The geometry of a landslide dam plays a critical role in its stability and failure mode,and is influenced by the damming process.However,there is a lack of understanding of the factors that affect the 3D geometry of a...The geometry of a landslide dam plays a critical role in its stability and failure mode,and is influenced by the damming process.However,there is a lack of understanding of the factors that affect the 3D geometry of a landslide dam.To address this gap,we conducted a study using the smoothed particle hydrodynamics numerical method to investigate the evolution of landslide dams.Our study included 17 numerical simulations to examine the effects of several factors on the geometry of landslide dams,including valley inclination,sliding angle,landslide velocity,and landslide mass repose angle.Based on this,three rapid prediction models were established for calculating the maximum height,the minimum height,and the maximum width of a landslide dam.The results show that the downstream width of a landslide dam remarkably increases with the valley inclination.The position of the maximum dam height along the valley direction is independent of external factors and is always located in the middle of the landslide width area.In contrast,that position of the maximum dam height across the valley direction is significantly influenced by the sliding angle and landslide velocity.To validate our models,we applied them to three typical landslide dams and found that the calculated values of the landslide dam geometry were in good agreement with the actual values.The findings of the current study provide a better understanding of the evolution and geometry of landslide dams,giving crucial guidance for the prediction and early warning of landslide dam disasters.展开更多
Slurry electrolysis(SE),as a hydrometallurgical process,has the characteristic of a multitank series connection,which leads to various stirring conditions and a complex solid suspension state.The computational fluid d...Slurry electrolysis(SE),as a hydrometallurgical process,has the characteristic of a multitank series connection,which leads to various stirring conditions and a complex solid suspension state.The computational fluid dynamics(CFD),which requires high computing resources,and a combination with machine learning was proposed to construct a rapid prediction model for the liquid flow and solid concentration fields in a SE tank.Through scientific selection of calculation samples via orthogonal experiments,a comprehensive dataset covering a wide range of conditions was established while effectively reducing the number of simulations and providing reasonable weights for each factor.Then,a prediction model of the SE tank was constructed using the K-nearest neighbor algorithm.The results show that with the increase in levels of orthogonal experiments,the prediction accuracy of the model improved remarkably.The model established with four factors and nine levels can accurately predict the flow and concentration fields,and the regression coefficients of average velocity and solid concentration were 0.926 and 0.937,respectively.Compared with traditional CFD,the response time of field information prediction in this model was reduced from 75 h to 20 s,which solves the problem of serious lag in CFD applied alone to actual production and meets real-time production control requirements.展开更多
Waste plastics, with their high hydrogen-to-carbon (H/C) atomic ratios, can act as hydrogen donors during coal pyrolysis, thereby enhancing tar yield and quality. Thus far, a study has been conducted on the co-pyrolys...Waste plastics, with their high hydrogen-to-carbon (H/C) atomic ratios, can act as hydrogen donors during coal pyrolysis, thereby enhancing tar yield and quality. Thus far, a study has been conducted on the co-pyrolysis characteristics of coal and waste plastic, along with a rapid prediction method for tar yield. An experimental system for the co-pyrolysis of coal and waste plastic is established to examine the distribution patterns of pyrolysis products, such as gas, tar, and char, at varying temperatures and coal-to-waste plastic ratios. The results indicate a significant synergistic effect during the co-pyrolysis of coal and plastic waste. As the blending ratio of waste plastic increases, the tar yield also increases, with the value of the synergistic effect parameter initially rising and then falling. As the blending ratio continues to increase, the formation of a liquid phase becomes more prevalent on the surface of coal particles during the pyrolysis process, which inhibits tar release and leads to a gradual decrease in the positive synergistic effect of the waste plastic on tar yield. Based on these findings, a rapid prediction model for tar yield has been developed using neural networks and optimized with a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), achieving a 10.52% reduction in the average prediction error under training conditions. The proposed model is utilized to predict the tar yield for new conditions in the database, with the relative error generally maintained within (−20%, 30%), demonstrating good accuracy and utility.展开更多
Traditional wildfire spread prediction models often struggle to simulate fire propagation accurately in complex terrain or under strong wind conditions due to their semi-empirical nature and simplified treatment of fi...Traditional wildfire spread prediction models often struggle to simulate fire propagation accurately in complex terrain or under strong wind conditions due to their semi-empirical nature and simplified treatment of fire-atmosphere interactions.This study presents a novel rapid fire spread model that integrates Briggs'buoyant plume theory,the Rothermel fire spread model,and Huygens'principle of wave propagation.The model is designed to simulate fire behavior in grasslands with complex terrain,enabling detailed representation of fire front dynamics.By analytically solving flame plume equations,the model quantifies the combustion heat production and its impact on surrounding wind fields through buoyant lifting effects.This innovative two-way coupling of atmosphere-fire interactions enhances the model's ability to simulate fire behavior under various environmental conditions.The model incorporates multiple factors,such as the wind speed,terrain,flame plume entrainment,fuel moisture content,packing ratio,and effective flame width,overcoming the limitations of traditional models in accurately capturing the shape of fire fronts.Validation through numerical simulations demonstrates that the model effectively reproduces both the temporal and spatial evolution of fire fronts across different wind and terrain conditions.In benchmark tests with a 100-meter ignition line,the model shows remarkable agreement with fully-coupled dynamic simulations,with a mere-10.38%deviation in fire spread rate while accurately replicating fire front patterns.The proposed model offers high computational efficiency and can serve as a valuable tool for wildfire risk assessment and emergency response.展开更多
Indoor airflow distribution significantly influences temperature regulation,humidity control,and pollutant dispersion,directly impacting thermal comfort and occupant health.Accurate and efficient prediction of airflow...Indoor airflow distribution significantly influences temperature regulation,humidity control,and pollutant dispersion,directly impacting thermal comfort and occupant health.Accurate and efficient prediction of airflow fields is essential for optimizing ventilation systems and enabling real-time control.However,existing computational approaches for dynamic ventilation are computationally intensive and have limited generalization capabilities.This study leverages the Fourier neural operator(FNO),a method rooted in operator learning and Fourier transform principles,to develop a three-dimensional(3D)airflow simulation model capable of predicting velocity and its components.The model was trained using 200 s of sinusoidal ventilation data(amplitude:0.4)and evaluated under diverse air supply patterns,including sinusoidal(amplitude:0.8),intermittent,and stepwise periodic ventilation.Additionally,the model’s performance was assessed with low-resolution training data and further tested for recursive prediction accuracy.Results reveal that the FNO method achieves high accuracy,with a mean square error of 9.906×10^(-5)for sinusoidal amplitude 0.8 and 4.004×10^(-5)over 400 time steps for sinusoidal,intermittent,and stepwise conditions.Further evaluations,including tests on low-resolution training data and recursive prediction,were conducted to examine the model’s resolution invariance and assess its performance in iterative forecasting.These findings demonstrate the FNO method’s potential for robust,efficient prediction of 3D unsteady airflow fields,providing a pathway for real-time ventilation system optimization.展开更多
Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities,resulting in great disaster losses.Therefore,in emergency management,we need to be timely in predicting urban floods.Alt...Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities,resulting in great disaster losses.Therefore,in emergency management,we need to be timely in predicting urban floods.Although the existing machine learning models can quickly predict the depth of stagnant water,these models only target single points and require large amounts of measured data,which are currently lacking.Although numerical models can accurately simulate and predict such events,it takes a long time to perform the associated calculations,especially two-dimensional large-scale calculations,which cannot meet the needs of emergency management.Therefore,this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas.Taking a drainage area in Tianjin Municipality,China,as an example,the results show that the simulation accuracy of this method is high,the Nash coefficient is 0.876,and the calculation time is 20 seconds.This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.展开更多
Myopia is the leading cause of visual impairment worldwide.The lack of a“rapid predictive index”for myopia development and progression hinders the clinic management and prevention of myopia.This article reviews the ...Myopia is the leading cause of visual impairment worldwide.The lack of a“rapid predictive index”for myopia development and progression hinders the clinic management and prevention of myopia.This article reviews the studies describing changes that occur in the choroid during myopia development and proposes that it is possible to detect myopia development at an earlier stage than is currently possible in a clinical setting using choroidal blood perfusion as a“rapid predictive index”of myopia.展开更多
Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy.Rapid prediction of the lignocellulose(cellulose,hemicellulose,and lignin)and organic elements(carbon,hydrog...Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy.Rapid prediction of the lignocellulose(cellulose,hemicellulose,and lignin)and organic elements(carbon,hydrogen,nitrogen,and sulfur)of rice straw would help to decipher its growth mechanisms and thereby improve its sustainable usages.In this study,364 rice straw samples featuring different rice subspecies(japonica and indica),growing seasons(early-,middle-,and late-season),and growing environments(irrigated and rainfed)were collected,the differences among which were examined by multivariate analysis of variance.Statistic results showed that the cellulose content exhibited significant differences among different growing seasons at a significant level(p<0.01),and the contents of cellulose and nitrogen had significant differences between different growing environments(p<0.01).Near infrared reflectance spectroscopy(NIRS)models for predicting the lignocellulosic and organic elements were developed based on two algorithms including partial least squares(PLS)and competitive adaptive reweighted sampling-partial least squares(CARS-PLS).Modeling results showed that most CARS-PLS models are of higher accuracy than the PLS models,possibly because the CARS-PLS models selected optimal combinations of wavenumbers,which might have enhanced the signal of chemical bonds and thereby improved the predictive efficiency.As a major contributor to the applications of rice straw,the nitrogen content was predicted precisely by the CARS-PLS model.Generally,the CARS-PLS models efficiently quantified the lignocellulose and organic elements of a wide variety of rice straw.The acceptable accuracy of the models allowed their practical applications.展开更多
Accurate and rapid determination of nitrite contents is an important step for guaranteeing sausage quality.This study attempted to mine hyperspectral data in the range of 900-1700 nm for non-destructive and rapid pred...Accurate and rapid determination of nitrite contents is an important step for guaranteeing sausage quality.This study attempted to mine hyperspectral data in the range of 900-1700 nm for non-destructive and rapid prediction of nitrite contents in sausages.The average spectra of 156 samples were collected to relate to the measured nitrite values by partial least squares(PLS)regression.Optimal wavelengths were respectively selected by successive projections algorithm(SPA)and regression coefficients(RC)to simplify the PLS model.The results indicated that PLS model established with 15 optimal wavelengths(900.5 nm,907.1 nm,908.8 nm,912.1 nm,915.4 nm,920.3 nm,922.0 nm,941.7 nm,979.6 nm,1083.2 nm,1213.2 nm,1353.0 nm,1460.2 nm,1595.6 nm and 1699.9 nm)selected by SPA had better performance with r C,r CV,r P of 0.92,0.89,0.89 and RMSEC,RMSECV,RMSEP of 0.41 mg/kg,0.89 mg/kg,0.49 mg/kg,respectively,for calibration set,cross-validation and prediction set.It was concluded that hyperspectral data could be mined by PLS&SPA for realizing the rapid evaluation of nitrite content in ham sausages.展开更多
Focusing on the rapid prediction of acoustic field uncertainty in environment with temporal and spatial sound speed perturbation, evolvement of sound speed structure over time is predicted based on the ocean-acoustic ...Focusing on the rapid prediction of acoustic field uncertainty in environment with temporal and spatial sound speed perturbation, evolvement of sound speed structure over time is predicted based on the ocean-acoustic coupled model to obtain the uncertainty distribution of the vertical structure of sound speed. Further, a method combining the arbitrary polynomial chaos expansion with the empirical orthogonal function is proposed to reduce the dimensionality of uncertain parameters and to obtain the uncertainty distribution of the acoustic field. Simulations have shown that the computational complexity can be reduced by 2 orders of magnitude compared to the conventional polynomial chaos expansion while ensures the same precision.Moreover, the computational complexity is not influenced by the complexity of the sound speed profile. The acoustic field and uncertainty predicted in uncertain environment by proposed method also have been tested with the experimental data.展开更多
Myopia is the leading cause of visual impairment worldwide.The lack of a"rapid predictive index"for myopia development and progression hinders the clinic management and prevention of myopia.This article revi...Myopia is the leading cause of visual impairment worldwide.The lack of a"rapid predictive index"for myopia development and progression hinders the clinic management and prevention of myopia.This article reviews the studies describing changes that occur in the choroid during myopia development and proposes that it is possible to detect myopia development at an earlier stage than is currently possible in a clinical setting using choroidal blood perfusion as a"rapid predictive index"of myopia.展开更多
Deep learning models demonstrate impressive performance in rapidly predicting urban floods,but there are still limitations in enhancing physical connectivity and interpretability.This study proposed an innovative mode...Deep learning models demonstrate impressive performance in rapidly predicting urban floods,but there are still limitations in enhancing physical connectivity and interpretability.This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton(CNN-WCA)to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results.The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP,and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA.Subsequently,the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City.The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP,with the mean absolute error concentrated within 5 mm,and the prediction time of CNN-WCA was only 0.8%that of LISFLOOD-FP.The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding,with the Nash-Sutcliffe e fficiency values of most flood-prone points exceeding 0.97.Furthermore,the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN.The CNN-WCA model with additional physical constraints exhibits a reduction of around 34%in instances of physical discontinuity compared to CNN.Our results prove that the CNN model with multiple physical constraints has signifi cant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.展开更多
In response to the growing necessity for rapid simulation techniques for long-term building energy forecasting,this study establishes a‘reference day’method.This method significantly alleviates computational load in...In response to the growing necessity for rapid simulation techniques for long-term building energy forecasting,this study establishes a‘reference day’method.This method significantly alleviates computational load in intricate simulation tasks by minimizing the redundancy of meteorological data.By employing a selected number of reference days to represent the meteorological profile over an extended period,we can estimate the total long-term energy consumption of buildings.The Finkelstein–Schafer statistic is utilized to identify these reference days.To evaluate the effectiveness of this proposed methodology,we analyzed sixteen prototype buildings—comprising two residential and fourteen commercial structures—and thirty years of meteorological data from Denver,USA.The findings indicate that the reference day approach effectively identifies days with representative weather conditions,enabling accurate energy consumption predictions while considerably reducing computational demands.Our case study suggests that selecting nine reference days strikes a good balance between predictive accuracy and computational efficiency over a long time span,even a 25-year period.In such a period,the margin of average error for predicting electricity and gas consumption was remarkably low,at−0.7%and−3.0%,respectively.It is important to note that the building’s operational schedule can significantly influence energy consumption.Hence,different sets of reference days should be designated for varied building operation categories.In summary,considering the high computational costs and lengthy durations of work associated with standard building simulations,our proposed reference day method could play a pivotal role in rapid energy consumption assessments.The efficacy and applicability of this method warrant further investigation.展开更多
基金funding from the National Natural Science Foundation of China(42207228,51879036,51579032)the Liaoning Revitalization Talents Program(XLYC2002036)the Sichuan Science and Technology Program(2022NSFSC1060)。
文摘The geometry of a landslide dam plays a critical role in its stability and failure mode,and is influenced by the damming process.However,there is a lack of understanding of the factors that affect the 3D geometry of a landslide dam.To address this gap,we conducted a study using the smoothed particle hydrodynamics numerical method to investigate the evolution of landslide dams.Our study included 17 numerical simulations to examine the effects of several factors on the geometry of landslide dams,including valley inclination,sliding angle,landslide velocity,and landslide mass repose angle.Based on this,three rapid prediction models were established for calculating the maximum height,the minimum height,and the maximum width of a landslide dam.The results show that the downstream width of a landslide dam remarkably increases with the valley inclination.The position of the maximum dam height along the valley direction is independent of external factors and is always located in the middle of the landslide width area.In contrast,that position of the maximum dam height across the valley direction is significantly influenced by the sliding angle and landslide velocity.To validate our models,we applied them to three typical landslide dams and found that the calculated values of the landslide dam geometry were in good agreement with the actual values.The findings of the current study provide a better understanding of the evolution and geometry of landslide dams,giving crucial guidance for the prediction and early warning of landslide dam disasters.
基金financially supported by the National Natural Science Foundation of China(No.51974018the Open Foundation of the State Key Laboratory of Process Automation in Mining and Metallurgy(No.BGRIMM-KZSKL-2022-9).
文摘Slurry electrolysis(SE),as a hydrometallurgical process,has the characteristic of a multitank series connection,which leads to various stirring conditions and a complex solid suspension state.The computational fluid dynamics(CFD),which requires high computing resources,and a combination with machine learning was proposed to construct a rapid prediction model for the liquid flow and solid concentration fields in a SE tank.Through scientific selection of calculation samples via orthogonal experiments,a comprehensive dataset covering a wide range of conditions was established while effectively reducing the number of simulations and providing reasonable weights for each factor.Then,a prediction model of the SE tank was constructed using the K-nearest neighbor algorithm.The results show that with the increase in levels of orthogonal experiments,the prediction accuracy of the model improved remarkably.The model established with four factors and nine levels can accurately predict the flow and concentration fields,and the regression coefficients of average velocity and solid concentration were 0.926 and 0.937,respectively.Compared with traditional CFD,the response time of field information prediction in this model was reduced from 75 h to 20 s,which solves the problem of serious lag in CFD applied alone to actual production and meets real-time production control requirements.
基金supported by National Natural Science Foundation of China(Grant No.52106133).
文摘Waste plastics, with their high hydrogen-to-carbon (H/C) atomic ratios, can act as hydrogen donors during coal pyrolysis, thereby enhancing tar yield and quality. Thus far, a study has been conducted on the co-pyrolysis characteristics of coal and waste plastic, along with a rapid prediction method for tar yield. An experimental system for the co-pyrolysis of coal and waste plastic is established to examine the distribution patterns of pyrolysis products, such as gas, tar, and char, at varying temperatures and coal-to-waste plastic ratios. The results indicate a significant synergistic effect during the co-pyrolysis of coal and plastic waste. As the blending ratio of waste plastic increases, the tar yield also increases, with the value of the synergistic effect parameter initially rising and then falling. As the blending ratio continues to increase, the formation of a liquid phase becomes more prevalent on the surface of coal particles during the pyrolysis process, which inhibits tar release and leads to a gradual decrease in the positive synergistic effect of the waste plastic on tar yield. Based on these findings, a rapid prediction model for tar yield has been developed using neural networks and optimized with a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), achieving a 10.52% reduction in the average prediction error under training conditions. The proposed model is utilized to predict the tar yield for new conditions in the database, with the relative error generally maintained within (−20%, 30%), demonstrating good accuracy and utility.
基金supported by the Innovation and Development Project of China Meteorological Administration(Grant No.CXFZ2024Q010)the National Natural Science Foundation of China(Grant Nos.42205075 and 42330608)+2 种基金the Beijing Natural Science Foundation(Grant No.8252025)supported by the youth innovation team of China Meteorological Administration(Grant No.CMA2024QN12)supported by the Key Innovation Team of China Meteorological Administration Innovation Team(Grant No.CMA2022ZD09)。
文摘Traditional wildfire spread prediction models often struggle to simulate fire propagation accurately in complex terrain or under strong wind conditions due to their semi-empirical nature and simplified treatment of fire-atmosphere interactions.This study presents a novel rapid fire spread model that integrates Briggs'buoyant plume theory,the Rothermel fire spread model,and Huygens'principle of wave propagation.The model is designed to simulate fire behavior in grasslands with complex terrain,enabling detailed representation of fire front dynamics.By analytically solving flame plume equations,the model quantifies the combustion heat production and its impact on surrounding wind fields through buoyant lifting effects.This innovative two-way coupling of atmosphere-fire interactions enhances the model's ability to simulate fire behavior under various environmental conditions.The model incorporates multiple factors,such as the wind speed,terrain,flame plume entrainment,fuel moisture content,packing ratio,and effective flame width,overcoming the limitations of traditional models in accurately capturing the shape of fire fronts.Validation through numerical simulations demonstrates that the model effectively reproduces both the temporal and spatial evolution of fire fronts across different wind and terrain conditions.In benchmark tests with a 100-meter ignition line,the model shows remarkable agreement with fully-coupled dynamic simulations,with a mere-10.38%deviation in fire spread rate while accurately replicating fire front patterns.The proposed model offers high computational efficiency and can serve as a valuable tool for wildfire risk assessment and emergency response.
基金supported by the National Natural Science Foundation of China[grant number 52078009]the Joint Research Project of the Wind Engineering Research Center,Tokyo Polytechnic University(MEXT(Japan)Promotion of the Distinctive Joint Research Center Program)[Joint Research Assignment number JURC 24242008].
文摘Indoor airflow distribution significantly influences temperature regulation,humidity control,and pollutant dispersion,directly impacting thermal comfort and occupant health.Accurate and efficient prediction of airflow fields is essential for optimizing ventilation systems and enabling real-time control.However,existing computational approaches for dynamic ventilation are computationally intensive and have limited generalization capabilities.This study leverages the Fourier neural operator(FNO),a method rooted in operator learning and Fourier transform principles,to develop a three-dimensional(3D)airflow simulation model capable of predicting velocity and its components.The model was trained using 200 s of sinusoidal ventilation data(amplitude:0.4)and evaluated under diverse air supply patterns,including sinusoidal(amplitude:0.8),intermittent,and stepwise periodic ventilation.Additionally,the model’s performance was assessed with low-resolution training data and further tested for recursive prediction accuracy.Results reveal that the FNO method achieves high accuracy,with a mean square error of 9.906×10^(-5)for sinusoidal amplitude 0.8 and 4.004×10^(-5)over 400 time steps for sinusoidal,intermittent,and stepwise conditions.Further evaluations,including tests on low-resolution training data and recursive prediction,were conducted to examine the model’s resolution invariance and assess its performance in iterative forecasting.These findings demonstrate the FNO method’s potential for robust,efficient prediction of 3D unsteady airflow fields,providing a pathway for real-time ventilation system optimization.
基金the Water Pollution Control and Treatment of Major National Science and Technology Project of China(2017ZX07106001)the National Natural Science Foundation of China(51509179)the Tianjin Natural Science Foundation(20JCQNJC01540).
文摘Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities,resulting in great disaster losses.Therefore,in emergency management,we need to be timely in predicting urban floods.Although the existing machine learning models can quickly predict the depth of stagnant water,these models only target single points and require large amounts of measured data,which are currently lacking.Although numerical models can accurately simulate and predict such events,it takes a long time to perform the associated calculations,especially two-dimensional large-scale calculations,which cannot meet the needs of emergency management.Therefore,this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas.Taking a drainage area in Tianjin Municipality,China,as an example,the results show that the simulation accuracy of this method is high,the Nash coefficient is 0.876,and the calculation time is 20 seconds.This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.
文摘Myopia is the leading cause of visual impairment worldwide.The lack of a“rapid predictive index”for myopia development and progression hinders the clinic management and prevention of myopia.This article reviews the studies describing changes that occur in the choroid during myopia development and proposes that it is possible to detect myopia development at an earlier stage than is currently possible in a clinical setting using choroidal blood perfusion as a“rapid predictive index”of myopia.
基金We would like to acknowledge the support given by the Innovation Team Project of the Ministry of Education(IRT_17R105)the China Agriculture Research System(CARS-36)Program for Changjiang Scholars.
文摘Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy.Rapid prediction of the lignocellulose(cellulose,hemicellulose,and lignin)and organic elements(carbon,hydrogen,nitrogen,and sulfur)of rice straw would help to decipher its growth mechanisms and thereby improve its sustainable usages.In this study,364 rice straw samples featuring different rice subspecies(japonica and indica),growing seasons(early-,middle-,and late-season),and growing environments(irrigated and rainfed)were collected,the differences among which were examined by multivariate analysis of variance.Statistic results showed that the cellulose content exhibited significant differences among different growing seasons at a significant level(p<0.01),and the contents of cellulose and nitrogen had significant differences between different growing environments(p<0.01).Near infrared reflectance spectroscopy(NIRS)models for predicting the lignocellulosic and organic elements were developed based on two algorithms including partial least squares(PLS)and competitive adaptive reweighted sampling-partial least squares(CARS-PLS).Modeling results showed that most CARS-PLS models are of higher accuracy than the PLS models,possibly because the CARS-PLS models selected optimal combinations of wavenumbers,which might have enhanced the signal of chemical bonds and thereby improved the predictive efficiency.As a major contributor to the applications of rice straw,the nitrogen content was predicted precisely by the CARS-PLS model.Generally,the CARS-PLS models efficiently quantified the lignocellulose and organic elements of a wide variety of rice straw.The acceptable accuracy of the models allowed their practical applications.
基金The authors acknowledge that this work was financially supported by the Key Scientific and Technological Project of Henan Province(Grant No.212102310491,No.182102310060)Major Scientific and Technological Project of Henan Province(No.161100110600)+2 种基金China Postdoctoral Science Foundation(No.2018M632767)Henan Postdoctoral Science Foundation(No.001801021)Youth Talents Lifting Project of Henan Province(No.2018HYTP008).
文摘Accurate and rapid determination of nitrite contents is an important step for guaranteeing sausage quality.This study attempted to mine hyperspectral data in the range of 900-1700 nm for non-destructive and rapid prediction of nitrite contents in sausages.The average spectra of 156 samples were collected to relate to the measured nitrite values by partial least squares(PLS)regression.Optimal wavelengths were respectively selected by successive projections algorithm(SPA)and regression coefficients(RC)to simplify the PLS model.The results indicated that PLS model established with 15 optimal wavelengths(900.5 nm,907.1 nm,908.8 nm,912.1 nm,915.4 nm,920.3 nm,922.0 nm,941.7 nm,979.6 nm,1083.2 nm,1213.2 nm,1353.0 nm,1460.2 nm,1595.6 nm and 1699.9 nm)selected by SPA had better performance with r C,r CV,r P of 0.92,0.89,0.89 and RMSEC,RMSECV,RMSEP of 0.41 mg/kg,0.89 mg/kg,0.49 mg/kg,respectively,for calibration set,cross-validation and prediction set.It was concluded that hyperspectral data could be mined by PLS&SPA for realizing the rapid evaluation of nitrite content in ham sausages.
基金supported by the National 530 Special 2015 First Batch of Research and Service Support Projectsthe National Defense Scientific and Technological Innovation Special Zone Project(17-H863-05-ZT-001-024-01)
文摘Focusing on the rapid prediction of acoustic field uncertainty in environment with temporal and spatial sound speed perturbation, evolvement of sound speed structure over time is predicted based on the ocean-acoustic coupled model to obtain the uncertainty distribution of the vertical structure of sound speed. Further, a method combining the arbitrary polynomial chaos expansion with the empirical orthogonal function is proposed to reduce the dimensionality of uncertain parameters and to obtain the uncertainty distribution of the acoustic field. Simulations have shown that the computational complexity can be reduced by 2 orders of magnitude compared to the conventional polynomial chaos expansion while ensures the same precision.Moreover, the computational complexity is not influenced by the complexity of the sound speed profile. The acoustic field and uncertainty predicted in uncertain environment by proposed method also have been tested with the experimental data.
文摘Myopia is the leading cause of visual impairment worldwide.The lack of a"rapid predictive index"for myopia development and progression hinders the clinic management and prevention of myopia.This article reviews the studies describing changes that occur in the choroid during myopia development and proposes that it is possible to detect myopia development at an earlier stage than is currently possible in a clinical setting using choroidal blood perfusion as a"rapid predictive index"of myopia.
基金supported by the General Program of National Natural Science Foundation of China(Grant No.42377467)。
文摘Deep learning models demonstrate impressive performance in rapidly predicting urban floods,but there are still limitations in enhancing physical connectivity and interpretability.This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton(CNN-WCA)to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results.The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP,and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA.Subsequently,the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City.The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP,with the mean absolute error concentrated within 5 mm,and the prediction time of CNN-WCA was only 0.8%that of LISFLOOD-FP.The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding,with the Nash-Sutcliffe e fficiency values of most flood-prone points exceeding 0.97.Furthermore,the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN.The CNN-WCA model with additional physical constraints exhibits a reduction of around 34%in instances of physical discontinuity compared to CNN.Our results prove that the CNN model with multiple physical constraints has signifi cant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.
基金funded by the National Natural Science Foundation of China under Grant No.52308016Opening Funds of State Key Laboratory of Building Safety and Built Environment&National Engineering Research Center of Building Technology(Grant No.BSBE2023-11)+1 种基金the Guangdong Basic and Applied Basic Research Foundation under Grant No.2023A1515011364the Science and Technology Program of Guangzhou University under Grant No.PT252022006.
文摘In response to the growing necessity for rapid simulation techniques for long-term building energy forecasting,this study establishes a‘reference day’method.This method significantly alleviates computational load in intricate simulation tasks by minimizing the redundancy of meteorological data.By employing a selected number of reference days to represent the meteorological profile over an extended period,we can estimate the total long-term energy consumption of buildings.The Finkelstein–Schafer statistic is utilized to identify these reference days.To evaluate the effectiveness of this proposed methodology,we analyzed sixteen prototype buildings—comprising two residential and fourteen commercial structures—and thirty years of meteorological data from Denver,USA.The findings indicate that the reference day approach effectively identifies days with representative weather conditions,enabling accurate energy consumption predictions while considerably reducing computational demands.Our case study suggests that selecting nine reference days strikes a good balance between predictive accuracy and computational efficiency over a long time span,even a 25-year period.In such a period,the margin of average error for predicting electricity and gas consumption was remarkably low,at−0.7%and−3.0%,respectively.It is important to note that the building’s operational schedule can significantly influence energy consumption.Hence,different sets of reference days should be designated for varied building operation categories.In summary,considering the high computational costs and lengthy durations of work associated with standard building simulations,our proposed reference day method could play a pivotal role in rapid energy consumption assessments.The efficacy and applicability of this method warrant further investigation.
