The index of Risk Frequency (RF) and other relative indices are used to analyze the temporal and spatial patterns of environmental riskevents in the past 30 years in Shenyang city. The results show that thereexists si...The index of Risk Frequency (RF) and other relative indices are used to analyze the temporal and spatial patterns of environmental riskevents in the past 30 years in Shenyang city. The results show that thereexists significant difference of the RFs between periods of 1966-1977 and1978-1991 (t=7.353**, t0.01=2.807). During the past 30 years, there areno significant changes of the spatial patterns of the environmental risk,while the proportions of environmental risk among the districts are extremely different. In Shenyang city, there exists a series of high riskenterprises, and the chemical industry is the one with highest risk.展开更多
Efficient flight path design for unmanned aerial vehicles(UAVs)in urban environmental event monitoring remains a critical challenge,particularly in prioritizing high-risk zones within complex urban landscapes.Current ...Efficient flight path design for unmanned aerial vehicles(UAVs)in urban environmental event monitoring remains a critical challenge,particularly in prioritizing high-risk zones within complex urban landscapes.Current UAV path planning methodologies often inadequately account for environmental risk factors and exhibit limitations in balancing global and local optimization efficiency.To address these gaps,this study proposes a hybrid path planning framework integrating an improved Ant Colony Optimization(ACO)algorithm with an Orthogonal Jump Point Search(OJPS)algorithm.Firstly,a two-dimensional grid model is constructed to simulate urban environments,with key monitoring nodes selected based on grid-specific environmental risk values.Subsequently,the improved ACO algorithm is used for global path planning,and the OJPS algorithm is integrated to optimize the local path.The improved ACO algorithm introduces the risk value of environmental events,which is used to direct the UAV to the area with higher risk.In the OJPS algorithm,the path search direction is restricted to the orthogonal direction,which improves the computational efficiency of local path optimization.In order to evaluate the performance of the model,this paper utilizes the metrics of the average risk value of the path,the flight time,and the number of turns.The experimental results demonstrate that the proposed improved ACO algorithm performs well in the average risk value of the paths traveled within the first 5 min,within the first 8 min,and within the first 10 min,with improvements of 48.33%,26.10%,and 6.746%,respectively,over the Particle Swarm Optimization(PSO)algorithm and 70.33%,19.08%,and 10.246%,respectively,over theArtificial Rabbits Optimization(ARO)algorithm.TheOJPS algorithmdemonstrates superior performance in terms of flight time and number of turns,exhibiting a reduction of 40%,40%and 57.1%in flight time compared to the other three algorithms,and a reduction of 11.1%,11.1%and 33.8%in the number of turns compared to the other three algorithms.These results highlight the effectiveness of the proposed method in improving the UAV’s ability to respond efficiently to urban environmental events,offering significant implications for the future of UAV path planning in complex urban settings.展开更多
Event evolution analysis which provides an effective approach to capture the main context of a story from explosive increased news texts has become the critical basis for many real applications,such as crisis and emer...Event evolution analysis which provides an effective approach to capture the main context of a story from explosive increased news texts has become the critical basis for many real applications,such as crisis and emergency management and decision making.Especially,the development of societal risk events which may cause some possible harm to society or individuals has been heavily concerned by both the government and the public.In order to capture the evolution and trends of societal risk events,this paper presents an improved algorithm based on the method of information maps.It contains an event-level cluster generation algorithm and an evaluation algorithm.The main work includes:1)Word embedding representation is adopted and event-level clusters are chosen as nodes of the events evolution chains which may comprehensively present the underlying structure of events.Meanwhile,clusters that consist of risk-labeled events enable to illustrate how events evolve along the time with transitions of risks.2)One real-world case,the event of"Chinese Red Cross",is studied and a series of experiments are conducted.3)An evaluation algorithm is proposed on the basis of indicators of map construction without massive human-annotated dataset.Our approach for event evolution analysis automatically generates a visual evolution of societal risk events,displaying a clear and structural picture of events development.展开更多
BACKGROUND Fulminant myocarditis is the critical form of myocarditis that is often associated with heart failure, malignant arrhythmia, and circulatory failure. Patients with fulminant myocarditis who end up with seve...BACKGROUND Fulminant myocarditis is the critical form of myocarditis that is often associated with heart failure, malignant arrhythmia, and circulatory failure. Patients with fulminant myocarditis who end up with severe multiple organic failure and death are not rare.AIM To analyze the predictors of in-hospital major adverse cardiovascular events(MACE) in patients diagnosed with fulminant myocarditis.METHODS We included a cohort of adult patients diagnosed with fulminant myocarditis who were admitted to Beijing Anzhen Hospital from January 2007 to December2017. The primary endpoint was defined as in-hospital MACE, including death,cardiac arrest, cardiac shock, and ventricular fibrillation. Baseline demographics,clinical history, characteristics of electrocardiograph and ultrasonic cardiogram,laboratory examination, and treatment were recorded. Multivariable logistic regression was used to examine risk factors for in-hospital MACE, and the variables were subsequently assessed by the area under the receiver operating characteristic curve(AUC).RESULTS The rate of in-hospital MACE was 40%. Multivariable logistic regression analysis revealed that baseline QRS duration > 120 ms was the independent risk factor for in-hospital MACE(odds ratio = 4.57, 95%CI: 1.23-16.94, P = 0.023). The AUC of QRS duration > 120 ms for predicting in-hospital MACE was 0.683(95%CI: 0.532-0.833, P = 0.03).CONCLUSION Patients with fulminant myocarditis has a poor outcome. Baseline QRS duration is the independent risk factor for poor outcome in those patients.展开更多
Background There are limited data on the prevalence of electrocardiographic (ECG) abnormalities, and their value for predicting a major adverse cardiovascular event (MACE) in patients at high cardiovascular risk. This...Background There are limited data on the prevalence of electrocardiographic (ECG) abnormalities, and their value for predicting a major adverse cardiovascular event (MACE) in patients at high cardiovascular risk. This study aimed to determine the prevalence of ECG abnormalities in patients at high risk for cardiovascular events, and to identify ECG abnormalities that significantly predict MACE. Methods Patients aged ≥ 45 years with established atherosclerotic disease (EAD) were consecutively enrolled from the outpatient clinics of the six participating hospitals during April 2011 to March 2014. The following data were collected: demographic data, cardiovascular risk factors, history of cardiovascular event, physical examination, ECG and medications. ECG was analyzed using Minnesota Code criteria. MACE included cardiovascular death, non-fatal myocardial infarction, and hospitalization due to unstable angina or heart failure. Results A total of 2009 patients were included, 1048 patients (52.2%) had established EAD, and 961 patients (47.8%) had multiple risk factors (MRF). ECG abnormalities included atrial fibrillation (6.7%), premature ventricular contraction (5.4%), pathological Q-wave (Q/QS)(21.3%), T-wave inversion (20.0%), intraventricular ventricular conduction delay (IVCD)(7.3%), left ventricular hypertrophy (LVH)(12.2%), and AV block (12.5%). MACE occurred in 88 patients (4.4%). Independent predictors of MACE were chronic kidney disease, EAD, and the presence of atrial fibrillation, Q/QS, IVCD or LVH by ECG. Conclusions A high prevalence of ECG abnormalities was found. The prevalence of ECG abnormalities was high even among those with risk factors without documented cardiovascular disease.展开更多
目前,空管各类安全管理信息化平台积累了大量非结构化文本数据,但未得到充分利用,为了挖掘空管不正常事件中潜藏的风险,研究利用收集的四千余条空管站不正常事件数据和自构建的4836个空管领域专业术语词,提出了一个基于空管专业信息词...目前,空管各类安全管理信息化平台积累了大量非结构化文本数据,但未得到充分利用,为了挖掘空管不正常事件中潜藏的风险,研究利用收集的四千余条空管站不正常事件数据和自构建的4836个空管领域专业术语词,提出了一个基于空管专业信息词抽取的双向编码器表征法和双向长短时记忆网络的深度学习模型(Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory,BERT-BiLSTM)。该模型通过对不正常事件文本进行信息抽取,过滤其中无用信息,并将双向编码器表征法(Bidirectional Encoder Representations from Transformers,BERT)模型输出的特征向量序列作为双向长短时记忆网络(Bidirectional Long Short-Term Memory,BiLSTM)的输入序列,以对空管不正常事件文本风险识别任务进行对比试验。试验结果显示,在风险识别试验中,基于空管专业信息词抽取的BERT-BiLSTM模型相比于通用领域的BERT模型,风险识别准确率提升了3百分点。可以看出该模型有效提升了空管安全信息处理能力,能够有效识别空管部门日常运行中出现的不正常事件所带来的风险,同时可以为空管安全领域信息挖掘相关任务提供基础参考。展开更多
文摘The index of Risk Frequency (RF) and other relative indices are used to analyze the temporal and spatial patterns of environmental riskevents in the past 30 years in Shenyang city. The results show that thereexists significant difference of the RFs between periods of 1966-1977 and1978-1991 (t=7.353**, t0.01=2.807). During the past 30 years, there areno significant changes of the spatial patterns of the environmental risk,while the proportions of environmental risk among the districts are extremely different. In Shenyang city, there exists a series of high riskenterprises, and the chemical industry is the one with highest risk.
基金supported by the Special Project forKey Fields of Ordinary Universities in Guangdong Province(Number:2023ZDZX1076).
文摘Efficient flight path design for unmanned aerial vehicles(UAVs)in urban environmental event monitoring remains a critical challenge,particularly in prioritizing high-risk zones within complex urban landscapes.Current UAV path planning methodologies often inadequately account for environmental risk factors and exhibit limitations in balancing global and local optimization efficiency.To address these gaps,this study proposes a hybrid path planning framework integrating an improved Ant Colony Optimization(ACO)algorithm with an Orthogonal Jump Point Search(OJPS)algorithm.Firstly,a two-dimensional grid model is constructed to simulate urban environments,with key monitoring nodes selected based on grid-specific environmental risk values.Subsequently,the improved ACO algorithm is used for global path planning,and the OJPS algorithm is integrated to optimize the local path.The improved ACO algorithm introduces the risk value of environmental events,which is used to direct the UAV to the area with higher risk.In the OJPS algorithm,the path search direction is restricted to the orthogonal direction,which improves the computational efficiency of local path optimization.In order to evaluate the performance of the model,this paper utilizes the metrics of the average risk value of the path,the flight time,and the number of turns.The experimental results demonstrate that the proposed improved ACO algorithm performs well in the average risk value of the paths traveled within the first 5 min,within the first 8 min,and within the first 10 min,with improvements of 48.33%,26.10%,and 6.746%,respectively,over the Particle Swarm Optimization(PSO)algorithm and 70.33%,19.08%,and 10.246%,respectively,over theArtificial Rabbits Optimization(ARO)algorithm.TheOJPS algorithmdemonstrates superior performance in terms of flight time and number of turns,exhibiting a reduction of 40%,40%and 57.1%in flight time compared to the other three algorithms,and a reduction of 11.1%,11.1%and 33.8%in the number of turns compared to the other three algorithms.These results highlight the effectiveness of the proposed method in improving the UAV’s ability to respond efficiently to urban environmental events,offering significant implications for the future of UAV path planning in complex urban settings.
基金This work has been supported by National Key Research and Development Program of)China,under Grant No.2016YFB1000902,Na-tional Natural Science Foundation of China,under Grant No.71731002 and No.71971190 and Beijing Postdoctoral Research Foundation,under Grant No.ZZ2019-92The main con-tents had been presented at the 19th Inter-national Symposium on Knowledge and Sys-tems Sciences(KSS2018)held in Tokyo during November 17-19,2018.The referees are greatly appreciated for their help to improve the qual-ity of the extended paper.
文摘Event evolution analysis which provides an effective approach to capture the main context of a story from explosive increased news texts has become the critical basis for many real applications,such as crisis and emergency management and decision making.Especially,the development of societal risk events which may cause some possible harm to society or individuals has been heavily concerned by both the government and the public.In order to capture the evolution and trends of societal risk events,this paper presents an improved algorithm based on the method of information maps.It contains an event-level cluster generation algorithm and an evaluation algorithm.The main work includes:1)Word embedding representation is adopted and event-level clusters are chosen as nodes of the events evolution chains which may comprehensively present the underlying structure of events.Meanwhile,clusters that consist of risk-labeled events enable to illustrate how events evolve along the time with transitions of risks.2)One real-world case,the event of"Chinese Red Cross",is studied and a series of experiments are conducted.3)An evaluation algorithm is proposed on the basis of indicators of map construction without massive human-annotated dataset.Our approach for event evolution analysis automatically generates a visual evolution of societal risk events,displaying a clear and structural picture of events development.
基金Supported by Beijing Natural Science Foundation,No.7184205Beijing Talents Fund,No.2017000021469G224Foundation of Beijing Anzhen Hospital,Capital Medical University,No.2016Z07
文摘BACKGROUND Fulminant myocarditis is the critical form of myocarditis that is often associated with heart failure, malignant arrhythmia, and circulatory failure. Patients with fulminant myocarditis who end up with severe multiple organic failure and death are not rare.AIM To analyze the predictors of in-hospital major adverse cardiovascular events(MACE) in patients diagnosed with fulminant myocarditis.METHODS We included a cohort of adult patients diagnosed with fulminant myocarditis who were admitted to Beijing Anzhen Hospital from January 2007 to December2017. The primary endpoint was defined as in-hospital MACE, including death,cardiac arrest, cardiac shock, and ventricular fibrillation. Baseline demographics,clinical history, characteristics of electrocardiograph and ultrasonic cardiogram,laboratory examination, and treatment were recorded. Multivariable logistic regression was used to examine risk factors for in-hospital MACE, and the variables were subsequently assessed by the area under the receiver operating characteristic curve(AUC).RESULTS The rate of in-hospital MACE was 40%. Multivariable logistic regression analysis revealed that baseline QRS duration > 120 ms was the independent risk factor for in-hospital MACE(odds ratio = 4.57, 95%CI: 1.23-16.94, P = 0.023). The AUC of QRS duration > 120 ms for predicting in-hospital MACE was 0.683(95%CI: 0.532-0.833, P = 0.03).CONCLUSION Patients with fulminant myocarditis has a poor outcome. Baseline QRS duration is the independent risk factor for poor outcome in those patients.
基金supported by the Heart Association of Thailand under the Royal Patronage of H.M. the King, National Research Council of Thailand
文摘Background There are limited data on the prevalence of electrocardiographic (ECG) abnormalities, and their value for predicting a major adverse cardiovascular event (MACE) in patients at high cardiovascular risk. This study aimed to determine the prevalence of ECG abnormalities in patients at high risk for cardiovascular events, and to identify ECG abnormalities that significantly predict MACE. Methods Patients aged ≥ 45 years with established atherosclerotic disease (EAD) were consecutively enrolled from the outpatient clinics of the six participating hospitals during April 2011 to March 2014. The following data were collected: demographic data, cardiovascular risk factors, history of cardiovascular event, physical examination, ECG and medications. ECG was analyzed using Minnesota Code criteria. MACE included cardiovascular death, non-fatal myocardial infarction, and hospitalization due to unstable angina or heart failure. Results A total of 2009 patients were included, 1048 patients (52.2%) had established EAD, and 961 patients (47.8%) had multiple risk factors (MRF). ECG abnormalities included atrial fibrillation (6.7%), premature ventricular contraction (5.4%), pathological Q-wave (Q/QS)(21.3%), T-wave inversion (20.0%), intraventricular ventricular conduction delay (IVCD)(7.3%), left ventricular hypertrophy (LVH)(12.2%), and AV block (12.5%). MACE occurred in 88 patients (4.4%). Independent predictors of MACE were chronic kidney disease, EAD, and the presence of atrial fibrillation, Q/QS, IVCD or LVH by ECG. Conclusions A high prevalence of ECG abnormalities was found. The prevalence of ECG abnormalities was high even among those with risk factors without documented cardiovascular disease.
文摘目前,空管各类安全管理信息化平台积累了大量非结构化文本数据,但未得到充分利用,为了挖掘空管不正常事件中潜藏的风险,研究利用收集的四千余条空管站不正常事件数据和自构建的4836个空管领域专业术语词,提出了一个基于空管专业信息词抽取的双向编码器表征法和双向长短时记忆网络的深度学习模型(Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory,BERT-BiLSTM)。该模型通过对不正常事件文本进行信息抽取,过滤其中无用信息,并将双向编码器表征法(Bidirectional Encoder Representations from Transformers,BERT)模型输出的特征向量序列作为双向长短时记忆网络(Bidirectional Long Short-Term Memory,BiLSTM)的输入序列,以对空管不正常事件文本风险识别任务进行对比试验。试验结果显示,在风险识别试验中,基于空管专业信息词抽取的BERT-BiLSTM模型相比于通用领域的BERT模型,风险识别准确率提升了3百分点。可以看出该模型有效提升了空管安全信息处理能力,能够有效识别空管部门日常运行中出现的不正常事件所带来的风险,同时可以为空管安全领域信息挖掘相关任务提供基础参考。
