BACKGROUND:Rapid and accurate identification of high-risk patients in the emergency departments(EDs)is crucial for optimizing resource allocation and improving patient outcomes.This study aimed to develop an early pre...BACKGROUND:Rapid and accurate identification of high-risk patients in the emergency departments(EDs)is crucial for optimizing resource allocation and improving patient outcomes.This study aimed to develop an early prediction model for identifying high-risk patients in EDs using initial vital sign measurements.METHODS:This retrospective cohort study analyzed initial vital signs from the Chinese Emergency Triage,Assessment,and Treatment(CETAT)database,which was collected between January 1^(st),2020,and June 25^(th),2023.The primary outcome was the identification of high-risk patients needing immediate treatment.Various machine learning methods,including a deep-learningbased multilayer perceptron(MLP)classifier were evaluated.Model performance was assessed using the area under the receiver operating characteristic curve(AUC-ROC).AUC-ROC values were reported for three scenarios:a default case,a scenario requiring sensitivity greater than 0.8(Scenario I),and a scenario requiring specificity greater than 0.8(Scenario II).SHAP values were calculated to determine the importance of each predictor within the MLP model.RESULTS:A total of 38,797 patients were analyzed,of whom 18.2%were identified as high-risk.Comparative analysis of the predictive models for high-risk patients showed AUC-ROC values ranging from 0.717 to 0.738,with the MLP model outperforming logistic regression(LR),Gaussian Naive Bayes(GNB),and the National Early Warning Score(NEWS).SHAP value analysis identified coma state,peripheral capillary oxygen saturation(SpO_(2)),and systolic blood pressure as the top three predictive factors in the MLP model,with coma state exerting the most contribution.CONCLUSION:Compared with other methods,the MLP model with initial vital signs demonstrated optimal prediction accuracy,highlighting its potential to enhance clinical decision-making in triage in the EDs.展开更多
Single-phase 25 kV traction networks of electrified alternating current(AC)railways create electromagnetic fields(EMFs)with significant levels of intensity.The most intense magnetic fields occur when short circuits ex...Single-phase 25 kV traction networks of electrified alternating current(AC)railways create electromagnetic fields(EMFs)with significant levels of intensity.The most intense magnetic fields occur when short circuits exist between the contact wire and rails or ground.Despite the short duration of exposure,they can adversely affect electronic devices and induce significant voltages in adjacent power lines,which is dangerous for operating personnel.Although numerous investigations have focused on modeling the EMF of traction networks and power lines,the challenge of determining the three-dimensional electromagnetic fields near metal supports during the flow of a short-circuit current through them is yet to be resolved.In this case,the field has a complex spatial structure that significantly complicates the calculations of intensities.This study proposes a methodology,algorithms,software,and digital models for determining the EMF in the described emergency scenarios.During the modeling process,the objects being studied were represented by segments of thin wires to analyze the distribution of the electric charge and calculate the intensities of the electric and magnetic fields.This approach was implemented in the Fazonord software,and the modeling results show a substantial increase in EMF levels close to the support,with a noticeable decrease in the levels as the distance from it increases.The procedure implemented in the commercial software Fazonord is universal and can be used to determine electromagnetic fields at any electrical power facility that includes live parts of limited length.Based on the proposed procedure,the EMF near the supports of overhead power lines and traction networks of various designs could be determined,the EMF levels at substations can be calculated,and the influence of metal structures located near traction networks,such as pedestrian crossings at railway stations,can be considered.展开更多
This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weat...This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.展开更多
BACKGROUND: This cross-sectional study aimed to assess the knowledge, attitude and anxiety pertaining to basic life support(BLS) and medical emergencies among interns in dental colleges of Mangalore city, Karnataka, I...BACKGROUND: This cross-sectional study aimed to assess the knowledge, attitude and anxiety pertaining to basic life support(BLS) and medical emergencies among interns in dental colleges of Mangalore city, Karnataka, India.METHODS: The study subjects comprised of interns who volunteered from the four dental colleges. The knowledge and attitude of interns were assessed using a 30-item questionnaire prepared based on the Basic Life Support Manual from American Heart Association and the anxiety of interns pertaining to BLS and medical emergencies were assessed using a State-Trait Anxiety Inventory(STAI) Questionnaire. Chi-square test was performed on SPSS 21.0(IBM Statistics, 2012) to determine statistically signifi cant differences(P<0.05) between assessed knowledge and anxiety.RESULTS: Out of 183 interns, 39.89% had below average knowledge. A total of 123(67.21%) reported unavailability of professional training. The majority(180, 98.36%) felt the urgent need of training in basic life support procedures. Assessment of stress showed a total of 27.1% participants to be above highstress level. Comparison of assessed knowledge and stress was found to be insignifi cant(P=0.983).CONCLUSION: There was an evident lack of knowledge pertaining to the management of medical emergencies among the interns. As oral health care providers moving out to the society, a focus should be placed on the training of dental interns with respect to Basic Life Support procedures.展开更多
BACKGROUND: Healthcare professionals have a duty to maintain basic life support(BLS) skills. This study aims to evaluate medical students' factual knowledge of BLS and the training they receive.METHODS: A cross-se...BACKGROUND: Healthcare professionals have a duty to maintain basic life support(BLS) skills. This study aims to evaluate medical students' factual knowledge of BLS and the training they receive.METHODS: A cross-sectional, closed-response questionnaire was distributed to the fi rst-and fourth-year students studying at institutions in the United Kingdom. The paper questionnaire sought to quantify respondent's previous BLS training, factual knowledge of the BLS algorithm using five multiple choice questions(MCQs), and valuate their desire for further BLS training. Students received 1 point for each correctly identifi ed answer to the 5 MCQ's.RESULTS: A total of 3,732 complete responses were received from 21 medical schools. Eighty percent(n=2,999) of students completed a BLS course as part of their undergraduate medical studies. There was a signifi cant difference(P<0.001) in the percentage of the fourth-year students selecting the correct answer in all the MCQ's compared to the fi rst-year students except in identifyingthe correct depth of compressions required during CPR(P=0.095). Overall 10.3%(95% CI 9.9% to 10.7%) of respondents correctly identified the answer to 5 MCQ's on BLS: 9% of the first-year students(n=194) and 12% of the fourth-year students(n=190). On an institutional level the proportion of students answering all MCQ's correctly ranged from 2% to 54% at different universities. Eighty-one percent of students(n=3,031) wished for more BLS training in their curriculum.CONCLUSION: Factual knowledge of BLS is poor among medical students in the UK. There is a disparity in standards of knowledge across institutions and respondents indicating that they would like more training.展开更多
为提高沿海城市轨道交通防台防汛应急响应的效率和处置能力,设计地铁防台防汛数字化系统,构建了概览、资源储备、水文气象、人员值守、人员疏散、抢险救灾等6个场景驾驶舱,在应急响应的全流程发挥预警研判、智能决策和应急协同作用。通...为提高沿海城市轨道交通防台防汛应急响应的效率和处置能力,设计地铁防台防汛数字化系统,构建了概览、资源储备、水文气象、人员值守、人员疏散、抢险救灾等6个场景驾驶舱,在应急响应的全流程发挥预警研判、智能决策和应急协同作用。通过地理信息系统(GIS,Geographic Information System)、BIM(Building Information Modeling)、数字孪生、人工智能、深度学习等关键技术,融合全区域全要素时空数据,建设数字孪生底座,打破数据壁垒。引入应急数据分析和预测分析算法引擎,使分析研判有据可依。在宁波轨道交通部分站点试点应用表明,该系统有助于提升应急响应速度和协同实战能力。展开更多
基金Applicable Funding Source University of Science and Technology of China(to YLL)National Natural Science Foundation of China(12126604)(to MPZ)+1 种基金R&D project of Pazhou Lab(Huangpu)(2023K0609)(to MPZ)Anhui Provincial Natural Science(grant number 2208085MH235)(to KJ)。
文摘BACKGROUND:Rapid and accurate identification of high-risk patients in the emergency departments(EDs)is crucial for optimizing resource allocation and improving patient outcomes.This study aimed to develop an early prediction model for identifying high-risk patients in EDs using initial vital sign measurements.METHODS:This retrospective cohort study analyzed initial vital signs from the Chinese Emergency Triage,Assessment,and Treatment(CETAT)database,which was collected between January 1^(st),2020,and June 25^(th),2023.The primary outcome was the identification of high-risk patients needing immediate treatment.Various machine learning methods,including a deep-learningbased multilayer perceptron(MLP)classifier were evaluated.Model performance was assessed using the area under the receiver operating characteristic curve(AUC-ROC).AUC-ROC values were reported for three scenarios:a default case,a scenario requiring sensitivity greater than 0.8(Scenario I),and a scenario requiring specificity greater than 0.8(Scenario II).SHAP values were calculated to determine the importance of each predictor within the MLP model.RESULTS:A total of 38,797 patients were analyzed,of whom 18.2%were identified as high-risk.Comparative analysis of the predictive models for high-risk patients showed AUC-ROC values ranging from 0.717 to 0.738,with the MLP model outperforming logistic regression(LR),Gaussian Naive Bayes(GNB),and the National Early Warning Score(NEWS).SHAP value analysis identified coma state,peripheral capillary oxygen saturation(SpO_(2)),and systolic blood pressure as the top three predictive factors in the MLP model,with coma state exerting the most contribution.CONCLUSION:Compared with other methods,the MLP model with initial vital signs demonstrated optimal prediction accuracy,highlighting its potential to enhance clinical decision-making in triage in the EDs.
文摘Single-phase 25 kV traction networks of electrified alternating current(AC)railways create electromagnetic fields(EMFs)with significant levels of intensity.The most intense magnetic fields occur when short circuits exist between the contact wire and rails or ground.Despite the short duration of exposure,they can adversely affect electronic devices and induce significant voltages in adjacent power lines,which is dangerous for operating personnel.Although numerous investigations have focused on modeling the EMF of traction networks and power lines,the challenge of determining the three-dimensional electromagnetic fields near metal supports during the flow of a short-circuit current through them is yet to be resolved.In this case,the field has a complex spatial structure that significantly complicates the calculations of intensities.This study proposes a methodology,algorithms,software,and digital models for determining the EMF in the described emergency scenarios.During the modeling process,the objects being studied were represented by segments of thin wires to analyze the distribution of the electric charge and calculate the intensities of the electric and magnetic fields.This approach was implemented in the Fazonord software,and the modeling results show a substantial increase in EMF levels close to the support,with a noticeable decrease in the levels as the distance from it increases.The procedure implemented in the commercial software Fazonord is universal and can be used to determine electromagnetic fields at any electrical power facility that includes live parts of limited length.Based on the proposed procedure,the EMF near the supports of overhead power lines and traction networks of various designs could be determined,the EMF levels at substations can be calculated,and the influence of metal structures located near traction networks,such as pedestrian crossings at railway stations,can be considered.
文摘This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.
文摘BACKGROUND: This cross-sectional study aimed to assess the knowledge, attitude and anxiety pertaining to basic life support(BLS) and medical emergencies among interns in dental colleges of Mangalore city, Karnataka, India.METHODS: The study subjects comprised of interns who volunteered from the four dental colleges. The knowledge and attitude of interns were assessed using a 30-item questionnaire prepared based on the Basic Life Support Manual from American Heart Association and the anxiety of interns pertaining to BLS and medical emergencies were assessed using a State-Trait Anxiety Inventory(STAI) Questionnaire. Chi-square test was performed on SPSS 21.0(IBM Statistics, 2012) to determine statistically signifi cant differences(P<0.05) between assessed knowledge and anxiety.RESULTS: Out of 183 interns, 39.89% had below average knowledge. A total of 123(67.21%) reported unavailability of professional training. The majority(180, 98.36%) felt the urgent need of training in basic life support procedures. Assessment of stress showed a total of 27.1% participants to be above highstress level. Comparison of assessed knowledge and stress was found to be insignifi cant(P=0.983).CONCLUSION: There was an evident lack of knowledge pertaining to the management of medical emergencies among the interns. As oral health care providers moving out to the society, a focus should be placed on the training of dental interns with respect to Basic Life Support procedures.
文摘BACKGROUND: Healthcare professionals have a duty to maintain basic life support(BLS) skills. This study aims to evaluate medical students' factual knowledge of BLS and the training they receive.METHODS: A cross-sectional, closed-response questionnaire was distributed to the fi rst-and fourth-year students studying at institutions in the United Kingdom. The paper questionnaire sought to quantify respondent's previous BLS training, factual knowledge of the BLS algorithm using five multiple choice questions(MCQs), and valuate their desire for further BLS training. Students received 1 point for each correctly identifi ed answer to the 5 MCQ's.RESULTS: A total of 3,732 complete responses were received from 21 medical schools. Eighty percent(n=2,999) of students completed a BLS course as part of their undergraduate medical studies. There was a signifi cant difference(P<0.001) in the percentage of the fourth-year students selecting the correct answer in all the MCQ's compared to the fi rst-year students except in identifyingthe correct depth of compressions required during CPR(P=0.095). Overall 10.3%(95% CI 9.9% to 10.7%) of respondents correctly identified the answer to 5 MCQ's on BLS: 9% of the first-year students(n=194) and 12% of the fourth-year students(n=190). On an institutional level the proportion of students answering all MCQ's correctly ranged from 2% to 54% at different universities. Eighty-one percent of students(n=3,031) wished for more BLS training in their curriculum.CONCLUSION: Factual knowledge of BLS is poor among medical students in the UK. There is a disparity in standards of knowledge across institutions and respondents indicating that they would like more training.
文摘为提高沿海城市轨道交通防台防汛应急响应的效率和处置能力,设计地铁防台防汛数字化系统,构建了概览、资源储备、水文气象、人员值守、人员疏散、抢险救灾等6个场景驾驶舱,在应急响应的全流程发挥预警研判、智能决策和应急协同作用。通过地理信息系统(GIS,Geographic Information System)、BIM(Building Information Modeling)、数字孪生、人工智能、深度学习等关键技术,融合全区域全要素时空数据,建设数字孪生底座,打破数据壁垒。引入应急数据分析和预测分析算法引擎,使分析研判有据可依。在宁波轨道交通部分站点试点应用表明,该系统有助于提升应急响应速度和协同实战能力。
