in the presence of missing or degraded inputs,and calibrated uncertainty usable in thresholdbased decisions.These pitfalls are common across fields:mismatch in the scale between a point sensor and a gridded product,objectives on proxies in remotely sensed measurements,domain shift in the extremes and changing baselines,and evaluation aspects,which overestimate generalization because of spatiotemporal autocorrelation.Based on these lessons,we present cross-domain proposals for strong validation,uncertainty quantification,provenance,and versioning,as well as fair performance evaluation.We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness,transparency,and operational responsibility,thus allowing the integrated environmental intelligence system to be deployed,which may be relied on to monitor human health,resource allocation,and survival in a more climate-adapted world.
