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.

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.

The Application of Big data Mining in Risk Warning for Food Safety

Comprehensive evaluation and warning is very important and difficult in food safety. This paper mainly focuses on introducing the application of using big data mining in food safety warning field. At first,we introduce the concept of big data miming and three big data methods. At the same time,we discuss the application of the three big data miming methods in food safety areas. Then we compare these big data miming methods,and propose how to apply Back Propagation Neural Network in food safety risk warning.

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.

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.

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.

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.

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.

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.

Flood Risk Forecasting and Early Warning Technology for Medium and Small Rivers in the Yellow River Basin Induced by Heavy Rain

The Yellow River Basin is one of the important sand-producing and sediment-transporting areas in China,and one of the three most important sand-producing areas in the world.The amount of sand and dust days in the“Three Norths”(Dongbei,Xibei,and Huabei)area has increased,and regional sand and dust storms have occurred frequently.There are generally more serious hidden danger points of debris flow geological disasters in small and medium-sized river basins.The technical achievements of flood risk forecasting and early warning for medium and small rivers in the Yellow River Basin based on rainstorm-induced floods are important technical supports for flood forecasting and early warning for medium and small rivers.Based on this,a case study was carried out on the problems such as the weak forecasting and early warning ability of flood disasters induced by heavy rain and the low accuracy of flood disaster loss assessment in the flood disasters of medium and small rivers,for the reference of relevant personnel.

Research about Online Security Warning and Risk Assessment of Power Grid Based on Energy Management System

In this paper, online security warning and risk assessment of power grid are proposed, based on data from EMS （Energy Management System）, combined with information of real-time operation state, component status and external operating environment. It combines the two factors, contingency likelihood and severity, that determine system reliability, into risk indices on different loads and operation modes, which provide precise evaluation of the power grid＇s security performance. According to these indices, it can know the vulnerable area of the system and whether the normal operating mode or repair mode is over-limited or not, and provide decision-making support for dispatchers. Common cause outages and equipment-aging are considered in terms of the establishment of outage model. Multiple risk indices are defined in order to reflect the risk level of the power grid more comprehensively.

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.

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.

Research on the Method of Shipping Risk Early-Warning Based on Matter-Element Theory

The purpose of shipping risk early-warning is that some effective measures are taken to reduce risk probability before the risk brings heavy loss.The shipping risk has the dynamic characteristic,so the key of early-warning is to choice risk early-warning index correctly and evaluate risk grade quantitatively.According to the element extension theory,the rhombus inference model is applied to establish the index system.And the problem of risk grade evaluation can be solved by the assessment model of multi-index performance parameter,which is developed by the extension engineering method.Finally,the main shipping risks and their grades are identified by the example analysis based on the statistical data,which shows the effective and feasible of the shipping risk early-warning method.

Analysis of Early-Warning Threshold for Metro Construction Collapse Risk Based on D-S Evidence Theory and Rough Set

The existing early-warning system in metro construction are generally based on traditional single-sensor data and simple analytic model, which makes it difficult to deal with the complex and comprehensive environment in metro construction. In this paper, the framework of early-warning threshold for metro construction collapse risk based on D-S evidence theory and rough set is built. By combining the primary data fusion collected based on rough set with the secondary data fusion which is based on D-S evidence theory, the integration of multiple information in metro construction is realized and the risk assessment methods are optimized. A case trial based on Hangzhou metro construction collapse accident is also carried out to exemplify the framework. The empirical analysis guarantees the completeness and independence of the prediction information, and realizes the dynamic prediction of the variation trend of metro construction collapse risk.

大数据下企业供应链风险管理与竞争情报融合模型构建--以华为公司为例

[研究目的]国际经贸对抗环境下,中美贸易摩擦将持续升级,国家层面的技术创新竞争使得企业面临的供应链风险大幅提升。同时,大数据改变了供应链风险管理模式,而竞争情报可以从理论、技术和方法等方面提升大数据下企业供应链风险管理能力,因此大数据下企业供应链风险管理与竞争情报的融合研究具有重要意义。[研究方法]采用文献分析、案例研究等方法,分析供应链风险管理与竞争情报的异同点,继而论证大数据下供应链风险管理与竞争情报融合的可行性和必要性,再辅之以标杆企业华为的供应链中断风险事件验证,最终构建大数据下华为供应链风险管理与竞争情报融合模型,并分析运行维护此模型的保障要素。[研究结论]研究表明,基于战略、组织、制度、技术和资源五要素的共同保障,大数据下供应链风险管理与竞争情报的融合能够实现供应链风险管理的全面性、及时性和有效性。

联合国全民早期预警目标下中国气候风险管理前景分析

全球气候变暖对自然生态系统和人类社会产生了广泛而深远的影响,特别是加剧了气候变化风险,未来这种风险将更加复杂且难以管理。早期预警是减轻灾害风险和适应气候变化的重要手段。文章系统回顾了国内外早期预警发展及早期预警技术的全球演变历程,归纳总结了国际早期预警从概念提出到多灾种早期预警系统发展的4个阶段,剖析了气候变化背景下联合国全民早期预警倡议的3方面内涵。结合我国早期预警服务的现状和发展模式,以及新形势下面临的气候风险、预警需求和技术差距,提出了在联合国全民早期预警目标下,我国在气候风险管理方面的3个优先方向。一是加强对复合型极端天气气候事件发生发展机理和预报预警的科学研究,以及人类社会经济系统中潜在临界要素的研究,评估极端事件和临界要素引爆后的潜在级联影响和规模,提高灾害风险认知能力,拓深、拓广已有多灾种早期预警系统,提升防范新型气候灾害风险能力;二是识别气候变化对行业、领域和区域绿色低碳转型可能造成的产业风险,加快构建面向气候和气候变化尺度的国家级气候安全早期预警平台,提升全社会应对气候风险的能力;三是开展多灾种早期预警国际合作,帮助尚无早期预警系统或早期预警系统效力不足的国家建立和完善早期预警系统,重点是厘清区域和国家存在的主要气候灾害风险和等级,以及适应气候变化的紧迫需求,提供多品类气象防灾减灾公共产品,提高国际影响力。