Purpose:The major limitation of tumor microwave ablation(MWA)operation is the lack of predictability of the ablation zone before surgery.Operators rely on their individual experience to select a treatment plan,which i...Purpose:The major limitation of tumor microwave ablation(MWA)operation is the lack of predictability of the ablation zone before surgery.Operators rely on their individual experience to select a treatment plan,which is prone to either inadequate or excessive ablation.This paper aims to establish an ablation prediction model that guides MWA tumor surgical planning.Methods:An MWA process was first simulated by incorporating electromagnetic radiation equations,thermal equations,and optimized biological tissue parameters(dynamic dielectric and thermophysical parameters).The temperature distributions(the short/long diameters,and the total volume of the ablation zone)were then generated and verified by 60 cases ex vivo porcine liver experiments.Subsequently,a series of data were obtained from the simulated temperature distributions and to further fit the novel ablation coagulated area prediction model(ACAPM),thus rendering the ablation-dose table for the guiding surgical plan.The MWA clinical patient data and clinical devices suggested data were used to validate the accuracy and practicability of the established predicted model.Results:The 60 cases ex vivo porcine liver experiments demonstrated the accuracy of the simulated temperature distributions.Compared to traditional simulation methods,our approach reduces the long-diameter error of the ablation zone from 1.1 cm to 0.29 cm,achieving a 74%reduction in error.Further,the clinical data including the patients'operation results and devices provided values were consistent well with our predicated data,indicating the great potential of ACAPM to assist preoperative planning.展开更多
Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables...Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.展开更多
The degradation of micropollutants in water via ultraviolet(UV)-based advanced oxidation processes(AOPs)is strongly dependent on the water matrix.Various reactive radicals(RRs)formed in UV-AOPs have different reaction...The degradation of micropollutants in water via ultraviolet(UV)-based advanced oxidation processes(AOPs)is strongly dependent on the water matrix.Various reactive radicals(RRs)formed in UV-AOPs have different reaction selectivities toward water matrices and degradation efficiencies for target micropollutants.Hence,process selection and optimization are crucial.This study developed a facilitated prediction method for the photon fluence-based rate constant for micropollutant degradation(K′_(p,MP))in various UV-AOPs by combining model simulation with portable measurement.Portable methods for measuring the scavenging capacities of the principal RRs(RRSCs)involved in UV-AOPs(i.e.,HO^(·),SO_(4)^(·-),and Cl^(·))using a mini-fluidic photoreaction system were proposed.The simulation models consisted of photochemical,quantitative structure–activity relationship,and radical concentration steady-state approximation models.The RRSCs were determined in eight test waters,and a higher RRSC was found to be associated with a more complex water matrix.Then,by taking sulfamethazine,caffeine,and carbamazepine as model micropollutants,the k′_(p,MP) values in various UV-AOPs were predicted and further verified experimentally.A lower k′_(p,MP) was found to be associated with a higher RRSC for a stronger RR competition;for example,k′_(p,MP) values of 130.9 and 332.5 m^(2) einstein^(–1),respectively,were obtained for carbamazepine degradation by UV/H_(2)O_(2) in the raw water(RRSC=9.47×10^(4) s^(-1))and sand-filtered effluent(RRSC=2.87×10^(4) s^(-1))of a drinking water treatment plant.The developed method facilitates process selection and optimization for UV-AOPs,which is essential for increasing the efficiency and cost-effectiveness of water treatment.展开更多
Compared with traditional feedback control,predictive control can eliminate the lag of pose control and avoid the snakelike motion of shield machines.Therefore,a shield pose prediction model was proposed based on dyna...Compared with traditional feedback control,predictive control can eliminate the lag of pose control and avoid the snakelike motion of shield machines.Therefore,a shield pose prediction model was proposed based on dynamic modeling.Firstly,the dynamic equations of shield thrust system were established to clarify the relationship between force and movement of shield machine.Secondly,an analytical model was proposed to predict future multistep pose of the shield machine.Finally,a virtual prototype model was developed to simulate the dynamic behavior of the shield machine and validate the accuracy of the proposed pose prediction method.Results reveal that the model proposed can predict the shield pose with high accuracy,which can provide a decision basis whether for manual or automatic control of shield pose.展开更多
Computer simulation permits answering theoretical and applied questions in animal and plant breeding.Blib is a novel multi-module simulation platform,which is able to handle more complicated genetic effects and models...Computer simulation permits answering theoretical and applied questions in animal and plant breeding.Blib is a novel multi-module simulation platform,which is able to handle more complicated genetic effects and models than most existing tools.In this study,we describe one major and unified application module of Blib,i.e.,ISB(abbreviated from in silico breeding),for simulating the three categories of breeding programs for developing clonal,pure-line and hybrid cultivars in plants.Genetic models on environments and breeding-targeted traits,one or several parental populations,and a number of breeding methods are key elements to run simulation experiments in ISB,which are arranged in three external input files by given formats.Applications of ISB are illustrated by three case studies,representing the three categories of plant breeding programs.Under the condition that 5000 F1 progenies were generated and tested from 50 heterozygous parents,Case study I showed that 50 crosses,each of 100 progenies,made the best balance between genetic achievement and field cost.In Case study II,one optimum breeding method was identified by which the pure lines with high yield and medium maturity could be developed.Case study III investigated the genetic consequence in hybrid breeding from five testers.One tester was identified for the simultaneous improvement in F1 hybrids and inbred lines.In summary,ISB identified a balanced crossing scheme,an optimum pure-line selection method,and an optimized tester in three case studies which are relevant to plant breeding.We believe the prediction by simulation would be highly required in front of the next generation of breeding to be driven by informatics and intelligence.展开更多
A nested regional climate model has been experimentally used in the seasonal prediction at the China National Climate Center （NCC） since 2001. The NCC/IAP （Institute of Atmospheric Physics） T63 coupled GCM （CGCM...A nested regional climate model has been experimentally used in the seasonal prediction at the China National Climate Center （NCC） since 2001. The NCC/IAP （Institute of Atmospheric Physics） T63 coupled GCM （CGCM） provides the boundary and initial conditions for driving the regional climate model （RegCM_NCC）. The latter has a 60-km horizontal resolution and improved physical parameterization schemes including the mass flux cumulus parameterization scheme, the turbulent kinetic energy closure scheme （TKE） and an improved land process model （LPM）. The large-scale terrain features such as the Tibetan Plateau are included in the larger domain to produce the topographic forcing on the rain-producing systems. A sensitivity study of the East Asian climate with regard to the above physical processes has been presented in the first part of the present paper. This is the second part, as a continuation of Part Ⅰ. In order to verify the performance of the nested regional climate model, a ten-year simulation driven by NCEP reanalysis datasets has been made to explore the performance of the East Asian climate simulation and to identify the model＇s systematic errors. At the same time, comparative simulation experiments for 5 years between the RegCM2 and RegCM_NCC have been done to further understand their differences in simulation performance. Also, a ten-year hindcast （1991-2000） for summer （June-August）, the rainy season in China, has been undertaken. The preliminary results have shown that the RegCM_NCC is capable of predicting the major seasonal rain belts. The best predicted regions with high anomaly correlation coefficient （ACC） are located in the eastern part of West China, in Northeast China and in North China, where the CGCM has maximum prediction skill as well. This fact may reflect the importance of the largescale forcing. One significant improvement of the prediction derived from RegCM_NCC is the increase of ACC in the Yangtze River valley where the CGCM has a very low, even a negative, ACC. The reason behind this improvement is likely to be related to the more realistic representation of the large-scale terrain features of the Tibetan Plateau. Presumably, many rain-producing systems may be generated over or near the Tibetan Plateau and may then move eastward along the Yangtze River basin steered by upper-level westerly airflow, thus leading to enhancement of rainfalls in the mid and lower basins of the Yangtze River. The real-time experimental predictions for summer in 2001, 2002, 2003 and 2004 by using this nested RegCM-NCC were made. The results are basically reasonable compared with the observations.展开更多
The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural ...The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).展开更多
Building the Yangtze River Economic Belt(YREB)is one of China’s three national development policies in the new era.The ecological environment of the Yangtze River Economic Belt must be protected not only for regional...Building the Yangtze River Economic Belt(YREB)is one of China’s three national development policies in the new era.The ecological environment of the Yangtze River Economic Belt must be protected not only for regional economic development but also for regional ecological security and ecological progress in this region.This paper takes the ecological space of the Yangtze River Economic Belt as the research object,based on land use data in 2010 and 2015,and uses the FLUS model to simulate and predict the ecological space of the research area in 2035.The variation of the research area’s ecological space area and its four sub-zones has remarkable stability under diverse situations.Both the production space priority scenarios(S1)and living space priority scenarios(S2)saw a fall in ecological space area,with the former experiencing the highest reduction(a total reduction of 25,212 km^(2)).Under the ecological space priority scenarios(S3)and comprehensive space optimization scenario(S4),the ecological space area increased,and the ecological space area expanded even more under the former scenario(a total growth of 23,648 km^(2)).In Yunnan-Guizhou,the ecological space is relatively stable,with minimal signs of change.In Sichuan-Chongqing,the Sichuan Basin,Zoige Grassland,and Longmen Mountains were significant regions of area changes in ecological space.In the middle reaches of the Yangtze River,the ecological space changes mainly occur in the Wuyi Mountains,Mufu Mountains,and Dabie Mountains,as well as the surrounding waters of Dongting Lake.The Yangtze River Delta’s changes were mainly observed in the eastern Dabie Mountains and Jianghuai Hills.展开更多
Various velocity models have been built for Southeast Qinghai-Xizang Plateau with the purpose of revealing the internal dynamics and estimating local seismic hazards.In this study,we use a 3-D full-waveform modeling p...Various velocity models have been built for Southeast Qinghai-Xizang Plateau with the purpose of revealing the internal dynamics and estimating local seismic hazards.In this study,we use a 3-D full-waveform modeling package to systematically validate three published continental-scale velocity models,that is,Shen2016,FWEA18,and USTClitho1.0,leveraging the ample datasets in Southeast Qinghai-Xizang Plateau region.Travel time residuals and waveform similarities are measured between observed empirical Green’s functions and synthetic waveforms.The results show that the Shen2016 model,derived from traditional surface wave tomography,performs best in fitting Rayleigh waves in the Southeast Qinghai-Xizang Plateau,followed by FWEA18,built from full-waveform inversion of long-period body and surface waves.The USTClitho1.0 model,although inverted from body wave datasets,is comparable with FWEA18 in fitting Rayleigh waves.The results also show that all the models are faster than the ground-truth model and show relatively large travel-time residuals and poor waveform similarities at shorter period bands,possibly caused by small-scale structural heterogeneities in the shallower crust.We further invert the time residuals for spatial velocity residuals and reveal that all three models underestimate the amplitudes of high-and low-velocity anomalies.The underestimated amplitude is up to 4%,which is non-negligible considering that the overall amplitude of anomalies is only 5%−10%in the crust.These results suggest that datasets and the inversion method are both essential to building accurate models and further refinements of these models are necessary.展开更多
With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration predict...With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration prediction system is of great scientific and practical significance for accurate and reliable predictions.This paper proposes a combination of pointinterval prediction system for pollutant concentration prediction by leveraging neural network,meta-heuristic optimization algorithm,and fuzzy theory.Fuzzy information granulation technology is used in data preprocessing to transform numerical sequences into fuzzy particles for comprehensive feature extraction.The golden Jackal optimization algorithm is employed in the optimization stage to fine-tune model hyperparameters.In the prediction stage,an ensemble learning method combines training results frommultiplemodels to obtain final point predictions while also utilizing quantile regression and kernel density estimation methods for interval predictions on the test set.Experimental results demonstrate that the combined model achieves a high goodness of fit coefficient of determination(R^(2))at 99.3% and a maximum difference between prediction accuracy mean absolute percentage error(MAPE)and benchmark model at 12.6%.This suggests that the integrated learning system proposed in this paper can provide more accurate deterministic predictions as well as reliable uncertainty analysis compared to traditionalmodels,offering practical reference for air quality early warning.展开更多
The prediction of the rolling force and thickness ratio plays an important role in the development and application of bimetallic composite plates.To analyze the rolling force of the bimetallic composite plate more acc...The prediction of the rolling force and thickness ratio plays an important role in the development and application of bimetallic composite plates.To analyze the rolling force of the bimetallic composite plate more accurately,a novel hypothesis based on Orowan's theory was proposed.The variation in the thickness of each differential element at different positions was considered to establish the analytical model.According to the characteristics of bimetallic composite plate rolling,the rolling deformation can be divided into forward and backward slip zones.The initial thickness ratio after rolling was predetermined by the thickness ratio before rolling;the rolling force balance of the upper and lower rollers was considered the convergence condition;and the final thickness ratio of the bimetallic composite plate was obtained by iterative calculation.The calculation results of the analytical model were compared with the measured and simulated data.The results showed that the errors in the calculation of the rolling force and thickness ratio were both less than 10%.The analytical model has high precision,meets engineering requirements,and has important reference significance for rolling process optimization and thickness ratio prediction.展开更多
Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)...Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)to clarify the contribution of each input feature in USS prediction.Three ML models,artificial neural network(ANN),extreme gradient boosting(XGBoost),and random forest(RF),were employed,with accuracy evaluated using mean squared error,mean absolute error,and coefficient of determination(R^(2)).The RF achieved the highest performance with an R^(2) of 0.82.SHAP analysis identified pre-consolidation stress as a key contributor to USS prediction.SHAP dependence plots reveal that the ANN captures smoother,linear feature-output relationships,while the RF handles complex,non-linear interactions more effectively.This suggests a non-linear relationship between USS and input features,with RF outperforming ANN.These findings highlight SHAP’s role in enhancing interpretability and promoting transparency and reliability in ML predictions for geotechnical applications.展开更多
The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which li...The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobodyeantigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.展开更多
Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasona...Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasonal precipitation anomalies during summer in China and reveals the contributions of possible driving factors.The results suggest that while single-model ensembles(SMEs)exhibit constrained predictive skills within a limited forecast lead time of three pentads,the MME illustrates an enhanced predictive skill at a lead time of up to four pentads,and even six pentads,in southern China.Based on both deterministic and probabilistic verification metrics,the MME consistently outperforms SMEs,with a more evident advantage observed in probabilistic forecasting.The superior performance of the MME is primarily attributable to the increase in ensemble size,and the enhanced model diversity is also a contributing factor.The reliability of probabilistic skill is largely improved due to the increase in ensemble members,while the resolution term does not exhibit consistent improvement.Furthermore,the Madden–Julian Oscillation(MJO)is revealed as the primary driving factor for the successful prediction of summer precipitation in China using the MME.The improvement by the MME is not solely attributable to the enhancement in the inherent predictive capacity of the MJO itself,but derives from its capability in capturing the more realistic relationship between the MJO and subseasonal precipitation anomalies in China.This study establishes a scientific foundation for acknowledging the advantageous predictive capability of the MME approach in subseasonal predictions of summer precipitation in China,and sheds light on further improving S2S predictions.展开更多
The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope ...The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope and image processing technology were employed to achieve a quantitative grain size distribution of NiTi alloys so as to provide experimental data for molecular dynamics modeling at the atomic scale.Considering the size effect of molecular dynamics model on material properties,a reasonable modeling size was provided by taking into account three characteristic dimensions from the perspective of macro,meso,and micro scales according to the Buckinghamπtheorem.Then,the corresponding MD simulation on deformation and fracture behavior was investigated to derive a parameterized traction-separation(T-S)law,and then it was embedded into cohesive elements of finite element software.Thus,the crack propagation behavior in NiTi alloys was reproduced by the finite element method(FEM).The experimental results show that the predicted initiation fracture toughness is in good agreement with experimental data.In addition,it is found that the dynamics initiation fracture toughness increases with decreasing grain size and increasing loading velocity.展开更多
Model-based system-of-systems(SOS)engineering(MBSoSE)is becoming a promising solution for the design of SoS with increasing complexity.However,bridging the models from the design phase to the simulation phase poses si...Model-based system-of-systems(SOS)engineering(MBSoSE)is becoming a promising solution for the design of SoS with increasing complexity.However,bridging the models from the design phase to the simulation phase poses significant challenges and requires an integrated approach.In this study,a unified requirement modeling approach is proposed based on unified architecture framework(UAF).Theoretical models are proposed which compose formalized descriptions from both topdown and bottom-up perspectives.Based on the description,the UAF profile is proposed to represent the SoS mission and constituent systems(CS)goal.Moreover,the agent-based simulation information is also described based on the overview,design concepts,and details(ODD)protocol as the complement part of the SoS profile,which can be transformed into different simulation platforms based on the eXtensible markup language(XML)technology and model-to-text method.In this way,the design of the SoS is simulated automatically in the early design stage.Finally,the method is implemented and an example is given to illustrate the whole process.展开更多
Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objective...Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.展开更多
In order to account for the effect of particle existence on gas-particle turbulence flow in large-eddy simulation (LES),a new gas-particle turbulent kinetic energy subgrid-scale (SGS) turbulence model is established,a...In order to account for the effect of particle existence on gas-particle turbulence flow in large-eddy simulation (LES),a new gas-particle turbulent kinetic energy subgrid-scale (SGS) turbulence model is established,and the effect of particle wake is also considered in gas turbulent kinetic energy SGS turbulence model.Simulation of gas-particle turbulence flow in backward-facing step is carried out by LES using present model and by unified second-order moment (USM) model.The prediction statistical results including mean velocity and fluctuation velocity by LES using present model are in reasonable agreement with the experimental results.It is shown that present model is with higher calculating accuracy than USM model,which indicates that the turbulent kinetic energy SGS turbulence model is suitable.展开更多
In this manuscript we present a nonlinear site amplification model for ground-motion prediction equations(GMPEs)in Japan,using a site period-based site class and a site impedance ratio as site parameters.We used a lar...In this manuscript we present a nonlinear site amplification model for ground-motion prediction equations(GMPEs)in Japan,using a site period-based site class and a site impedance ratio as site parameters.We used a large number of shear-wave velocity profiles from the Kiban-Kyoshin network(KiK-net)and the Kyoshin network(K-NET)to construct the one-dimensional(1D)numerical models.The strong-motion records from rock-sites in Japan with different earthquake categories and taken from the Pacific Earthquake Engineering Research Center dataset were used in this study.We fit a set of 1D site amplification models using the spectral amplification ratios derived from 1D equivalent linear analyses.Parameters of site impedance ratios for both linear and nonlinear site response were included in the 1D model.The 1D model could be implemented into GMPEs using a new proposed adjustment method.The adjusted site amplification ratios retain the nonlinear characteristics of the 1D model for strong motions and match the linear amplification ratio in GMPE for weak motions.The nonlinearity of the present site model is reasonably similar to that of the historical models,and the present site model could satisfactorily capture the nonlinear site response in empirical data.展开更多
Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Reg...Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.展开更多
基金supported by the National Major Scientific Instruments and Equipment Development Project Funded by the National Natural Science Foundation of China(81827803)the Jiangsu Province Key Research and Development Program(Social Development)Project(BE2020705).
文摘Purpose:The major limitation of tumor microwave ablation(MWA)operation is the lack of predictability of the ablation zone before surgery.Operators rely on their individual experience to select a treatment plan,which is prone to either inadequate or excessive ablation.This paper aims to establish an ablation prediction model that guides MWA tumor surgical planning.Methods:An MWA process was first simulated by incorporating electromagnetic radiation equations,thermal equations,and optimized biological tissue parameters(dynamic dielectric and thermophysical parameters).The temperature distributions(the short/long diameters,and the total volume of the ablation zone)were then generated and verified by 60 cases ex vivo porcine liver experiments.Subsequently,a series of data were obtained from the simulated temperature distributions and to further fit the novel ablation coagulated area prediction model(ACAPM),thus rendering the ablation-dose table for the guiding surgical plan.The MWA clinical patient data and clinical devices suggested data were used to validate the accuracy and practicability of the established predicted model.Results:The 60 cases ex vivo porcine liver experiments demonstrated the accuracy of the simulated temperature distributions.Compared to traditional simulation methods,our approach reduces the long-diameter error of the ablation zone from 1.1 cm to 0.29 cm,achieving a 74%reduction in error.Further,the clinical data including the patients'operation results and devices provided values were consistent well with our predicated data,indicating the great potential of ACAPM to assist preoperative planning.
基金supported by the National Natural Science Foundation of China(No.42061065)the Third Xinjiang Comprehensive Scientific Expedition,China(No.2022xjkk03010102).
文摘Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.
基金supported by the National Natural Science Foundation of China(52222002)Bureau of International Cooperation of Chinese Academy of Sciences(032GJHZ2022035MI)State Key Laboratory of Environmental Aquatic Chemistry(23Z01ESPCR).
文摘The degradation of micropollutants in water via ultraviolet(UV)-based advanced oxidation processes(AOPs)is strongly dependent on the water matrix.Various reactive radicals(RRs)formed in UV-AOPs have different reaction selectivities toward water matrices and degradation efficiencies for target micropollutants.Hence,process selection and optimization are crucial.This study developed a facilitated prediction method for the photon fluence-based rate constant for micropollutant degradation(K′_(p,MP))in various UV-AOPs by combining model simulation with portable measurement.Portable methods for measuring the scavenging capacities of the principal RRs(RRSCs)involved in UV-AOPs(i.e.,HO^(·),SO_(4)^(·-),and Cl^(·))using a mini-fluidic photoreaction system were proposed.The simulation models consisted of photochemical,quantitative structure–activity relationship,and radical concentration steady-state approximation models.The RRSCs were determined in eight test waters,and a higher RRSC was found to be associated with a more complex water matrix.Then,by taking sulfamethazine,caffeine,and carbamazepine as model micropollutants,the k′_(p,MP) values in various UV-AOPs were predicted and further verified experimentally.A lower k′_(p,MP) was found to be associated with a higher RRSC for a stronger RR competition;for example,k′_(p,MP) values of 130.9 and 332.5 m^(2) einstein^(–1),respectively,were obtained for carbamazepine degradation by UV/H_(2)O_(2) in the raw water(RRSC=9.47×10^(4) s^(-1))and sand-filtered effluent(RRSC=2.87×10^(4) s^(-1))of a drinking water treatment plant.The developed method facilitates process selection and optimization for UV-AOPs,which is essential for increasing the efficiency and cost-effectiveness of water treatment.
基金Project(2023JBZY030)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(U1834208)supported by the National Natural Science Foundation of China。
文摘Compared with traditional feedback control,predictive control can eliminate the lag of pose control and avoid the snakelike motion of shield machines.Therefore,a shield pose prediction model was proposed based on dynamic modeling.Firstly,the dynamic equations of shield thrust system were established to clarify the relationship between force and movement of shield machine.Secondly,an analytical model was proposed to predict future multistep pose of the shield machine.Finally,a virtual prototype model was developed to simulate the dynamic behavior of the shield machine and validate the accuracy of the proposed pose prediction method.Results reveal that the model proposed can predict the shield pose with high accuracy,which can provide a decision basis whether for manual or automatic control of shield pose.
基金supported by Biological Breeding-National Science and Technology Major Project(2023ZD0407501)National Natural Science Foundation of China(31861143003)Innovation Program of Chinese Academy of Agricultural Sciences.
文摘Computer simulation permits answering theoretical and applied questions in animal and plant breeding.Blib is a novel multi-module simulation platform,which is able to handle more complicated genetic effects and models than most existing tools.In this study,we describe one major and unified application module of Blib,i.e.,ISB(abbreviated from in silico breeding),for simulating the three categories of breeding programs for developing clonal,pure-line and hybrid cultivars in plants.Genetic models on environments and breeding-targeted traits,one or several parental populations,and a number of breeding methods are key elements to run simulation experiments in ISB,which are arranged in three external input files by given formats.Applications of ISB are illustrated by three case studies,representing the three categories of plant breeding programs.Under the condition that 5000 F1 progenies were generated and tested from 50 heterozygous parents,Case study I showed that 50 crosses,each of 100 progenies,made the best balance between genetic achievement and field cost.In Case study II,one optimum breeding method was identified by which the pure lines with high yield and medium maturity could be developed.Case study III investigated the genetic consequence in hybrid breeding from five testers.One tester was identified for the simultaneous improvement in F1 hybrids and inbred lines.In summary,ISB identified a balanced crossing scheme,an optimum pure-line selection method,and an optimized tester in three case studies which are relevant to plant breeding.We believe the prediction by simulation would be highly required in front of the next generation of breeding to be driven by informatics and intelligence.
文摘A nested regional climate model has been experimentally used in the seasonal prediction at the China National Climate Center （NCC） since 2001. The NCC/IAP （Institute of Atmospheric Physics） T63 coupled GCM （CGCM） provides the boundary and initial conditions for driving the regional climate model （RegCM_NCC）. The latter has a 60-km horizontal resolution and improved physical parameterization schemes including the mass flux cumulus parameterization scheme, the turbulent kinetic energy closure scheme （TKE） and an improved land process model （LPM）. The large-scale terrain features such as the Tibetan Plateau are included in the larger domain to produce the topographic forcing on the rain-producing systems. A sensitivity study of the East Asian climate with regard to the above physical processes has been presented in the first part of the present paper. This is the second part, as a continuation of Part Ⅰ. In order to verify the performance of the nested regional climate model, a ten-year simulation driven by NCEP reanalysis datasets has been made to explore the performance of the East Asian climate simulation and to identify the model＇s systematic errors. At the same time, comparative simulation experiments for 5 years between the RegCM2 and RegCM_NCC have been done to further understand their differences in simulation performance. Also, a ten-year hindcast （1991-2000） for summer （June-August）, the rainy season in China, has been undertaken. The preliminary results have shown that the RegCM_NCC is capable of predicting the major seasonal rain belts. The best predicted regions with high anomaly correlation coefficient （ACC） are located in the eastern part of West China, in Northeast China and in North China, where the CGCM has maximum prediction skill as well. This fact may reflect the importance of the largescale forcing. One significant improvement of the prediction derived from RegCM_NCC is the increase of ACC in the Yangtze River valley where the CGCM has a very low, even a negative, ACC. The reason behind this improvement is likely to be related to the more realistic representation of the large-scale terrain features of the Tibetan Plateau. Presumably, many rain-producing systems may be generated over or near the Tibetan Plateau and may then move eastward along the Yangtze River basin steered by upper-level westerly airflow, thus leading to enhancement of rainfalls in the mid and lower basins of the Yangtze River. The real-time experimental predictions for summer in 2001, 2002, 2003 and 2004 by using this nested RegCM-NCC were made. The results are basically reasonable compared with the observations.
文摘The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).
基金The Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan),No.CUG2018123。
文摘Building the Yangtze River Economic Belt(YREB)is one of China’s three national development policies in the new era.The ecological environment of the Yangtze River Economic Belt must be protected not only for regional economic development but also for regional ecological security and ecological progress in this region.This paper takes the ecological space of the Yangtze River Economic Belt as the research object,based on land use data in 2010 and 2015,and uses the FLUS model to simulate and predict the ecological space of the research area in 2035.The variation of the research area’s ecological space area and its four sub-zones has remarkable stability under diverse situations.Both the production space priority scenarios(S1)and living space priority scenarios(S2)saw a fall in ecological space area,with the former experiencing the highest reduction(a total reduction of 25,212 km^(2)).Under the ecological space priority scenarios(S3)and comprehensive space optimization scenario(S4),the ecological space area increased,and the ecological space area expanded even more under the former scenario(a total growth of 23,648 km^(2)).In Yunnan-Guizhou,the ecological space is relatively stable,with minimal signs of change.In Sichuan-Chongqing,the Sichuan Basin,Zoige Grassland,and Longmen Mountains were significant regions of area changes in ecological space.In the middle reaches of the Yangtze River,the ecological space changes mainly occur in the Wuyi Mountains,Mufu Mountains,and Dabie Mountains,as well as the surrounding waters of Dongting Lake.The Yangtze River Delta’s changes were mainly observed in the eastern Dabie Mountains and Jianghuai Hills.
基金supported by the Special Fund of the Institute of Geophysics,China Earthquake Administration(Nos.DQJB23R28 and DQJB22K40)the National Natural Science Foundation of China(Nos.42304078,U1839210 and 42104043).
文摘Various velocity models have been built for Southeast Qinghai-Xizang Plateau with the purpose of revealing the internal dynamics and estimating local seismic hazards.In this study,we use a 3-D full-waveform modeling package to systematically validate three published continental-scale velocity models,that is,Shen2016,FWEA18,and USTClitho1.0,leveraging the ample datasets in Southeast Qinghai-Xizang Plateau region.Travel time residuals and waveform similarities are measured between observed empirical Green’s functions and synthetic waveforms.The results show that the Shen2016 model,derived from traditional surface wave tomography,performs best in fitting Rayleigh waves in the Southeast Qinghai-Xizang Plateau,followed by FWEA18,built from full-waveform inversion of long-period body and surface waves.The USTClitho1.0 model,although inverted from body wave datasets,is comparable with FWEA18 in fitting Rayleigh waves.The results also show that all the models are faster than the ground-truth model and show relatively large travel-time residuals and poor waveform similarities at shorter period bands,possibly caused by small-scale structural heterogeneities in the shallower crust.We further invert the time residuals for spatial velocity residuals and reveal that all three models underestimate the amplitudes of high-and low-velocity anomalies.The underestimated amplitude is up to 4%,which is non-negligible considering that the overall amplitude of anomalies is only 5%−10%in the crust.These results suggest that datasets and the inversion method are both essential to building accurate models and further refinements of these models are necessary.
基金supported by General Scientific Research Funding of the Science and Technology Development Fund(FDCT)in Macao(No.0150/2022/A)the Faculty Research Grants of Macao University of Science and Technology(No.FRG-22-074-FIE).
文摘With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration prediction system is of great scientific and practical significance for accurate and reliable predictions.This paper proposes a combination of pointinterval prediction system for pollutant concentration prediction by leveraging neural network,meta-heuristic optimization algorithm,and fuzzy theory.Fuzzy information granulation technology is used in data preprocessing to transform numerical sequences into fuzzy particles for comprehensive feature extraction.The golden Jackal optimization algorithm is employed in the optimization stage to fine-tune model hyperparameters.In the prediction stage,an ensemble learning method combines training results frommultiplemodels to obtain final point predictions while also utilizing quantile regression and kernel density estimation methods for interval predictions on the test set.Experimental results demonstrate that the combined model achieves a high goodness of fit coefficient of determination(R^(2))at 99.3% and a maximum difference between prediction accuracy mean absolute percentage error(MAPE)and benchmark model at 12.6%.This suggests that the integrated learning system proposed in this paper can provide more accurate deterministic predictions as well as reliable uncertainty analysis compared to traditionalmodels,offering practical reference for air quality early warning.
基金Supported by National Key Research and Development Program of China(Grant No.2018YFA0707300)Major Program of National Natural Science Foundation of China(Grant No.U22A20188)+1 种基金General Program of National Natural Science Foundation of China(Grant No.51974196)Postdoctoral Science Foundation of China(Grant No.201903D421047)。
文摘The prediction of the rolling force and thickness ratio plays an important role in the development and application of bimetallic composite plates.To analyze the rolling force of the bimetallic composite plate more accurately,a novel hypothesis based on Orowan's theory was proposed.The variation in the thickness of each differential element at different positions was considered to establish the analytical model.According to the characteristics of bimetallic composite plate rolling,the rolling deformation can be divided into forward and backward slip zones.The initial thickness ratio after rolling was predetermined by the thickness ratio before rolling;the rolling force balance of the upper and lower rollers was considered the convergence condition;and the final thickness ratio of the bimetallic composite plate was obtained by iterative calculation.The calculation results of the analytical model were compared with the measured and simulated data.The results showed that the errors in the calculation of the rolling force and thickness ratio were both less than 10%.The analytical model has high precision,meets engineering requirements,and has important reference significance for rolling process optimization and thickness ratio prediction.
基金Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study
文摘Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)to clarify the contribution of each input feature in USS prediction.Three ML models,artificial neural network(ANN),extreme gradient boosting(XGBoost),and random forest(RF),were employed,with accuracy evaluated using mean squared error,mean absolute error,and coefficient of determination(R^(2)).The RF achieved the highest performance with an R^(2) of 0.82.SHAP analysis identified pre-consolidation stress as a key contributor to USS prediction.SHAP dependence plots reveal that the ANN captures smoother,linear feature-output relationships,while the RF handles complex,non-linear interactions more effectively.This suggests a non-linear relationship between USS and input features,with RF outperforming ANN.These findings highlight SHAP’s role in enhancing interpretability and promoting transparency and reliability in ML predictions for geotechnical applications.
基金supported by the National Natural Science Foundation of China(Grant Nos.:92477103,22273023,12474285 and 22373116)the National Key R&D Program of China(Grant No.:2019YFA0905200)+5 种基金Shanghai Municipal Natural Science Foundation(Grant No.:23ZR1418200)Natural Science Foundation of Chongqing,China(Grant No.:CSTB2023NSCQ-MSX0616)Shanghai Frontiers Science Center of Molecule Intelligent SynthesesShanghai Future Discipline Program(Quantum Science and Tech-nology)Shanghai Municipal Education Commission’s“Artificial Intelligence-Driven Research Paradigm Reform and Discipline Advancement Program”the Fundamental Research Funds for the Central Universities.
文摘The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobodyeantigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.42175052 and U2442206)the Joint Research Project for Meteorological Capacity Improvement(Grant No.23NLTSQ007,23NLTSZ003)+2 种基金the Innovative Development Special Project of the China Meteorological Administration(Grant No.CXFZ2023J002)the National Key R&D Program of China(Grant No.2023YFC3007700,2024YFC3013100)the China Meteorological Administration Youth Innovation Team(Grant No.CMA2024QN06)。
文摘Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasonal precipitation anomalies during summer in China and reveals the contributions of possible driving factors.The results suggest that while single-model ensembles(SMEs)exhibit constrained predictive skills within a limited forecast lead time of three pentads,the MME illustrates an enhanced predictive skill at a lead time of up to four pentads,and even six pentads,in southern China.Based on both deterministic and probabilistic verification metrics,the MME consistently outperforms SMEs,with a more evident advantage observed in probabilistic forecasting.The superior performance of the MME is primarily attributable to the increase in ensemble size,and the enhanced model diversity is also a contributing factor.The reliability of probabilistic skill is largely improved due to the increase in ensemble members,while the resolution term does not exhibit consistent improvement.Furthermore,the Madden–Julian Oscillation(MJO)is revealed as the primary driving factor for the successful prediction of summer precipitation in China using the MME.The improvement by the MME is not solely attributable to the enhancement in the inherent predictive capacity of the MJO itself,but derives from its capability in capturing the more realistic relationship between the MJO and subseasonal precipitation anomalies in China.This study establishes a scientific foundation for acknowledging the advantageous predictive capability of the MME approach in subseasonal predictions of summer precipitation in China,and sheds light on further improving S2S predictions.
基金Funded by the National Natural Science Foundation of China Academy of Engineering Physics and Jointly Setup"NSAF"Joint Fund(No.U1430119)。
文摘The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope and image processing technology were employed to achieve a quantitative grain size distribution of NiTi alloys so as to provide experimental data for molecular dynamics modeling at the atomic scale.Considering the size effect of molecular dynamics model on material properties,a reasonable modeling size was provided by taking into account three characteristic dimensions from the perspective of macro,meso,and micro scales according to the Buckinghamπtheorem.Then,the corresponding MD simulation on deformation and fracture behavior was investigated to derive a parameterized traction-separation(T-S)law,and then it was embedded into cohesive elements of finite element software.Thus,the crack propagation behavior in NiTi alloys was reproduced by the finite element method(FEM).The experimental results show that the predicted initiation fracture toughness is in good agreement with experimental data.In addition,it is found that the dynamics initiation fracture toughness increases with decreasing grain size and increasing loading velocity.
基金Fifth Electronic Research Institute of the Ministry of Industry and Information Technology(HK07202200877)Pre-research Project on Civil Aerospace Technologies of CNSA(D020101)+2 种基金Zhejiang Provincial Science and Technology Plan Project(2022C01052)Frontier Scientific Research Program of Deep Space Exploration Laboratory(2022-QYKYJHHXYF-018,2022-QYKYJH-GCXD-001)Zhiyuan Laboratory(ZYL2024001)。
文摘Model-based system-of-systems(SOS)engineering(MBSoSE)is becoming a promising solution for the design of SoS with increasing complexity.However,bridging the models from the design phase to the simulation phase poses significant challenges and requires an integrated approach.In this study,a unified requirement modeling approach is proposed based on unified architecture framework(UAF).Theoretical models are proposed which compose formalized descriptions from both topdown and bottom-up perspectives.Based on the description,the UAF profile is proposed to represent the SoS mission and constituent systems(CS)goal.Moreover,the agent-based simulation information is also described based on the overview,design concepts,and details(ODD)protocol as the complement part of the SoS profile,which can be transformed into different simulation platforms based on the eXtensible markup language(XML)technology and model-to-text method.In this way,the design of the SoS is simulated automatically in the early design stage.Finally,the method is implemented and an example is given to illustrate the whole process.
基金supported by the National Natural Science Foundation of China(41561088 and 61501314)the Science&Technology Nova Program of Xinjiang Production and Construction Corps,China(2018CB020)
文摘Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.
基金the National Natural Science Foundation of China (Nos.50736006 and 51066006)the Aero-Science Fund (No.2009ZB56004)the Jiangxi Provincial Natural Science Foundation (Nos.2009GZC0100 and 2008GZW0016)
文摘In order to account for the effect of particle existence on gas-particle turbulence flow in large-eddy simulation (LES),a new gas-particle turbulent kinetic energy subgrid-scale (SGS) turbulence model is established,and the effect of particle wake is also considered in gas turbulent kinetic energy SGS turbulence model.Simulation of gas-particle turbulence flow in backward-facing step is carried out by LES using present model and by unified second-order moment (USM) model.The prediction statistical results including mean velocity and fluctuation velocity by LES using present model are in reasonable agreement with the experimental results.It is shown that present model is with higher calculating accuracy than USM model,which indicates that the turbulent kinetic energy SGS turbulence model is suitable.
基金National Science Foundation of China under Grant No.51578470。
文摘In this manuscript we present a nonlinear site amplification model for ground-motion prediction equations(GMPEs)in Japan,using a site period-based site class and a site impedance ratio as site parameters.We used a large number of shear-wave velocity profiles from the Kiban-Kyoshin network(KiK-net)and the Kyoshin network(K-NET)to construct the one-dimensional(1D)numerical models.The strong-motion records from rock-sites in Japan with different earthquake categories and taken from the Pacific Earthquake Engineering Research Center dataset were used in this study.We fit a set of 1D site amplification models using the spectral amplification ratios derived from 1D equivalent linear analyses.Parameters of site impedance ratios for both linear and nonlinear site response were included in the 1D model.The 1D model could be implemented into GMPEs using a new proposed adjustment method.The adjusted site amplification ratios retain the nonlinear characteristics of the 1D model for strong motions and match the linear amplification ratio in GMPE for weak motions.The nonlinearity of the present site model is reasonably similar to that of the historical models,and the present site model could satisfactorily capture the nonlinear site response in empirical data.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd..(Grant No.H20230317)。
文摘Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.
