Ultra-tight GPS/INS integration based long-range rocket projectile navigation method

Accurate navigation is important for long-range rocket projectile＇s precise striking. To obtain stable and high-per- formance navigation result, a ultra-tight global positioning system/inertial navigation system （GPS/INS） integration based nav- igation approach is proposed. The accurate short-time output of INS is used by GPS receiver to assist in acquisition of signal, and output information of INS and GPS is fused based on federated filter. Meanwhile, the improved cubature Kalman filter with strong tracking ability is chosen to serve as the local filter, and then the federated filter is enhanced based on vector sharing theory. Finally, simulation results show that the navigation accuracy with the proposed method is higher than that with traditional methods. It provides reference for long-range rocket projectile navigation.

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.

In- and Anti-transition of Firing Patterns Induced by Random Long-range Connections in Coupled Hindmarsh-Rose Neurons System

The effects of random long-range connections （shortcuts） on the transitions of neural firing patterns in coupled Hindmarsh-Rose neurons are investigated, where each neuron is subjected to an external current. It is found that, on one hand, the system can achieve the transition of neural firing patterns from the fewer-period state to the multi-period one, when the number of the added shortcuts in the neural network is greater than a threshold value, indicating the occurrence of in-transition of neural firing patterns. On the other hand, for a stronger coupling strength, we can also find the similar but reverse results by adding some proper random connections. In addition, the influences of system size and coupling strength on such transition behavior, as well as the internality between the transition degree of firing patterns and its critical characteristics for different external stimulation current, are also discussed.

Ag、Cu、Au、Ni、Pd、Pt的Long-range Finis-Sinclair势构建及其二元合金形成焓的计算

构建了fcc金属Cu、Ag、Au、Ni、Pd、Pt的Long-range Finnis-Sinclair势,并利用晶格常数、体弹性模量和金属的内聚能的实验数据拟合了这几种金属的Long-range Finnis-Sinclair势参数;利用Miedema理论和所构建的原子间相互作用势计算了Cu,Ag,Au,Ni,Pd,Pt的二元合金的形成焓.对两种计算结果进行了比较,并与已有的实验结果进行了比较.结果表明,使用Long-range Finnis-Sinclair势计算合金的形成焓能够得到令人满意的结果.

SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer

This study proposes a novel general image fusion framework based on cross-domain long-range learning and Swin Transformer,termed as SwinFusion.On the one hand,an attention-guided cross-domain module is devised to achieve sufficient integration of complementary information and global interaction.More specifically,the proposed method involves an intra-domain fusion unit based on self-attention and an interdomain fusion unit based on cross-attention,which mine and integrate long dependencies within the same domain and across domains.Through long-range dependency modeling,the network is able to fully implement domain-specific information extraction and cross-domain complementary information integration as well as maintaining the appropriate apparent intensity from a global perspective.In particular,we introduce the shifted windows mechanism into the self-attention and cross-attention,which allows our model to receive images with arbitrary sizes.On the other hand,the multi-scene image fusion problems are generalized to a unified framework with structure maintenance,detail preservation,and proper intensity control.Moreover,an elaborate loss function,consisting of SSIM loss,texture loss,and intensity loss,drives the network to preserve abundant texture details and structural information,as well as presenting optimal apparent intensity.Extensive experiments on both multi-modal image fusion and digital photography image fusion demonstrate the superiority of our SwinFusion compared to the state-of-theart unified image fusion algorithms and task-specific alternatives.Implementation code and pre-trained weights can be accessed at https://github.com/Linfeng-Tang/SwinFusion.

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.

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.

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.

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.

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.

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.

Long-range electron synergy over Pt_(1)-Co_(1)/CN bimetallic single-atom catalyst in enhancing charge separation for photocatalytic hydrogen production

The development of novel single-atom catalysts with optimal electron configuration and economical noble-metal cocatalyst for efficient photocatalytic hydrogen production is of great importance,but still challenging.Herein,we fabricate Pt and Co single-atom sites successively on polymeric carbon nitride(CN).In this Pt_(1)-Co_(1)/CN bimetallic single-atom catalyst,the noble-metal active sites are maximized,and the single-atomic Co_(1)N_4sites are tuned to Co_(1)N_3sites by photogenerated electrons arising from the introduced single-atomic Pt_(1)N_4sites.Mechanism studies and density functional theory(DFT)calculations reveal that the 3d orbitals of Co_(1)N_3single sites are filled with unpaired d-electrons,which lead to the improved visible-light response,carrier separation and charge migration for CN photocatalysts.Thereafter,the protons adsorption and activation are promoted.Taking this advantage of long-range electron synergy in bimetallic single atomic sites,the photocatalytic hydrogen evolution activity over Pt_(1)-Co_(1)/CN achieves 915.8 mmol g^(-1)Pt h^(-1),which is 19.8 times higher than Co_(1)/CN and 3.5 times higher to Pt_(1)/CN.While this electron-synergistic effect is not so efficient for Pt nanoclusters.These results demonstrate the synergistic effect at electron-level and provide electron-level guidance for the design of efficient photocatalysts.

Super long-range diffusion of carbon during proeutectoid ferrite transformation

In order to explore the possible diffusion distance of carbon during proeutectoid ferrite transformation, a slow cooling test of low carbon steel was carried out under vacuum of the thermal simulator. The microstructure and thermal expansion curve were discussed and the carbon concentration inside the sample was measured. The ferrite layer of about 450 μm thickness was obtained without pearlite on the surface of the sample in the microstructure. The thermal expansion curve shows that the ferrite layer without pearlite is formed during the local phase transformation, which is followed by the global transformation. The carbon concentration in the core of the sample (0.061%) is significantly higher than that of the bulk material (0.054%). All results show that carbon has long-range diffusion from the outer layer to the inner layer of the sample. The transformation is predominantly interface-controlled mode during local transformation, and the interface migration rate is about 2.25 μm/s.

The influence of magnetic field on the beam quality of relativistic electron beam long-range propagation in near-Earth environment

In recent years,it has been proposed to use satellite-mounted radio-frequency(RF)accelerators to produce high-current relativistic electron beams to complete debris removal tasks.However,when simulating the long-range propagation(km-range)process of the electron beam,it is difficult to directly use the particle-in-cell method to simultaneously consider the space charge effect of beam and the influence of the geomagnetic field.Owing to these limitations,in this paper,we proposed a simplified method.The ps-range electronic micropulses emitted by the RF accelerator were transmitted and fused to form a ns-range electron beam;then,combined with the improved moving window technology,the model was constructed to simulate the long-range propagation process of the relativistic electron beam in near-Earth environment.Finally,by setting the direction of movement of the beam to be parallel,perpendicular and at an inclination of 3°to the magnetic field,we analyzed and compared the effects of the applied magnetic fields in different directions on the quality of the beam during long-range propagation.The simulation results showed that the parallel state of the beam motion and magnetic fields should be achieved as much as possible to ensure the feasibility of the space debris removal.

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.

Self-sustained target waves in excitable media with only a long-range link

In this paper we investigate spatiotemporal pattern formation in excitable media with only a long-range link. Besides the trivial solutions of spiral patterns, we find the asymptotic self-sustained target waves in the autonomous tissues. The wave source supporting this kind of new pattern is the oscillatory one-dimensional Winfree-loop self- organized under the presence of a long-range link, which is explored by the dominant phase-advanced driving method. Based on this understanding we can effectively regulate the oscillations of excitable media by suitably arranging the long-range link, including construction of self-sustained target waves with controllable period and wave length, or manipulation of system states between different patterns.

Preliminary research on the relationship between long-range correlations and predictability

By establishing the Markov model for a long-range correlated time series （LRCS） and analysing its evolutionary characteristics, this paper defines a physical effective correlation length （ECL） T, which reflects the predictability of the LRCS. It also finds that the ECL has a better power law relation with the long-range correlated exponent γ of the LRCS： T = Kexp（-γ/0.3） ＋ Y, （0 〈 γ〈 1） the predictability of the LRCS decays exponentially with the increase of γ It is then applied to a daily maximum temperature series （DMTS） recorded at 740 stations in China between the years 1960-2005 and calculates the ECL of the DMTS. The results show the remarkable regional distributive feature that the ECL is about 10-14 days in west, northwest and northern China, and about 5-10 days in east, southeast and southern China. Namely, the predictability of the DMTS is higher in central-west China than in east and southeast China. In addition, the ECL is reduced by 1-8 days in most areas of China after subtracting the seasonal oscillation signal of the DMTS from its original DMTS; however, it is only slightly altered when the decadal linear trend is removed from the original DMTS. Therefore, it is shown that seasonal oscillation is a significant component of daily maximum temperature evolution and may provide a basis for predicting daily maximum temperatures. Seasonal oscillation is also significant for guiding general weather predictions, as well as seasonal weather predictions.

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 VLASOV-POISSON-BOLTZMANN SYSTEM NEAR MAXWELLIANS FOR LONG-RANGE INTERACTIONS

In this article, we are concerned with the construction of global smooth small-amplitude solutions to the Cauchy problem of the one species Vlasov-Poisson-Boltzmann system near Maxwellians for long-range interactions. Compared with the former result obtained by Duan and Liu in [12] for the two species model, we do not ask the initial perturbation to satisfy the neutral condition and our result covers all physical collision kernels for the full range of intermolecular repulsive potentials.