The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warni...The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects.展开更多
0 INTRODUCTION Due to the sudden and highly destructive nature of slope rock collapse,developing effective early warning systems has become an urgent challenge in geotechnical engineering(Cai and Detournay,2024;Loew e...0 INTRODUCTION Due to the sudden and highly destructive nature of slope rock collapse,developing effective early warning systems has become an urgent challenge in geotechnical engineering(Cai and Detournay,2024;Loew et al.,2017).Traditional monitoring methods primarily target the acceleration stage preceding disasters(such as displacement monitoring for landslides and debris flows),which is effective for early warning of plastic collapse disasters but often inadequate for brittle failure modes(Walter et al.,2019;Chao et al.,2018;Crosta et al.,2017).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
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
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
The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A da...The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A data set was established by collecting academic warning data of students in a certain university.The importance of the school,major,grade,and warning level for the students was analyzed using the Pearson correlation coefficient,random forest variable importance,and permutation importance.It was found that the characteristic of the major has a great impact on the academic warning level.Countermeasures such as dynamic adjustment of majors,reform of cognitive adaptation of courses,full-cycle academic support,and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’learning ability.展开更多
Due to batteries inconsistencies and potential faults in battery management systems,slight overcharging remains a common yet insufficiently understood safety risk,lacking effective warning methods.To illuminate the de...Due to batteries inconsistencies and potential faults in battery management systems,slight overcharging remains a common yet insufficiently understood safety risk,lacking effective warning methods.To illuminate the degradation behavior and failure mechanism of various overcharged states(100%SOC,105%SOC,110%SOC,and 115%SOC),multiple advanced in-situ characterization techniques(accelerating rate calorimeter,electrochemical impedance spectroscopy,ultrasonic scanning,and expansion instrument)were utilized.Additionally,re-overcharge-induced thermal runaway(TR)tests were conducted,with a specific emphasis on the evolution of the expansion force signal.Results indicated significant degradation at 110%SOC including conductivity loss,loss of lithium inventory,and loss of active material accompanied by internal gas generation.These failure behaviors slow down the expansion force rate during reovercharging,reducing the efficacy of active warnings that depend on rate thresholds of expansion force.Specifically,the warning time for 115%SOC battery is only 144 s,which is 740 s shorter than that for fresh battery,and the time to TR is advanced by 9 min.Moreover,the initial self-heating temperature(T1)is reduced by 62.4℃compared to that of fresh battery,reaching only 70.8℃.To address the low safety of overcharged batteries,a passive overcharge warning method utilizing relaxation expansion force was proposed,based on the continued gas generation after stopping charging,leading to a sustained increase in force.Compared to active methods that rely on thresholds of expansion force rate,the passive method can issue warnings 115 s earlier.By combining the passive and active warning methods,guaranteed effective overcharge warning can be issued 863-884 s before TR.This study introduces a novel perspective for enhancing the safety of batteries.展开更多
Coating microdefects and localized corrosion in coating/metal system are inevitable,accelerating the degradation of metal infrastructure.Early evaluating coating microdefects and detecting corrosion sites are urgent y...Coating microdefects and localized corrosion in coating/metal system are inevitable,accelerating the degradation of metal infrastructure.Early evaluating coating microdefects and detecting corrosion sites are urgent yet remain challenge to achieve.Herein,we propose a robust,universal and efficient fluorescence-based strategy for hierarchical warning of coating damage and metal corrosion by introducing the concepts of damage-induced fluorescence enhancement effect(DIE)and ionic-recognition induced quenching effect(RIQ).The coatings with dualresponsiveness for coating defect and steel corrosion are constructed by incorporating synthesized nanoprobes composed of metal organic frameworks(Ni–Zn-MOFs)loaded with Rhodamine B(RhB@MOFs).The initial damage to the coating causes an immediate intensification of fluorescence,while the specific ionic-recognition characteristic of RhB with Fe3t results in an evident fluorescence quenching,enabling the detection of coating damage and corrosion.Importantly,this nanoprobes are insensitive to the coating matrix and exhibit stable corrosion warning capability across various coating systems.Meanwhile,electrochemical investigations indicate that the impedance values of RM/EP maintain above 10^(8)Ωcm^(2)even after 60 days of immersion.Therefore,the incorporation of fluorescent nanoprobes greatly inhibits the intrusion of electrolytes into polymer and improves the corrosion protection performance of the coating.This powerful strategy towards dual-level damage warning provides insights for the development of long-term smart protective materials.展开更多
As oil and gas exploration continues to progress into deeper and unconventional reservoirs,the likelihood of kick risk increases,making kick warning a critical factor in ensuring drilling safety and efficiency.Due to ...As oil and gas exploration continues to progress into deeper and unconventional reservoirs,the likelihood of kick risk increases,making kick warning a critical factor in ensuring drilling safety and efficiency.Due to the scarcity of kick samples,traditional supervised models perform poorly,and significant fluctuations in field data lead to high false alarm rates.This study proposes an unsupervised graph autoencoder(GAE)-based kick warning method,which effectively reduces false alarms by eliminating the influence of field engineer operations and incorporating real-time model updates.The method utilizes the GAE model to process time-series data during drilling,accurately identifying kick risk while overcoming challenges related to small sample sizes and missing features.To further reduce false alarms,the weighted dynamic time warping(WDTW)algorithm is introduced to identify fluctuations in logging data caused by field engineer operations during drilling,with real-time updates applied to prevent normal conditions from being misclassified as kick risk.Experimental results show that the GAE-based kick warning method achieves an accuracy of 92.7%and significantly reduces the false alarm rate.The GAE model continues to operate effectively even under conditions of missing features and issues kick warnings 4 min earlier than field engineers,demonstrating its high sensitivity and robustness.After integrating the WDTW algorithm and real-time updates,the false alarm rate is reduced from 17.3%to 5.6%,further improving the accuracy of kick warnings.The proposed method provides an efficient and reliable approach for kick warning in drilling operations,offering strong practical value and technical support for the intelligent management of future drilling operations.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
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
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
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
An effective warning system for flash floods along the upper River des Peres, a small urban stream in eastern Missouri, USA, is based on three enterprise-level, automated rain gauges.Because floods in this 25 km~2 bas...An effective warning system for flash floods along the upper River des Peres, a small urban stream in eastern Missouri, USA, is based on three enterprise-level, automated rain gauges.Because floods in this 25 km~2 basin develop rapidly and are commonly caused by small but intense thunderstorm cells, these rain gauges were necessarily deployed within the watershed, and immediate telemetry and processing of rainfall delivered in 5-minute intervals is required. Available data show that damaging floods in this area occur only 30 min to 3 h following the delivery of 38 mm of rainfall or more in a single hour. Water levels along this stream can rise more than 3 m/h. Since full deployment in Nov. 2021, our system has successfully predicted 3 significant floods with one false positive.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
The earthquake early warning system is an effective means of disaster reduction to reduce losses caused by earthquakes,it can release earthquake warning information to the public before destructive seismic waves reach...The earthquake early warning system is an effective means of disaster reduction to reduce losses caused by earthquakes,it can release earthquake warning information to the public before destructive seismic waves reach the warning target area,and carry out automatic disposal of lifeline engineering facilities.Through the construction of the National Earthquake Intensity Rapid Reporting and Early Warning Project,an earthquake early warning network consisting of over 1900 monitoring stations has been established in the Beijing-Tianjin-Hebei Urban Agglomeration.The early warning system has achieved second level earthquake warning and minute level intensity rapid reporting.The implementation of these functions relies on the system's ability to timely,accurately,and reliably identify seismic waves.But with the development of social economy,the background noise of earthquake observation environment is becoming increasingly complex,which brings certain challenges to earthquake wave recognition,some interference events have the risk of triggering the earthquake warning system incorrectly.Therefore,this article focuses on seismic wave recognition in complex noise environments and proposes a seismic wave detection method based on triangulation to enhance the antiinterference ability and recognition accuracy of early warning systems.展开更多
The collapse of rock masses in fault-developed zones poses significant safety challenges during the excavation of high-stress underground caverns. This study investigates the spatiotemporal evolution of the collapse m...The collapse of rock masses in fault-developed zones poses significant safety challenges during the excavation of high-stress underground caverns. This study investigates the spatiotemporal evolution of the collapse mechanisms of the cavern in the Yebatan Hydropower Station through using microseismic (MS) monitoring and displacement measurements. We developed a multi-parameter deformation early warning model that integrates three critical indicators: deformation rate, rate increment, and tangential angle of the deformation time curve. The results of the early warning model show a significant and abrupt increase in the deformation of the rock mass during the collapse process. The safety and stability of the local cavern in the face of excavation-induced disturbances are meticulously assessed utilizing MS data. Spatiotemporal analysis of the MS monitoring indicates a high frequency of MS events during the blasting phase, with a notable clustering of these events in the vicinity of the fault. These research results provide a valuable reference for risk warnings and stability assessments in the fault development zones of analogous caverns.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
To explore the clinical characteristics,pathogenesis,and treatment efficacy of patients with stroke warning syndrome(SWS).Methods:Ten patients with internal capsule early warning syndrome who visited the Neurology Dep...To explore the clinical characteristics,pathogenesis,and treatment efficacy of patients with stroke warning syndrome(SWS).Methods:Ten patients with internal capsule early warning syndrome who visited the Neurology Department of our hospital from 2019 to 2023 were enrolled.Their clinical data were summarized,and the etiology,pathogenesis,imaging features,and treatment outcomes were discussed in light of previous literature.Results:Among the patients,there were 5 males and 5 females,with an average age of 60 years.The number of attacks ranged from 4 to 9,and the duration of each attack varied from 3 to 60 minutes.Five patients underwent intravenous thrombolysis.Four patients still experienced recurrent attacks after thrombolysis.They were then provided with volume expansion,anticoagulation,antiplatelet aggregation,vasospasm-relief therapies,and symptomatic treatment as appropriate.Three months later,the mRs score of all patients was≤2 points.Conclusion:Although SWS is not common,it is associated with a high risk of early infarction and a poor clinical prognosis.Clinicians should be fully aware of stroke warning syndrome and strive to prevent its clinical progression.展开更多
To enhance the prevention capability of meteorological disasters in semi-arid mountainous areas,in view of the characteristics of strong suddenness,significant locality and high basin correlation of hazardous weather ...To enhance the prevention capability of meteorological disasters in semi-arid mountainous areas,in view of the characteristics of strong suddenness,significant locality and high basin correlation of hazardous weather in Liangcheng County,the progress of risk warning technology of hazardous weather and the innovative achievements of the construction of the basin joint prevention mechanism were systematically analyzed based on the recent prevention and mitigation practices of meteorological disasters in Liangcheng Meteorological Bureau.The results show that firstly,Liangcheng County has established an"ground-air-space"integrated monitoring network to achieve 5-min full coverage monitoring of core meteorological elements throughout the region.Secondly,relying on a multi-level forecasting system and intelligent technologies,the lead time of early warnings of hazardous weather has been extended to several hours,and the accuracy has significantly improved.Thirdly,a multi-channel and targeted warning dissemination system has been constructed,with a public coverage rate of over 90%.Fourthly,a basin joint prevention innovation model of"information sharing,joint discussion,and hierarchical linkage"has been formed,and the collaborative effectiveness of disaster response across regions and departments has been significantly enhanced.This research results can provide technical references and practical models for optimizing the prevention and mitigation system of meteorological disasters in regions with similar climate and geographical characteristics.展开更多
基金support from the Sichuan Science and Technology Program(Grant No.2023NSFSC0812).
文摘The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects.
基金supported by the National Key R&D Program of China(No.2023YFC3081400)the National Natural Science Foundation of China(No.41702371)+3 种基金the Open Fund Project of State Key Laboratory of Mining Response and Disaster Prevention in Deep Coal Mines(No.SKLMRDPC22KF13)the Supported by State key Laboratory of Mining Disaster Prevention and Control(Shandong University of Science and Technology),Ministry of Education(No.DPEPM202502)the Open Fund Research Project Supported by State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology(No.SICGM202503)the Fund of Chongqing Key Laboratory of Facility Damage Mechanism and Protection in Highland Mountain Environment(No.LQ24KFJJ09)。
文摘0 INTRODUCTION Due to the sudden and highly destructive nature of slope rock collapse,developing effective early warning systems has become an urgent challenge in geotechnical engineering(Cai and Detournay,2024;Loew et al.,2017).Traditional monitoring methods primarily target the acceleration stage preceding disasters(such as displacement monitoring for landslides and debris flows),which is effective for early warning of plastic collapse disasters but often inadequate for brittle failure modes(Walter et al.,2019;Chao et al.,2018;Crosta et al.,2017).
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.
基金supported by the Basic Ability Improvement Project of Young and Middle-Aged Teachers in Colleges and Universities of Guangxi(2022KY1922,2021KY1938).
文摘The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A data set was established by collecting academic warning data of students in a certain university.The importance of the school,major,grade,and warning level for the students was analyzed using the Pearson correlation coefficient,random forest variable importance,and permutation importance.It was found that the characteristic of the major has a great impact on the academic warning level.Countermeasures such as dynamic adjustment of majors,reform of cognitive adaptation of courses,full-cycle academic support,and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’learning ability.
基金supported by the National Natural Science Foundation of China(52476200,52106244)the Guangdong Basic and Applied Basic Research Foundation(2024A1515030124)+1 种基金the Science and Technology Project of China Southern Power Grid under Grant GDKJXM20230246(030100KC23020017)the Fundamental Research Funds for the Central Universities。
文摘Due to batteries inconsistencies and potential faults in battery management systems,slight overcharging remains a common yet insufficiently understood safety risk,lacking effective warning methods.To illuminate the degradation behavior and failure mechanism of various overcharged states(100%SOC,105%SOC,110%SOC,and 115%SOC),multiple advanced in-situ characterization techniques(accelerating rate calorimeter,electrochemical impedance spectroscopy,ultrasonic scanning,and expansion instrument)were utilized.Additionally,re-overcharge-induced thermal runaway(TR)tests were conducted,with a specific emphasis on the evolution of the expansion force signal.Results indicated significant degradation at 110%SOC including conductivity loss,loss of lithium inventory,and loss of active material accompanied by internal gas generation.These failure behaviors slow down the expansion force rate during reovercharging,reducing the efficacy of active warnings that depend on rate thresholds of expansion force.Specifically,the warning time for 115%SOC battery is only 144 s,which is 740 s shorter than that for fresh battery,and the time to TR is advanced by 9 min.Moreover,the initial self-heating temperature(T1)is reduced by 62.4℃compared to that of fresh battery,reaching only 70.8℃.To address the low safety of overcharged batteries,a passive overcharge warning method utilizing relaxation expansion force was proposed,based on the continued gas generation after stopping charging,leading to a sustained increase in force.Compared to active methods that rely on thresholds of expansion force rate,the passive method can issue warnings 115 s earlier.By combining the passive and active warning methods,guaranteed effective overcharge warning can be issued 863-884 s before TR.This study introduces a novel perspective for enhancing the safety of batteries.
基金support by the National Natural Science Foundation of China(52201077)the Natural Science Foundation of Shandong Province(ZR2022QE191)+1 种基金Elite Scheme of Shandong University of Science and Technology(0104060541123)Talent introduction and Research Start-up Fund of Shandong University of Science and Technology(0104060510124).
文摘Coating microdefects and localized corrosion in coating/metal system are inevitable,accelerating the degradation of metal infrastructure.Early evaluating coating microdefects and detecting corrosion sites are urgent yet remain challenge to achieve.Herein,we propose a robust,universal and efficient fluorescence-based strategy for hierarchical warning of coating damage and metal corrosion by introducing the concepts of damage-induced fluorescence enhancement effect(DIE)and ionic-recognition induced quenching effect(RIQ).The coatings with dualresponsiveness for coating defect and steel corrosion are constructed by incorporating synthesized nanoprobes composed of metal organic frameworks(Ni–Zn-MOFs)loaded with Rhodamine B(RhB@MOFs).The initial damage to the coating causes an immediate intensification of fluorescence,while the specific ionic-recognition characteristic of RhB with Fe3t results in an evident fluorescence quenching,enabling the detection of coating damage and corrosion.Importantly,this nanoprobes are insensitive to the coating matrix and exhibit stable corrosion warning capability across various coating systems.Meanwhile,electrochemical investigations indicate that the impedance values of RM/EP maintain above 10^(8)Ωcm^(2)even after 60 days of immersion.Therefore,the incorporation of fluorescent nanoprobes greatly inhibits the intrusion of electrolytes into polymer and improves the corrosion protection performance of the coating.This powerful strategy towards dual-level damage warning provides insights for the development of long-term smart protective materials.
基金Youth Foundation of National Natural Science Foundation of China (No. 52204020)Distinguished Young Foundation of National Natural Science Foundation of China (No. 52125401).
文摘As oil and gas exploration continues to progress into deeper and unconventional reservoirs,the likelihood of kick risk increases,making kick warning a critical factor in ensuring drilling safety and efficiency.Due to the scarcity of kick samples,traditional supervised models perform poorly,and significant fluctuations in field data lead to high false alarm rates.This study proposes an unsupervised graph autoencoder(GAE)-based kick warning method,which effectively reduces false alarms by eliminating the influence of field engineer operations and incorporating real-time model updates.The method utilizes the GAE model to process time-series data during drilling,accurately identifying kick risk while overcoming challenges related to small sample sizes and missing features.To further reduce false alarms,the weighted dynamic time warping(WDTW)algorithm is introduced to identify fluctuations in logging data caused by field engineer operations during drilling,with real-time updates applied to prevent normal conditions from being misclassified as kick risk.Experimental results show that the GAE-based kick warning method achieves an accuracy of 92.7%and significantly reduces the false alarm rate.The GAE model continues to operate effectively even under conditions of missing features and issues kick warnings 4 min earlier than field engineers,demonstrating its high sensitivity and robustness.After integrating the WDTW algorithm and real-time updates,the false alarm rate is reduced from 17.3%to 5.6%,further improving the accuracy of kick warnings.The proposed method provides an efficient and reliable approach for kick warning in drilling operations,offering strong practical value and technical support for the intelligent management of future drilling operations.
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.
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An effective warning system for flash floods along the upper River des Peres, a small urban stream in eastern Missouri, USA, is based on three enterprise-level, automated rain gauges.Because floods in this 25 km~2 basin develop rapidly and are commonly caused by small but intense thunderstorm cells, these rain gauges were necessarily deployed within the watershed, and immediate telemetry and processing of rainfall delivered in 5-minute intervals is required. Available data show that damaging floods in this area occur only 30 min to 3 h following the delivery of 38 mm of rainfall or more in a single hour. Water levels along this stream can rise more than 3 m/h. Since full deployment in Nov. 2021, our system has successfully predicted 3 significant floods with one false positive.
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.
基金supported by the Spark Program of Earthquake Science and Technology(No.XH23003C)。
文摘The earthquake early warning system is an effective means of disaster reduction to reduce losses caused by earthquakes,it can release earthquake warning information to the public before destructive seismic waves reach the warning target area,and carry out automatic disposal of lifeline engineering facilities.Through the construction of the National Earthquake Intensity Rapid Reporting and Early Warning Project,an earthquake early warning network consisting of over 1900 monitoring stations has been established in the Beijing-Tianjin-Hebei Urban Agglomeration.The early warning system has achieved second level earthquake warning and minute level intensity rapid reporting.The implementation of these functions relies on the system's ability to timely,accurately,and reliably identify seismic waves.But with the development of social economy,the background noise of earthquake observation environment is becoming increasingly complex,which brings certain challenges to earthquake wave recognition,some interference events have the risk of triggering the earthquake warning system incorrectly.Therefore,this article focuses on seismic wave recognition in complex noise environments and proposes a seismic wave detection method based on triangulation to enhance the antiinterference ability and recognition accuracy of early warning systems.
基金Projects(52209132, 52309156) supported by the National Natural Science Foundation of ChinaProject(BK20251905) supported by the Natural Science Foundation of Jiangsu Province,China+2 种基金Project(252102320037) supported by the Henan Province Science and Technology Research,ChinaProject(CKWV20231173/KY) supported by the CRSRI Open Research Program,ChinaProject(2023KSD15) supported by the Open Research Fund of Hubei Provincial Key Laboratory of Construction and Management in Hydropower Engineering,China。
文摘The collapse of rock masses in fault-developed zones poses significant safety challenges during the excavation of high-stress underground caverns. This study investigates the spatiotemporal evolution of the collapse mechanisms of the cavern in the Yebatan Hydropower Station through using microseismic (MS) monitoring and displacement measurements. We developed a multi-parameter deformation early warning model that integrates three critical indicators: deformation rate, rate increment, and tangential angle of the deformation time curve. The results of the early warning model show a significant and abrupt increase in the deformation of the rock mass during the collapse process. The safety and stability of the local cavern in the face of excavation-induced disturbances are meticulously assessed utilizing MS data. Spatiotemporal analysis of the MS monitoring indicates a high frequency of MS events during the blasting phase, with a notable clustering of these events in the vicinity of the fault. These research results provide a valuable reference for risk warnings and stability assessments in the fault development zones of analogous caverns.
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Project of Chengde Municipal Science and Technology Bureau,Project Name:A Comparative Study on the Efficacy and Safety of Tirofiban and Argatroban in the Treatment of Acute Cerebral Infarction.Project Code:202402A045.
文摘To explore the clinical characteristics,pathogenesis,and treatment efficacy of patients with stroke warning syndrome(SWS).Methods:Ten patients with internal capsule early warning syndrome who visited the Neurology Department of our hospital from 2019 to 2023 were enrolled.Their clinical data were summarized,and the etiology,pathogenesis,imaging features,and treatment outcomes were discussed in light of previous literature.Results:Among the patients,there were 5 males and 5 females,with an average age of 60 years.The number of attacks ranged from 4 to 9,and the duration of each attack varied from 3 to 60 minutes.Five patients underwent intravenous thrombolysis.Four patients still experienced recurrent attacks after thrombolysis.They were then provided with volume expansion,anticoagulation,antiplatelet aggregation,vasospasm-relief therapies,and symptomatic treatment as appropriate.Three months later,the mRs score of all patients was≤2 points.Conclusion:Although SWS is not common,it is associated with a high risk of early infarction and a poor clinical prognosis.Clinicians should be fully aware of stroke warning syndrome and strive to prevent its clinical progression.
文摘To enhance the prevention capability of meteorological disasters in semi-arid mountainous areas,in view of the characteristics of strong suddenness,significant locality and high basin correlation of hazardous weather in Liangcheng County,the progress of risk warning technology of hazardous weather and the innovative achievements of the construction of the basin joint prevention mechanism were systematically analyzed based on the recent prevention and mitigation practices of meteorological disasters in Liangcheng Meteorological Bureau.The results show that firstly,Liangcheng County has established an"ground-air-space"integrated monitoring network to achieve 5-min full coverage monitoring of core meteorological elements throughout the region.Secondly,relying on a multi-level forecasting system and intelligent technologies,the lead time of early warnings of hazardous weather has been extended to several hours,and the accuracy has significantly improved.Thirdly,a multi-channel and targeted warning dissemination system has been constructed,with a public coverage rate of over 90%.Fourthly,a basin joint prevention innovation model of"information sharing,joint discussion,and hierarchical linkage"has been formed,and the collaborative effectiveness of disaster response across regions and departments has been significantly enhanced.This research results can provide technical references and practical models for optimizing the prevention and mitigation system of meteorological disasters in regions with similar climate and geographical characteristics.
