Low-visibility phenomena strongly impact the environment,as well as transportation,aviation and other fields that are closely related to people's livelihoods;thus,they represent important ecological issues of soci...Low-visibility phenomena strongly impact the environment,as well as transportation,aviation and other fields that are closely related to people's livelihoods;thus,they represent important ecological issues of social concern.Based on observation data concerning low-visibility phenomena derived from 105 national meteorological stations in Xinjiang,China over the past 20 years,we systematically analyzed the differences between manual and instrument observations for six types of low-visibility phenomena,with a focus on exploring their spatiotemporal distribution characteristics using instrument data.The results revealed that low-visibility phenomena were dominated by fog-and haze-related events(mist,fog,and haze)in northern Xinjiang and dust-related events(dust storms,blowing sand,and floating dust)in southern Xinjiang,with transitional characteristics observed in eastern Xinjiang.Compared with manual observations,the instrument measurements significantly improved the fine-scale low-visibility phenomenon identification process.On the basis of the instrument observation data,spatial-dimension analysis results indicated that low-visibility phenomena in Xinjiang were significantly influenced by terrain factors.Constrained by the Tianshan Mountains,haze-like phenomena formed a core agglomeration area in northern Xinjiang,whereas dust-and sand-related phenomena radiated outward,with the Taklimakan Desert at the center.Moreover,the gripping effect of the terrain promoted dust transmission along low-altitude channels.Temporally,fog-and haze-related phenomena occurred mainly during autumn and winter,and the proportion of these events decreased from 76.7%to 55.1%.The fog-and haze-related phenomena demonstrated a U-shaped rebound trend,while the proportion of mist phenomena decreased by 34.2%.Dust storms occurred during spring,accounting for 23.3%to 44.9%of all storms.Instrument measurement technology has the advantages of high spatial and temporal resolutions and multiparameter coordination but provides a limited dust-haze mixed-pollution identification capacity.This study provides crucial reference data for enhancing the understanding of low-visibility events in Xinjiang and the potential responses while improving the accuracy of pollution source tracking and meteorological process diagnosis tasks.展开更多
Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threat...Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threaten flight safety and mission success.Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths,without accounting for constraints imposed by enemy detection and strike capabilities.Such a simplified approach can yield safety-compromising routes in highly complex urban airspace.To address these limitations,this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs,which integrates UAV performance constraints,environmental limitations,and exposure risks.An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length.In the case study set in a typical mixed-use urban area,analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments.Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index,which represents the likelihood of enemy detection,and the path length,which corresponds to mission execution time.展开更多
BACKGROUND Glucagon-like peptide-1 receptor agonists(GLP-1RAs)are increasingly being used to treat type 2 diabetes mellitus(T2DM)and obesity.Although GLP-1RAs delay gastric emptying,their impact on gastric mucosal vis...BACKGROUND Glucagon-like peptide-1 receptor agonists(GLP-1RAs)are increasingly being used to treat type 2 diabetes mellitus(T2DM)and obesity.Although GLP-1RAs delay gastric emptying,their impact on gastric mucosal visibility during upper endoscopy remains uncertain,especially in Asian patients.AIM To investigate the association between GLP-1RA treatment and gastric mucosal visibility during upper endoscopy in Asian patients with T2DM.METHODS The study population included Korean patients who underwent esophagogastroduodenoscopy(EGD)with concomitant GLP-1RA or dipeptidyl peptidase 4 inhibitor(DPP4i)for the treatment of T2DM.A 1:2 propensity score matching between GLP-1RA and DPP4i users resulted in 198 matched patients and 295 matched patients in each group,respectively.Gastric mucosal visibility was assessed by reviewing endoscopy images with a validated scale(POLPREP).In addition,the rates of aborted and repeat EGD and pulmonary aspiration were also assessed.RESULTS Of the 493 matched patients,mean body mass index was 26.0 kg/m^(2).The rate of inadequate gastric mucosal visibility(gastric POLPREP score 0 or 1)was significantly higher in GLP-1RA group than matched DPP4i group(8.6%vs 1.4%,P=0.0007).The rates of aborted EGD and repeat EGD were also significantly higher in GLP-1RA than DPP4i group(7.6%vs 0.7%in both aborted and repeat EGD,P=0.0011).Multivariable logistic regression revealed GLP-1RA use as an independent risk factor for both inadequate gastric mucosal visibility(odds ratio=6.143,95%confidence interval:2.289,20.318,P=0.0008)and aborted EGD(odds ratio=11.099,95%confidence interval:3.172,63.760,P=0.0010).Despite gastric residue,no pulmonary aspiration was reported in either group.CONCLUSION GLP-1RA use was associated with a higher risk of inadequate gastric mucosal visibility and aborted and repeat procedures during upper gastrointestinal endoscopy in Korean patients with T2DM while pulmonary aspiration was not observed.展开更多
Low visibility conditions,particularly those caused by fog,significantly affect road safety and reduce drivers’ability to see ahead clearly.The conventional approaches used to address this problem primarily rely on i...Low visibility conditions,particularly those caused by fog,significantly affect road safety and reduce drivers’ability to see ahead clearly.The conventional approaches used to address this problem primarily rely on instrument-based and fixed-threshold-based theoretical frameworks,which face challenges in adaptability and demonstrate lower performance under varying environmental conditions.To overcome these challenges,we propose a real-time visibility estimation model that leverages roadside CCTV cameras to monitor and identify visibility levels under different weather conditions.The proposedmethod begins by identifying specific regions of interest(ROI)in the CCTVimages and focuses on extracting specific features such as the number of lines and contours detected within these regions.These features are then provided as an input to the proposed hierarchical clusteringmodel,which classifies them into different visibility levels without the need for predefined rules and threshold values.In the proposed approach,we used two different distance similaritymetrics,namely dynamic time warping(DTW)and Euclidean distance,alongside the proposed hierarchical clustering model and noted its performance in terms of numerous evaluation measures.The proposed model achieved an average accuracy of 97.81%,precision of 91.31%,recall of 91.25%,and F1-score of 91.27% using theDTWdistancemetric.We also conducted experiments for other deep learning(DL)-based models used in the literature and compared their performances with the proposed model.The experimental results demonstrate that the proposedmodel ismore adaptable and consistent compared to themethods used in the literature.The proposedmethod provides drivers real-time and accurate visibility information and enhances road safety during low visibility conditions.展开更多
Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions.These phenomena exhibit complex nonlinear dependencies on atmospheric processes,particularly during moist...Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions.These phenomena exhibit complex nonlinear dependencies on atmospheric processes,particularly during moisture-driven weather events such as fog,rain,and snow.To address this challenge,we propose a dual-branch neural architecture that synergistically processes optical imagery and multi-source meteorological data(temperature,humidity,and wind speed).The framework employs a convolutional neural network(CNN)branch to extract visibility-related visual features from video imagery sequences,while a parallel artificial neural network(ANN)branch decodes nonlinear relationships among the meteorological factors.Cross-modal feature fusion is achieved through an adaptive weighting layer.To validate the framework,multimodal Backpropagation-VGG(BP-VGG)and Backpropagation-ResNet(BP-ResNet)models are developed and trained/tested using historical imagery and meteorological observations from Nanjing Lukou International Airport.The results demonstrate that the multimodal networks reduce retrieval errors by approximately 8%–10%compared to unimodal networks relying solely on imagery.Among the multimodal models,BP-ResNet exhibits the best performance with a mean absolute percentage error(MAPE)of 8.5%.Analysis of typical case studies reveals that visibility fluctuates rapidly while meteorological factors change gradually,highlighting the crucial role of high-frequency imaging data in intelligent visibility retrieval models.The superior performance of BP-ResNet over BP-VGG is attributed to its use of residual blocks,which enables BP-ResNet to excel in multimodal processing by effectively leveraging data complementarity for synergistic improvements.This study presents an end-to-end intelligent visibility inversion framework that directly retrieves visibility values,enhancing its applicability across industries.However,while this approach boosts accuracy and applicability,its performance in critical low-visibility scenarios remains suboptimal,necessitating further research into more advanced retrieval techniques—particularly under extreme visibility conditions.展开更多
The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiologi...The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiological measurements,including electroencephalograph(EEG)and functional magnetic resonance imaging(fMRI),and are crucial for insights into physiological phenomena.This study introduces a novel method,the baseline perspective visibility graph(BPVG),which can analyze time series by accurately capturing connectivity across data points both above and below the baseline.We present the BPVG construction process and validate its performance using simulated signals.Results demonstrate that BPVG accurately translates periodic,random,and fractal signals into regular,random,and scale-free networks respectively,exhibiting diverse degree distribution traits.Furthermore,we apply BPVG to classify Alzheimer’s disease(AD)patients from healthy controls using EEG data and identify non-demented adults at varying dementia risk using resting-state fMRI(rs-fMRI)data.Utilizing degree distribution entropy derived from BPVG networks,our results exceed the best accuracy benchmark(77.01%)in EEG analysis,especially at channels F4(78.46%)and O1(81.54%).Additionally,our rs-fMRI analysis achieves a statistically significant classification accuracy of 76.74%.These findings highlight the effectiveness of BPVG in distinguishing various time series types and its practical utility in EEG and rs-fMRI analysis for early AD detection and dementia risk assessment.In conclusion,BPVG’s validation across both simulated and real data confirms its capability to capture comprehensive information from time series,irrespective of baseline constraints,providing a novel method for studying neural physiological signals.展开更多
基金supported by the Central Government Guidance Funds for Local Science and Technology Development Program(grant no.ZYYD2025ZY21)the Science and Technology Plan Project of the Xinjiang Production and Construction Corps(2023AB036)+1 种基金the Xinjiang Meteorological Bureau High-Level Key Talent Programthe Natural Science Foundation of the Xinjiang Uygur Autonomous Region(2023D01A17 and 2025D01A109).
文摘Low-visibility phenomena strongly impact the environment,as well as transportation,aviation and other fields that are closely related to people's livelihoods;thus,they represent important ecological issues of social concern.Based on observation data concerning low-visibility phenomena derived from 105 national meteorological stations in Xinjiang,China over the past 20 years,we systematically analyzed the differences between manual and instrument observations for six types of low-visibility phenomena,with a focus on exploring their spatiotemporal distribution characteristics using instrument data.The results revealed that low-visibility phenomena were dominated by fog-and haze-related events(mist,fog,and haze)in northern Xinjiang and dust-related events(dust storms,blowing sand,and floating dust)in southern Xinjiang,with transitional characteristics observed in eastern Xinjiang.Compared with manual observations,the instrument measurements significantly improved the fine-scale low-visibility phenomenon identification process.On the basis of the instrument observation data,spatial-dimension analysis results indicated that low-visibility phenomena in Xinjiang were significantly influenced by terrain factors.Constrained by the Tianshan Mountains,haze-like phenomena formed a core agglomeration area in northern Xinjiang,whereas dust-and sand-related phenomena radiated outward,with the Taklimakan Desert at the center.Moreover,the gripping effect of the terrain promoted dust transmission along low-altitude channels.Temporally,fog-and haze-related phenomena occurred mainly during autumn and winter,and the proportion of these events decreased from 76.7%to 55.1%.The fog-and haze-related phenomena demonstrated a U-shaped rebound trend,while the proportion of mist phenomena decreased by 34.2%.Dust storms occurred during spring,accounting for 23.3%to 44.9%of all storms.Instrument measurement technology has the advantages of high spatial and temporal resolutions and multiparameter coordination but provides a limited dust-haze mixed-pollution identification capacity.This study provides crucial reference data for enhancing the understanding of low-visibility events in Xinjiang and the potential responses while improving the accuracy of pollution source tracking and meteorological process diagnosis tasks.
基金supported by the Ministry of Industry and Information Technology(No.23100002022102001)。
文摘Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threaten flight safety and mission success.Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths,without accounting for constraints imposed by enemy detection and strike capabilities.Such a simplified approach can yield safety-compromising routes in highly complex urban airspace.To address these limitations,this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs,which integrates UAV performance constraints,environmental limitations,and exposure risks.An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length.In the case study set in a typical mixed-use urban area,analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments.Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index,which represents the likelihood of enemy detection,and the path length,which corresponds to mission execution time.
文摘BACKGROUND Glucagon-like peptide-1 receptor agonists(GLP-1RAs)are increasingly being used to treat type 2 diabetes mellitus(T2DM)and obesity.Although GLP-1RAs delay gastric emptying,their impact on gastric mucosal visibility during upper endoscopy remains uncertain,especially in Asian patients.AIM To investigate the association between GLP-1RA treatment and gastric mucosal visibility during upper endoscopy in Asian patients with T2DM.METHODS The study population included Korean patients who underwent esophagogastroduodenoscopy(EGD)with concomitant GLP-1RA or dipeptidyl peptidase 4 inhibitor(DPP4i)for the treatment of T2DM.A 1:2 propensity score matching between GLP-1RA and DPP4i users resulted in 198 matched patients and 295 matched patients in each group,respectively.Gastric mucosal visibility was assessed by reviewing endoscopy images with a validated scale(POLPREP).In addition,the rates of aborted and repeat EGD and pulmonary aspiration were also assessed.RESULTS Of the 493 matched patients,mean body mass index was 26.0 kg/m^(2).The rate of inadequate gastric mucosal visibility(gastric POLPREP score 0 or 1)was significantly higher in GLP-1RA group than matched DPP4i group(8.6%vs 1.4%,P=0.0007).The rates of aborted EGD and repeat EGD were also significantly higher in GLP-1RA than DPP4i group(7.6%vs 0.7%in both aborted and repeat EGD,P=0.0011).Multivariable logistic regression revealed GLP-1RA use as an independent risk factor for both inadequate gastric mucosal visibility(odds ratio=6.143,95%confidence interval:2.289,20.318,P=0.0008)and aborted EGD(odds ratio=11.099,95%confidence interval:3.172,63.760,P=0.0010).Despite gastric residue,no pulmonary aspiration was reported in either group.CONCLUSION GLP-1RA use was associated with a higher risk of inadequate gastric mucosal visibility and aborted and repeat procedures during upper gastrointestinal endoscopy in Korean patients with T2DM while pulmonary aspiration was not observed.
文摘Low visibility conditions,particularly those caused by fog,significantly affect road safety and reduce drivers’ability to see ahead clearly.The conventional approaches used to address this problem primarily rely on instrument-based and fixed-threshold-based theoretical frameworks,which face challenges in adaptability and demonstrate lower performance under varying environmental conditions.To overcome these challenges,we propose a real-time visibility estimation model that leverages roadside CCTV cameras to monitor and identify visibility levels under different weather conditions.The proposedmethod begins by identifying specific regions of interest(ROI)in the CCTVimages and focuses on extracting specific features such as the number of lines and contours detected within these regions.These features are then provided as an input to the proposed hierarchical clusteringmodel,which classifies them into different visibility levels without the need for predefined rules and threshold values.In the proposed approach,we used two different distance similaritymetrics,namely dynamic time warping(DTW)and Euclidean distance,alongside the proposed hierarchical clustering model and noted its performance in terms of numerous evaluation measures.The proposed model achieved an average accuracy of 97.81%,precision of 91.31%,recall of 91.25%,and F1-score of 91.27% using theDTWdistancemetric.We also conducted experiments for other deep learning(DL)-based models used in the literature and compared their performances with the proposed model.The experimental results demonstrate that the proposedmodel ismore adaptable and consistent compared to themethods used in the literature.The proposedmethod provides drivers real-time and accurate visibility information and enhances road safety during low visibility conditions.
基金Foundation of Key Laboratory of Smart Earth(KF2023ZD03-02)China Meteorological Administration Innovation development project(CXFZ2025J116)+1 种基金National Natural Science Foundation of China(42205197)Basic Research Fund of CAMS(2022Y023,2022Y025)。
文摘Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions.These phenomena exhibit complex nonlinear dependencies on atmospheric processes,particularly during moisture-driven weather events such as fog,rain,and snow.To address this challenge,we propose a dual-branch neural architecture that synergistically processes optical imagery and multi-source meteorological data(temperature,humidity,and wind speed).The framework employs a convolutional neural network(CNN)branch to extract visibility-related visual features from video imagery sequences,while a parallel artificial neural network(ANN)branch decodes nonlinear relationships among the meteorological factors.Cross-modal feature fusion is achieved through an adaptive weighting layer.To validate the framework,multimodal Backpropagation-VGG(BP-VGG)and Backpropagation-ResNet(BP-ResNet)models are developed and trained/tested using historical imagery and meteorological observations from Nanjing Lukou International Airport.The results demonstrate that the multimodal networks reduce retrieval errors by approximately 8%–10%compared to unimodal networks relying solely on imagery.Among the multimodal models,BP-ResNet exhibits the best performance with a mean absolute percentage error(MAPE)of 8.5%.Analysis of typical case studies reveals that visibility fluctuates rapidly while meteorological factors change gradually,highlighting the crucial role of high-frequency imaging data in intelligent visibility retrieval models.The superior performance of BP-ResNet over BP-VGG is attributed to its use of residual blocks,which enables BP-ResNet to excel in multimodal processing by effectively leveraging data complementarity for synergistic improvements.This study presents an end-to-end intelligent visibility inversion framework that directly retrieves visibility values,enhancing its applicability across industries.However,while this approach boosts accuracy and applicability,its performance in critical low-visibility scenarios remains suboptimal,necessitating further research into more advanced retrieval techniques—particularly under extreme visibility conditions.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFF1204803)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20190736)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.NJ2024029)the National Natural Science Foundation of China(Grant Nos.81701346 and 62201265).
文摘The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiological measurements,including electroencephalograph(EEG)and functional magnetic resonance imaging(fMRI),and are crucial for insights into physiological phenomena.This study introduces a novel method,the baseline perspective visibility graph(BPVG),which can analyze time series by accurately capturing connectivity across data points both above and below the baseline.We present the BPVG construction process and validate its performance using simulated signals.Results demonstrate that BPVG accurately translates periodic,random,and fractal signals into regular,random,and scale-free networks respectively,exhibiting diverse degree distribution traits.Furthermore,we apply BPVG to classify Alzheimer’s disease(AD)patients from healthy controls using EEG data and identify non-demented adults at varying dementia risk using resting-state fMRI(rs-fMRI)data.Utilizing degree distribution entropy derived from BPVG networks,our results exceed the best accuracy benchmark(77.01%)in EEG analysis,especially at channels F4(78.46%)and O1(81.54%).Additionally,our rs-fMRI analysis achieves a statistically significant classification accuracy of 76.74%.These findings highlight the effectiveness of BPVG in distinguishing various time series types and its practical utility in EEG and rs-fMRI analysis for early AD detection and dementia risk assessment.In conclusion,BPVG’s validation across both simulated and real data confirms its capability to capture comprehensive information from time series,irrespective of baseline constraints,providing a novel method for studying neural physiological signals.
