Early warning method for thermal runaway of lithium-ion batteries under thermal abuse condition based on online electrochemical impedance monitoring

Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application scenarios.Timely and effective TR early warning technology is urgently required considering the current fire safety situation of LIBs.In this work,we report an early warning method of TR with online electrochemical impedance spectroscopy(EIS)monitoring,which overcomes the shortcomings of warning methods based on traditional signals such as temperature,gas,and pressure with obvious delay and high cost.With in-situ data acquisition through accelerating rate calorimeter(ARC)-EIS test,the crucial features of TR were extracted using the RReliefF algorithm.TR mechanisms corresponding to the features at specific frequencies were analyzed.Finally,a three-level warning strategy for single battery,series module,and parallel module was formulated,which can successfully send out an early warning signal ahead of the self-heating temperature of battery under thermal abuse condition.The technology can provide a reliable basis for the timely intervention of battery thermal management and fire protection systems and is expected to be applied to electric vehicles and energy storage devices to realize early warning and improve battery safety.

Effect of preload forces on multidimensional signal dynamic behaviours for battery early safety warning

Providing early safety warning for batteries in real-world applications is challenging.In this study,comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces.The time-sequence relationship among expansion force,voltage,and temperature during thermal abuse under five categorised stages is revealed.Three characteristic peaks are identified for the expansion force,which correspond to venting,internal short-circuiting,and thermal runaway.In particular,an abnormal expansion force signal can be detected at temperatures as low as 42.4°C,followed by battery thermal runaway in approximately 6.5 min.Moreover,reducing the preload force can improve the effectiveness of the early-warning method via the expansion force.Specifically,reducing the preload force from 6000 to 1000 N prolongs the warning time(i.e.,227 to 398 s)before thermal runaway is triggered.Based on the results,a notable expansion force early-warning method is proposed that can successfully enable early safety warning approximately 375 s ahead of battery thermal runaway and effectively prevent failure propagation with module validation.This study provides a practical reference for the development of timely and accurate early-warning strategies as well as guidance for the design of safer battery systems.

Comparing 11 early warning scores and three shock indices in early sepsis prediction in the emergency department

BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong.The primary outcome was sepsis(Sepsis-3 definition)within 48 h of ED presentation.Using c-statistics and the DeLong test,we compared 11 EWSs,including the National Early Warning Score 2(NEWS2),Modified Early Warning Score,and Worthing Physiological Scoring System(WPS),etc.,and three shock indices(the shock index[SI],modified shock index[MSI],and diastolic shock index[DSI]),with Systemic Inflammatory Response Syndrome(SIRS)and quick Sequential Organ Failure Assessment(qSOFA)in predicting the primary outcome,intensive care unit admission,and mortality at different time points.RESULTS:We analyzed 601 patients,of whom 166(27.6%)developed sepsis.NEWS2 had the highest point estimate(area under the receiver operating characteristic curve[AUROC]0.75,95%CI 0.70-0.79)and was significantly better than SIRS,qSOFA,other EWSs and shock indices,except WPS,at predicting the primary outcome.However,the pooled sensitivity and specificity of NEWS2≥5 for the prediction of sepsis were 0.45(95%CI 0.37-0.52)and 0.88(95%CI 0.85-0.91),respectively.The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point.CONCLUSION:NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.

ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

Effectiveness of Combined Application of Shock Index and Early Warning Scoring System in Patients with Acute Gastrointestinal Hemorrhage

Objective:To explore the effect of the combined application of the Shock Index(SI)and the Early Warning Score(EWS)in patients with acute gastrointestinal bleeding.Methods:Seventy patients with acute gastrointestinal bleeding admitted to a hospital from June 2022 to May 2024 were selected and randomly divided into two groups:the control group and the observation group,with 35 patients in each group.The control group received conventional emergency care measures,while the observation group received SI combined with NEWS emergency care measures.The treatment effects in both groups were compared.Results:The observation group had shorter waiting times for consultation(4.45±1.59 minutes),intravenous access establishment(6.79±2.52 minutes),hemostasis time(4.41±1.52 hours),and hospital stays(8.39±2.13 days)compared to the control group,which had times of 5.46±1.34 minutes,8.41±2.16 minutes,5.16±1.47 hours,and 10.26±2.98 days,respectively.The differences were statistically significant(P<0.05).Before management,there were no significant differences in the levels of hemoglobin,prealbumin,and serum protein between the two groups(P>0.05).However,after systematic emergency management,the serum indexes in both groups significantly improved,with the observation group showing greater improvement than the control group,and these differences were statistically significant(P<0.05).In the observation group,only one case of cardiovascular complications occurred during the rescue period,with an incidence rate of 2.86%.In contrast,the control group experienced eight cases of complications,including hemorrhagic shock,anemia,multi-organ failure,cardiovascular complications,and gastrointestinal rebleeding,with an incidence rate of 22.85%.The difference between the groups was statistically significant(P<0.05).Conclusion:The application of SI combined with EWS emergency care measures in patients with acute gastrointestinal hemorrhage can effectively improve serum indexes,shorten resuscitation time and hospital stay,and reduce the risk of complications such as hemorrhagic shock,anemia,infection,multi-organ failure,cardiovascular complications,acute renal failure,and gastrointestinal rebleeding.This approach has positive clinical application value.

WebGIS-based Early Warning System of Farmland Environmental Pollution Caused by Livestock Waste

[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.

Research on Monitoring and Early-warning Standards of Ancient and Famous Trees Protection in the Humble Administrator's Garden

Based on the current situation and symptoms of the trees' growth in the Humble Administrator's Garden,this paper put forward corresponding monitoring and early-warning standards and technical measures of the ancient and famous trees protection in the Humble Administrator's Garden specifically.The aim of doing this is to establish a scientific basis for the protection of the ancient and famous trees in the Humble Administrator's Garden by setting up systematic fundamental data,dynamic protection standard grades and technique measures of protecting the trees.The main symptom of trees in the Humble Administrator's Garden is the erosion and decay of the tree trunks.Fifteen tree trunks need technical protection,which holds 65.22% of the total sum of trees in the Humble Administrator's Garden.Therefore,much more emphasis should be paid in strengthening technical protection procedures of monitoring and early warning of the tree trunks in the future protection of the ancient and famous trees in the garden.Besides,the rejuvenation technique of rooting zone and rooting system,tree pruning technique as well as tree supporting measures according to the specific condition and symptom of the trees should be concerned with in order to protect the ancient and famous trees in the Humble Administrator's Garden in a more scientific and effective way.

Early-warning of Mud Expansion in Sewage Treatment Plants by A/O Technology

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.

Design and Realization of Cold Disaster Early Warning System for Subtropics Aquaculture

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.

Preliminary Study on Early-warning System of Ecological Security in Water-Level-Fluctuating Zone (WLFZ) of Three Gorges Reservoir

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.

Comprehensive early warning of rock burst utilizing microseismic multi-parameter indices

Rock bursts have become one of the most severe risks in underground coal mining and its early warning is an important component in the safety management. Microseismic(MS) monitoring is considered potentially as a powerful tool for the early warning of rock burst. In this study, an MS multi-parameter index system was established and the critical values of each index were estimated based on the normalized multi-information warning model of coal-rock dynamic failure. This index system includes bursting strain energy(BSE) index, time-space-magnitude independent information(TSMII) indices and timespace-magnitude compound information(TSMCI) indices. On the basis of this multi-parameter index system, a comprehensive analysis was conducted via introducing the R-value scoring method to calculate the weights of each index. To calibrate the multi-parameter index system and the associated comprehensive analysis, the weights of each index were first confirmed using historical MS data occurred in LW402102 of Hujiahe Coal Mine(China) over a period of four months. This calibrated comprehensive analysis of MS multi-parameter index system was then applied to pre-warn the occurrence of a subsequent rock burst incident in LW 402103. The results demonstrate that this multi-parameter index system combined with the comprehensive analysis are capable of quantitatively pre-warning rock burst risk.

Mechanism and monitoring and early warning technology for rockburst in coal mines

On the basis of the massive amount of published literature and the long-term practice of our research group in the field of prevention and control of rockburst,the research progress and shortcomings in understanding the rockburst phenomenon have been comprehensively in-vestigated.This study focuses on the occurrence mechanism and monitoring and early warning technology for rockburst in coal mines.Results showed that the prevention and control of rockburst had made significant progress.However,with the increasing mining depth,several unre-solved concerns remain challenging.From the in-depth research and analysis,it can be inferred that rockburst disasters involve three main problems,i.e.,the induction factors are complicated,the mechanism is still unclear,and the accuracy of the monitoring equipment and multi-source stereo monitoring technology is insufficient.The monitoring and warning standards of rockburst need to be further clarified and im-proved.Combined with the Internet of Things,cloud computing,and big data,a study of the trend of rockburst needs to be conducted.Further-more,the mechanism of multiphase and multi-field coupling induced by rockburst on a large scale needs to be explored.A multisystem and multiparameter integrated monitoring and early warning system and remote monitoring cloud platform for rockburst should be explored and developed.High-reliability sensing technology and equipment and perfect monitoring and early warning standards are considered to be the de-velopment direction of rockburst in the future.This research will help experts and technicians adopt effective measures for controlling rock-burst disasters.

Conceptual design and RCS performance research of shipborne early warning aircraft

In order to improve the survivability of the aircraft,conceptual design and radar cross section（RCS） performance research are done. The CATIA software is used to design the 3D digital model of the shipborne early warning aircraft, and some measures are taken to reduce the RCS characteristics of the early warning aircraft at the same time. Based on the physical optics method and the equivalent electromagnetic flow method,the aircraft＇s RCS characteristics and strength distribution characteristics are simulated numerically, and compared with the foreign advanced shipborne early warning aircraft. The simulation results show that under the X radar band, when the incident wave pitching angle is 0？, compared with the foreign advanced shipborne early warning aircraft, the forward RCS average value of the conceptual shipborne early warning aircraft is reduced to 24.49%, the lateral RCS average value is reduced to 5.04%, and the backward RCS average value is reduced to 39.26%. The research results of this paper are expected to provide theoretical basis and technical support for the conceptual design and the stealth design of the shipborne early warning aircraft.

Interactive early warning technique based on SVDD

After reviewing current researches on early warning, it is found that “bad”data of some systems is not easy to obtain, which makes methods proposed by these researches unsuitable for monitored systems. An interactive early warning technique based on SVDD （support vector data description） is proposed to adopt “good” data as samples to overcome the difficulty in obtaining the “bad” data. The process consists of two parts： （1） A hypersphere is fitted on “good” data using SVDD. If the data object are outside the hypersphere, it would be taken as “suspicious”; （2） A group of experts would decide whether the suspicious data is “bad” or “good”, early warning messages would be issued according to the decisions. And the detailed process of implementation is proposed. At last, an experiment based on data of a macroeconomic system is conducted to verify the proposed technique.

Early warning of rock mass instability based on multi-field coupling analysis and microseismic monitoring

In order to overcome the limitation that rock mass instability warnings are caused by a lack of deep consideration of the inherent mechanism of disaster formation, early warning signs of rock mass instability were detected and multi-field coupling was analyzed. A multi-field coupling model of a damaged rock mass was established. The relationship between microseismic activity parameters and rock mass stability was analyzed, and a multi-parameter early warning index system was established and its solution program was compiled. Based on the D-S data fusion theory,an early warning model of rock mass instability combining multi-field coupling analysis and microseismic monitoring was constructed. Taking an underground mine stope as an object, the multi-field coupling model and its solution program were used to analyze mining response characteristics. The seismic field data were used to verify the accuracy of the multi-field coupling analysis. The early warning model was used to predict the instability of stope rock mass,and the early warning result is consistent with a real-world scenario.

Early Warning of Acute Altitude Sickness by Physiological Variables and Noninvasive Cardiovascular Indicators

Objective To examine if the variations at sea level would be able to predict subsequent susceptibility to acute altitude sickness in subjects upon a rapid ascent to high altitude.Methods One hundred and six Han nationality male individuals were recruited to this research.Dynamic electrocardiogram,treadmill exercise test,echocardiography,routine blood examination and biochemical analysis were performed when subjects at sea level and entering the plateau respectively.Then multiple regression analysis was performed to construct a multiple linear regression equation using the Lake Louise Score as dependent variable to predict the risk factors at sea level related to acute mountain sickness(AMS).Results Approximately 49.05%of the individuals developed AMS.The tricuspid annular plane systolic excursion(22.0+2.66 vs.23.2+3.19 mm,t=l.998,P=0.048)was significantly lower in the AMS group at sea level,while count of eosinophil[(0.264+0.393)×109/L vs.(0.126+0.084)×109/L,t=-2.040,P—0.045],percentage of diflerences exceeding 50 ms between adjacent normal number of intervals(PNN50,9.66%±5.40%vs.6.98%±5.66%,t=-2.229,P=0.028)and heart rate variability triangle index(57.1+16.1 vs.50.6+12.7,t=-2.271,P=0.025)were significantly higher.After acute exposure to high altitude,C-reactive protein(0.098+0.103 vs.0.062+0.045 g/L,t=-2.132,P=0.037),aspartate aminotransferase(19.7+6.7275.17,3±3.95 U/L,t=-2.231,P=0.028)and creatinine(85.1±12.9 vs.77.7±11.2 mmol/L,t=3.162,P=0.002)were significantly higher in the AMS group,while alkaline phosphatase(71.7+18.2 vs.80.6+20.2 U/L,t=2.389,P=0.019),standard deviation of normal-to-normal RR intervals(126.5+35.9 vs.143.3+36.4 ms,t—2.320,P—0.022),ejection time(276.9+50.8 vs.313.8+48.9 ms,t—3.641,P—0.001)and heart rate variability triangle index(37.1+12.9 vs.41.9+11.1,t=2.O2O,P=0.047)were significantly lower.Using the Lake Louise Score as the dependent variable,prediction equation were established to estimate AMS:Lake Louise Score=3.783+0.281Xeosinophil-0.219Xalkaline phosphatase+O.O32XPNN50.Conclusions We elucidated the differences of pl^siological variables as well as noninvasive cardiovascular indicators for subjects after high altitude exposure compared with those at sea level.We also created an acute high altitude reaction early warning equation based on the physiological variables and noninvasive cardiovascular indicators at sea level.

Disaster reduction stick equipment: A method for monitoring and early warning of pipeline-landslide hazards

Oil and gas pipelines are of great importance in China,and pipeline security problems pose a serious threat to society and the environment.Pipeline safety has therefore become an integral part of the entire national economy.Landslides are the most harmful type of pipeline accident,and have directed increasing public attention to safety issues.Although some useful results have been obtained in the investigation and prevention of pipeline-landslide hazards,there remains a need for effective monitoring and early warning methods,especially when the complexity of pipeline-landslides is considered.Because oil and gas pipeline-landslides typically occur in the superficial soil layers,monitoring instruments must be easy to install and must cause minimal disturbance to the surrounding soil and pipeline.To address the particular characteristics of pipelinelandslides,we developed a multi-parameter integrated monitoring system called disaster reduction stick equipment.In this paper,we detail this monitoring and early warning system for pipeline-landslide hazards based on an on-site monitoring network and early warning algorithms.The functionality of our system was verified by its successful application to the Chongqing Loujiazhuang pipeline-landslide in China.The results presented here provide guidelines for the monitoring,early warning,and prevention of pipeline geological hazards.

Poor performance of the modified early warning score for predicting mortality in critically ill patients presenting to an emergency department

BACKGROUND:This study was undertaken to validate the use of the modified early warning score(MEWS) as a predictor of patient mortality and intensive care unit(ICU)/ high dependency(HD)admission in an Asian population.METHODS:The MEWS was applied to a retrospective cohort of 1 024 critically ill patients presenting to a large Asian tertiary emergency department(ED) between November 2006 and December2007.Individual MEWS was calculated based on vital signs parameters on arrival at ED.Outcomes of mortality and ICU/HD admission were obtained from hospital records.The ability of the composite MEWS and its individual components to predict mortality within 30 days from ED visit was assessed.Sensitivity,specificity,positive and negative predictive values were derived and compared with values from other cohorts.A MEWS of ≥4 was chosen as the cut-off value for poor prognosis based on previous studies.RESULTS:A total of 311(30.4%) critically ill patients were presented with a MEWS ≥4.Their mean age was 61.4 years(SD 18.1) with a male to female ratio of 1.10.Of the 311 patients,53(17%)died within 30 days,64(20.6%) were admitted to ICU and 86(27.7%) were admitted to HD.The area under the receiver operating characteristic curve was 0.71 with a sensitivity of 53.0%and a specificity of 72.1%in addition to a positive predictive value(PPV) of 17.0%and a negative predictive value(NPV)of 93.4%(MEWS cut-off of ≥4) for predicting mortality.CONCLUSION:The composite MEWS did not perform well in predicting poor patient outcomes for critically ill patients presenting to an ED.

Effect of an inpatient nursing risk early warning and control system in Shanghai:A retrospective study of adverse events

Purpose:To avoid the nursing risk of inpatients,reduce the occurrence of nursing errors and improve the safety of inpatients.Methods:We established a nursing risk early warning and control system,which includes a safety supervisory network,risk screening and early warning tools,and a risk control process.Results:The qualified rates of risk control measures to prevent pressure ulcers,unplanned extubation and fall/fall from bed all increased.The incidence of reported nursing errors decreased.The number of mistakes in medication-giving decreased.Conclusion:The establishment of an inpatient early warning and control system could effectively avoid nursing risk,improve risk prevention abilities,improve patient safety,and improve nursing quality.

Deep learning for P-wave arrival picking in earthquake early warning

Fast and accurate P-wave arrival picking significantly affects the performance of earthquake early warning(EEW)systems.Automated P-wave picking algorithms used in EEW have encountered problems of falsely picking up noise,missing P-waves and inaccurate P-wave arrival estimation.To address these issues,an automatic algorithm based on the convolution neural network(DPick)was developed,and trained with a moderate number of data sets of 17,717 accelerograms.Compared to the widely used approach of the short-term average/long-term average of signal characteristic function(STA/LTA),DPick is 1.6 times less likely to detect noise as a P-wave,and 76 times less likely to miss P-waves.In terms of estimating P-wave arrival time,when the detection task is completed within 1 s,DPick′s detection occurrence is 7.4 times that of STA/LTA in the 0.05 s error band,and 1.6 times when the error band is 0.10 s.This verified that the proposed method has the potential for wide applications in EEW.