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.

Ecological risk assessment and early warning of heavy metal cumulation in the soils near the Luanchuan molybdenum polymetallic mine concentration area,Henan Province,central China

The Luanchuan molybdenum polymetallic mine concentration area is rich in mineral resources and has a long history of mining.The environmental impact of long-term mining activities cannot be ignored.It is of great significance to study the ecological risk and the accumulation trends of heavy metals in the soil of mining areas for scientific prevention and control of heavy metal pollution.Taking the Taowanbeigou River Basin in the mine concentration area as the research object,the ecological pollution risk and cumulative effect of heavy metals in the soil of the basin were studied by using the comprehensive pollution index method,potential ecological risk assessment method and geoaccumulation index method.On this basis,the cumulative exceeding years of specific heavy metals were predicted by using the early warning model.The comprehensive potential ecological risk of heavy metals in the soil near the Luanchuan mine concentration area is moderate,and the single element Cd is the main ecological risk factor,with a contribution rate of 53.6%.The overall cumulative degrees of Cu and Pb in the soil are“none-moderate”,Zn and Cd are moderate,Mo has reached an extremely strong cumulative level,Hg,As and Cr risks are not obvious,and the overall cumulative risks order is Mo>Cd>Zn>Cu>Pb>Hg.According to the current accumulation rate and taking the risk screening values for soil contamination of agricultural land as the reference standard,the locations over standard rates of Cu,Zn and Cd will exceed 78%in 90years,and the over standard rate of Pb will reach approximately 57%in 200 years.The cumulative exceeding standard periods of As,Cr and Hg are generally long,which basically indicates that these elements do not pose a significant potential threat to the ecological environment.Mining activities will accelerate the accumulation of heavy metals in soil.With the continuous development of mining activities,the potential pollution risk of heavy metals in the soil of mining areas will also increase.

Pattern changes and early risk warning of Spartina alterniflora invasion:a study of mangrove-dominated wetlands in northeastern Fujian,China

The exotic saltmarsh cordgrass,Spartina alterniflora(Loisel)Peterson&Saarela,is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature.The species has rapidly spread wildly across coastal wetlands,challenging resource managers for control of its further spread.An investigation of S.alterniflora invasion and associated ecological risk is urgent in China's coastal wetlands.In this study,an ecological risk invasive index system was developed based on the Driving Force-Pressure-State-Impact-Response framework.Predictions were made of'warning degrees':zero warning and light,moderate,strong,and extreme warning,by developing a back propagation(BP)artificial neural network model for coastal wetlands in eastern Fujian Province.Our results suggest that S.alterniflora mainly has invaded Kandelia candel beaches and farmlands with clustered distributions.An early warning indicator system assessed the ecological risk of the invasion and showed a ladder-like distribution from high to low extending from the urban area in the central inland region with changes spread to adjacent areas.Areas of light warning and extreme warning accounted for43%and 7%,respectively,suggesting the BP neural network model is reliable prediction of the ecological risk of S.alterniflora invasion.The model predicts that distribution pattern of this invasive species will change little in the next 10 years.However,the invaded patches will become relatively more concentrated without warning predicted.We suggest that human factors such as land use activities may partially determine changes in warning degree.Our results emphasize that an early warning system for S.alterniflora invasion in China's eastern coastal wetlands is significant,and comprehensive control measures are needed,particularly for K.candel beach.

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.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

Integrated water risk early warning framework of the semi-arid transitional zone based on the water environmental carrying capacity (WECC)

Water risk early warning systems based on the water environmental carrying capacity(WECC)are powerful and effective tools to guarantee the sustainability of rivers.Existing work on the early warning of WECC has mainly concerned the comprehensive evaluation of the status quo and lacked a quantitative prejudgement and warning of future overload.In addition,existing quantitative methods for short-term early warning have rarely focused on the integrated change trends of the early warning indicators.Given the periodicity of the socioeconomic system,however,the water environmental system also follows a trend of cyclical fluctuations.Thus,it is meaningful to monitor and use this periodicity for the early warning of the WECC.In this study,we first adopted and improved the prosperity index method to develop an integrated water risk early warning framework.We also constructed a forecast model to qualitatively and quantitatively prejudge and warn about the development trends of the water environmental system.We selected the North Canal Basin(an essential connection among the Beijing-Tianjin-Hebei region)in China as a case study and predicted the WECC in 25 water environmental management units of the basin in 2018–2023.We found that the analysis of the prosperity index was helpful in predicting the WECC,to some extent.The result demonstrated that the early warning system provided reliable prediction(root mean square error of 0.0651 and mean absolute error of 0.1418),and the calculation results of the comprehensive early warning index(CEWI)conformed to the actual situation and related research in the river basin.From 2008 to 2023,the WECC of most water environmental management units in the basin had improved but with some spatial differences:the CEWI was generally poor in areas with many human disturbances,while it was relatively good in the upstream regions with higher forest and grass covers as well as in the downstream areas with larger water volume.Finally,through a sensitivity analysis of the indicators,we proposed specific management measures for the sustainability of the water environmental system in the North Canal Basin.Overall,the integrated water risk early warning framework could provide an appropriate method for the water environmental administration department to predict the WECC of the basin in the future.This framework could also assist in implementing corresponding management measures in advance,especially for the performance evaluation and the arrangement of key short-term tasks in the River Chief System in China.

Designing of Commercial Bank Loans Risk Early Warning System Based on BP Neural Networks

According to the index early warning method, a commercial bank loans risk early warning system based on BP neural networks is proposed. The warning signal is mainly involved with the financial situation signal of loaning corporation. Except the structure description of the system structure the demonstration of attemptive designing is also elaborated.

Study of Enterprises Marketing Risk Early Warning System Based on BP Neural Network Model

For effectively early warning the marketing risk caused along with the varied environment, a BP neural network method was introduced on the basis of analyzing the shortcomings of the risk early warning method, and combined with the practical conditions of dairy enterprises, the index system caused by the marketing risk was also studied. The＇principal component method was used for screening the indexes, the grades and critical values of the marketing risk were determined. Through the configuration of BP network, node processing and error analysis, the early warning resuits of the marketing risk were obtained. The results indicate that BP neural network method can be effectively applied through the function approach in the marketing early warning with incomplete information and complex varied conditions.

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.

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.

Study on Positioning System of the Risk Early Warning for Enterprise Overseas Investment

From the perspective of internal and external environment analysis,we construct the risk identification index system for overseas investment enterprises.Combined with the theory of comprehensive evaluation and risk early warning,the risk location system of overseas investment is established.The risk intelligence decision model is constructed by rough set theory,and the risk identification,risk location and risk decision of overseas investment are studied,and are empirically analyzed with cases in overseas investment.

Early Warning of Commercial Housing Market Based on Bagging-GWO-SVM

A number of risks exist in commercial housing,and it is critical for the government,the real estate industry,and consumers to establish an objective early warning indicator system for commercial housing risks and to conduct research regarding its measurement and early warning.In this paper,we examine the commodity housing market and construct a risk index for the commodity housing market at three levels:market level,the real estate industry and the national economy.Using the Bootstrap aggregating-grey wolf optimizer-support vector machine(Bagging-GWO-SVM)model after synthesizing the risk index by applying the CRITIC objective weighting method,the commercial housing market can be monitored for risks and early warnings.Based on the empirical study,the following conclusions have been drawn:(1)The commodity housing market risk index accurately reflect the actual risk situation in Tianjin;(2)Based on comparisons with other models,the Bagging-GWO-SVM model provides higher accuracy in early warning.A final set of suggestions is presented based on the empirical study.

Chinese public participation monitoring and warning system for geological hazards

In China,many geological hazards occurred in remote mountainous regions,and it was time-consuming to disseminate disaster information for the responsible parties to make timely judgements.Besides,only relying on professionals to monitor and manage disasters was demanding and costly.The Chinese government created a system to engage residents in the process of disaster risk management,namely’Public Participation Monitoring and Warning’(PPMW),to disseminate timely disaster information and bring down management costs.The objective of this system was to reduce casualties with minimum cost by organizing residents to evacuate from disasters in advance.This paper introduced the PPMW system,including its structure,operation mechanism by reviewing government documents and research articles,and its implementation by a case study of a landslide at Boli village(E 101°01’,N 27°29’),Yanyuan County,Sichuan Province,China on July 19th 2018.Further,this paper analyzed the strengths and limitations of the system and discussed its future development.It had the potential to become an affordable disaster risk management tool for other countries facing similar situations to China.

Decision-making method for high-speed rail early warning system in complex earthquake situations

To address the shortcomings in decision-making methods for ground motion threshold warning models in high-speed rail earthquake early warning systems(HSREEWs),we propose a dual judgement method and corresponding early warning process for earthquake early warning decisions based on joint peak ground acceleration(PGA)and complex earthquake environmental risk evaluation(ERE)values.First,we analyse the characteristics of four complex earthquake environments based on the characteristics of high-speed rail(HSR)operating environments.Second,we establish an earthquake environmental risk evaluation index system and propose an adversarial interpretive structure modelling method-based complex earthquake situation evaluation model(AISM-based ESEM).The AISM method firstly evaluates the proximity by the TOPSIS(technique for order preference by similarity to an ideal solution)method,then effectively rank targets with fuzzy attributes through opposite hierarchical extraction rules without sacrificing system functionality.Since PGA can reflect the current size of earthquake energy,combining PGA thresholds with ESEM-derived values of ERE can effectively determine the risk status of each train and make decisions on the most appropriate alarm form and control measures for that status.Finally,case analysis results under the background of Wenchuan Earthquake show that the new early warning decisionmaking method accurately assesses environmental risks in affected areas and provides corresponding warning levels as a supplement to existing HSREEWs warning models.

Oral microbiome and risk of malignant esophageal lesions in a high-risk area of China:A nested case-control study

Objective:We aimed to prospectively evaluate the association of oral microbiome with malignant esophageal lesions and its predictive potential as a biomarker of risk.Methods:We conducted a case-control study nested within a population-based cohort with up to 8 visits of oral swab collection for each subject over an 11-year period in a high-risk area for esophageal cancer in China.The oral microbiome was evaluated with 16 S ribosomal RNA(rRNA)gene sequencing in 428 pre-diagnostic oral specimens from 84 cases with esophageal lesions of severe squamous dysplasia and above(SDA)and 168 matched healthy controls.DESeq analysis was performed to identify taxa of differential abundance.Differential oral species together with subject characteristics were evaluated for their potential in predicting SDA risk by constructing conditional logistic regression models.Results:A total of 125 taxa including 37 named species showed significantly different abundance between SDA cases and controls(all P<0.05&false discovery rate-adjusted Q<0.10).A multivariate logistic model including 11 SDA lesion-related species and family history of esophageal cancer provided an area under the receiver operating characteristic curve(AUC)of 0.89(95%CI,0.84-0.93).Cross-validation and sensitivity analysis,excluding cases diagnosed within 1 year of collection of the baseline specimen and their matched controls,or restriction to screenendoscopic-detected or clinically diagnosed case-control triads,or using only bacterial data measured at the baseline,yielded AUCs>0.84.Conclusions:The oral microbiome may play an etiological and predictive role in esophageal cancer,and it holds promise as a non-invasive early warning biomarker for risk stratification for esophageal cancer screening programs.

Early warning for geo-hazards based on the weather condition in China

According to national early warning practice for geo-hazards from 2003 to 2005,it is systematically concluded that the basic characteristics of geo-hazards,early warning method and forecast result based on the geological maps of China in a scale 1∶6 000 000.With the contrast of different characters between sustained rainfall and typhoon rainfall inducing geo-hazards,the disaster reduction result and some problems are preliminarily analyzed.Some basic recognition is that early warning to geo-hazards is feasible,national scale forecast is only to call attention,but can't immediately be used to disaster reduction decision-making.And,the future direction is to build a united disaster reduction framework of early warning system including national,provincial and county levels based on weather factors in different scale of area.

Old vs new: Risk factors predicting early onset colorectal cancer

BACKGROUND Colorectal cancer(CRC)is the second leading cause of all cancer related deaths in the United States and Europe.Although the incidence has been decreasing for individuals’≥50,it has been on the rise for individuals<50.AIM To identify potential risk factors for early-onset CRC.METHODS A population-based cohort analysis using a national database,Explorys,screened all patients with an active electronic medical record from January 2012 to December 2016 with a diagnosis of CRC.Subgroups were stratified based on age(25–49 years vs≥50 years).Demographics,comorbidities,and symptom profiles were recorded and compared between both age groups.Furthermore,the younger group was also compared with a control group consisting of individuals aged 25-49 years within the same timeframe without a diagnosis of CRC.Twentydata points for CRC related factors were analyzed to identify potential risk factors specific to early-onset CRC.RESULTS A total of 68860 patients were identified with CRC,of which 5710(8.3%)were younger than 50 years old,with 4140(73%)between 40-49 years of age.Multivariable analysis was reported using odds ratio(OR)with 95%CI and demonstrated that several factors were associated with an increased risk of CRC in the early-onset group versus the later-onset group.These factors included:African-American race(OR 1.18,95%CI:1.09-1.27,P<0.001),presenting symptoms of abdominal pain(OR 1.82,95%CI:1.72-1.92,P<0.001),rectal pain(OR 1.50,95%CI:1.28-1.77,P<0.001),altered bowel function(OR 1.12,95%CI:1.05-1.19,P=0.0005),having a family history of any cancer(OR 1.78,95%CI:1.67-1.90,P<0.001),gastrointestinal(GI)malignancy(OR 2.36,95%CI:2.18-2.55,P<0.001),polyps(OR 1.41,95%CI:1.08-1.20,P<0.001),and obesity(OR 1.14,95%CI:1.08-1.20,P<0.001).Comparing the early-onset cohort versus the control group,factors that were associated with an increased risk of CRC were:male gender(OR 1.34,95%CI:1.27-1.41),P<0.001),Caucasian(OR 1.48,95%CI:1.40-1.57,P<0.001)and African-American race(OR 1.25,95%CI:1.17-1.35,P<0.001),presenting symptoms of abdominal pain(OR 4.73,95%CI:4.49-4.98,P<0.001),rectal pain(OR 7.48,95%CI:6.42-8.72,P<0.001),altered bowel function(OR 5.51,95%CI:5.19-5.85,P<0.001),rectal bleeding(OR 9.83,95%CI:9.12-10.6,P<0.001),weight loss(OR 7.43,95%CI:6.77-8.15,P<0.001),having a family history of cancer(OR 11.66,95%CI:10.97-12.39,P<0.001),GI malignancy(OR 28.67,95%CI:26.64-30.86,P<0.001),polyps(OR 8.15,95%CI:6.31-10.52,P<0.001),tobacco use(OR 2.46,95%CI:2.33-2.59,P<0.001),alcohol use(OR 1.71,95%CI:1.62-1.80,P<0.001),presence of colitis(OR 4.10,95%CI:3.79-4.43,P<0.001),and obesity(OR 2.88,95%CI:2.74-3.04,P<0.001).CONCLUSION Pending further investigation,these potential risk factors should lower the threshold of suspicion for early CRC and potentially be used to optimize guidelines for early screening.

Prospective study and validation of early warning marker discovery based on integrating multi-omics analysis in severe burn patients with sepsis

Background:Early detection,timely diagnosis and rapid response are essential for case management and precautions of burn-associated sepsis.However,studies on indicators for early warning and intervention have rarely been conducted.This study was performed to better understand the pathophysiological changes and targets for prevention of severe burn injuries.Methods:We conducted a multi-center,prospective multi-omics study,including genomics,microRNAomics,proteomics and single-cell transcriptomics,in 60 patients with severe burn injuries.A mouse model of severe burn injuries was also constructed to verify the early warning ability and therapeutic effects of potential markers.Results:Through genomic analysis,we identified seven important susceptibility genes(DNAH11,LAMA2,ABCA2,ZFAND4,CEP290,MUC20 and ENTPD1)in patients with severe burn injuries complicated with sepsis.Through plasma miRNAomics studies,we identified four miRNAs(hsamiR-16-5p,hsa-miR-185-5p,hsa-miR-451a and hsa-miR-423-5p)that may serve as early warning markers of burn-associated sepsis.A proteomic study indicated the changes in abundance of major proteins at different time points after severe burn injury and revealed the candidate early warning markers S100A8 and SERPINA10.In addition,the proteomic analysis indicated that neutrophils play an important role in the pathogenesis of severe burn injuries,as also supported by findings from single-cell transcriptome sequencing of neutrophils.Through further studies on severely burned mice,we determined that S100A8 is also a potential early therapeutic target for severe burn injuries,beyond being an early warning indicator.Conclusions:Our multi-omics study identified seven susceptibility genes,four miRNAs and two proteins as early warning markers for severe burn-associated sepsis.In severe burn-associated sepsis,the protein S100A8 has both warning and therapeutic effects.

An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells

Sticking is the most serious cause of failure in complex drilling operations.In the present work a novel“early warning”method based on an artificial intelligence algorithm is proposed to overcome some of the known pro-blems associated with existing sticking-identification technologies.The method is tested against a practical case study(Southern Sichuan shale gas drilling operations).It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state;furthermore,the results from four groups of verification samples are also consistent with the actual downhole state.This shows that the pro-posed training-based model can effectively be applied to practical situations.

Review of big-data and AI application in typhoon-related disaster risk early warning in Typhoon Committee region

ESCAP/WMO Typhoon Committee Members are directly or indirectly affected by typhoons every year.Members have accumulated rich experiences dealing with typhoons'negative impact and developed the technologies and measures on typhoon-related disaster risk forecasting and early warning in various ways to reduce the damage caused by typhoon.However,it is still facing many difficulties and challenges to accurately forecast the occurrence of typhoons and warning the potential impacts in an early stage due to the continuously changing weather conditions.With the development of information technology(IT)and computing science,and increasing accumulated hydro-meteorological data in recent decades,scientists,researchers and operationers keep trying to improve forecasting models based on the application of big data and artificial intelligent(AI)technology to promote the capacity of typhoon-related disaster risk forecasting and early warning.This paper reviewed the current status of application of big data and AI technology in the aspect of typhoon-related disaster risk forecasting and early warning,and discussed the challenges and limitations that must be addressed to effectively harness the power of big data and AI technology application in typhoon-related disaster risk reduction in the future.