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.

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.

Research on Technology Early-Warning System Based on Dynamic Information Monitoring

Relying on the advanced information technologies, such as information monitoring, data mining, natural language processing etc., the dynamic technology early-warning system is constructed. The system consists of technology information automatic retrieval, technology information monitoring, technology threat evaluation, and crisis response and management subsystem, which implements uninterrupted dynamic monitoring, trace and crisis early-warning to the specific technology. Empirical study testifies that the system improves the accuracy, timeliness and reliability of technology early-warning.

Failure process and monitoring data of an extra-large landslide at the Nanfen Open-pit Iron Mine

An extra-large landslide occurred on June 19,2021,on the footwall slope of the Nanfen Open-pit Iron Mine in Liaoning Province,China,with a volume of approximately 1.2×107 m3.To elucidate the causative factors,development process,and destructive mechanisms of this catastrophic landslide,comprehensive field tests,investigations,and laboratory experiments were conducted.Initially,the heavily weathered rock mass of the slope was intersected by faults and joint fissures,facilitating rainwater infiltration.Moreover,the landslide contained a substantial clay mineral with highly developed micro-cracks and micro-pores,exhibiting strong water-absorption properties.As moisture content increased,the rock mass underwent softening,resulting in reduced strength.Ultimately,continuous heavy rainfall infiltration amplified the slope's weight,diminishing the weak structural plane's strength,leading to fracture propagation,slip plane penetration,and extensive tensile-shear and uplift failure of the slope.The study highlights poor geological conditions as the decisive factor for this landslide,with continuous heavy rainfall as the triggering factor.Presently,adverse environmental factors persistently affect the landslide,and deformation and failure continue to escalate.Hence,it is imperative to urgently implement integrated measures encompassing slope reinforcement,monitoring,and early-warning to real-time monitor the landslide's deformation and deep mechanical evolution trends.

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.

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.

Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams

Analyzing the service behavior of high dams and establishing early-warning systems for them have become increasingly important in ensuring their long-term service.Current analysis methods used to obtain safety monitoring data are suited only to single survey point data.Unreliable or even paradoxical results are inevitably obtained when processing large amounts of monitoring data,thereby causing difficulty in acquiring precise conclusions.Therefore,we have developed a new method based on multi-source information fusion for conducting a comprehensive analysis of prototype monitoring data of high dams.In addition,we propose the use of decision information entropy analysis for building a diagnosis and early-warning system for the long-term service of high dams.Data metrics reduction is achieved using information fusion at the data level.A Bayesian information fusion is then conducted at the decision level to obtain a comprehensive diagnosis.Early-warning outcomes can be released after sorting analysis results from multi-positions in the dam according to importance.A case study indicates that the new method can effectively handle large amounts of monitoring data from numerous survey points.It can likewise obtain precise real-time results and export comprehensive early-warning outcomes from multi-positions of high dams.

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.

长生命史飑线在强、弱对流降水过程中的异同点分析

利用常规气象资料、自动站资料、NCEP再分析资料和多普勒雷达资料,对发生在我国中纬度地区不同对流环境下两次长生命史飑线过程的物理机制和中尺度特征进行了分析。结果表明:(1)飑线在近地面层有较强的水汽辐合,但强对流降水过程中的飑线湿层深厚,水汽辐合的层次更为深广、强度更强,存在较低的抬升凝结高度。(2)高层强冷平流与低层暖平流的叠加是飑线的共同特征之一;不同之处在于弱对流降水过程中飑线不稳定层结的建立更多地依靠高层冷平流的作用,有更高的温度直减率,具有弱降水超级单体的一些特征;强对流降水过程中飑线低层暖平流的加强也是造成大气不稳定的重要原因,θse在中层呈现出湿中性层结特征,存在更大的热力不稳定度,是典型的强降水超级单体特性。(3)长生命史飑线的发展离不开强环境风垂直风切变;强对流降水过程中垂直风切变主要是风速随高度变化而产生的,弱对流降水过程中垂直风切变主要表现为风向随高度的变化。(4)飑线沿着出流边界和引导气流方向移动。(5)飑线在雷达回波上的共同点:都出现典型弓形回波,减弱的标志亦是阵风锋逐渐远离回波主体,弓形回波逐渐断裂,强回波后侧的层状云回波面积开始增大;不同点在于弓形回波的演变方式不同,弱对流降水过程中的弓形回波有超级单体风暴的典型结构,而强对流降水过程中弓形回波的形成是由强降水超级单体的发展而来,单体结构明显不同于经典超级单体中非降水或弱降水超级单体。(6)速度场上低层存在着径向速度的大值区,中低层有辐合,并伴有中气旋存在,中层存在明显的MARC(Mid-Altitude Radial Convergence)。1km高度以下的径向速度大值区、MARC和中气旋对地面灾害性大风有提前预警作用。

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 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.