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.

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.

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.

Ultrafast Response and Threshold Adjustable Intelligent Thermoelectric Systems for Next‑Generation Self‑Powered Remote IoT Fire Warning

Fire warning is vital to human life,economy and ecology.However,the development of effective warning systems faces great challenges of fast response,adjustable threshold and remote detecting.Here,we propose an intelligent self-powered remote IoT fire warning system,by employing single-walled carbon nanotube/titanium carbide thermoelectric composite films.The flexible films,prepared by a convenient solution mixing,display p-type characteristic with excellent high-temperature stability,flame retardancy and TE(power factor of 239.7±15.8μW m^(-1) K^(-2))performances.The comprehensive morphology and structural analyses shed light on the underlying mechanisms.And the assembled TE devices(TEDs)exhibit fast fire warning with adjustable warning threshold voltages(1–10 mV).Excitingly,an ultrafast fire warning response time of~0.1 s at 1 mV threshold voltage is achieved,rivaling many state-of-the-art systems.Furthermore,TE fire warning systems reveal outstanding stability after 50 repeated cycles and desired durability even undergoing 180 days of air exposure.Finally,a TED-based wireless intelligent fire warning system has been developed by coupling an amplifier,analogto-digital converter and Bluetooth module.By combining TE characteristics,high-temperature stability and flame retardancy with wireless IoT signal transmission,TE-based hybrid system developed here is promising for next-generation self-powered remote IoT fire warning applications.

Method for rapid warning and activity concentration estimates in online waterγ-spectrometry systems

Onlineγ-spectrometry systems for inland waters,most of which extract samples in situ and in real time,are able to produce reliable activity concentration measurements for waterborne radionuclides only when they are distributed relatively uniformly and enter into a steady-state diffusion regime in the measurement chamber.To protect residents’health and ensure the safety of the living environment,better timeliness is required for this measurement method.To address this issue,this study established a mathematical model of the online waterγ-spectrometry system so that rapid warning and activity estimates can be obtained for water under non-steady-state(NSS)conditions.In addition,the detection efficiency of the detector for radionuclides during the NSS diffusion process was determined by applying the computational fluid dynamics technique in conjunction with Monte Carlo simulations.On this basis,a method was developed that allowed the online waterγ-spectrometry system to provide rapid warning and activity concentration estimates for radionuclides in water.Subsequent analysis of the NSS-mode measurements of^(40)K radioactive solutions with different activity concentrations determined the optimum warning threshold and measurement time for producing accurate activity concentration estimates for radionuclides.The experimental results show that the proposed NSS measurement method is able to give warning and yield accurate activity concentration estimates for radionuclides 55.42 and 69.42 min after the entry of a 10 Bq/L^(40)K radioactive solution into the measurement chamber,respectively.These times are much shorter than the 90 min required by the conventional measurement method.Furthermore,the NSS measurement method allows the measurement system to give rapid(within approximately 15 min)warning when the activity concentrations of some radionuclides reach their respective limits stipulated in the Guidelines for Drinking-water Quality of the WHO,suggesting that this method considerably enhances the warning capacity of in situ online waterγ-spectrometry systems.

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.

Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention

The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accurately identifying warning signals of infectious diseases in a timely manner,especially emerging infectious diseases,can be challenging.Consequently,there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals.This paper examines the role of medical data in the early identification of infectious diseases,explores early warning technologies for infectious disease recognition,and assesses monitoring and early warning mechanisms for infectious diseases.We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems,in compliance with national strategies to integrate clinical treatment and disease prevention.Furthermore,hospitals should establish institution-specific,clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.

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.

Street Lamp Status Warning System Based on Internet of Things Technology

This paper proposes a street light warning system based on Internet of Things(IoT)technology,which uses cameras to detect moving targets such as vehicles and pedestrians around the system and adjust the brightness of street lights according to road conditions to reduce unnecessary power waste.The system has a mature self-fault detection mechanism and is equipped with a wireless communication device for data exchange and timely communication with the host computer terminal.The intelligent street lamp system in this paper can be used to reduce the occurrence of pedestrian and vehicle accidents at intersections,and at the same time reduce the consumption of manpower and material resources for street lamp troubleshooting,to achieve energy conservation and emission reduction.

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.

China agricultural outlook for 2015–2024 based on China Agricultural Monitoring and Early-warning System(CAMES)

The primary goal of Chinese agricultural development is to guarantee national food security and supply of major agricultural products. Hence, the scientiifc work on agricultural monitoring and early warning as wel as agricultural outlook must be strengthened. In this study, we develop the China Agricultural Monitoring and Early-warning System (CAMES) on the basis of a comparative study of domestic and international agricultural outlook models. The system is a dynamic and multi-market partial equilibrium model that integrates biological mechanisms with economic mechanisms. This system, which includes 11 categories of 953 kinds of agricultural products, could dynamical y project agricultural market supply and demand, assess food security, and conduct scenario analysis at different spatial levels, time scale levels, and macro-micro levels. Based on the CAMES, the production, consumption, and trade of the major agricultural products in China over the next decade are projected. The fol owing conclusions are drawn:i) The production of major agricultural products wil continue to grow steadily, mainly because of the increase in yield. i ) The growth of agricultural consumption wil be slightly higher than that of agricultural production. Meanwhile, a high self-sufifciency rate is expected for cereals such as rice, wheat, and maize, with the rate being stable at around 97%. i i) Agricultural trade wil continue to thrive. The growth of soybean and milk im-ports wil slow down, but the growth of traditional agricultural exports such as vegetables and fruits is expected to continue.

Characteristics Analysis and the Early-warning Service System of Heavy Fog in Chizhou City

By analyzing the heavy fog data in Chizhou City in recent 50 years(1959-2007),the general rules of meteorological elements variations were found when the heavy fog happened.The meteorological elements included the temperature,humidity,wind direction,wind speed,air pressure and so on.The conceptual models of high-altitude and ground situation were established when the heavy fog happened in Chizhou City.Based on considering sufficiently the special geographical environment in Chizhou City,we found the key factors which affected the local heavy fog via the relative analyses.By using the statistical forecast methods which included the second-level judgment method and regression method of event probability and so on,the forecast mode equation of heavy fog was established.Moreover,the objective forecast system of heavy fog in Chizhou City was also manufactured.It provided the basis and platform which could be referred for the heavy fog forecast,service and the release of early-warning signal.

A new early-warning prediction system for monitoring shear force of fault plane in the active fault

The most common method used to describe earthquake activity is based on the changes in physical parameters of the earth＇s surface such as displacement of active fault and seismic wave.However,such approach is not successful in forecasting the movement behaviors of faults.In the present study,a new mechanical model of fault activity,considering the shear strength on the fault plane and the influence of the resistance force,is established based on the occurrence condition of earthquake.A remote real-time monitoring system is correspondingly developed to obtain the changes in mechanical components within fault.Taking into consideration the local geological conditions and the history of fault activity in Zhangjiakou of China,an active fault exposed in the region of Zhangjiakou is selected to be directly monitored by the real-time monitoring technique.A thorough investigation on local fault structures results in the selection of two suitable sites for monitoring potential active tectonic movements of Zhangjiakou fault.Two monitoring curves of shear strength,recorded during a monitoring period of 6 months,turn out to be steady,which indicates that the potential seismic activities hardly occur in the adjacent region in the near future.This monitoring technique can be used for early-warning prediction of the movement of active fault,and can help to further gain an insight into the interaction between fault activity and relevant mechanisms.

Risk monitoring and early-warning technology of coal mine production

This article was written according to the secudty information theory and the secudty cybernetics basic principle, for reducing the accident risk effectively and safeguarding the production safety in coal mine. First, each kind of risk characteristic has carried on the earnest analysis to the coal-mining production process. Then it proposed entire wrap technology system of the risk management and the risk monitoring early warning in the coal-mining production process, and developed the application software-coal mine risk monitoring and the early warning system which runs on the local area network. The coal-mining production risk monitoring and early warning technology system includes risk information gathering, risk identification and management, risk information transmission; saving and analysis, early warning prompt of accident risk, safety dynamic monitoring, and safety control countermeasure and so on. The article specifies implementation method and step of this technology system, and introduces application situations in cooperating mine enterprise, e.g. Xiezhuang coal mine. It may supply the risk management and the accident prevention work of each kind of mine reference.

The Innovation Research of Financial Early-Warning Index Measurement

The period economic fluctuation is vital for an enterprise to exist and further develop, it directly affect the enterprise financial health. So, it is significant to build up financial early-warning index and measure the warning condition that the enterprise faces and take the effective measures to eliminate. We criticize Altman’sZ calculating model and build up some new indexes for enterprise financial early-warning condition measuring and making sound decision.

Risk Early-Warning Method for Natural Disasters Based on Integration of Entropy and DEA Model

Risk early-warning of natural disasters is a very intricate non-deterministic prediction, and it was difficult to resolve the conflicts and incompatibility of the risk structure. Risk early-warning factors of natural disasters were differentiated into essential attributes and external characters, and its workflow mode was established on risk early-warning structure with integrated Entropy and DEA model, whose steps were put forward. On the basis of standard risk early-warning DEA model of natural disasters, weight coefficient of risk early-warning factors was determined with Information Entropy method, which improved standard risk early-warning DEA model with non-Archimedean infinitesimal, and established risk early-warning preference DEA model based on integrated entropy weight and DEA Model. Finally, model was applied into landslide risk early-warning case in earthquake-damaged emergency process on slope engineering, which exemplified the outcome could reflect more risk information than the method of standard DEA model, and reflected the rationality, feasibility, and impersonality, revealing its better ability on comprehensive safety and structure risk.

Introduction to marine emergency forecasting and early-warning system(MEFES)

Marine emergencies especially oil spill may bring irreversible harm to the marine environment,and will cause immeasurable economic losses.In recent years,the demand for crude oil is increasing year by year in China with the high-speed economic development,leading to the high risk of marine oil spill.Therefore,it is necessary that promoting emergency response on marine oil spill in China and improving oil spill forecasting and early-warning techniques.This paper introduces the Marine Emergency Forecasting and Early-warning System(MEFES)developed by National Marine Data and Information Service(NMDIS).The system consists of one database,two modelling subsystems and a GIS platform.The database is the marine emergency database,and two subsystems include the marine environmental forecasting subsystem and the oil spill behaviour forecasting subsystem.MEFES has been applied in the emergency response of some major oil spill accidents occurred in recent years.The operational applications of the system can provide some theoretical basis and reference for marine oil spill emergency response.

Early-warning signals for an outbreak of the influenza pandemic

Over the course of human history, influenza pandemics have been seen as major disasters, so studies on the influenza virus have become an important issue for many experts and scholars. Comprehensive research has been performed over the years on the biological properties, chemical characteristics, external environmental factors and other aspects of the virus, and some results have been achieved. Based on the chaos game representation walk model, this paper uses the time series analysis method to study the DNA sequences of the influenza virus from 1913 to 2010, and works out the early-warning signals indicator value for the outbreak of an influenza pandemic. The variances in the CCR wall〈 sequences for the pandemic years （or ＋ -1 to 2 years） are significantly higher than those for the adjacent years, while those in the non-pandemic years are usually smaller. In this way we can provide an influenza early-warning mechanism so that people can take precautions and be well prepared prior to a pandemic.