BACKGROUND:Airway management is a core competence in emergency medicine.International registries have described indications,techniques,and outcomes of endotracheal intubation,yet contemporary data from German emergenc...BACKGROUND:Airway management is a core competence in emergency medicine.International registries have described indications,techniques,and outcomes of endotracheal intubation,yet contemporary data from German emergency departments(EDs) are scarce.We conducted a multicenter prospective registry study in Thuringia,to characterize indications,techniques,success rates,and complications of ED intubations.METHODS:From February 2023 to January 2024,six Thuringian EDs participated in a prospective observational registry(www.airwayregistry.eu).All consecutive intubations were documented anonymously using a standardized digital form.Demographics,indications,methods,equipment,operator characteristics,first pass success(FPS),overall success,and complications were captured.Descriptive statistics were used.RESULTS:We analyzed 117 intubations(63.2% male;mean age 68.4 years,range 2–98 years).FPS was 88.9%(104/117),second pass success was 4.3% and third pass success was 6.8%;overall success was 100%.Rapid sequence intubation(RSI) was used in 77.8% of intubations and delayed sequence intubation(DSI) in 21.4%;10.3% were performed without medication.Direct laryngoscopy(DL) was used in 65.0% and videolaryngoscopy(VL) in 34.2%.FPS was higher with VL than DL(92.5% vs.88.2%).The most common indications were cardiopulmonary resuscitation(14.5%) and stroke/ischemia(13.7%);intracranial hemorrhage accounted for 13.7%.Complications occurred in 39% of cases,most frequently hypotension(23.9%) and catecholamine requirement(12.0%).A difficult airway was anticipated in 30.8%.CONCLUSION:In this multicenter snapshot from German EDs,overall intubation success was high but complications—especially peri-intubation hypotension—were common.VL yielded higher FPS yet was used less frequently than DL.Standardized protocols,hemodynamic optimization,and broader VL adoption may improve safety and performance.展开更多
Objective:To analyze the impact of improved emergency integrated nursing on the treatment effectiveness and safety of emergency trauma patients.Methods:Study duration:December 2024 to December 2025.Observation target:...Objective:To analyze the impact of improved emergency integrated nursing on the treatment effectiveness and safety of emergency trauma patients.Methods:Study duration:December 2024 to December 2025.Observation target:emergency trauma patients in our hospital.Sample size:92 cases.Using computer-based grouping,the 92 patients were divided into two equally sized groups:a control group of 46 patients who received conventional emergency nursing care,and an observation group of 46 patients who underwent an improved emergency integrated nursing model.The treatment-related indicators,treatment effectiveness,and incidence of adverse events were evaluated in both groups.Results:After intervention,the pre-hospital emergency care time,emergency diagnosis time,total emergency rescue duration,and examination waiting time in the control group were all longer than those in the observation group(p<0.05);the treatment effectiveness in the control group(effective rate:82.61%)was worse than that in the observation group(effective rate:95.65%),p<0.05;compared with the control group,the observation group had a lower incidence of adverse events,p<0.05.Conclusion:Implementing an improved emergency integrated nursing model for emergency trauma patients helps streamline the treatment process,enhance treatment effectiveness,and reduce the incidence of adverse events.展开更多
Physician well-being is vital to delivering high-quality emergency care.A supported and healthy emergency medicine workforce leads to better patient outcomes,fewer medical errors,and greater job satisfaction and staff...Physician well-being is vital to delivering high-quality emergency care.A supported and healthy emergency medicine workforce leads to better patient outcomes,fewer medical errors,and greater job satisfaction and staff retention.[1,2]Emergency physicians(EPs)face unique pressures,including shift work,high patient volumes and acuities,overcrowding,and systemic inefficiencies that escalate their risk of burnout.As a result,EPs have reported the highest rates of burnout among physician specialties.展开更多
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
Unmanned aerial vehicles(UAVs)face the challenge of autonomous obstacle avoidance in complex,multi-obstacle environments.Behavior cloning offers a promising approach to rapidly acquire a learning policy from limited e...Unmanned aerial vehicles(UAVs)face the challenge of autonomous obstacle avoidance in complex,multi-obstacle environments.Behavior cloning offers a promising approach to rapidly acquire a learning policy from limited expert demonstrations.However,pure imitation learning inherently suffers from poor exploration and limited generalization,typically necessitating extensive datasets to train competent student policies.We utilize a cross-modal variational autoencoder(CM-VAE)to extract compact features from raw visual inputs and UAV states,which then feed into a policy network.We evaluated our approach in a simulated environment featuring a challenging circular trajectory with eight gate obstacles.The results demonstrate that the policy trained with pure behavior cloning consistently failed.In stark contrast,our DAgger-augmented behavior cloning method successfully traversed all gates without collision.Our findings confirm that DAgger effectively mitigates the shortcomings of behavior cloning,enabling the creation of reliable and sample-efficient navigation policies for UAVs.展开更多
BACKGROUND:Acute pain is a sudden experience secondary to injuries and varies in perception among individuals.In trauma patients,it can negatively aff ect respiratory function,immune response,and wound healing,making ...BACKGROUND:Acute pain is a sudden experience secondary to injuries and varies in perception among individuals.In trauma patients,it can negatively aff ect respiratory function,immune response,and wound healing,making it a signifi cant public health concern.This study is to determine the prevalence and factors associated with acute pain among emergency trauma patients.METHODS:A multicenter cross-sectional study was conducted.Data were collected via interviewer-administered questionnaires and patient chart review.The data were analyzed via the statistical package for social science version 25.Bivariable and multivariable logistic regression analyses were used.Variables with a P-value<0.05 were considered statistically signifi cant.RESULTS:A total of 397 patients were included in the study,for a response rate of 96.8%.The prevalence of pain during admission was 91.9%(95%confi dence intervals[95%CIs]:88.8%-94.4%).Blunt trauma(adjusted odds ratio[aOR]=2.82;95%CI:1.23-6.45),analgesia before admission to the emergency department(aOR=2.71;95%CI:1.16-6.36),documentation of pain severity in the chart(aOR=2.71;95%CI:1.16-6.36),analgesia provided within two hours after admission(aOR=7.60;95%CI:2.79-20.68),use of non-pharmacological pain management methods(aOR=3.09;95%CI:1.35-7.08)and availability of analgesia(aOR=3.95;95%CI:1.36-11.43)were associated with acute pain experience.CONCLUSION:The prevalence of acute pain among emergency trauma patients was high in the study area.Analgesia should be administered prior to admission,and non-pharmacological pain management should be implemented.Moreover,training on pain assessment and management should be provided for healthcare providers in the emergency department.展开更多
Objective:To compare the therapeutic efficacy of intravenous pantoprazole and famotidine for the treatment of epigastric pain in patients presenting to the emergency department.Methods:In this triple-blind randomized ...Objective:To compare the therapeutic efficacy of intravenous pantoprazole and famotidine for the treatment of epigastric pain in patients presenting to the emergency department.Methods:In this triple-blind randomized clinical trial,eligible patients presenting with epigastric pain were randomly assigned to receive intravenous pantoprazole or famotidine.Block randomization was used,and patients,treating physicians,and outcome assessors were blinded to treatment allocation.Pain intensity was assessed at baseline and at 30 and 60 minutes after drug administration.Results:Eighty patients were enrolled,with a mean age of 36.6 years(SD,15.0),and 42.5%were male.Mean pain scores decreased significantly over time in both treatment groups.In the pantoprazole group,pain scores declined from 8.02±1.28 at baseline to 4.75±1.31 at 30 minutes and 1.62±1.29 at 60 minutes,whereas in the famotidine group scores decreased from 8.12±1.48 to 5.37±1.23 and 2.35±1.54,respectively.There was no significant difference in baseline pain scores between groups(P=.92).Pantoprazole resulted in greater pain reduction compared with famotidine at both 30 minutes(P=.04)and 60 minutes(P=.05).Conclusions:Both medications were effective in relieving epigastric pain;however,pantoprazole provided greater and more sustained pain reduction,supporting its preferential use in emergency settings.展开更多
Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential f...Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.展开更多
Objective:Early sepsis can be treated if recognised early,but progression to severe sepsis and septic shock and multiple organ dysfunction syndrome substantially increases mortality.The objectives of our study were to...Objective:Early sepsis can be treated if recognised early,but progression to severe sepsis and septic shock and multiple organ dysfunction syndrome substantially increases mortality.The objectives of our study were to assess morbidity and mortality of patients with sepsis and to compare the effectiveness of a simple bedside satisfiable Quick Sequential Organ Failure Assessment(qSOFA)score with National Early Warning Score(NEWS)in prognosticating sepsis.Methods:This prospective observational study was conducted among patients>18 years old presenting with sepsis at B.J.Medical College.The SOFA,qSOFA and NEWS scores were calculated.The effectiveness in predicting mortality was evaluated using receiver operating characteristic curve analysis.Results:A total of 200 patients were evaluated(56%male)with a mean age of 51.7 years.The mortality rate was 23%.Patients categorized under high risk according to SOFA score>8,qSOFA score of 2-3 and NEWS>7 had a mortality rate of 33.3%,27.5%and 28.4%,respectively.AUC for mortality prediction was 0.695 using SOFA score,0.665 using qSOFA and 0.725 using NEWS.At a cut off of 7.50,NEWS demonstrated a sensitivity of 97.8%with a specificity of 28.0%and outperformed both SOFA and qSOFA which yielded a sensitivity of 43.5%and 91.3%and a specificity of 77.9%and 27.9%,respectively.Conclusions:The NEWS score outperforms SOFA and qSOFA in predicting mortality among sepsis patients.However,qSOFA is more helpful in identifying high risk patients and performs better in intensive care setting.展开更多
To address the critical challenge of risk perception and assessment for autonomous vehicles in dynamic interactive envi-ronments,this study proposes a semi-supervised spatiotemporal interaction risk cognition network ...To address the critical challenge of risk perception and assessment for autonomous vehicles in dynamic interactive envi-ronments,this study proposes a semi-supervised spatiotemporal interaction risk cognition network with attention mecha-nism(SS-SIRCN),inspired by the behavioral adaptation patterns of biological groups under external threats.First,by thoroughly analyzing the dynamic interaction characteristics exhibited by typical biological collectives when exposed to risk,the study reveals the underlying patterns of trajectory changes influenced by external danger.Then,an attention-based spatiotemporal risk cognition network is designed to establish a mapping between driving behavior features and potential driving risks.Finally,a semi-supervised learning framework is employed to enable risk assessment for autono-mous vehicles using only a small amount of labeled data.Experimental results on real-world vehicle trajectory datasets demonstrate that the proposed method achieves a risk prediction accuracy of 90.76%,outperforming other baseline models in performance.展开更多
Objective: To analyze the impact of whole-process nursing on the rescue of emergency critically ill patients by setting a control group and an experimental group and comparing their experimental results. Methods: A to...Objective: To analyze the impact of whole-process nursing on the rescue of emergency critically ill patients by setting a control group and an experimental group and comparing their experimental results. Methods: A total of 50 critically ill patients admitted to the Emergency Department from October 2022 to October 2023 were randomly divided into the experimental group (25 cases) and the control group (25 cases). The control group received routine nursing, while the experimental group received whole-process nursing. The rescue success rate and nursing satisfaction were compared between the two groups. Results: In the experimental group, 24 patients were rescued successfully, with a success rate of 96%;in the control group, 19 patients were rescued successfully, with a success rate of 76%, showing a significant difference (χ2 = 4.1528, p = 0.0415 < 0.05). The nursing satisfaction was 92% in the experimental group and 68% in the control group. Conclusion: Whole-process nursing can effectively improve the rescue success rate of critically ill patients, enhance the satisfaction of patients and their families, and improve patients’ quality of life.展开更多
Against the backdrop of continuous social development and growing public health demands,the efficiency and scientific nature of the emergency care system are of paramount importance.This paper focuses on researching t...Against the backdrop of continuous social development and growing public health demands,the efficiency and scientific nature of the emergency care system are of paramount importance.This paper focuses on researching the construction of an emergency care system based on the concept of“linkage”,delving into its theoretical foundations,exploring innovative construction models,and analyzing practical cases.The study indicates that an emergency care system under the“linkage”concept can effectively integrate resources and enhance efficiency,providing new insights for improving the construction of the emergency care system.It aims to promote the development of the emergency care system towards a more scientific,efficient,and collaborative direction.展开更多
Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelli...Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelligent question answering,decision support,and fault diagnosis.As high-speed train systems become increasingly intelligent and interconnected,fault patterns have grown more complex and dynamic.Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge,addressing key requirements such as interpretability,accuracy,and continuous evolution in intelligent diagnostic systems.However,conventional knowledge graph construction relies heavily on domain expertise and specialized tools,resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios.To address this limitation,this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs.The method employs a seven-layer logic structure—comprising fault name,applicable vehicles,diagnostic logic,signal parameters,verification conditions,fault causes,and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation.A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs,enabling dynamic reasoning and knowledge reuse.Furthermore,the proposed method establishes a three-layer architecture—logic structuring,knowledge graph transformation,and dynamic inference—to bridge human-expert logic with machinebased reasoning.Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability.It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain.展开更多
BACKGROUND Appropriate care for individuals who attempt suicide and are admitted to the emergency department(ED)can prevent future suicidal behavior.It is vital to understand their sociodemographic characteristics and...BACKGROUND Appropriate care for individuals who attempt suicide and are admitted to the emergency department(ED)can prevent future suicidal behavior.It is vital to understand their sociodemographic characteristics and the effects of targeted psychological care.AIM To analyze sociodemographic characteristics of suicide attempters treated in the ED and evaluate the efficacy of psychological care.METHODS Data from 239 suicide attempters treated in the ED of the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture(Hubei Province,China)between January 2021 and February 2025 were divided into 2:Control(n=108)and psychological care(n=131).The demographic characteristics and effects of the psychological care were analyzed.RESULTS The mean(±SD)age of the 239 patients[114 male(47.7%),125 female(52.3%)]was 26.25±9.3 years,of whom 122(45.2%)were single,117(48.9%)were married,and 106(44.4%)had secondary education.Thirty-eight(15.9%)patients had suicidal intent,with a mean of 1.26±0.59 suicide attempts each.Twenty-two(9.21%)patients had a family history of suicide,while 8(3.34%)had a family history of suicide attempt(s).Before intervention,mean Suicidal Intent Scale scores in the psychological nursing and control groups were 21.57±5.28 and 19.86±5.92,respectively(P>0.05).After 1 month of nursing intervention,the respective scores were 10.09±1.11 and 16.48±0.87(P<0.001);and the re-suicide rates were 11.45%(15/131)and 24.07%(26/108)(P<0.001).CONCLUSION Psychological care significantly reduces suicide risk;EDs should provide comprehensive mental health care.展开更多
Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary...Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency.展开更多
BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suita...BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.展开更多
As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,t...As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.展开更多
The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and...The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.展开更多
Aortic saddle embolism(ASE)is a rare but catastrophic vascular emergency characterized by acute occlusion of the aortic bifurcation,leading to bilateral lower limb ischemia and multiorgan dysfunction.Despite advances ...Aortic saddle embolism(ASE)is a rare but catastrophic vascular emergency characterized by acute occlusion of the aortic bifurcation,leading to bilateral lower limb ischemia and multiorgan dysfunction.Despite advances in imaging and surgical techniques,ASE has high morbidity and mortality rates,particularly when diagnosis or intervention is delayed.Here,we report two patients admitted to our center to increase awareness among emergency physicians.展开更多
This study investigated the application and the application value of intelligent emergency in emergency management in the big data environment.It addresses the neglect of the application value(performance)measurement ...This study investigated the application and the application value of intelligent emergency in emergency management in the big data environment.It addresses the neglect of the application value(performance)measurement of intelligent emergency,further improving the effectiveness of intelligent emergency management.First,approximately 3,900 documents from the intelligent emergency field are analyzed to determine the future research trend in intelligent emergency management.The socio-technical theory concerning technical and social systems is introduced.The emergency management system concepts of“technology enabling”and“enabling value creation”are defined according to bibliometric analysis and socio-technical theory.Second,a research framework that includes technology enabling and enabling value creation for the decision-making paradigm in emergency management according to the big data environment is constructed.A detailed analysis approach from intelligent emergency technology enabling to enabling value creation in emergency management is proposed.Finally,earthquake disasters are taken as examples,and specific analyses of the intelligent emergency enabling and enabling value creation are explored;enabling value creation is discussed based on measurable indicators.The clear concept of emergency management system technology enabling and enabling value creation,as well as the detailed analysis approach from intelligent emergency technology enabling to enabling value creation,provide a theoretical bases for scholars and practitioners to evaluate the value(performance)of intelligent emergency for the first time.展开更多
文摘BACKGROUND:Airway management is a core competence in emergency medicine.International registries have described indications,techniques,and outcomes of endotracheal intubation,yet contemporary data from German emergency departments(EDs) are scarce.We conducted a multicenter prospective registry study in Thuringia,to characterize indications,techniques,success rates,and complications of ED intubations.METHODS:From February 2023 to January 2024,six Thuringian EDs participated in a prospective observational registry(www.airwayregistry.eu).All consecutive intubations were documented anonymously using a standardized digital form.Demographics,indications,methods,equipment,operator characteristics,first pass success(FPS),overall success,and complications were captured.Descriptive statistics were used.RESULTS:We analyzed 117 intubations(63.2% male;mean age 68.4 years,range 2–98 years).FPS was 88.9%(104/117),second pass success was 4.3% and third pass success was 6.8%;overall success was 100%.Rapid sequence intubation(RSI) was used in 77.8% of intubations and delayed sequence intubation(DSI) in 21.4%;10.3% were performed without medication.Direct laryngoscopy(DL) was used in 65.0% and videolaryngoscopy(VL) in 34.2%.FPS was higher with VL than DL(92.5% vs.88.2%).The most common indications were cardiopulmonary resuscitation(14.5%) and stroke/ischemia(13.7%);intracranial hemorrhage accounted for 13.7%.Complications occurred in 39% of cases,most frequently hypotension(23.9%) and catecholamine requirement(12.0%).A difficult airway was anticipated in 30.8%.CONCLUSION:In this multicenter snapshot from German EDs,overall intubation success was high but complications—especially peri-intubation hypotension—were common.VL yielded higher FPS yet was used less frequently than DL.Standardized protocols,hemodynamic optimization,and broader VL adoption may improve safety and performance.
文摘Objective:To analyze the impact of improved emergency integrated nursing on the treatment effectiveness and safety of emergency trauma patients.Methods:Study duration:December 2024 to December 2025.Observation target:emergency trauma patients in our hospital.Sample size:92 cases.Using computer-based grouping,the 92 patients were divided into two equally sized groups:a control group of 46 patients who received conventional emergency nursing care,and an observation group of 46 patients who underwent an improved emergency integrated nursing model.The treatment-related indicators,treatment effectiveness,and incidence of adverse events were evaluated in both groups.Results:After intervention,the pre-hospital emergency care time,emergency diagnosis time,total emergency rescue duration,and examination waiting time in the control group were all longer than those in the observation group(p<0.05);the treatment effectiveness in the control group(effective rate:82.61%)was worse than that in the observation group(effective rate:95.65%),p<0.05;compared with the control group,the observation group had a lower incidence of adverse events,p<0.05.Conclusion:Implementing an improved emergency integrated nursing model for emergency trauma patients helps streamline the treatment process,enhance treatment effectiveness,and reduce the incidence of adverse events.
文摘Physician well-being is vital to delivering high-quality emergency care.A supported and healthy emergency medicine workforce leads to better patient outcomes,fewer medical errors,and greater job satisfaction and staff retention.[1,2]Emergency physicians(EPs)face unique pressures,including shift work,high patient volumes and acuities,overcrowding,and systemic inefficiencies that escalate their risk of burnout.As a result,EPs have reported the highest rates of burnout among physician specialties.
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
基金supported by the National Natural Science Foundation of China(No.62576349)。
文摘Unmanned aerial vehicles(UAVs)face the challenge of autonomous obstacle avoidance in complex,multi-obstacle environments.Behavior cloning offers a promising approach to rapidly acquire a learning policy from limited expert demonstrations.However,pure imitation learning inherently suffers from poor exploration and limited generalization,typically necessitating extensive datasets to train competent student policies.We utilize a cross-modal variational autoencoder(CM-VAE)to extract compact features from raw visual inputs and UAV states,which then feed into a policy network.We evaluated our approach in a simulated environment featuring a challenging circular trajectory with eight gate obstacles.The results demonstrate that the policy trained with pure behavior cloning consistently failed.In stark contrast,our DAgger-augmented behavior cloning method successfully traversed all gates without collision.Our findings confirm that DAgger effectively mitigates the shortcomings of behavior cloning,enabling the creation of reliable and sample-efficient navigation policies for UAVs.
文摘BACKGROUND:Acute pain is a sudden experience secondary to injuries and varies in perception among individuals.In trauma patients,it can negatively aff ect respiratory function,immune response,and wound healing,making it a signifi cant public health concern.This study is to determine the prevalence and factors associated with acute pain among emergency trauma patients.METHODS:A multicenter cross-sectional study was conducted.Data were collected via interviewer-administered questionnaires and patient chart review.The data were analyzed via the statistical package for social science version 25.Bivariable and multivariable logistic regression analyses were used.Variables with a P-value<0.05 were considered statistically signifi cant.RESULTS:A total of 397 patients were included in the study,for a response rate of 96.8%.The prevalence of pain during admission was 91.9%(95%confi dence intervals[95%CIs]:88.8%-94.4%).Blunt trauma(adjusted odds ratio[aOR]=2.82;95%CI:1.23-6.45),analgesia before admission to the emergency department(aOR=2.71;95%CI:1.16-6.36),documentation of pain severity in the chart(aOR=2.71;95%CI:1.16-6.36),analgesia provided within two hours after admission(aOR=7.60;95%CI:2.79-20.68),use of non-pharmacological pain management methods(aOR=3.09;95%CI:1.35-7.08)and availability of analgesia(aOR=3.95;95%CI:1.36-11.43)were associated with acute pain experience.CONCLUSION:The prevalence of acute pain among emergency trauma patients was high in the study area.Analgesia should be administered prior to admission,and non-pharmacological pain management should be implemented.Moreover,training on pain assessment and management should be provided for healthcare providers in the emergency department.
文摘Objective:To compare the therapeutic efficacy of intravenous pantoprazole and famotidine for the treatment of epigastric pain in patients presenting to the emergency department.Methods:In this triple-blind randomized clinical trial,eligible patients presenting with epigastric pain were randomly assigned to receive intravenous pantoprazole or famotidine.Block randomization was used,and patients,treating physicians,and outcome assessors were blinded to treatment allocation.Pain intensity was assessed at baseline and at 30 and 60 minutes after drug administration.Results:Eighty patients were enrolled,with a mean age of 36.6 years(SD,15.0),and 42.5%were male.Mean pain scores decreased significantly over time in both treatment groups.In the pantoprazole group,pain scores declined from 8.02±1.28 at baseline to 4.75±1.31 at 30 minutes and 1.62±1.29 at 60 minutes,whereas in the famotidine group scores decreased from 8.12±1.48 to 5.37±1.23 and 2.35±1.54,respectively.There was no significant difference in baseline pain scores between groups(P=.92).Pantoprazole resulted in greater pain reduction compared with famotidine at both 30 minutes(P=.04)and 60 minutes(P=.05).Conclusions:Both medications were effective in relieving epigastric pain;however,pantoprazole provided greater and more sustained pain reduction,supporting its preferential use in emergency settings.
文摘Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.
文摘Objective:Early sepsis can be treated if recognised early,but progression to severe sepsis and septic shock and multiple organ dysfunction syndrome substantially increases mortality.The objectives of our study were to assess morbidity and mortality of patients with sepsis and to compare the effectiveness of a simple bedside satisfiable Quick Sequential Organ Failure Assessment(qSOFA)score with National Early Warning Score(NEWS)in prognosticating sepsis.Methods:This prospective observational study was conducted among patients>18 years old presenting with sepsis at B.J.Medical College.The SOFA,qSOFA and NEWS scores were calculated.The effectiveness in predicting mortality was evaluated using receiver operating characteristic curve analysis.Results:A total of 200 patients were evaluated(56%male)with a mean age of 51.7 years.The mortality rate was 23%.Patients categorized under high risk according to SOFA score>8,qSOFA score of 2-3 and NEWS>7 had a mortality rate of 33.3%,27.5%and 28.4%,respectively.AUC for mortality prediction was 0.695 using SOFA score,0.665 using qSOFA and 0.725 using NEWS.At a cut off of 7.50,NEWS demonstrated a sensitivity of 97.8%with a specificity of 28.0%and outperformed both SOFA and qSOFA which yielded a sensitivity of 43.5%and 91.3%and a specificity of 77.9%and 27.9%,respectively.Conclusions:The NEWS score outperforms SOFA and qSOFA in predicting mortality among sepsis patients.However,qSOFA is more helpful in identifying high risk patients and performs better in intensive care setting.
基金the Jilin Provincial Department of Science and Technology Youth Science and Technology Talent Cultivation Project(20250602051RC)Fundamental Research Funds for the Central Universities(2025-JCXK-19)National Natural Science Foundation of China under Grant 52272417.
文摘To address the critical challenge of risk perception and assessment for autonomous vehicles in dynamic interactive envi-ronments,this study proposes a semi-supervised spatiotemporal interaction risk cognition network with attention mecha-nism(SS-SIRCN),inspired by the behavioral adaptation patterns of biological groups under external threats.First,by thoroughly analyzing the dynamic interaction characteristics exhibited by typical biological collectives when exposed to risk,the study reveals the underlying patterns of trajectory changes influenced by external danger.Then,an attention-based spatiotemporal risk cognition network is designed to establish a mapping between driving behavior features and potential driving risks.Finally,a semi-supervised learning framework is employed to enable risk assessment for autono-mous vehicles using only a small amount of labeled data.Experimental results on real-world vehicle trajectory datasets demonstrate that the proposed method achieves a risk prediction accuracy of 90.76%,outperforming other baseline models in performance.
文摘Objective: To analyze the impact of whole-process nursing on the rescue of emergency critically ill patients by setting a control group and an experimental group and comparing their experimental results. Methods: A total of 50 critically ill patients admitted to the Emergency Department from October 2022 to October 2023 were randomly divided into the experimental group (25 cases) and the control group (25 cases). The control group received routine nursing, while the experimental group received whole-process nursing. The rescue success rate and nursing satisfaction were compared between the two groups. Results: In the experimental group, 24 patients were rescued successfully, with a success rate of 96%;in the control group, 19 patients were rescued successfully, with a success rate of 76%, showing a significant difference (χ2 = 4.1528, p = 0.0415 < 0.05). The nursing satisfaction was 92% in the experimental group and 68% in the control group. Conclusion: Whole-process nursing can effectively improve the rescue success rate of critically ill patients, enhance the satisfaction of patients and their families, and improve patients’ quality of life.
文摘Against the backdrop of continuous social development and growing public health demands,the efficiency and scientific nature of the emergency care system are of paramount importance.This paper focuses on researching the construction of an emergency care system based on the concept of“linkage”,delving into its theoretical foundations,exploring innovative construction models,and analyzing practical cases.The study indicates that an emergency care system under the“linkage”concept can effectively integrate resources and enhance efficiency,providing new insights for improving the construction of the emergency care system.It aims to promote the development of the emergency care system towards a more scientific,efficient,and collaborative direction.
基金support from the Scientific Funding for the Center of National Railway Intelligent Transportation System Engineering and Technology,China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ354)。
文摘Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelligent question answering,decision support,and fault diagnosis.As high-speed train systems become increasingly intelligent and interconnected,fault patterns have grown more complex and dynamic.Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge,addressing key requirements such as interpretability,accuracy,and continuous evolution in intelligent diagnostic systems.However,conventional knowledge graph construction relies heavily on domain expertise and specialized tools,resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios.To address this limitation,this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs.The method employs a seven-layer logic structure—comprising fault name,applicable vehicles,diagnostic logic,signal parameters,verification conditions,fault causes,and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation.A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs,enabling dynamic reasoning and knowledge reuse.Furthermore,the proposed method establishes a three-layer architecture—logic structuring,knowledge graph transformation,and dynamic inference—to bridge human-expert logic with machinebased reasoning.Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability.It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain.
文摘BACKGROUND Appropriate care for individuals who attempt suicide and are admitted to the emergency department(ED)can prevent future suicidal behavior.It is vital to understand their sociodemographic characteristics and the effects of targeted psychological care.AIM To analyze sociodemographic characteristics of suicide attempters treated in the ED and evaluate the efficacy of psychological care.METHODS Data from 239 suicide attempters treated in the ED of the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture(Hubei Province,China)between January 2021 and February 2025 were divided into 2:Control(n=108)and psychological care(n=131).The demographic characteristics and effects of the psychological care were analyzed.RESULTS The mean(±SD)age of the 239 patients[114 male(47.7%),125 female(52.3%)]was 26.25±9.3 years,of whom 122(45.2%)were single,117(48.9%)were married,and 106(44.4%)had secondary education.Thirty-eight(15.9%)patients had suicidal intent,with a mean of 1.26±0.59 suicide attempts each.Twenty-two(9.21%)patients had a family history of suicide,while 8(3.34%)had a family history of suicide attempt(s).Before intervention,mean Suicidal Intent Scale scores in the psychological nursing and control groups were 21.57±5.28 and 19.86±5.92,respectively(P>0.05).After 1 month of nursing intervention,the respective scores were 10.09±1.11 and 16.48±0.87(P<0.001);and the re-suicide rates were 11.45%(15/131)and 24.07%(26/108)(P<0.001).CONCLUSION Psychological care significantly reduces suicide risk;EDs should provide comprehensive mental health care.
基金the Natural Science Foundation of China(Project for Young Scientists:Grant No.52105010,Regular Project:Grant No.62173096)Natural Science Foundationof Guangdong Province(Regular Project:Grant No.2025A1515012124,Grant No.2022A1515010327)Guangdong-Hong Kong-Macao Key Laboratory of Multi-scaleInformation Fusion and Collaborative Optimization Control Manufacturing Process.
文摘Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency.
基金supported by the special fund of the National Clinical Key Specialty Construction Program[(2022)301-2305].
文摘BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.
基金supported by the National Key Research and Development Program of China(No.2022YFB4300902).
文摘As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.
文摘The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.
文摘Aortic saddle embolism(ASE)is a rare but catastrophic vascular emergency characterized by acute occlusion of the aortic bifurcation,leading to bilateral lower limb ischemia and multiorgan dysfunction.Despite advances in imaging and surgical techniques,ASE has high morbidity and mortality rates,particularly when diagnosis or intervention is delayed.Here,we report two patients admitted to our center to increase awareness among emergency physicians.
基金the National Natural Science Foundation of China(Grant No.:71771061).
文摘This study investigated the application and the application value of intelligent emergency in emergency management in the big data environment.It addresses the neglect of the application value(performance)measurement of intelligent emergency,further improving the effectiveness of intelligent emergency management.First,approximately 3,900 documents from the intelligent emergency field are analyzed to determine the future research trend in intelligent emergency management.The socio-technical theory concerning technical and social systems is introduced.The emergency management system concepts of“technology enabling”and“enabling value creation”are defined according to bibliometric analysis and socio-technical theory.Second,a research framework that includes technology enabling and enabling value creation for the decision-making paradigm in emergency management according to the big data environment is constructed.A detailed analysis approach from intelligent emergency technology enabling to enabling value creation in emergency management is proposed.Finally,earthquake disasters are taken as examples,and specific analyses of the intelligent emergency enabling and enabling value creation are explored;enabling value creation is discussed based on measurable indicators.The clear concept of emergency management system technology enabling and enabling value creation,as well as the detailed analysis approach from intelligent emergency technology enabling to enabling value creation,provide a theoretical bases for scholars and practitioners to evaluate the value(performance)of intelligent emergency for the first time.
