0 INTRODUCTION The lunar surface lacks an atmosphere and is continuously subjected to a combination of space weathering factors such as cosmic rays,solar wind,and micrometeorite impacts,forming a several-meter-thick l...0 INTRODUCTION The lunar surface lacks an atmosphere and is continuously subjected to a combination of space weathering factors such as cosmic rays,solar wind,and micrometeorite impacts,forming a several-meter-thick lunar regolith(Sorokin et al.,2020).展开更多
The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flo...The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flow prevention simulations.Thus,this paper proposes a multi-source data fusion method.First,we constructed 3D models of debris flow prevention using virtual reality technology according to the relevant specifications.The 3D spatial data generated by 3D modeling were converted into DEM data for debris flow prevention engineering.Then,the accuracy and applicability of the DEM data were verified by the error analysis testing and fusion testing of the debris flow prevention simulation.Finally,we propose the Levels of Detail algorithm based on the quadtree structure to realize the visualization of a large-scale disaster prevention scene.The test results reveal that the data fusion method controlled the error rate of the DEM data of the debris flow prevention engineering within an allowable range and generated 3D volume data(obj format)to compensate for the deficiency of the DEM data whereby the 3D internal entity space is not expressed.Additionally,the levels of detailed method can dispatch the data of a large-scale debris flow hazard scene in real time to ensure a realistic 3D visualization.In summary,the proposed methods can be applied to the planning of debris flow prevention engineering and to the simulation of the debris flow prevention process.展开更多
It may be difcult for existing methods to make full use of the correlation and complementarity of various kinds of information when processing multi-source information.In order to accurately perceive the security situ...It may be difcult for existing methods to make full use of the correlation and complementarity of various kinds of information when processing multi-source information.In order to accurately perceive the security situation of distribution automation and ensure the safe and stable operation of distribution network,the multi-source information fusion distribution automation security situation awareness technology based on risk transmission path is studied.Based on the risk transmission path,the distribution automation security situational awareness factors are analyzed,and the main factors afecting the distribution automation security situation are divided into two dimensions:internal source and external source,and eight main awareness factors;Diferent types of sensors are set in the main areas of security situational awareness factors to collect data of diferent awareness factors.Using ant colony algorithm to optimize DS evidence fusion method,data with diferent perception factors are fused,and data fusion results with diferent perception factors are obtained.The distribution automation security situational awareness model is constructed,and the security situational awareness results are obtained based on the data fusion results of the awareness factors.If the results are higher than the set threshold,the abnormal signal can be output to determine the area where the distribution automation abnormal equipment is located.The experimental results show that the multi-source data fusion efect of this method is good,and it can accurately perceive the security status of diferent nodes of the experimental object at diferent time nodes.展开更多
Introduction:This study presents empirical evidence from the implementation of an automated infectious disease warning system utilizing multi-source surveillance and multi-point triggers in Yuhang District,Hangzhou Ci...Introduction:This study presents empirical evidence from the implementation of an automated infectious disease warning system utilizing multi-source surveillance and multi-point triggers in Yuhang District,Hangzhou City,Zhejiang Province,so as to provide reference for more extensive practice of infectious disease surveillance and early warning in the future.Methods:The data were obtained from the Health Emergency Intelligent Control Platform of Yuhang District from January 1 to April 30,2024,encompassing warning signal issuance and response documentation.Descriptive epidemiological method was used to analyze the early warning signals.Results:From January 1 to April 30,2024,the Health Emergency Intelligent Control Platform in Yuhang District generated 4,598 valid warning signals,with a warning signal positive rate of 36.43%.The early warning system detected 71 infectious disease outbreaks reported through the Intelligent Control Platform,including 24 single-source early warning and 47 multi-source early warning.The sensitivity was 78.02%,demonstrating improved performance compared to existing infectious disease surveillance and warning systems.Conclusions:This represents the first domestic publication evaluating an automated multi-source surveillance and multi-point trigger warning system.By integrating and correlating multi-source data,the system can efficiently and accurately detect warning signals of infectious disease incidents,which has significant practical implications for early surveillance,warning,and management of infectious diseases.展开更多
We introduce a novel stretchable photodetector with enhanced multi-light source detection,capable of discriminating light sources using artificial intelligence(AI).These features highlight the application potential of...We introduce a novel stretchable photodetector with enhanced multi-light source detection,capable of discriminating light sources using artificial intelligence(AI).These features highlight the application potential of deep learning enhanced photodetectors in applications that require accurate for visual light communication(VLC).Experimental results showcased its excellent potential in real-world traffic system.This photodetector,fabricated using a composite structure of silver nanowires(AgNWs)/zinc sulfide(ZnS)-polyurethane acrylate(PUA)/AgNWs,maintained stable performance under 25%tensile strain and 2 mm bending radius.It shows high sensitivity at both 448 and 505 nm wavelengths,detecting light sources under mechanical deformations,different wavelengths and frequencies.By integrating a one-dimensional convolutional neural network(1D-CNN)model,we classified the light source power level with 96.52%accuracy even the light of two wavelengths is mixed.The model’s performance remains consistent across flat,bent,and stretched states,setting a precedent for flexible electronics combined with AI in dynamic environments.展开更多
Paddy rice mapping is crucial for cultivation management,yield estimation,and food security.Guangdong,straddling tropics and subtropics,is a major rice-producing region in China.Mapping paddy rice in Guangdong is esse...Paddy rice mapping is crucial for cultivation management,yield estimation,and food security.Guangdong,straddling tropics and subtropics,is a major rice-producing region in China.Mapping paddy rice in Guangdong is essential.However,there are 2 main difficulties in tropical and subtropical paddy rice mapping,including the lack of high-quality optical images and differences in paddy rice planting times.This study proposed a paddy rice mapping framework using phenology matching,integrating Sentinel-1 and Sentinel-2 data to incorporate prior knowledge into the classifiers.The transplanting periods of paddy rice were identified with Sentinel-1 data,and the subsequent 3 months were defined as the growth periods.Features during growth periods obtained by Sentinel-1 and Sentinel-2 were inputted into machine learning classifiers.The classifiers using matched features substantially improved mapping accuracy compared with those using unmatched features,both for early and late rice mapping.The proposed method also improved the accuracy by 6.44%to 16.10%compared with 3 other comparison methods.The model,utilizing matched features,was applied to early and late rice mapping in Guangdong in 2020.Regression results between mapping area and statistical data validate paddy rice mapping credibility.Our analysis revealed that thermal conditions,especially cold severity during growing stages,are the primary determinant of paddy rice phenology.Spatial patterns of paddy rice in Guangdong result from a blend of human and physical factors,with slope and minimum temperature emerging as the most important limitations.These findings enhance our understanding of rice ecosystems’dynamics,offering insights for formulating relevant agricultural policies.展开更多
Background: Population-based cancer survival is a key metric in evaluating the overall effectiveness of health services and cancer control activities. Advancement in information technology enables accurate vital statu...Background: Population-based cancer survival is a key metric in evaluating the overall effectiveness of health services and cancer control activities. Advancement in information technology enables accurate vital status tracking through multi-source data linkage. However, its reliability for survival estimates in China is unclear.Methods: We analyzed data from Dalian Cancer Registry to evaluate the reliability of multi-source data linkage for population-based cancer survival estimates in China. Newly diagnosed cancer patients in 2015 were included and followed until June 2021. We conducted single-source data linkage by linking patients to Dalian Vital Statistics System, and multi-source data linkage by further linking to Dalian Household Registration System and the hospital medical records. Patient vital status was subsequently determined through active follow-up via telephone calls, referred to as comprehensive follow-up, which served as the gold standard. Using the cohort method, we calculated 5-year observed survival and age-standardized relative survival for 20 cancer types and all cancers combined.Results: Compared to comprehensive follow-up, single-source data linkage overestimated 5-year observed survival by 3.2% for all cancers combined, ranging from 0.1% to 8.6% across 20 cancer types. Multi-source data linkage provided a relatively complete patient vital status, with an observed survival estimate of only 0.3% higher for all cancers, ranging from 0% to1.5% across 20 cancer types.Conclusion: Multi-source data linkage contributes to reliable population-based cancer survival estimates in China. Linkage of multiple databases might be of great value in improving the efficiency of follow-up and the quality of survival data for cancer patients in developing countries.展开更多
Remote sensing underpins environmental monitoring and Earth science.The expansion of satellites and observation platforms drives a substantial increase in multi-source remote sensing data.The land-air-space multi-sens...Remote sensing underpins environmental monitoring and Earth science.The expansion of satellites and observation platforms drives a substantial increase in multi-source remote sensing data.The land-air-space multi-sensor stereoscopic observation heralds a new era of intelligent photogrammetry and digital infrastructure.However,the inherent complexity of multi-source data(spanning spatial,spectral,and temporal domains)poses challenges for observation,interpretation,and decision.1 The rapid advancement of artificial intelligence(AI)injects new vitality into the intelligent remote sensing by reshaping systems:from overcoming imaging limitations through enhanced visual observation to elevating knowledge dimensions via semantic understanding and ultimately enabling intelligent decision-making(Figure 1).This commentary examines how AI enhances visual observation,facilitates semantic transition,and empowers intelligent decision-making.These advancements provide support for the paradigm shift from data acquisition to cognitive services.展开更多
基金supported by the National Major Scientific and Technological Infrastructure Project“Space Environment Simulation and Research Infrastructure”financially supported in part by the National Natural Science Foundation of China(No.52275241)the Fund for National Key Laboratory of Space Environment and Matter Behaviors(No.2023059)。
文摘0 INTRODUCTION The lunar surface lacks an atmosphere and is continuously subjected to a combination of space weathering factors such as cosmic rays,solar wind,and micrometeorite impacts,forming a several-meter-thick lunar regolith(Sorokin et al.,2020).
基金support provided by the National Natural Sciences Foundation of China(No.41771419)Student Research Training Program of Southwest Jiaotong University(No.191510,No.182117)。
文摘The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flow prevention simulations.Thus,this paper proposes a multi-source data fusion method.First,we constructed 3D models of debris flow prevention using virtual reality technology according to the relevant specifications.The 3D spatial data generated by 3D modeling were converted into DEM data for debris flow prevention engineering.Then,the accuracy and applicability of the DEM data were verified by the error analysis testing and fusion testing of the debris flow prevention simulation.Finally,we propose the Levels of Detail algorithm based on the quadtree structure to realize the visualization of a large-scale disaster prevention scene.The test results reveal that the data fusion method controlled the error rate of the DEM data of the debris flow prevention engineering within an allowable range and generated 3D volume data(obj format)to compensate for the deficiency of the DEM data whereby the 3D internal entity space is not expressed.Additionally,the levels of detailed method can dispatch the data of a large-scale debris flow hazard scene in real time to ensure a realistic 3D visualization.In summary,the proposed methods can be applied to the planning of debris flow prevention engineering and to the simulation of the debris flow prevention process.
基金supported by Innovation and innovation project of State Grid Qinghai Electric Power Company"Development and application of the Reactive Power Compensation Intelligent Control Device based on Automatic Synchronous Control"(No.B7280723E028).
文摘It may be difcult for existing methods to make full use of the correlation and complementarity of various kinds of information when processing multi-source information.In order to accurately perceive the security situation of distribution automation and ensure the safe and stable operation of distribution network,the multi-source information fusion distribution automation security situation awareness technology based on risk transmission path is studied.Based on the risk transmission path,the distribution automation security situational awareness factors are analyzed,and the main factors afecting the distribution automation security situation are divided into two dimensions:internal source and external source,and eight main awareness factors;Diferent types of sensors are set in the main areas of security situational awareness factors to collect data of diferent awareness factors.Using ant colony algorithm to optimize DS evidence fusion method,data with diferent perception factors are fused,and data fusion results with diferent perception factors are obtained.The distribution automation security situational awareness model is constructed,and the security situational awareness results are obtained based on the data fusion results of the awareness factors.If the results are higher than the set threshold,the abnormal signal can be output to determine the area where the distribution automation abnormal equipment is located.The experimental results show that the multi-source data fusion efect of this method is good,and it can accurately perceive the security status of diferent nodes of the experimental object at diferent time nodes.
基金Supported by the Major Science and Technology Project of the Science and Technology Department of Zhejiang Province(2021C03038,2022C03109)the Medical and Health Science and Technology Project of Zhejiang Province(2024KY895,WKJ-ZJ-2522,2025KY774).
文摘Introduction:This study presents empirical evidence from the implementation of an automated infectious disease warning system utilizing multi-source surveillance and multi-point triggers in Yuhang District,Hangzhou City,Zhejiang Province,so as to provide reference for more extensive practice of infectious disease surveillance and early warning in the future.Methods:The data were obtained from the Health Emergency Intelligent Control Platform of Yuhang District from January 1 to April 30,2024,encompassing warning signal issuance and response documentation.Descriptive epidemiological method was used to analyze the early warning signals.Results:From January 1 to April 30,2024,the Health Emergency Intelligent Control Platform in Yuhang District generated 4,598 valid warning signals,with a warning signal positive rate of 36.43%.The early warning system detected 71 infectious disease outbreaks reported through the Intelligent Control Platform,including 24 single-source early warning and 47 multi-source early warning.The sensitivity was 78.02%,demonstrating improved performance compared to existing infectious disease surveillance and warning systems.Conclusions:This represents the first domestic publication evaluating an automated multi-source surveillance and multi-point trigger warning system.By integrating and correlating multi-source data,the system can efficiently and accurately detect warning signals of infectious disease incidents,which has significant practical implications for early surveillance,warning,and management of infectious diseases.
基金supported by National Research Foundation of Korea(NRF)grants(Number RS-2023-00247545)funded by the Korean government(MSIP)funded and conducted under the Competency Development Program for Industry Specialists of the Korean Ministry of Trade,Industry and Energy(MOTIE),operated by Korea Institute for Advancement of Technology(KIAT)(No.P0023704,SemiconductorTrack Graduate School(SKKU)).
文摘We introduce a novel stretchable photodetector with enhanced multi-light source detection,capable of discriminating light sources using artificial intelligence(AI).These features highlight the application potential of deep learning enhanced photodetectors in applications that require accurate for visual light communication(VLC).Experimental results showcased its excellent potential in real-world traffic system.This photodetector,fabricated using a composite structure of silver nanowires(AgNWs)/zinc sulfide(ZnS)-polyurethane acrylate(PUA)/AgNWs,maintained stable performance under 25%tensile strain and 2 mm bending radius.It shows high sensitivity at both 448 and 505 nm wavelengths,detecting light sources under mechanical deformations,different wavelengths and frequencies.By integrating a one-dimensional convolutional neural network(1D-CNN)model,we classified the light source power level with 96.52%accuracy even the light of two wavelengths is mixed.The model’s performance remains consistent across flat,bent,and stretched states,setting a precedent for flexible electronics combined with AI in dynamic environments.
基金supported in part by the National Key R&D Program of China under grant 2022YFB3903402in part by the National Natural Science Foundation of China under grant 42222106in part by the National Natural Science Foundation of China under grant 61976234.
文摘Paddy rice mapping is crucial for cultivation management,yield estimation,and food security.Guangdong,straddling tropics and subtropics,is a major rice-producing region in China.Mapping paddy rice in Guangdong is essential.However,there are 2 main difficulties in tropical and subtropical paddy rice mapping,including the lack of high-quality optical images and differences in paddy rice planting times.This study proposed a paddy rice mapping framework using phenology matching,integrating Sentinel-1 and Sentinel-2 data to incorporate prior knowledge into the classifiers.The transplanting periods of paddy rice were identified with Sentinel-1 data,and the subsequent 3 months were defined as the growth periods.Features during growth periods obtained by Sentinel-1 and Sentinel-2 were inputted into machine learning classifiers.The classifiers using matched features substantially improved mapping accuracy compared with those using unmatched features,both for early and late rice mapping.The proposed method also improved the accuracy by 6.44%to 16.10%compared with 3 other comparison methods.The model,utilizing matched features,was applied to early and late rice mapping in Guangdong in 2020.Regression results between mapping area and statistical data validate paddy rice mapping credibility.Our analysis revealed that thermal conditions,especially cold severity during growing stages,are the primary determinant of paddy rice phenology.Spatial patterns of paddy rice in Guangdong result from a blend of human and physical factors,with slope and minimum temperature emerging as the most important limitations.These findings enhance our understanding of rice ecosystems’dynamics,offering insights for formulating relevant agricultural policies.
基金supported by the National Key R&D Program of China (2022YFC3600805 and 2021YFC2501900)
文摘Background: Population-based cancer survival is a key metric in evaluating the overall effectiveness of health services and cancer control activities. Advancement in information technology enables accurate vital status tracking through multi-source data linkage. However, its reliability for survival estimates in China is unclear.Methods: We analyzed data from Dalian Cancer Registry to evaluate the reliability of multi-source data linkage for population-based cancer survival estimates in China. Newly diagnosed cancer patients in 2015 were included and followed until June 2021. We conducted single-source data linkage by linking patients to Dalian Vital Statistics System, and multi-source data linkage by further linking to Dalian Household Registration System and the hospital medical records. Patient vital status was subsequently determined through active follow-up via telephone calls, referred to as comprehensive follow-up, which served as the gold standard. Using the cohort method, we calculated 5-year observed survival and age-standardized relative survival for 20 cancer types and all cancers combined.Results: Compared to comprehensive follow-up, single-source data linkage overestimated 5-year observed survival by 3.2% for all cancers combined, ranging from 0.1% to 8.6% across 20 cancer types. Multi-source data linkage provided a relatively complete patient vital status, with an observed survival estimate of only 0.3% higher for all cancers, ranging from 0% to1.5% across 20 cancer types.Conclusion: Multi-source data linkage contributes to reliable population-based cancer survival estimates in China. Linkage of multiple databases might be of great value in improving the efficiency of follow-up and the quality of survival data for cancer patients in developing countries.
基金funded by the National Natural Science Foundation of China(U21B2027)the Natural Science Foundation of Jiangsu Province(BK20241280)the Start-up Research Fund of Southeast University(RF1028623006 and RF1028624061).
文摘Remote sensing underpins environmental monitoring and Earth science.The expansion of satellites and observation platforms drives a substantial increase in multi-source remote sensing data.The land-air-space multi-sensor stereoscopic observation heralds a new era of intelligent photogrammetry and digital infrastructure.However,the inherent complexity of multi-source data(spanning spatial,spectral,and temporal domains)poses challenges for observation,interpretation,and decision.1 The rapid advancement of artificial intelligence(AI)injects new vitality into the intelligent remote sensing by reshaping systems:from overcoming imaging limitations through enhanced visual observation to elevating knowledge dimensions via semantic understanding and ultimately enabling intelligent decision-making(Figure 1).This commentary examines how AI enhances visual observation,facilitates semantic transition,and empowers intelligent decision-making.These advancements provide support for the paradigm shift from data acquisition to cognitive services.
