Studies on Early-warning System for Social Stability: Vital to China

As a large country in the world, it is both necessary and possible to develop and operate a pre-warning system for social stability, says Prof. Niu Wenyuan, chief scientist for strate-

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 scientific work on agricultural monitoring and early warning as well 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 dynamically 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 following conclusions are drawn: i) The production of major agricultural products will continue to grow steadily, mainly because of the increase in yield. ii) The growth of agricultural consumption will be slightly higher than that of agricultural production. Meanwhile, a high self-sufficiency rate is expected for cereals such as rice, wheat, and maize, with the rate being stable at around 97%. iii) Agricultural trade will continue to thrive. The growth of soybean and milk imports will slow down, but the growth of traditional agricultural exports such as vegetables and fruits is expected to continue.

Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-Cat Boost

As the banking industry gradually steps into the digital era of Bank 4.0,business competition is becoming increasingly fierce,and banks are also facing the problem of massive customer churn.To better maintain their customer resources,it is crucial for banks to accurately predict customers with a tendency to churn.Aiming at the typical binary classification problem like customer churn,this paper establishes an early-warning model for credit card customer churn.That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm(GSA)and an Improved Beetle Antennae Search(IBAS)is proposed to optimize the parameters of the CatBoost algorithm,which forms the GSAIBAS-CatBoost model.Especially,considering that the BAS algorithm has simple parameters and is easy to fall into local optimum,the Sigmoid nonlinear convergence factor and the lane flight equation are introduced to adjust the fixed step size of beetle.Then this improved BAS algorithm with variable step size is fused with the GSA to form a GSAIBAS algorithm which can achieve dual optimization.Moreover,an empirical analysis is made according to the data set of credit card customers from Analyttica official platform.The empirical results show that the values of Area Under Curve(AUC)and recall of the proposedmodel in this paper reach 96.15%and 95.56%,respectively,which are significantly better than the other 9 common machine learning models.Compared with several existing optimization algorithms,GSAIBAS algorithm has higher precision in the parameter optimization for CatBoost.Combined with two other customer churn data sets on Kaggle data platform,it is further verified that the model proposed in this paper is also valid and feasible.

Deformation early-warning index for heightened gravity dam during impoundment period

The mechanical parameters of materials in a dam body and dam foundation tend to change when dams are reinforced in aging processes.It is important to use an early-warning index to reflect the safety status of dams,particularly of heightened projects in the impoundment period.Herein,a new method for monitoring the safety status of heightened dams is proposed based on the deformation monitoring data of a dam structure,a statistical model,and finite-element numerical simulation.First,a fast optimization inversion method for estimation of dam mechanical parameters was developed,which used the water pressure component extracted from a statistical model,an improved inversion objective function,and a genetic optimization iterative algorithm.Then,a finite element model of a heightened concrete gravity dam was established,and the deformation behavior of the dam with rising water levels in the impoundment period was simulated.Subsequently,mechanical parameters of aged dam parts were calculated using the fast optimization inversion method with simulated deformation and the water pressure deformation component obtained by the statistical model under the same conditions of water pressure change.Finally,a new earlywarning index of dam deformation was constructed by means of the forward-simulated deformation and other components of the statistical model.The early-warning index is useful for forecasting dam deformation under different water levels,especially high water levels.

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.

A study on the early-warning technique concerning debris flow disasters

According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward. The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified. The key techniques of building the debris flow disaster neural network (NN)real time forecasting model are given detailed explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters, which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day, the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware are applied, which makes the system′s performance steady with good expansibility. The system is a visual information system that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention.

The Management Platform for Online Rate of Meteorological Early-warning Loudspeakers

The meteorological early-warning loudspeaker is a specific initiative for the meteorological departments to address the issues concerning issues concerning agriculture,countryside and farmers.Its significance is that it can promptly deliver the early-warning information concerning some meteorological disasters(such as torrential rains,typhoons,cold wave,hail)to the areas affected,so as to provide reference and protection for agricultural production and effectively reduce the loss of agricultural producers.Up to now,the meteorological early-warning loudspeakers in Benxi have covered the villages.However,due to irregular occurrence of meteorological disasters,the listeners will turn off the information receivers of meteorological early-warning loudspeakers when they fail to receive meteorological information for a long time,so that the users can not promptly know the early-warning information regarding some sudden meteorological disasters.In view of this,the meteorological departments have introduced a series of management measures,such as the daily use of loudspeakers to publish weather forecast information,aimed at improving the online rate and usage rate of meteorological loudspeakers.And the management platform for online rate of meteorological early-warning loudspeakers is an important part of the management system.

Financial crisis early-warning model of listed companies based on predicted value

To establish a financial early-warning model with high accuracy of discrimination and achieve the aim of long-term prediction, principal component analysis (PCA), Fisher discriminant, together with grey forecasting models are used at the same time. 110 A-share companies listed on the Shanghai and Shenzhen stock exchange are selected as research samples. And 10 extractive factors with 89.746% of all the original information are determined by applying PCA, which obtains the goal of dimension reduction without information loss. Based on the index system, the early-warning model is constructed according to the Fisher rules. And then the GM(1,1) is adopted to predict financial ratios in 2004, according to 40 testing samples from 2000 to 2003. Finally, two different methods, a self-validated and a forecasting-validated, are used to test the validity of the financial crisis warning model. The empirical results show that the model has better predictability and feasibility, and GM(1,1) contributes to the ability to make long-term predictions.

Spread of invasive migratory pest Spodoptera frugiperda and management practices throughout China

The fall armyworm(FAW),Spodoptera frugiperda(J.E.Smith)(Lepidoptera:Noctuidae),a notorious migratory pest native to tropical and subtropical America,invaded China in December 2018,then spread through 26 provinces(autonomous regions,municipalities)in 2019 and 27 in 2020,damaging 1.125 and 1.278 million hectares of crops,respectively.Maize was the most severely affected crop,although wheat and other plants were also ruined.Considering the biological characteristics,incidence regularity and migration patterns of the FAW populations,Chinese government implemented a regional control strategy and divided the areas infested with FAW into the annual breeding grounds in Southwest and South China,the transitional migration area in Jiangnan and Jianghuai and the key preventive area in the Huang-Huai-Hai region and North China.The National Agro-Tech Extension and Service Center constructed"the National Information Platform for the Prevention and Control of the Fall Armyworm"at the county level,which would entail people reporting and mapping the spread of fall armyworm.According to forecasting information,millions of extension workers and small-scale growers in entire country were rallied by local governments to fight the pest through comprehensive control tactics including chemical,physical,biological and ecological measures.Thanks to the joint prevention and control,the final loss of crops infested was controlled within 5%of the total in 2019 and 2020.This review also gives a discussion on existing problems and future management scenarios.

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.

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.

Design of an agricultural economic statistical digitalized system

To improve the efficiency of the management of large farms, a digitalized system of economic statistics is designed based on the Internet platform of the digitalized agricultural integrated system of Friendship Farm, the largest farm in the world. The system can also realize data storage by using the access databank technology. A dynamic website system, based on ASP technology, is used to implement the on-line inquiry of the statistical index of the agricultural economy and the diagrams of the index every year. Furthermore, it can provide the value of comprehensive indicators of farms' economic profits for every year and a trend chart of the comprehensive appraisal of economic development, by using principal component analysis. An early-warning indicator boundary is decided based on the majority principle. The system can realize the farm's terminal data input with effective data-collecting channels and a normative gathering scope and system. This system breaks through the stand-alone database system in agricultural digitalized research to realize the database system in the Internet environment by integrating the existing technologies in China. The system lays a foundation for the further integrated research on the network platform of the digitalized agricultural integrated system in Friendship Farm.

Development of Crisis Management Models Combined with Cloud Computing

Three monitor models of enterprise crisis were introduced,i.e.,the monitoring model of enterprise crisis based on intelligent Meta search,the enterprise crisis management model based on artificial neural network and the combined early-warning model.Combined with the advantages of cloud computing,the prominent crisis management models are improved and more efficient,comprehensive and accurate in enterprise crisis management.Through the empirical study of the models,cloud computing makes the early warning structures of enterprise crisis tend to be more simple and efficient,cloud computing can effectively enhance the recognition ability and learning ability of the crisis management,and cloud computing can keep data information updating and realize the dynamic management of enterprise joint early-warning.At the same time,according to the comparative analysis and the experimental result,the crisis management models based on cloud computing also need some improvements.

Option of Fiscal and Financial Polices Based on Monitoring Boom of Urban Employment

With downward pressure of economy facing, monitoring boom index of employment continue to decline. This paper will research on how to cope with urban employment problem using fiscal and financial polices,through building system of boom of urban employment, and identifying risk signal of Chinese urban employment.

An Early Warning System for Regional Rain-Induced Landslide Hazard

Landslide in alpine regions often causes heavy losses of both human lives and properties, most of the landslides are induced by heavy rainfall. In this paper, we put forward an early warning system of rain-induced landslide. From 2002, we carried on the demonstrative work of landslide monitoring and early warning in Yaan, Sichuan Province, China, and constructed the first county-scale landslide monitoring and early warning region. Yucheng District of Yaan City is located in the west of the Sichuan Basin, right in the intersection of SichuanBasin and the Tibetan Plateau. The slopes are made of Mesozoic sedimentary rock, sandstone inter-bedded with mudstone. Yucheng District has the title “sky funnel” because of the high precipitation, the annual precipitation is about 1750 mm. We carried out detailed landslide survey, and obtained the location, scale, characteristics, influence and triggering factors of the landslides. Then we assessed the regional landslide susceptibility. Based on the evolution law of the landslides, we selected ten factors to study the relationship between the factors and landslide. Using the bi-variate statistics method, we calculated the contribution to landslide from each factor, classified the susceptibility into four categories. We set up the regional rainfall monitoring network with 13 automatic CAWS600R rain gauges. Using the landslide survey data, we studied the rainfall influencing of the regional landslides. The one-day and three-day rainfall controls the occurrence of regional landslide. We also classified the triggering effect of rainfall into four categories. We presented a method to calculate the landslide danger degree using the susceptibility and triggering category. Utilizing the predicted rainfall data and real-time monitored rainfall data, together with the landslide susceptibility map, we developed a WebGIS-based landslide warning system, which greatly strengthened the capability for geohazard control.

Key technologies of earthquake early warning system for China’s high-speed railway

Purpose–The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China,which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.Design/methodology/approach–The paper describes the key technologies that are involved in the development of the system,such as P-wave identification and earthquake early-warning,multi-source seismic information fusion and earthquake emergency treatment technologies.The paper also presents the test results of the system,which show that it has complete functions and its major performance indicators meet the design requirements.Findings–The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety.The key technical indicators of the system have excellent performance:The first report time of the P-wave is less than three seconds.From the first arrival of P-wave to the beginning of train braking,the total delay of onboard emergency treatment is 3.63 seconds under 95%probability.The average total delay for power failures triggered by substations is 3.3 seconds.Originality/value–The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions.It also contributes to the earthquake prevention and disaster reduction efforts.

Gestational dexamethasone exposure impacts hippocampal excitatory synaptic transmission and learning and memory function with transgenerational effects

The formation of learning and memory is regulated by synaptic plasticity in hippocampal neurons.Here we explored how gestational exposure to dexamethasone,a synthetic glucocorticoid commonly used in clinical practice,has lasting effects on offspring's learning and memory.Adult offspring rats of prenatal dexamethasone exposure(PDE)displayed significant impairments in novelty recognition and spatial learning memory,with some phenotypes maintained transgenerationally.PDE impaired synaptic transmission of hippocampal excitatory neurons in offspring of F1 to F3 generations,and abnormalities of neurotransmitters and receptors would impair synaptic plasticity and lead to impaired learning and memory,but these changes failed to carry over to offspring of F5 and F7 generations.Mechanistically,altered hippocampal miR-133a-3p-SIRT1-CDK5-NR2B signaling axis in PDE multigeneration caused inhibition of excitatory synaptic transmission,which might be related to oocyte-specific high expression and transmission of miR-133a-3p.Together,PDE affects hippocampal excitatory synaptic transmission,with lasting consequences across generations,and CDK5 in offspring's peripheral blood might be used as an early-warning marker for fetal-originated learning and memory impairment.

A dynamic risk-based early warning monitoring system for population-based management of cardiovascular disease

Risk prediction tools are crucial for population-based management of cardiovascular disease(CVD).However,most prediction models are currently used to assess long-term risk instead of the risk of short-term CVD onset.We developed a Dynamic Risk-based Early wAming Monitoring(DREAM)system using large-scale,real-time electronic health record data from 2010 to 2020 from the CHinese Electronic health Records Research in Yinzhou study.The dynamic risk scores were derived from a 1:5 matched nested case-control set comprising 70,470 individuals(11,745 CVD events)and then validated in a cohort of 81,205 individuals(5950 CVD events).The individuals were Chinese adults aged 40-79 years without a history of CVD at baseline.Eleven predictors related to vital signs,laboratory tests,and health service utilization were selected to establish the dynamic scores.The proposed scores were significantly associated with the subsequent CVD onset(adjusted odds ratio,1.21;95%confidence interval,1.20-1.23).The area under the receiver operating characteristic curves(AUCs)was 0.6010(0.5929-0.6092)and 0.6021(0.5937-0.6105)for the long-term 10-year CVD risk<10%and≥10%groups in the derivation set,respectively.In the long-term 10-year CVD risk>10%group in the validation set,the change in AUC in addition to the long-term risk was 0.0235(0.0155-0.0315).By increasing the risk threshold from 7 to 16 points,the proportion of true subsequent CVD cases among those given alerts increased from 40.61%to 85.31%.In terms of management efficiency,the number needed to manage per CVD case ranged from 2.46 to 1.17 using the risk scores.With the increasing popularity and integration of EHR systems with wearable technology,the DREAM scores can be incorporated into an early-warning system and applied in dynamic,real-time,EHR-based,automated management to support healthcare decision making for individuals,general practitioners,and policymakers.

An antibiotic composite electrode for improving the sensitivity of electrochemically active biofilm biosensor

Extensive research has been carried out for improved sensitivity of electroactive biofilm-based sensor(EAB-sensor),which is recognized as a useful tool in water quality early-warning.Antibiotic that is employed widely to treat infection has been proved feasible in this study to regulate the EAB and to increase the EAB-biosensor’s sensitivity.A novel composite electrode was prepared using azithromycin(AZM)and graphite powder(GP),namely AZM@GP electrode,and was employed as the anode in EAB-biosensor.Different dosages of AZM,i.e.,2 mg,4 mg,and 8 mg,referred to as 0.25%,0.5%and 1%AZM@GP were under examination.Results showed that EAB-biosensor was greatly benefited from appropriate dosage of AZM(0.5%AZM@GP)with reduced start-up time period,comparatively higher voltage output,more readable electrical signal and increased inhibition rate(30%-65%higher than control sensor with GP electrode)when exposing to toxic formaldehyde.This may be attributed to the fact that AZM inhibited the growth of non-EAM without much influence on the physiologic or metabolism activities of EAM under proper dosage.Further investigation of the biofilm morphology and microbial community analysis suggested that the biofilm formation was optimized with reduced thickness and enriched Geobacter with 0.5%AZM@GP dosage.This novel electrode is easily fabricated and equipped,and therefore would be a promising way to facilitate the practical application of EAB-sensors.