Rainfall-induced landslides have occurred frequently in Southwestern China since the Wenchuan earthquake,resulting in massive loss of people’s life and property.Fortunately,landslide early-warning is one of the most ...Rainfall-induced landslides have occurred frequently in Southwestern China since the Wenchuan earthquake,resulting in massive loss of people’s life and property.Fortunately,landslide early-warning is one of the most important tools for landslide hazard prevention and mitigation.However,the accumulation of historical data of the landslides induced by rainfall is limited in many remote mountain areas and the stability of the slope is easily affected by human engineering activities and environmental changes,leading to difficulties to accurately realize early warning of landslide hazards by statistical methods.The proposed warning method is divided into rainfall warning component and deformation warning component because the deformation induced by rainfall has the characteristic of hysteretic nature.Rainfall,tilted angle and crack width are chosen as monitoring indexes.Rainfall grade level that contains rainfall intensity and duration information is graded according to the variation of the safety factor calculated by 3-D finite difference numerical simulation method,and then is applied using the strength reduction method and unascertained information theory to obtain the deformation grade level of several monitored points.Finally,based on the system reliability theory,we establish a comprehensive landslide warning level method that provides four early warning levels to reflect the safety factor reductions during and post rainfall events.The application of this method at a landslide site yield generally satisfactory results and provide a new method for performing multi-index and multi-level landslide early warnings.展开更多
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
Emphysematous pyelonephritis(EPN)is a severe,a lethal necrotizing upper urinary tract infection,characterized by gas production within the renal pa-renchyma,collecting system,or perinephric tissue.EPN is emerging as a...Emphysematous pyelonephritis(EPN)is a severe,a lethal necrotizing upper urinary tract infection,characterized by gas production within the renal pa-renchyma,collecting system,or perinephric tissue.EPN is emerging as a sig-nificant concern,necessitating early diagnosis,severity assessment,and timely intervention to improve outcomes.This study proposes a modified National Early Warning Score 2(mNEWS 2)to enhance risk stratification and predictive accuracy in EPN management.The mNEWS 2 refines the original NEWS 2 system,which aggregates 6 physiological indicators(body temperature,systolic blood pressure,pulse rate,oxygen saturation,breathing rate,and degree of consciousness),by incorporating weighted risk stratification indices and specific cutoff values derived from clinical observations,statistical modeling,and predictive per-formance analysis.A pilot study identified optimal thresholds,with a score of 15 maximizing predictive performance for mortality risk and intervention needs,validated through receiver operating characteristic curve analysis.So,the mNEWS 2 score represents a significant advancement in EPN management,offering improved risk stratification and treatment outcomes.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
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
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
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
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
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
BACKGROUND Primary liver cancer is a globally prevalent malignancy,with China accounting for approximately 55%of new cases,and is linked to hepatitis B,aflatoxin,and cirrhosis.Its rupture with hemorrhagic shock is a l...BACKGROUND Primary liver cancer is a globally prevalent malignancy,with China accounting for approximately 55%of new cases,and is linked to hepatitis B,aflatoxin,and cirrhosis.Its rupture with hemorrhagic shock is a lethal complication with high mortality,and traditional triage struggles with timely risk stratification,necessitating better tools,such as the integrated shock index(SI)-early warning score(EWS).AIM To study and analyze the combined effect of the SI and EWS in primary liver cancer patients with ruptured hemorrhage and shock.METHODS In total,118 patients who visited the Emergency Department of Nantong Third People's Hospital from January 2023 to December 2024 were selected and randomly divided into a control group(59 patients who received routine emergency treatment)and an observation group(59 patients who received condition assessment and intervention by combining the SI and EWS based on routine emergency treatment).The clinical treatment outcomes,respiratory function indicators,serological indicators,complications,and satisfaction with emergency intervention before and after the emergency intervention were compared between the two groups.RESULTS The emergency,triage,waiting,and hemostasis times,as well as hospital stay were shorter in the observation group than in the control group(P<0.05).After 48 hours of emergency intervention,blood oxygen saturation and partial pressure of oxygen in the observation group were higher than those in the control group(P<0.05).Seven days after emergency intervention,the hemoglobin,prealbumin,and albumin levels were higher in the observation group than in the control group(P<0.05).The complication rate in the observation group was 3.39%,lower than that in the control group(13.56%;P<0.05).Satisfaction with emergency intervention in the observation group was 94.92%,higher than 83.05%in the control group(P<0.05).CONCLUSION The combined application of the SI and EWS in patients with primary liver cancer rupture,hemorrhage,and shock can significantly shorten emergency treatment time,improve respiratory function and serological indicators,reduce the incidence of complications,and enhance patient satisfaction with emergency interventions,with higher clinical treatment efficiency and quality.Therefore,it is worthy of promotion and application.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
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
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
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
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
[Objective]The paper was to quickly get the real-time dynamic status of regional farmland environmental pollution caused by livestock wastes.[Method] With WebGIS as spatial information platform,the network and digital...[Objective]The paper was to quickly get the real-time dynamic status of regional farmland environmental pollution caused by livestock wastes.[Method] With WebGIS as spatial information platform,the network and digital early warning system of farmland environmental pollution caused by livestock wastes was established.[Result] The system realized the functions such as livestock wastes calculation,livestock information query and analysis,nitrogen load quantity estimation of livestock waste,early warning of farmland environmental pollution caused by livestock wastes and visual display of result.[Conclusion] The paper provided scientific basis for the relevant research on farmland environmental pollution caused by livestock wastes.展开更多
Early-warning is an effective way to control mud expansion in sewage treatment plants with A/O technology. In the research, warning indices and technology of active mud were explored and it is concluded that bacteria ...Early-warning is an effective way to control mud expansion in sewage treatment plants with A/O technology. In the research, warning indices and technology of active mud were explored and it is concluded that bacteria growth in mud can be obtained by observation of mud appearance and microorganism variety, and measurement of the number of filamentous bacteria, water quality, mud load and age, dissolved oxygen, temperature and pH. Furthermore, filamentous bacteria in mud can be researched through fluorescence in situ hybridization, PCR-temperature denaturing gradient gel electrophoresis and PCR-single-stranded conformation polymorphism in order to determine the characters and states of active mud to achieve early warning of mud expansion.展开更多
By analyzing the subtropics aquaculture present situation,the necessity of the construction of cold disaster early warning system for subtropics aquaculture,the research goal and the duty were expounded. The system st...By analyzing the subtropics aquaculture present situation,the necessity of the construction of cold disaster early warning system for subtropics aquaculture,the research goal and the duty were expounded. The system structure and the frame were introduced in detail. Several key questions and their solutions of the cold disaster early warning system for subtropics aquaculture were put forward.展开更多
The area,the scope as well as some ecological environment questions in Three Gorges Reservoir was briefly introduced. Then its early warning-system frame was preliminarily constructed,which includes ecological securit...The area,the scope as well as some ecological environment questions in Three Gorges Reservoir was briefly introduced. Then its early warning-system frame was preliminarily constructed,which includes ecological security dynamic monitoring,ecological security appraisal,ecological security forecast and ecological security decision-making management. The synthetic evaluation indicator system of the ecological security quality were initially established,which includes ecological environment pollution,land use and land cover change,geological hazard and epidemic outbreaks. At the same time,29 evaluating indicators were selected,divides into the basic factors,response factors and inducing factors,which need to be Real-time monitored.展开更多
基金sponsored by National KeyResearch and Development Program(2018YFC0809400)"Safety Guarantee Technology of Power Grid Facilities in Large Region under Extreme Conditions"and Scientific Program of State Grid Corporation of China(GCB17201800051)"Research for application of geological hazard analysis technology on strategic transmission channel of Sichuan-Tibet Plateau based on synthetic aperture radar"。
文摘Rainfall-induced landslides have occurred frequently in Southwestern China since the Wenchuan earthquake,resulting in massive loss of people’s life and property.Fortunately,landslide early-warning is one of the most important tools for landslide hazard prevention and mitigation.However,the accumulation of historical data of the landslides induced by rainfall is limited in many remote mountain areas and the stability of the slope is easily affected by human engineering activities and environmental changes,leading to difficulties to accurately realize early warning of landslide hazards by statistical methods.The proposed warning method is divided into rainfall warning component and deformation warning component because the deformation induced by rainfall has the characteristic of hysteretic nature.Rainfall,tilted angle and crack width are chosen as monitoring indexes.Rainfall grade level that contains rainfall intensity and duration information is graded according to the variation of the safety factor calculated by 3-D finite difference numerical simulation method,and then is applied using the strength reduction method and unascertained information theory to obtain the deformation grade level of several monitored points.Finally,based on the system reliability theory,we establish a comprehensive landslide warning level method that provides four early warning levels to reflect the safety factor reductions during and post rainfall events.The application of this method at a landslide site yield generally satisfactory results and provide a new method for performing multi-index and multi-level landslide early warnings.
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Emphysematous pyelonephritis(EPN)is a severe,a lethal necrotizing upper urinary tract infection,characterized by gas production within the renal pa-renchyma,collecting system,or perinephric tissue.EPN is emerging as a sig-nificant concern,necessitating early diagnosis,severity assessment,and timely intervention to improve outcomes.This study proposes a modified National Early Warning Score 2(mNEWS 2)to enhance risk stratification and predictive accuracy in EPN management.The mNEWS 2 refines the original NEWS 2 system,which aggregates 6 physiological indicators(body temperature,systolic blood pressure,pulse rate,oxygen saturation,breathing rate,and degree of consciousness),by incorporating weighted risk stratification indices and specific cutoff values derived from clinical observations,statistical modeling,and predictive per-formance analysis.A pilot study identified optimal thresholds,with a score of 15 maximizing predictive performance for mortality risk and intervention needs,validated through receiver operating characteristic curve analysis.So,the mNEWS 2 score represents a significant advancement in EPN management,offering improved risk stratification and treatment outcomes.
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.
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.
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.
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.
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Supported by Clinical Medicine Special Research Fund Project of Nantong University,No.2024HZ001 and No.2022HY009。
文摘BACKGROUND Primary liver cancer is a globally prevalent malignancy,with China accounting for approximately 55%of new cases,and is linked to hepatitis B,aflatoxin,and cirrhosis.Its rupture with hemorrhagic shock is a lethal complication with high mortality,and traditional triage struggles with timely risk stratification,necessitating better tools,such as the integrated shock index(SI)-early warning score(EWS).AIM To study and analyze the combined effect of the SI and EWS in primary liver cancer patients with ruptured hemorrhage and shock.METHODS In total,118 patients who visited the Emergency Department of Nantong Third People's Hospital from January 2023 to December 2024 were selected and randomly divided into a control group(59 patients who received routine emergency treatment)and an observation group(59 patients who received condition assessment and intervention by combining the SI and EWS based on routine emergency treatment).The clinical treatment outcomes,respiratory function indicators,serological indicators,complications,and satisfaction with emergency intervention before and after the emergency intervention were compared between the two groups.RESULTS The emergency,triage,waiting,and hemostasis times,as well as hospital stay were shorter in the observation group than in the control group(P<0.05).After 48 hours of emergency intervention,blood oxygen saturation and partial pressure of oxygen in the observation group were higher than those in the control group(P<0.05).Seven days after emergency intervention,the hemoglobin,prealbumin,and albumin levels were higher in the observation group than in the control group(P<0.05).The complication rate in the observation group was 3.39%,lower than that in the control group(13.56%;P<0.05).Satisfaction with emergency intervention in the observation group was 94.92%,higher than 83.05%in the control group(P<0.05).CONCLUSION The combined application of the SI and EWS in patients with primary liver cancer rupture,hemorrhage,and shock can significantly shorten emergency treatment time,improve respiratory function and serological indicators,reduce the incidence of complications,and enhance patient satisfaction with emergency interventions,with higher clinical treatment efficiency and quality.Therefore,it is worthy of promotion and application.
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.
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.
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.
基金Supported by B Category Projects of Fujian Provincial Department ofEducation (JB10132)Technology Start-up Projects of MinjiangUniversity (YKQ09003)~~
文摘[Objective]The paper was to quickly get the real-time dynamic status of regional farmland environmental pollution caused by livestock wastes.[Method] With WebGIS as spatial information platform,the network and digital early warning system of farmland environmental pollution caused by livestock wastes was established.[Result] The system realized the functions such as livestock wastes calculation,livestock information query and analysis,nitrogen load quantity estimation of livestock waste,early warning of farmland environmental pollution caused by livestock wastes and visual display of result.[Conclusion] The paper provided scientific basis for the relevant research on farmland environmental pollution caused by livestock wastes.
基金Supported by National Natural Science Foundation of China(51208068)~~
文摘Early-warning is an effective way to control mud expansion in sewage treatment plants with A/O technology. In the research, warning indices and technology of active mud were explored and it is concluded that bacteria growth in mud can be obtained by observation of mud appearance and microorganism variety, and measurement of the number of filamentous bacteria, water quality, mud load and age, dissolved oxygen, temperature and pH. Furthermore, filamentous bacteria in mud can be researched through fluorescence in situ hybridization, PCR-temperature denaturing gradient gel electrophoresis and PCR-single-stranded conformation polymorphism in order to determine the characters and states of active mud to achieve early warning of mud expansion.
基金Supported by National Scientific Department National Science and Technology Supporting Plan Scheme (2008BADB9B05-02)Guangdong Science Technology Plan Program (2010B010600037)Guangdong Ocean University Personnel Project (0512049)~~
文摘By analyzing the subtropics aquaculture present situation,the necessity of the construction of cold disaster early warning system for subtropics aquaculture,the research goal and the duty were expounded. The system structure and the frame were introduced in detail. Several key questions and their solutions of the cold disaster early warning system for subtropics aquaculture were put forward.
基金funded by National Natural Science Foundation Project (40801077)Ministry of Education Key Project (209100)+1 种基金Natural Science Foundation of Chongqing ( CSTC, 2008BB7367 )Chongqing Municipal Education Commission of Science and Technology Research Grant Project (KJ070811)~~
文摘The area,the scope as well as some ecological environment questions in Three Gorges Reservoir was briefly introduced. Then its early warning-system frame was preliminarily constructed,which includes ecological security dynamic monitoring,ecological security appraisal,ecological security forecast and ecological security decision-making management. The synthetic evaluation indicator system of the ecological security quality were initially established,which includes ecological environment pollution,land use and land cover change,geological hazard and epidemic outbreaks. At the same time,29 evaluating indicators were selected,divides into the basic factors,response factors and inducing factors,which need to be Real-time monitored.
