Spatial distribution prediction and benefits assessment of green manure in the Pinggu District,Beijing,based on the CLUE-S model

Green manure use in China has declined rapidly since the 1980 s with the extensive use of chemical fertilizers.The deterioration of field environments and the demand for green agricultural products have resulted in more attention to green manure.Human intervention and policy-oriented behaviors likely have large impacts on promoting green manure planting.However,little information is available regarding on where,at what rates,and in which ways（i.e.,intercropping green manure in orchards or rotating green manure in cropland） to develop green manure and what benefits could be gained by incorporating green manure in fields at the county scale.This paper presents the conversion of land use and its effects at small region extent（CLUE-S） model,which is specifically developed for the simulation of land use changes originally,to predict spatial distribution of green manure in cropland and orchards in 2020 in Pinggu District located in Beijing,China.Four types of land use for planting or not planting green manure were classified and the future land use dynamics（mainly croplands and orchards） were considered in the prediction.Two scenarios were used to predict the spatial distribution of green manure based on data from 2011：The promotion of green manure planting in orchards（scenario 1） and the promotion of simultaneous green manure planting in orchards and croplands（scenario 2）.The predictions were generally accurate based on the receiver operating characteristic（ROC） and Kappa indices,which validated the effectiveness of the CLUE-S model in the prediction.In addition,the spatial distribution of the green manure was acquired,which indicated that green manure mainly located in the orchards of the middle and southern regions of Dahuashan,the western and southern regions of Wangxinzhuang,the middle region of Shandongzhuang,the eastern region of Pinggu and the middle region of Xiagezhuang under scenario 1.Green manure planting under scenario 2 occurred in orchards in the middle region of Wangxinzhuang,and croplands in most regions of Daxingzhuang,southern Pinggu,northern Xiagezhuang and most of Mafang.The spatially explicit results allowed for the assessment of the benefits of these changes based on different economic and ecological indicators.The economic and ecological gains of scenarios 1 and 2 were 175691 900 and143000 300 CNY,respectively,which indicated that the first scenario was more beneficial for promoting the same area of green manure.These results can facilitate policies of promoting green manure and guide the extensive use of green manure in local agricultural production in suitable ways.

RESEARCH PROGRESS ON STRUCTURAL FATIGUE RELIABILITY DESIGN AND ANALYSIS METHODS OF CHINESE RAILWAY VEHICLES

A state-of-art review is given to the new advances on fatigue reliability design and analysis methods of Chinese railway vehicle＇s structures. First, the structures are subject to a complicated random fatigue stressing history and this history should be determined by combining dynamic simulation and on-line inspection. Second, the random fatigue constitutions belong to an intrinsic fatigue phenomenon and a probabilistic model is developed to well describe them with two measurements of survival probability and confidence, similar model is also presented for the random stress-life rela- tions and extrapolated appropriately into Song fatigue life regime. Third, concept of the fatigue limit should be understood as the fatigue strength at a given fatigue life and a so-called local Basquin model method is proposed for measuring the random strengths. In addition, drawing and application methods of the Goodman-Smith diagram for integrally characterizing the random fatigue strengths are established in terms of ten kilometers. Fourth, a reliability stress-based method is constructed with a consideration of the random constitutive relations. These new advances form a new frame work for railway fatigue reliability design and analysis.

Digital Twin-Driven Intelligent Construction:Features and Trends

Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intelligent perception networks,etc.,and it can drive the rapid conceptual development of intelligent construction(IC)such as smart factories,smart cities,and smart medical care.Nevertheless,the actual use of DT in IC is partially pending,with numerous scientific factors still not clarified.An overall survey on pending issues and unsolved scientific factors is needed for the development of DT-driven IC.To this end,this study aims to provide a comprehensive review of the state of the art and state of the use of DT-driven IC.The use of DT in planning,design,manufacturing,operation,and maintenance management of IC is demonstrated and analyzed,following which the driving functions of DT in IC are detailed from four aspects:information perception and analysis,data mining and modeling,state assessment and prediction,intelligent optimization and decision-making.Furthermore,the future direction of research,using DT in IC,is presented with some comments and suggestions.This work will help researchers gain in-depth and systematic understanding of the use of DT,and help practitioners to better promote its implementation in IC.

An intelligent approach for flight risk prediction under icing conditions

Flight risk prediction is significant in improving the flight crew's situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions,especially aircraft icing conditions.The flight risk space representing the nonlinear mapping relations between risk degree and the three-dimensional commanded vector(commanded airspeed,commanded bank angle,and commanded vertical velocity)is developed to provide the crew with practical risk information.However,the construction of flight risk space by means of computational flight dynamics suffers from certain defects,including slow computing speed.Accordingly,an intelligent approach for flight risk prediction is proposed to address these defects based on neural networks.Radial Basis Function Neural Network(RBFNN)is optimized using Adaptive Particle Swarm Optimization(APSO).To optimize both the parameters and the structure of APSO-RBFNN,a fitness function containing the training accuracy and network structure size is proposed.Extensive experimental results demonstrate that the flight risk predicted by APSO-RBFNN is very close to that obtained via computational flight dynamics.The average error(RMSE)is less than 10^(-1).The approach achieves a speedup close to 1000x compared with computational flight dynamics.In addition,some flight upset and recovery cases are presented to illustrate the efficiency of the intelligent approach for flight risk prediction.

The potential risk assessment for different arsenic species in the aquatic environment

The different toxicity characteristics of arsenic species result in discrepant ecological risk.The predicted no-effect concentrations（PNECs） 43.65, 250.18, and 2.00 × 10^3μg/L were calculated for As（III）, As（V）, and dimethylarsinic acid in aqueous phase, respectively. With these PNECs, the ecological risk from arsenic species in Pearl River Delta in China and Kwabrafo stream in Ghana was evaluated. It was found that the risk from As（III） and As（V）in the samples from Pearl River Delta was low, while much high in Kwabrafo stream. This study implies that ecological risk of arsenic should be evaluated basing on its species.

A New Hidden Markov Model for Protein Quality Assessment Using Compatibility Between Protein Sequence and Structure

Protein structure Quality Assessment（QA） is an essential component in protein structure prediction and analysis. The relationship between protein sequence and structure often serves as a basis for protein structure QA.In this work, we developed a new Hidden Markov Model（HMM） to assess the compatibility of protein sequence and structure for capturing their complex relationship. More specifically, the emission of the HMM consists of protein local structures in angular space, secondary structures, and sequence profiles. This model has two capabilities：（1） encoding local structure of each position by jointly considering sequence and structure information, and（2）assigning a global score to estimate the overall quality of a predicted structure, as well as local scores to assess the quality of specific regions of a structure, which provides useful guidance for targeted structure refinement. We compared the HMM model to state-of-art single structure quality assessment methods OPUSCA, DFIRE, GOAP,and RW in protein structure selection. Computational results showed our new score HMM.Z can achieve better overall selection performance on the benchmark datasets.

Establishment and assessment of a nomogram for predicting the differentiation syndrome of acute promyelocytic leukemia

Objective By analyzing the risk factors for occurrence of differentiation syndrome(DS)during induction therapy in newly-diagnosed acute promyelocytic leukemia(APL)patients,a prediction nomogram for DS was established and the accuracy of this nomogram was validated.Methods The modeling group was made up of 130classical APL patients during the period of 1st。