Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
BACKGROUND Emphysematous pyelonephritis(EPN)is a life-threatening necrotizing renal parenchyma infection characterized by gas formation due to severe bacterial infection,predominantly affecting diabetic and immunocomp...BACKGROUND Emphysematous pyelonephritis(EPN)is a life-threatening necrotizing renal parenchyma infection characterized by gas formation due to severe bacterial infection,predominantly affecting diabetic and immunocompromised patients.It carries high morbidity and mortality,requiring early diagnosis and timely intervention.Various prognostic scoring systems help in triaging critically ill patients.The National Early Warning Score 2(NEWS 2)scoring system is a widely used physiological assessment tool that evaluates clinical deterioration based on vital parameters,but its standard form lacks specificity for risk stratification in EPN,necessitating modifications to improve treatment decisionmaking and prognostic accuracy in this critical condition.AIM To highlight the need to modify the NEWS 2 score to enable more intense monitoring and better treatment outcomes.METHODS This prospective study was done on all EPN patients admitted to our hospital over the past 12 years.A weighted average risk-stratification index was calculated for each of the three groups,mortality risk was calculated for each of the NEWS 2 scores,and the need for intervention for each of the three groups was calculated.The NEWS 2 score was subsequently modified with 0-6,7-14 and 15-20 scores included in groups 1,2 and 3,respectively.RESULTS A total of 171 patients with EPN were included in the study,with a predominant association with diabetes(90.6%)and a female-to-male ratio of 1.5:1.The combined prognostic scoring of the three groups was 10.7,13.0,and 21.9,respectively(P<0.01).All patients managed conservatively belonged to group 1(P<0.01).Eight patients underwent early nephrectomy,with six from group 3(P<0.01).Overall mortality was 8(4.7%),with seven from group 3(87.5%).The cutoff NEWS 2 score for mortality was identified to be 15,with a sensitivity of 87.5%,specificity of 96.9%,and an overall accuracy rate of 96.5%.The area under the curve to predict mortality based on the NEWS 2 score was 0.98,with a confidence interval of(0.97,1.0)and P<0.001.CONCLUSION Modified NEWS 2(mNEWS 2)score dramatically aids in the appropriate assessment of treatment-related outcomes.MNEWS 2 scores should become the practice standard to reduce the morbidity and mortality associated with this dreaded illness.展开更多
In order to solve the problems of high coupling and poor scalability of the traditional monomer early warning release system architecture,multi-level deployment in a complex network environment will lead to high inves...In order to solve the problems of high coupling and poor scalability of the traditional monomer early warning release system architecture,multi-level deployment in a complex network environment will lead to high investment in software and hardware and cannot achieve intensive multi-level deployment.This paper realizes the goal of system scalability by introducing micro service architecture and technology stack and realizes the goal of resource intensification by introducing the idea of a data forwarding agent.The designed architecture scheme has been practically applied in the“Jiangxi emergency early warning information release system software platform(phase I)project”(hereinafter referred to as“provincial emergency”),which meets the needs of flexible deployment of multi-level applications across meteorological wide area network(WAN),business private network of other commissions,offices,and bureaus,government extranet,Internet and other complex networks,and fully verifies the scientificity and rationality of the scheme.It has achieved the goal of intensive and scalable construction of provincial emergencies under the complex network environment,greatly improved the early warning capacity and communication capacity of emergencies and comprehensive disasters,provided a reliable guarantee for disaster prevention and reduction,and provided a reference for the construction of current and future early warning release system and capacity improvement project.展开更多
Thermal power generation systems have stringent requirements for water and steam quality,i.e.,condensate water quality is one of the critical issues.In this paper,we designed a two-layer model based on an autoencoder ...Thermal power generation systems have stringent requirements for water and steam quality,i.e.,condensate water quality is one of the critical issues.In this paper,we designed a two-layer model based on an autoencoder and expert knowledge to achieve the early warning and causal analysis of condensate water quality abnormalities.An early warning model using an autoencoder model is built based on the historical data affecting the condensate water quality.Next,an analytical model of condensate water quality abnormalities was then developed by combining expert knowledge and trend test algorithms.Two different datasets were used to test the proposed model,respectively.The accuracy of the autoencoder model in the short-period test set is 88.83%,which shows that the early warning model can accurately analyze the condensate water quality data and achieve the purpose of early warning.For the long-time period test set,the model can correctly identify each abnormality and simultaneously indicates the cause of the abnormal condensate water quality.The proposed model can correctly identify abnormal working conditions and it is applicable to other thermal power plants.展开更多
With the continuous advancement of the tiered diagnosis and treatment system,the medical consortium model has gained increasing attention as an important approach to promoting the vertical integration of healthcare re...With the continuous advancement of the tiered diagnosis and treatment system,the medical consortium model has gained increasing attention as an important approach to promoting the vertical integration of healthcare resources.Within this context,laboratory data,as a key component of healthcare information systems,urgently requires efficient sharing and intelligent analysis.This paper designs and constructs an intelligent early warning system for laboratory data based on a cloud platform tailored to the medical consortium model.Through standardized data formats and unified access interfaces,the system enables the integration and cleaning of laboratory data across multiple healthcare institutions.By combining medical rule sets with machine learning models,the system achieves graded alerts and rapid responses to abnormal key indicators and potential outbreaks of infectious diseases.Practical deployment results demonstrate that the system significantly improves the utilization efficiency of laboratory data,strengthens public health event monitoring,and optimizes inter-institutional collaboration.The paper also discusses challenges encountered during system implementation,such as inconsistent data standards,security and compliance concerns,and model interpretability,and proposes corresponding optimization strategies.These findings provide a reference for the broader application of intelligent medical early warning systems.展开更多
In the face of the unrelenting challenge posed by earthquakes-a natural hazard of unpredictable nature with a legacy of significant loss of life,destruction of infrastructure,and profound economic and social impacts-t...In the face of the unrelenting challenge posed by earthquakes-a natural hazard of unpredictable nature with a legacy of significant loss of life,destruction of infrastructure,and profound economic and social impacts-the scientific community has pursued advancements in earthquake early warning systems(EEWSs).These systems are vital for pre-emptive actions and decision-making that can save lives and safeguard critical infrastructure.This study proposes and validates a domain-informed deep learning-based EEWS called the hybrid earthquake early warning framework for estimating response spectra(HEWFERS),which represents a significant leap forward in the capabilities to predict ground shaking intensity in real-time,aligning with the United Nations’disaster risk reduction goals.HEWFERS ingeniously integrates a domain-informed variational autoencoder for physics-based latent variable(LV)extraction,a feed-forward neural network for on-site prediction,and Gaussian process regression for spatial prediction.Adopting explainable artificial intelligence-based Shapley explanations further elucidates the predictive mechanisms,ensuring stakeholder-informed decisions.By conducting an extensive analysis of the proposed framework under a large database of approximately 14000 recorded ground motions,this study offers insights into the potential of integrating machine learning with seismology to revolutionize earthquake preparedness and response,thus paving the way for a safer and more resilient future.展开更多
Purpose–This study aims to design and validate an emergency response method for high-speed railway earthquake early warning(EEW)systems based on the Propagation of Local Undamped Motion(PLUM)principle in order to enh...Purpose–This study aims to design and validate an emergency response method for high-speed railway earthquake early warning(EEW)systems based on the Propagation of Local Undamped Motion(PLUM)principle in order to enhance the timeliness and accuracy of warnings under seismic threats.Design/methodology/approach–A hierarchical architecture of the railway EEW system was adopted,in which self-built stations along the railway serve as the backbone and the national seismic network provides supplementary data.Warning zones were designed along the railway using overlapping trapezoidal layouts to cover seismic stations and reduce inter-regional time delays.Offline replay experiments were conducted using 82 historical earthquake events and records from 61 seismic stations to evaluate the timeliness and accuracy of warning information.Findings–The results indicate that the PLUM-based early warning method can issue emergency response information before destructive seismic waves arrive.Multiple earthquake experiments demonstrated high reliability and stability,with effective detection across different magnitudes and epicentral distances.Furthermore,the trapezoidal overlapping zone design improved regional consistency and significantly reduced missed alerts.Originality/value–This work represents the first systematic application of the PLUM method to high-speed railway EEW in China.By integrating railway operational requirements,the proposed method provides a practical and robust emergency response strategy,offering new insights into seismic risk mitigation for China’s high-speed railways.展开更多
BACKGROUND Enhancing postoperative recovery is a critical goal in clinical practice and the application of innovative nursing models can significantly contribute to this objective.AIM To investigate the effects of mot...BACKGROUND Enhancing postoperative recovery is a critical goal in clinical practice and the application of innovative nursing models can significantly contribute to this objective.AIM To investigate the effects of motivational and early warning nursing interventions on wound healing and sociopsychological adaptability in patients undergoing hepatobiliary surgery.METHODS A total of 160 patients who underwent surgical treatment in the hepatobiliary department of our hospital from January 2022 to June 2024 were selected and randomly divided into a control group and an observation group,with 80 patients in each group.The control group received routine nursing care,while the observation group received a combination of motivational and early warning nursing interventions.The wound healing status(class A,B,and C wound healing and healing time),social psychological adaptability,complications,postoperative recovery,and quality of life were compared between the two groups.RESULTS The wound healing rate in the observation group was higher than that in the control group,while the wound healing time was shorter(P<0.05).The social adaptability scores in the observation group were higher than those in the control group(P<0.05).The incidence of complications was lower in the observation group than in the control group(P<0.05).Postoperative recovery and quality of life were better in the observation group than in the control group(P<0.05).CONCLUSION Motivational and early warning nursing interventions are beneficial for promoting wound healing in patients undergoing hepatobiliary surgery,reducing the incidence of complications and improving socio-psychological adaptability and postoperative quality of life.These interventions should be promoted in clinical nursing practice.展开更多
One of the primary tasks of earthquake early warning(EEW)systems is to predict potential earthquake damage rapidly and accurately.Cumulative absolute velocity(CAV),Arias intensity(I_(A)),and spectrum intensity(SI)are ...One of the primary tasks of earthquake early warning(EEW)systems is to predict potential earthquake damage rapidly and accurately.Cumulative absolute velocity(CAV),Arias intensity(I_(A)),and spectrum intensity(SI)are important parameters for measuring ground motion intensity and assessing earthquake damage.Due to the limited available information in EEW,CAV,I_(A),and SI cannot be accurately predicted using traditional EEW methods.In this paper,we propose an end-to-end deep learning-based Ground motion Intensity prediction Network(ENGINet)for on-site EEW.The aim of the ENGINet is to predict CAV,I_(A),and SI rapidly and reliably.ENGINet is based on a convolutional neural network and recurrent neural network.The inputs of the network are three-component acceleration records,three-component velocity records,and three-component displacement records obtained by a single station.The results from the test dataset show that at 3 s after the P-wave arrival,compared with the baseline models and other traditional methods,ENGINet has better performance in predicting CAV,I_(A),and SI.Our results indicate that ENGINet can quickly and accurately predict CAV,I_(A),and SI to some extent and has good potential in EEW efforts.展开更多
Extensive urban areas worldwide face significant landslide hazards, impacting inhabitants, buildings, and critical infrastructures alike. In the case of slow-moving deep-seated landslides involving huge areas and char...Extensive urban areas worldwide face significant landslide hazards, impacting inhabitants, buildings, and critical infrastructures alike. In the case of slow-moving deep-seated landslides involving huge areas and characterized by complex patterns, when the cost of repairing infrastructures, relocating communities, and restoring cultural sites might be such that it is unsustainable for the community, the exposed structures require significant effort for their surveillance and protection, which can be supported by the development of innovative monitoring systems. For this purpose, a smart extenso-inclinometer, realized by equipping a conventional inclinometer tube with distributed strain and temperature transducers based on optical fiber sensing technology, is presented. In situ monitoring of the active deep-seated San Nicola landslide in Centola (Campania, southern Italy) demonstrated its ability to capture the main features of movements and reconstruct a tridimensional evolution of the landslide pattern, even when the entity of both vertical and horizontal soil strain components is comparable. Although further tests are needed to definitively ascertain the extensometer function of the new device, by interpreting the strain profiles of the landslide body and identifying the achievement of predetermined thresholds, this system could provide a warning of the trigger of a landslide event. The use of the smart extenso-inclinometer within an early warning system for slow-moving landslides holds immense potential for reducing the impact of landslide events.展开更多
This study employs deformation monitoring data acquired during the construction of the Haoji railway large-scale bridge to investigate the displacement behavior of the subgrades,catenary columns,and tracks.Emphasis is...This study employs deformation monitoring data acquired during the construction of the Haoji railway large-scale bridge to investigate the displacement behavior of the subgrades,catenary columns,and tracks.Emphasis is placed on data acquisition and processing methods using total stations and automated monitoring systems.Through a comprehensive analysis of lateral,longitudinal,and vertical displacement data from 26 subgrade monitoring points,catenary columns,and track sections,this research evaluates how construction activities influence railway structures.The results show that displacement variations in the subgrades,catenary columns,and tracks remained within the established alert thresholds,exhibiting stable deformation trends and indicating that any adverse environmental impact was effectively contained.Furthermore,this paper proposes an early warning mechanism based on an automated monitoring system,which can promptly detect abnormal deformations and initiate emergency response procedures,thereby ensuring the safe operation of the railway.The integration of big data analysis and deformation prediction models offers a practical foundation for future safety management in railway construction.展开更多
The stability of reservoir bank slopes during the impoundment period has become a critical issue in the construction and operation of large-scale hydropower projects.A predictive and early warning method for reservoir...The stability of reservoir bank slopes during the impoundment period has become a critical issue in the construction and operation of large-scale hydropower projects.A predictive and early warning method for reservoir bank slopes is proposed,based on slip resistance stability evolution analysis.Using a refined three-dimensional numerical calculation model of the bank slope,the creep damage model is employed for simulation and analysis,enabling the derivation of stress field and strain field evolution from bank slope excavation to the long-term impoundment period.Subsequently,for the stress field of the bank slope at any given moment,the safety factors of the sliding blocks are determined by using the multigrid method and vector sum method.Accordingly,the evolutionary law of the sliding safety factor for the bank slope can be derived.By integrating the long-term stability evolution trend of the slope with specific engineering practices,the safety factors for graded warning can be determined.Based on the time correspondence,the graded warning moment and the deformation warning index for slope measurement points can be determined.In this study,the proposed method is applied to the left bank slope of the Jinping I Hydropower Station.The results indicate that from excavation to June 2022,the left bank slope exhibits a strong correlation with excavation elevation and the number of reservoir water cycles.The initial,maximum,and minimum safety factors are 2.01,3.07,and 1.58,respectively.The deep fracture SL44-1 serves as the primary stress-bearing slip surface of the left bank slope,while the safety margin of the fault f42-9 and lamprophyre X is slightly insufficient.Based on the long-term stability evolution trend of the slope and in accordance with relevant standards,the safety factors for graded warning indicators—K_(w1),K_(w2),K_(w3),and K_(w4)—are determined as 1.350,1.325,1.300,and 1.275,respectively.Correspondingly,the estimated warning times are 12/30/2066,12/30/2084,and 12/30/2120.Accordingly,the deformation graded warning indexes for slope measurement points are established.展开更多
Food safety has always been a critical issue concerning people s livelihoods.The complexity and diversity of hazardous substances in food make systematical and scientifical identification of potential risk factors aff...Food safety has always been a critical issue concerning people s livelihoods.The complexity and diversity of hazardous substances in food make systematical and scientifical identification of potential risk factors affecting food safety and accurate assessment and early warning for such risks one of the urgent problems for food safety inspection authorities.This paper explored food safety risk identification technologies,food safety risk monitoring technologies,and food safety risk early warning methods,aiming to provide theoretical support and research insights for improving food risk early warning systems.展开更多
Leveraging the achievements of the smart meteorological system nationwide,a meteorological monitoring and early warning system for alfalfa pests and diseases can be formed through the establishment of four systems,nam...Leveraging the achievements of the smart meteorological system nationwide,a meteorological monitoring and early warning system for alfalfa pests and diseases can be formed through the establishment of four systems,namely,"real-time monitoring system,forecasting and prediction system,monitoring and early warning system,and smart service system".It will enable intelligent,dynamic meteorological monitoring,early warning,and forecasting services for the occurrence and development of alfalfa pests and diseases,providing technical support for scientifically controlling their harm and improving yield and quality.展开更多
Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application scenarios.Timely and effective TR early warning technology is urgently required considering the curre...Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application scenarios.Timely and effective TR early warning technology is urgently required considering the current fire safety situation of LIBs.In this work,we report an early warning method of TR with online electrochemical impedance spectroscopy(EIS)monitoring,which overcomes the shortcomings of warning methods based on traditional signals such as temperature,gas,and pressure with obvious delay and high cost.With in-situ data acquisition through accelerating rate calorimeter(ARC)-EIS test,the crucial features of TR were extracted using the RReliefF algorithm.TR mechanisms corresponding to the features at specific frequencies were analyzed.Finally,a three-level warning strategy for single battery,series module,and parallel module was formulated,which can successfully send out an early warning signal ahead of the self-heating temperature of battery under thermal abuse condition.The technology can provide a reliable basis for the timely intervention of battery thermal management and fire protection systems and is expected to be applied to electric vehicles and energy storage devices to realize early warning and improve battery safety.展开更多
BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We per...BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong.The primary outcome was sepsis(Sepsis-3 definition)within 48 h of ED presentation.Using c-statistics and the DeLong test,we compared 11 EWSs,including the National Early Warning Score 2(NEWS2),Modified Early Warning Score,and Worthing Physiological Scoring System(WPS),etc.,and three shock indices(the shock index[SI],modified shock index[MSI],and diastolic shock index[DSI]),with Systemic Inflammatory Response Syndrome(SIRS)and quick Sequential Organ Failure Assessment(qSOFA)in predicting the primary outcome,intensive care unit admission,and mortality at different time points.RESULTS:We analyzed 601 patients,of whom 166(27.6%)developed sepsis.NEWS2 had the highest point estimate(area under the receiver operating characteristic curve[AUROC]0.75,95%CI 0.70-0.79)and was significantly better than SIRS,qSOFA,other EWSs and shock indices,except WPS,at predicting the primary outcome.However,the pooled sensitivity and specificity of NEWS2≥5 for the prediction of sepsis were 0.45(95%CI 0.37-0.52)and 0.88(95%CI 0.85-0.91),respectively.The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point.CONCLUSION:NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.展开更多
Electric vehicles have been rapidly developing worldwide due to the use of new energy.However,at the same time,serious traffic accidents caused by driver fatigue in emergency situations have also drawn widespread atte...Electric vehicles have been rapidly developing worldwide due to the use of new energy.However,at the same time,serious traffic accidents caused by driver fatigue in emergency situations have also drawn widespread attention.The lack of datasets in real vehicle test environments has always been a bottleneck in the research of driver fatigue in electric vehicles.Therefore,this study establishes a dataset from real vehicle test,applies the Bayesian optimization support vector machine(BOA-SVM)algorithm to take features of electromyography(EMG)and electrocardiography(ECG)signals as input and develop an early warning model for driving fatigue detection.Firstly,the driver’s EMG and ECG signals are collected through real vehicle testing experiments and then combined with the driver’s subjective fatigue evaluation scores to establish the dataset.Secondly,the study establishes a driver fatigue early warning model for emergency situations.Time-domain and frequency-domain features are extracted from the EMG signals.Principal component analysis(PCA)is applied for dimensionality reduction of these features.The experimental results show that based on the input of dimensionality reduced EMG features and ECG features,the BOA-SVM algorithm achieved an accuracy of 94.4%in classification.展开更多
A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in vari...A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.展开更多
Earthquake has a significant impact on operation safety of the high speed railway,and for Jakarta-Bandung High Speed Railway(HSR)in Indonesia where it is earthquake-prone,it is necessary to establish an earthquake ear...Earthquake has a significant impact on operation safety of the high speed railway,and for Jakarta-Bandung High Speed Railway(HSR)in Indonesia where it is earthquake-prone,it is necessary to establish an earthquake early warning system to strengthen its earthquake resistance.Based on the principle and technical characteristics of China's high speed railway earthquake early warning system and combining the actual situations of Jakarta-Bandung HSR in Indonesia,this paper describes how to implement China's high speed railway earthquake early warning system in Jakarta-Bandung HSR.It focuses on optimizations in environmental adaptation design and seismic network interface design,earthquake attenuation model parameter adjustment and terminal software interface adjustment,so as to make the system better suit the local situations,and meet operation requirements and guarantee safe operation of Jakarta-Bandung HSR.展开更多
Objective:To explore the effect of the combined application of the Shock Index(SI)and the Early Warning Score(EWS)in patients with acute gastrointestinal bleeding.Methods:Seventy patients with acute gastrointestinal b...Objective:To explore the effect of the combined application of the Shock Index(SI)and the Early Warning Score(EWS)in patients with acute gastrointestinal bleeding.Methods:Seventy patients with acute gastrointestinal bleeding admitted to a hospital from June 2022 to May 2024 were selected and randomly divided into two groups:the control group and the observation group,with 35 patients in each group.The control group received conventional emergency care measures,while the observation group received SI combined with NEWS emergency care measures.The treatment effects in both groups were compared.Results:The observation group had shorter waiting times for consultation(4.45±1.59 minutes),intravenous access establishment(6.79±2.52 minutes),hemostasis time(4.41±1.52 hours),and hospital stays(8.39±2.13 days)compared to the control group,which had times of 5.46±1.34 minutes,8.41±2.16 minutes,5.16±1.47 hours,and 10.26±2.98 days,respectively.The differences were statistically significant(P<0.05).Before management,there were no significant differences in the levels of hemoglobin,prealbumin,and serum protein between the two groups(P>0.05).However,after systematic emergency management,the serum indexes in both groups significantly improved,with the observation group showing greater improvement than the control group,and these differences were statistically significant(P<0.05).In the observation group,only one case of cardiovascular complications occurred during the rescue period,with an incidence rate of 2.86%.In contrast,the control group experienced eight cases of complications,including hemorrhagic shock,anemia,multi-organ failure,cardiovascular complications,and gastrointestinal rebleeding,with an incidence rate of 22.85%.The difference between the groups was statistically significant(P<0.05).Conclusion:The application of SI combined with EWS emergency care measures in patients with acute gastrointestinal hemorrhage can effectively improve serum indexes,shorten resuscitation time and hospital stay,and reduce the risk of complications such as hemorrhagic shock,anemia,infection,multi-organ failure,cardiovascular complications,acute renal failure,and gastrointestinal rebleeding.This approach has positive clinical application value.展开更多
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
文摘BACKGROUND Emphysematous pyelonephritis(EPN)is a life-threatening necrotizing renal parenchyma infection characterized by gas formation due to severe bacterial infection,predominantly affecting diabetic and immunocompromised patients.It carries high morbidity and mortality,requiring early diagnosis and timely intervention.Various prognostic scoring systems help in triaging critically ill patients.The National Early Warning Score 2(NEWS 2)scoring system is a widely used physiological assessment tool that evaluates clinical deterioration based on vital parameters,but its standard form lacks specificity for risk stratification in EPN,necessitating modifications to improve treatment decisionmaking and prognostic accuracy in this critical condition.AIM To highlight the need to modify the NEWS 2 score to enable more intense monitoring and better treatment outcomes.METHODS This prospective study was done on all EPN patients admitted to our hospital over the past 12 years.A weighted average risk-stratification index was calculated for each of the three groups,mortality risk was calculated for each of the NEWS 2 scores,and the need for intervention for each of the three groups was calculated.The NEWS 2 score was subsequently modified with 0-6,7-14 and 15-20 scores included in groups 1,2 and 3,respectively.RESULTS A total of 171 patients with EPN were included in the study,with a predominant association with diabetes(90.6%)and a female-to-male ratio of 1.5:1.The combined prognostic scoring of the three groups was 10.7,13.0,and 21.9,respectively(P<0.01).All patients managed conservatively belonged to group 1(P<0.01).Eight patients underwent early nephrectomy,with six from group 3(P<0.01).Overall mortality was 8(4.7%),with seven from group 3(87.5%).The cutoff NEWS 2 score for mortality was identified to be 15,with a sensitivity of 87.5%,specificity of 96.9%,and an overall accuracy rate of 96.5%.The area under the curve to predict mortality based on the NEWS 2 score was 0.98,with a confidence interval of(0.97,1.0)and P<0.001.CONCLUSION Modified NEWS 2(mNEWS 2)score dramatically aids in the appropriate assessment of treatment-related outcomes.MNEWS 2 scores should become the practice standard to reduce the morbidity and mortality associated with this dreaded illness.
文摘In order to solve the problems of high coupling and poor scalability of the traditional monomer early warning release system architecture,multi-level deployment in a complex network environment will lead to high investment in software and hardware and cannot achieve intensive multi-level deployment.This paper realizes the goal of system scalability by introducing micro service architecture and technology stack and realizes the goal of resource intensification by introducing the idea of a data forwarding agent.The designed architecture scheme has been practically applied in the“Jiangxi emergency early warning information release system software platform(phase I)project”(hereinafter referred to as“provincial emergency”),which meets the needs of flexible deployment of multi-level applications across meteorological wide area network(WAN),business private network of other commissions,offices,and bureaus,government extranet,Internet and other complex networks,and fully verifies the scientificity and rationality of the scheme.It has achieved the goal of intensive and scalable construction of provincial emergencies under the complex network environment,greatly improved the early warning capacity and communication capacity of emergencies and comprehensive disasters,provided a reliable guarantee for disaster prevention and reduction,and provided a reference for the construction of current and future early warning release system and capacity improvement project.
基金supported by the Jingneng Shiyan Thermal Power Co.,Ltd.(TPRI/TR-CA-006-2023)Huaihe Energy Power Group Co.,Ltd.(TPRI/TR-CA-040-2023)Xi'an Thermal Power Research Institute Co.,Ltd.(TPRI/TR-CA-110-2021A/H1).
文摘Thermal power generation systems have stringent requirements for water and steam quality,i.e.,condensate water quality is one of the critical issues.In this paper,we designed a two-layer model based on an autoencoder and expert knowledge to achieve the early warning and causal analysis of condensate water quality abnormalities.An early warning model using an autoencoder model is built based on the historical data affecting the condensate water quality.Next,an analytical model of condensate water quality abnormalities was then developed by combining expert knowledge and trend test algorithms.Two different datasets were used to test the proposed model,respectively.The accuracy of the autoencoder model in the short-period test set is 88.83%,which shows that the early warning model can accurately analyze the condensate water quality data and achieve the purpose of early warning.For the long-time period test set,the model can correctly identify each abnormality and simultaneously indicates the cause of the abnormal condensate water quality.The proposed model can correctly identify abnormal working conditions and it is applicable to other thermal power plants.
文摘With the continuous advancement of the tiered diagnosis and treatment system,the medical consortium model has gained increasing attention as an important approach to promoting the vertical integration of healthcare resources.Within this context,laboratory data,as a key component of healthcare information systems,urgently requires efficient sharing and intelligent analysis.This paper designs and constructs an intelligent early warning system for laboratory data based on a cloud platform tailored to the medical consortium model.Through standardized data formats and unified access interfaces,the system enables the integration and cleaning of laboratory data across multiple healthcare institutions.By combining medical rule sets with machine learning models,the system achieves graded alerts and rapid responses to abnormal key indicators and potential outbreaks of infectious diseases.Practical deployment results demonstrate that the system significantly improves the utilization efficiency of laboratory data,strengthens public health event monitoring,and optimizes inter-institutional collaboration.The paper also discusses challenges encountered during system implementation,such as inconsistent data standards,security and compliance concerns,and model interpretability,and proposes corresponding optimization strategies.These findings provide a reference for the broader application of intelligent medical early warning systems.
基金the financial support from the Chilean National Research and Development Agency(Agencia Nacional de Investigación y Desarrollo,ANID)through Fondo Nacional de Desarrollo Científico y Tecnológico(FONDECYT)Regular 1240503Fondo de Valorización de la Investigación(FOVI)230030 projectsthe financial support from the ANID through FONDECYT Reg-ular 1240501.
文摘In the face of the unrelenting challenge posed by earthquakes-a natural hazard of unpredictable nature with a legacy of significant loss of life,destruction of infrastructure,and profound economic and social impacts-the scientific community has pursued advancements in earthquake early warning systems(EEWSs).These systems are vital for pre-emptive actions and decision-making that can save lives and safeguard critical infrastructure.This study proposes and validates a domain-informed deep learning-based EEWS called the hybrid earthquake early warning framework for estimating response spectra(HEWFERS),which represents a significant leap forward in the capabilities to predict ground shaking intensity in real-time,aligning with the United Nations’disaster risk reduction goals.HEWFERS ingeniously integrates a domain-informed variational autoencoder for physics-based latent variable(LV)extraction,a feed-forward neural network for on-site prediction,and Gaussian process regression for spatial prediction.Adopting explainable artificial intelligence-based Shapley explanations further elucidates the predictive mechanisms,ensuring stakeholder-informed decisions.By conducting an extensive analysis of the proposed framework under a large database of approximately 14000 recorded ground motions,this study offers insights into the potential of integrating machine learning with seismology to revolutionize earthquake preparedness and response,thus paving the way for a safer and more resilient future.
文摘Purpose–This study aims to design and validate an emergency response method for high-speed railway earthquake early warning(EEW)systems based on the Propagation of Local Undamped Motion(PLUM)principle in order to enhance the timeliness and accuracy of warnings under seismic threats.Design/methodology/approach–A hierarchical architecture of the railway EEW system was adopted,in which self-built stations along the railway serve as the backbone and the national seismic network provides supplementary data.Warning zones were designed along the railway using overlapping trapezoidal layouts to cover seismic stations and reduce inter-regional time delays.Offline replay experiments were conducted using 82 historical earthquake events and records from 61 seismic stations to evaluate the timeliness and accuracy of warning information.Findings–The results indicate that the PLUM-based early warning method can issue emergency response information before destructive seismic waves arrive.Multiple earthquake experiments demonstrated high reliability and stability,with effective detection across different magnitudes and epicentral distances.Furthermore,the trapezoidal overlapping zone design improved regional consistency and significantly reduced missed alerts.Originality/value–This work represents the first systematic application of the PLUM method to high-speed railway EEW in China.By integrating railway operational requirements,the proposed method provides a practical and robust emergency response strategy,offering new insights into seismic risk mitigation for China’s high-speed railways.
文摘BACKGROUND Enhancing postoperative recovery is a critical goal in clinical practice and the application of innovative nursing models can significantly contribute to this objective.AIM To investigate the effects of motivational and early warning nursing interventions on wound healing and sociopsychological adaptability in patients undergoing hepatobiliary surgery.METHODS A total of 160 patients who underwent surgical treatment in the hepatobiliary department of our hospital from January 2022 to June 2024 were selected and randomly divided into a control group and an observation group,with 80 patients in each group.The control group received routine nursing care,while the observation group received a combination of motivational and early warning nursing interventions.The wound healing status(class A,B,and C wound healing and healing time),social psychological adaptability,complications,postoperative recovery,and quality of life were compared between the two groups.RESULTS The wound healing rate in the observation group was higher than that in the control group,while the wound healing time was shorter(P<0.05).The social adaptability scores in the observation group were higher than those in the control group(P<0.05).The incidence of complications was lower in the observation group than in the control group(P<0.05).Postoperative recovery and quality of life were better in the observation group than in the control group(P<0.05).CONCLUSION Motivational and early warning nursing interventions are beneficial for promoting wound healing in patients undergoing hepatobiliary surgery,reducing the incidence of complications and improving socio-psychological adaptability and postoperative quality of life.These interventions should be promoted in clinical nursing practice.
基金Scientific Research Fund of Institute of Engineering Mechanics,China Earthquake Administration under Grant No.2024B08。
文摘One of the primary tasks of earthquake early warning(EEW)systems is to predict potential earthquake damage rapidly and accurately.Cumulative absolute velocity(CAV),Arias intensity(I_(A)),and spectrum intensity(SI)are important parameters for measuring ground motion intensity and assessing earthquake damage.Due to the limited available information in EEW,CAV,I_(A),and SI cannot be accurately predicted using traditional EEW methods.In this paper,we propose an end-to-end deep learning-based Ground motion Intensity prediction Network(ENGINet)for on-site EEW.The aim of the ENGINet is to predict CAV,I_(A),and SI rapidly and reliably.ENGINet is based on a convolutional neural network and recurrent neural network.The inputs of the network are three-component acceleration records,three-component velocity records,and three-component displacement records obtained by a single station.The results from the test dataset show that at 3 s after the P-wave arrival,compared with the baseline models and other traditional methods,ENGINet has better performance in predicting CAV,I_(A),and SI.Our results indicate that ENGINet can quickly and accurately predict CAV,I_(A),and SI to some extent and has good potential in EEW efforts.
基金supported by Universita della Campania“L.Vanvitelli”,Program VALERE“VAnviteLli pEr la RicErca”(Grant No.516/2018)Italian Ministry of Economic Development#NOACRONYM Project,PoC MISE 2021.
文摘Extensive urban areas worldwide face significant landslide hazards, impacting inhabitants, buildings, and critical infrastructures alike. In the case of slow-moving deep-seated landslides involving huge areas and characterized by complex patterns, when the cost of repairing infrastructures, relocating communities, and restoring cultural sites might be such that it is unsustainable for the community, the exposed structures require significant effort for their surveillance and protection, which can be supported by the development of innovative monitoring systems. For this purpose, a smart extenso-inclinometer, realized by equipping a conventional inclinometer tube with distributed strain and temperature transducers based on optical fiber sensing technology, is presented. In situ monitoring of the active deep-seated San Nicola landslide in Centola (Campania, southern Italy) demonstrated its ability to capture the main features of movements and reconstruct a tridimensional evolution of the landslide pattern, even when the entity of both vertical and horizontal soil strain components is comparable. Although further tests are needed to definitively ascertain the extensometer function of the new device, by interpreting the strain profiles of the landslide body and identifying the achievement of predetermined thresholds, this system could provide a warning of the trigger of a landslide event. The use of the smart extenso-inclinometer within an early warning system for slow-moving landslides holds immense potential for reducing the impact of landslide events.
文摘This study employs deformation monitoring data acquired during the construction of the Haoji railway large-scale bridge to investigate the displacement behavior of the subgrades,catenary columns,and tracks.Emphasis is placed on data acquisition and processing methods using total stations and automated monitoring systems.Through a comprehensive analysis of lateral,longitudinal,and vertical displacement data from 26 subgrade monitoring points,catenary columns,and track sections,this research evaluates how construction activities influence railway structures.The results show that displacement variations in the subgrades,catenary columns,and tracks remained within the established alert thresholds,exhibiting stable deformation trends and indicating that any adverse environmental impact was effectively contained.Furthermore,this paper proposes an early warning mechanism based on an automated monitoring system,which can promptly detect abnormal deformations and initiate emergency response procedures,thereby ensuring the safe operation of the railway.The integration of big data analysis and deformation prediction models offers a practical foundation for future safety management in railway construction.
基金supported by the National Natural Science Foundation of China(Grant No.41961134032).
文摘The stability of reservoir bank slopes during the impoundment period has become a critical issue in the construction and operation of large-scale hydropower projects.A predictive and early warning method for reservoir bank slopes is proposed,based on slip resistance stability evolution analysis.Using a refined three-dimensional numerical calculation model of the bank slope,the creep damage model is employed for simulation and analysis,enabling the derivation of stress field and strain field evolution from bank slope excavation to the long-term impoundment period.Subsequently,for the stress field of the bank slope at any given moment,the safety factors of the sliding blocks are determined by using the multigrid method and vector sum method.Accordingly,the evolutionary law of the sliding safety factor for the bank slope can be derived.By integrating the long-term stability evolution trend of the slope with specific engineering practices,the safety factors for graded warning can be determined.Based on the time correspondence,the graded warning moment and the deformation warning index for slope measurement points can be determined.In this study,the proposed method is applied to the left bank slope of the Jinping I Hydropower Station.The results indicate that from excavation to June 2022,the left bank slope exhibits a strong correlation with excavation elevation and the number of reservoir water cycles.The initial,maximum,and minimum safety factors are 2.01,3.07,and 1.58,respectively.The deep fracture SL44-1 serves as the primary stress-bearing slip surface of the left bank slope,while the safety margin of the fault f42-9 and lamprophyre X is slightly insufficient.Based on the long-term stability evolution trend of the slope and in accordance with relevant standards,the safety factors for graded warning indicators—K_(w1),K_(w2),K_(w3),and K_(w4)—are determined as 1.350,1.325,1.300,and 1.275,respectively.Correspondingly,the estimated warning times are 12/30/2066,12/30/2084,and 12/30/2120.Accordingly,the deformation graded warning indexes for slope measurement points are established.
基金Supported by Hebei Provincial Outstanding Talent Development ProgramTangshan Talent Funding Project(A202202005).
文摘Food safety has always been a critical issue concerning people s livelihoods.The complexity and diversity of hazardous substances in food make systematical and scientifical identification of potential risk factors affecting food safety and accurate assessment and early warning for such risks one of the urgent problems for food safety inspection authorities.This paper explored food safety risk identification technologies,food safety risk monitoring technologies,and food safety risk early warning methods,aiming to provide theoretical support and research insights for improving food risk early warning systems.
文摘Leveraging the achievements of the smart meteorological system nationwide,a meteorological monitoring and early warning system for alfalfa pests and diseases can be formed through the establishment of four systems,namely,"real-time monitoring system,forecasting and prediction system,monitoring and early warning system,and smart service system".It will enable intelligent,dynamic meteorological monitoring,early warning,and forecasting services for the occurrence and development of alfalfa pests and diseases,providing technical support for scientifically controlling their harm and improving yield and quality.
基金supported by the National Natural Science Foundation of China(U2033204,51976209)the Natural Science Foundation of Hefei(2022019)supported by Youth Innovative Promotion Association CAS(Y201768)。
文摘Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application scenarios.Timely and effective TR early warning technology is urgently required considering the current fire safety situation of LIBs.In this work,we report an early warning method of TR with online electrochemical impedance spectroscopy(EIS)monitoring,which overcomes the shortcomings of warning methods based on traditional signals such as temperature,gas,and pressure with obvious delay and high cost.With in-situ data acquisition through accelerating rate calorimeter(ARC)-EIS test,the crucial features of TR were extracted using the RReliefF algorithm.TR mechanisms corresponding to the features at specific frequencies were analyzed.Finally,a three-level warning strategy for single battery,series module,and parallel module was formulated,which can successfully send out an early warning signal ahead of the self-heating temperature of battery under thermal abuse condition.The technology can provide a reliable basis for the timely intervention of battery thermal management and fire protection systems and is expected to be applied to electric vehicles and energy storage devices to realize early warning and improve battery safety.
基金supported by the Health and Medical Research Fund of the Food and Health Bureau of the Hong Kong Special Administrative Region(Project No.19201161)Seed Fund from the University of Hong Kong.
文摘BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong.The primary outcome was sepsis(Sepsis-3 definition)within 48 h of ED presentation.Using c-statistics and the DeLong test,we compared 11 EWSs,including the National Early Warning Score 2(NEWS2),Modified Early Warning Score,and Worthing Physiological Scoring System(WPS),etc.,and three shock indices(the shock index[SI],modified shock index[MSI],and diastolic shock index[DSI]),with Systemic Inflammatory Response Syndrome(SIRS)and quick Sequential Organ Failure Assessment(qSOFA)in predicting the primary outcome,intensive care unit admission,and mortality at different time points.RESULTS:We analyzed 601 patients,of whom 166(27.6%)developed sepsis.NEWS2 had the highest point estimate(area under the receiver operating characteristic curve[AUROC]0.75,95%CI 0.70-0.79)and was significantly better than SIRS,qSOFA,other EWSs and shock indices,except WPS,at predicting the primary outcome.However,the pooled sensitivity and specificity of NEWS2≥5 for the prediction of sepsis were 0.45(95%CI 0.37-0.52)and 0.88(95%CI 0.85-0.91),respectively.The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point.CONCLUSION:NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.
基金Supported by the Key Research and Development Program of Ningbo(No.2023Z218)the Joint Funds of the National Natural Science Founda-tion of China(No.U21A20121)+1 种基金the National Natural Science Foundation of China(No.51775325)the Young Eastern Scholars Program of Shanghai(No.QD2016033).
文摘Electric vehicles have been rapidly developing worldwide due to the use of new energy.However,at the same time,serious traffic accidents caused by driver fatigue in emergency situations have also drawn widespread attention.The lack of datasets in real vehicle test environments has always been a bottleneck in the research of driver fatigue in electric vehicles.Therefore,this study establishes a dataset from real vehicle test,applies the Bayesian optimization support vector machine(BOA-SVM)algorithm to take features of electromyography(EMG)and electrocardiography(ECG)signals as input and develop an early warning model for driving fatigue detection.Firstly,the driver’s EMG and ECG signals are collected through real vehicle testing experiments and then combined with the driver’s subjective fatigue evaluation scores to establish the dataset.Secondly,the study establishes a driver fatigue early warning model for emergency situations.Time-domain and frequency-domain features are extracted from the EMG signals.Principal component analysis(PCA)is applied for dimensionality reduction of these features.The experimental results show that based on the input of dimensionality reduced EMG features and ECG features,the BOA-SVM algorithm achieved an accuracy of 94.4%in classification.
文摘A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.
文摘Earthquake has a significant impact on operation safety of the high speed railway,and for Jakarta-Bandung High Speed Railway(HSR)in Indonesia where it is earthquake-prone,it is necessary to establish an earthquake early warning system to strengthen its earthquake resistance.Based on the principle and technical characteristics of China's high speed railway earthquake early warning system and combining the actual situations of Jakarta-Bandung HSR in Indonesia,this paper describes how to implement China's high speed railway earthquake early warning system in Jakarta-Bandung HSR.It focuses on optimizations in environmental adaptation design and seismic network interface design,earthquake attenuation model parameter adjustment and terminal software interface adjustment,so as to make the system better suit the local situations,and meet operation requirements and guarantee safe operation of Jakarta-Bandung HSR.
文摘Objective:To explore the effect of the combined application of the Shock Index(SI)and the Early Warning Score(EWS)in patients with acute gastrointestinal bleeding.Methods:Seventy patients with acute gastrointestinal bleeding admitted to a hospital from June 2022 to May 2024 were selected and randomly divided into two groups:the control group and the observation group,with 35 patients in each group.The control group received conventional emergency care measures,while the observation group received SI combined with NEWS emergency care measures.The treatment effects in both groups were compared.Results:The observation group had shorter waiting times for consultation(4.45±1.59 minutes),intravenous access establishment(6.79±2.52 minutes),hemostasis time(4.41±1.52 hours),and hospital stays(8.39±2.13 days)compared to the control group,which had times of 5.46±1.34 minutes,8.41±2.16 minutes,5.16±1.47 hours,and 10.26±2.98 days,respectively.The differences were statistically significant(P<0.05).Before management,there were no significant differences in the levels of hemoglobin,prealbumin,and serum protein between the two groups(P>0.05).However,after systematic emergency management,the serum indexes in both groups significantly improved,with the observation group showing greater improvement than the control group,and these differences were statistically significant(P<0.05).In the observation group,only one case of cardiovascular complications occurred during the rescue period,with an incidence rate of 2.86%.In contrast,the control group experienced eight cases of complications,including hemorrhagic shock,anemia,multi-organ failure,cardiovascular complications,and gastrointestinal rebleeding,with an incidence rate of 22.85%.The difference between the groups was statistically significant(P<0.05).Conclusion:The application of SI combined with EWS emergency care measures in patients with acute gastrointestinal hemorrhage can effectively improve serum indexes,shorten resuscitation time and hospital stay,and reduce the risk of complications such as hemorrhagic shock,anemia,infection,multi-organ failure,cardiovascular complications,acute renal failure,and gastrointestinal rebleeding.This approach has positive clinical application value.
