Construction of Early-warning Model for Plant Diseases and Pests Based on Improved Neural Network

By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.

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.

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.

Data Processing Model of Coalmine Gas Early-Warning System

The data processing mode is vital to the performance of an entire coalmine gas early-warning system, especially in real-time performance. Our objective was to present the structural features of coalmine gas data, so that the data could be processed at different priority levels in C language. Two different data processing models, one with priority and the other without priority, were built based on queuing theory. Their theoretical formulas were determined via a M/M/I model in order to calculate average occupation time of each measuring point in an early-warning program. We validated the model with the gas early-warning system of the Huaibei Coalmine Group Corp. The results indicate that the average occupation time for gas data processing by using the queuing system model with priority is nearly 1/30 of that of the model without priority.

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

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

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

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

Risk 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.

The Innovation Research of Financial Early-Warning Index Measurement

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

Early-warning signals for an outbreak of the influenza pandemic

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

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.

240 nm AlGaN-based deep ultraviolet micro-LEDs:size effect versus edge effect

240 nm AlGaN-based micro-LEDs with different sizes are designed and fabricated.Then,the external quantum efficiency(EQE)and light extraction efficiency(LEE)are systematically investigated by comparing size and edge effects.Here,it is revealed that the peak optical output power increases by 81.83%with the size shrinking from 50.0 to 25.0μm.Thereinto,the LEE increases by 26.21%and the LEE enhancement mainly comes from the sidewall light extraction.Most notably,transversemagnetic(TM)mode light intensifies faster as the size shrinks due to the tilted mesa side-wall and Al reflector design.However,when it turns to 12.5μm sized micro-LEDs,the output power is lower than 25.0μm sized ones.The underlying mechanism is that even though protected by SiO2 passivation,the edge effect which leads to current leakage and Shockley-Read-Hall(SRH)recombination deteriorates rapidly with the size further shrinking.Moreover,the ratio of the p-contact area to mesa area is much lower,which deteriorates the p-type current spreading at the mesa edge.These findings show a role of thumb for the design of high efficiency micro-LEDs with wavelength below 250 nm,which will pave the way for wide applications of deep ultraviolet(DUV)micro-LEDs.

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.

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.

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.

Evaluation of the intracellular lipid-lowering effect of polyphenols extract from highland barley in HepG2 cells

Active ingredients from highland barley have received considerable attention as natural products for developing treatments and dietary supplements against obesity.In practical application,the research of food combinations is more significant than a specific food component.This study investigated the lipid-lowering effect of highland barley polyphenols via lipase assay in vitro and HepG2 cells induced by oleic acid(OA).Five indexes,triglyceride(TG),total cholesterol(T-CHO),low density lipoprotein-cholesterol(LDL-C),aspartate aminotransferase(AST),and alanine aminotransferase(ALT),were used to evaluate the lipidlowering effect of highland barley extract.We also preliminary studied the lipid-lowering mechanism by Realtime fluorescent quantitative polymerase chain reaction(q PCR).The results indicated that highland barley extract contains many components with lipid-lowering effects,such as hyperoside and scoparone.In vitro,the lipase assay showed an 18.4%lipase inhibition rate when the additive contents of highland barley extract were 100μg/m L.The intracellular lipid-lowering effect of highland barley extract was examined using 0.25 mmol/L OA-induced HepG2 cells.The results showed that intracellular TG,LDL-C,and T-CHO content decreased by 34.4%,51.2%,and 18.4%,respectively.ALT and AST decreased by 51.6%and 20.7%compared with the untreated hyperlipidemic HepG2 cells.q PCR results showed that highland barley polyphenols could up-regulation the expression of lipid metabolism-related genes such as PPARγand Fabp4.

Engineering Strategies for Suppressing the Shuttle Effect in Lithium–Sulfur Batteries

Lithium–sulfur(Li–S)batteries are supposed to be one of the most potential next-generation batteries owing to their high theoretical capacity and low cost.Nevertheless,the shuttle effect of firm multi-step two-electron reaction between sulfur and lithium in liquid electrolyte makes the capacity much smaller than the theoretical value.Many methods were proposed for inhibiting the shuttle effect of polysulfide,improving corresponding redox kinetics and enhancing the integral performance of Li–S batteries.Here,we will comprehensively and systematically summarize the strategies for inhibiting the shuttle effect from all components of Li–S batteries.First,the electrochemical principles/mechanism and origin of the shuttle effect are described in detail.Moreover,the efficient strategies,including boosting the sulfur conversion rate of sulfur,confining sulfur or lithium polysulfides(LPS)within cathode host,confining LPS in the shield layer,and preventing LPS from contacting the anode,will be discussed to suppress the shuttle effect.Then,recent advances in inhibition of shuttle effect in cathode,electrolyte,separator,and anode with the aforementioned strategies have been summarized to direct the further design of efficient materials for Li–S batteries.Finally,we present prospects for inhibition of the LPS shuttle and potential development directions in Li–S batteries.

The risk early-warning of gas hazard in coal mine based on Rough Set-neural network

This article proposed the risk early-warning model of gas hazard based on Rough Set and neural network. The attribute quantity was reduced by Rough Set, the main characteristic attributes were withdrawn, the complexity of neural network system and the computing time was reduced, as well. Because of fault-tolerant ability, parallel processing ability, anti-jamming ability and processing non-linear problem ability of neural network system, the methods of Rough Set and neural network were combined. The examples research indicate： applying Rough Set and BP neural network to the gas hazard risk early-warning coal mines in coal mine, the BPNN structure is greatly simplified, the network computation quantity is reduced and the convergence rate is speed up.

Experimental study on the effect of water absorption level on rockburst occurrence of sandstone

To investigate the mechanism of rockburst prevention by spraying water onto the surrounding rocks,15 experiments are performed considering different water absorption levels on a single face.High-speed photography and acoustic emission(AE)system are used to monitor the rockburst process.The effect of water on sandstone rockburst and the prevention mechanism of water on sandstone rockburst are analyzed from the perspective of energy and failure mode.The results show that the higher the ab-sorption degree,the lower the intensity of the rockburst after absorbing water on single side of sand-stone.This is reflected in the fact that with the increase in the water absorption level,the ejection velocity of rockburst fragments is smaller,the depth of the rockburst pit is shallower,and the AE energy is smaller.Under the water absorption level of 100%,the magnitude of rockburst intensity changes from medium to slight.The prevention mechanism of water on sandstone rockburst is that water reduces the capacity of sandstone to store strain energy and accelerates the expansion of shear cracks,which is not conducive to the occurrence of plate cracking before rockburst,and destroys the conditions for rockburst incubation.

High responsivity photodetectors based on graphene/WSe_(2) heterostructure by photogating effect

Graphene, with its zero-bandgap electronic structure, is a highly promising ultra-broadband light absorbing material.However, the performance of graphene-based photodetectors is limited by weak absorption efficiency and rapid recombination of photoexcited carriers, leading to poor photodetection performance. Here, inspired by the photogating effect, we demonstrated a highly sensitive photodetector based on graphene/WSe_(2) vertical heterostructure where the WSe_(2) layer acts as both the light absorption layer and the localized grating layer. The graphene conductive channel is induced to produce more carriers by capacitive coupling. Due to the strong light absorption and high external quantum efficiency of multilayer WSe_(2), as well as the high carrier mobility of graphene, a high photocurrent is generated in the vertical heterostructure. As a result, the photodetector exhibits ultra-high responsivity of 3.85×10~4A/W and external quantum efficiency of 1.3 × 10~7%.This finding demonstrates that photogating structures can effectively enhance the sensitivity of graphene-based photodetectors and may have great potential applications in future optoelectronic devices.