Martensite is an important microstructure in ultrahigh-strength steels,and enhancing the strength of martensitic steels often involves the introduction of precipitated phases within the martensitic matrix.Despite cons...Martensite is an important microstructure in ultrahigh-strength steels,and enhancing the strength of martensitic steels often involves the introduction of precipitated phases within the martensitic matrix.Despite considerable research efforts devoted to this area,a systematic summary of these advancements is lacking.This review focuses on the precipitates prevalent in ultrahigh-strength martensitic steel,primarily carbides(e.g.,MC,M_(2)C,and M_(3)C)and intermetallic compounds(e.g.,Ni Al,Ni_(3)X,and Fe_(2)Mo).The precipitation-strengthening effect of these precipitates on ultrahigh-strength martensitic steel is discussed from the aspects of heat treatment processes,microstructure of precipitate-strengthened martensite matrix,and mechanical performance.Finally,a perspective on the development of precipitation-strengthened martensitic steel is presented to contribute to the advancement of ultrahigh-strength martensitic steel.This review highlights significant findings,ongoing challenges,and opportunities in the development of ultrahigh-strength martensitic steel.展开更多
Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the int...Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.展开更多
Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the nume...Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.展开更多
Extreme weather events,such as floods and droughts,are expected to rise significantly worldwide as a result of climate change.Investigating future drought patterns is therefore a key approach for elaborating anticipat...Extreme weather events,such as floods and droughts,are expected to rise significantly worldwide as a result of climate change.Investigating future drought patterns is therefore a key approach for elaborating anticipatory water resources management responses to climate change.In this paper,future meteorological drought conditions are investigated based on the SPEI(Standardised Precipitation Evapotranspiration Index).This study makes use of observed and projected data.The simulated data were retrieved from the CMIP6(Coupled Model Intercomparison Project Phase 6)over the period 2025-2050,and the Delta change method was adopted to remove the bias in the dataset.Then SPEI at various scales has been estimated under four future scenarios(SSP1-2.6,SSP2-4.5,SSP3-7.0 and SSP5-8.5).The trend analysis of the projected SPEI was performed at p<0.05 using the MMK(Modified Mann-Kendall)test in order to detect the statistically significant trend of the drought against the null hypothesis of no trend.Results show large variability in the magnitude of drought in the past and future.Based on SPEI at 24 months accumulation,the result shows that under SSP1-2.6,the basin will experience a wet period during the first decade(SPEI=0.60),the second decade will be dry(SPEI24=-0.43).The remaining years will be also dry(SPEI=-0.34).Under SSP2-4.5,SSP3-7.0 and SSP5-8.5 scenarios,the district will experience a wet period during the first two decades with SPEI ranging from 0.38 to 0.59.This wet period will be followed by a dry period under these scenarios ranging from-0.14 to-0.06.Overall,under SSPs scenarios,two main periods characterized by a rainfall recovery spanning from followed by a moderately prolonged drought are identified within the study area.The findings of this study may provide valuable information for developing proactive measures to reduce water insecurity in Fada N’Gourma through effective drought mitigation.展开更多
Enzyme-induced carbonate precipitation(EICP)is an emanating,eco-friendly and potentially sound technique that has presented promise in various geotechnical applications.However,the durability and microscopic character...Enzyme-induced carbonate precipitation(EICP)is an emanating,eco-friendly and potentially sound technique that has presented promise in various geotechnical applications.However,the durability and microscopic characteristics of EICP-treated specimens against the impact of drying-wetting(D-W)cycles is under-explored yet.This study investigates the evolution of mechanical behavior and pore charac-teristics of EICP-treated sea sand subjected to D-W cycles.The uniaxial compressive strength(UCS)tests,synchrotron radiation micro-computed tomography(micro-CT),and three-dimensional(3D)recon-struction of CT images were performed to study the multiscale evolution characteristics of EICP-reinforced sea sand under the effect of D-W cycles.The potential correlations between microstructure characteristics and macro-mechanical property deterioration were investigated using gray relational analysis(GRA).Results showed that the UCS of EICP-treated specimens decreases by 63.7% after 15 D-W cycles.The proportion of mesopores gradually decreases whereas the proportion of macropores in-creases due to the exfoliated calcium carbonate with increasing number of D-W cycles.The micro-structure in EICP-reinforced sea sand was gradually disintegrated,resulting in increasing pore size and development of pore shape from ellipsoidal to columnar and branched.The gray relational degree suggested that the weight loss rate and UCS deterioration were attributed to the development of branched pores with a size of 100-1000 m m under the action of D-W cycles.Overall,the results in this study provide a useful guidancee for the long-term stability and evolution characteristics of EICP-reinforced sea sand under D-W weathering conditions.展开更多
Whether climate change or anthropogenic activities play a more pivotal role in regulating vegetation growth on the Tibetan Plateau is still controversial.A better understanding on grassland changes at a fine scale may...Whether climate change or anthropogenic activities play a more pivotal role in regulating vegetation growth on the Tibetan Plateau is still controversial.A better understanding on grassland changes at a fine scale may provide important guidance for local government policy and grassland management.Using two of the most reliable satellite NDVI products(MODIS NDVI and SPOT NDVI),we evaluated the dynamic of grasslands in the Zhegucuo valley on the southern Tibetan Plateau from 2000 to 2020,and analyzed its driving factors and relative influences of climate change and anthropogenic activities.Here,the key indicators of climate change were assumed to be precipitation and temperature.The main results were:(1)the grassland NDVI in Zhegucuo valley did not reflect a significant temporal change during the last 21 years.The variation of precipitation during the early growing season(GSP)resembled that of NDVI,and the GSP was positively correlated with NDVI.At the pixel level,the partial correlation analysis showed that 37.79%of the pixels depicted a positive relationship between GSP and NDVI,while 11.32%of the pixels showed a negative relationship between temperature during the early growing season(GST)and NDVI.(2)In view of the spatial distribution,the areas mainly controlled by GSP were generally distributed in the southern part,while those affected by GST stood in the eastern part,mainly around the Zhegucuo lake where most population in Cuomei County settled down.(3)Decreasing NDVI trends were mainly occurred in alpine steppe at lower elevations rather than alpine meadow at higher elevations.(4)The residual trend(RESTREND)analysis further indicated that the anthropogenic activities played a more pivotal role in regulating the annual changes of NDVI rather than climate factors in this area.Future studies should pay more attention on climate extremes rather than the simple temporal trends.Also,the influence of human activities on alpine grassland needs to be accessed and fully considered in future sustainable management.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52122408 and 52071023)financial support from the Fundamental Research Funds for the Central Universities(University of Science and Technology Beijing,No.FRF-TP-2021-04C1,and 06500135)。
文摘Martensite is an important microstructure in ultrahigh-strength steels,and enhancing the strength of martensitic steels often involves the introduction of precipitated phases within the martensitic matrix.Despite considerable research efforts devoted to this area,a systematic summary of these advancements is lacking.This review focuses on the precipitates prevalent in ultrahigh-strength martensitic steel,primarily carbides(e.g.,MC,M_(2)C,and M_(3)C)and intermetallic compounds(e.g.,Ni Al,Ni_(3)X,and Fe_(2)Mo).The precipitation-strengthening effect of these precipitates on ultrahigh-strength martensitic steel is discussed from the aspects of heat treatment processes,microstructure of precipitate-strengthened martensite matrix,and mechanical performance.Finally,a perspective on the development of precipitation-strengthened martensitic steel is presented to contribute to the advancement of ultrahigh-strength martensitic steel.This review highlights significant findings,ongoing challenges,and opportunities in the development of ultrahigh-strength martensitic steel.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the National Natural Science Foundation of China(Grant No.42030605)+1 种基金CAAI-MindSpore Academic Fund Research Projects(CAAIXSJLJJ2023MindSpore11)the program of China Scholarships Council(No.CXXM2101180001)。
文摘Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.
基金jointly supported by the National Natural Science Foundation of China(Grant Nos.42122034,42075043,42330609)the Second Tibetan Plateau Scientific Expedition and Research program(2019QZKK0103)+2 种基金Key Talent Project in Gansu and Central Guidance Fund for Local Science and Technology Development Projects in Gansu(No.24ZYQA031)the Youth Innovation Promotion Association of Chinese Academy of Sciences(2021427)West Light Foundation of the Chinese Academy of Sciences(xbzg-zdsys-202215)。
文摘Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.
文摘Extreme weather events,such as floods and droughts,are expected to rise significantly worldwide as a result of climate change.Investigating future drought patterns is therefore a key approach for elaborating anticipatory water resources management responses to climate change.In this paper,future meteorological drought conditions are investigated based on the SPEI(Standardised Precipitation Evapotranspiration Index).This study makes use of observed and projected data.The simulated data were retrieved from the CMIP6(Coupled Model Intercomparison Project Phase 6)over the period 2025-2050,and the Delta change method was adopted to remove the bias in the dataset.Then SPEI at various scales has been estimated under four future scenarios(SSP1-2.6,SSP2-4.5,SSP3-7.0 and SSP5-8.5).The trend analysis of the projected SPEI was performed at p<0.05 using the MMK(Modified Mann-Kendall)test in order to detect the statistically significant trend of the drought against the null hypothesis of no trend.Results show large variability in the magnitude of drought in the past and future.Based on SPEI at 24 months accumulation,the result shows that under SSP1-2.6,the basin will experience a wet period during the first decade(SPEI=0.60),the second decade will be dry(SPEI24=-0.43).The remaining years will be also dry(SPEI=-0.34).Under SSP2-4.5,SSP3-7.0 and SSP5-8.5 scenarios,the district will experience a wet period during the first two decades with SPEI ranging from 0.38 to 0.59.This wet period will be followed by a dry period under these scenarios ranging from-0.14 to-0.06.Overall,under SSPs scenarios,two main periods characterized by a rainfall recovery spanning from followed by a moderately prolonged drought are identified within the study area.The findings of this study may provide valuable information for developing proactive measures to reduce water insecurity in Fada N’Gourma through effective drought mitigation.
基金The authors gratefully acknowledge the financial support of National NaturalScience Foundation of China(Grant No.41972276)Natural Science Foundation of Fujian Province,China(Grant No.2020J06013)"Foal Eagle Program"Youth Top-notch Talent Project of Fujian Province,China(Grant No.00387088).
文摘Enzyme-induced carbonate precipitation(EICP)is an emanating,eco-friendly and potentially sound technique that has presented promise in various geotechnical applications.However,the durability and microscopic characteristics of EICP-treated specimens against the impact of drying-wetting(D-W)cycles is under-explored yet.This study investigates the evolution of mechanical behavior and pore charac-teristics of EICP-treated sea sand subjected to D-W cycles.The uniaxial compressive strength(UCS)tests,synchrotron radiation micro-computed tomography(micro-CT),and three-dimensional(3D)recon-struction of CT images were performed to study the multiscale evolution characteristics of EICP-reinforced sea sand under the effect of D-W cycles.The potential correlations between microstructure characteristics and macro-mechanical property deterioration were investigated using gray relational analysis(GRA).Results showed that the UCS of EICP-treated specimens decreases by 63.7% after 15 D-W cycles.The proportion of mesopores gradually decreases whereas the proportion of macropores in-creases due to the exfoliated calcium carbonate with increasing number of D-W cycles.The micro-structure in EICP-reinforced sea sand was gradually disintegrated,resulting in increasing pore size and development of pore shape from ellipsoidal to columnar and branched.The gray relational degree suggested that the weight loss rate and UCS deterioration were attributed to the development of branched pores with a size of 100-1000 m m under the action of D-W cycles.Overall,the results in this study provide a useful guidancee for the long-term stability and evolution characteristics of EICP-reinforced sea sand under D-W weathering conditions.
基金funded by the Second Tibetan Plateau Scientific Expedition and Research program(2019QZKK0301)the Natural Science Foundation of Xizang Autonomous Region(XZ202301ZR0027G).
文摘Whether climate change or anthropogenic activities play a more pivotal role in regulating vegetation growth on the Tibetan Plateau is still controversial.A better understanding on grassland changes at a fine scale may provide important guidance for local government policy and grassland management.Using two of the most reliable satellite NDVI products(MODIS NDVI and SPOT NDVI),we evaluated the dynamic of grasslands in the Zhegucuo valley on the southern Tibetan Plateau from 2000 to 2020,and analyzed its driving factors and relative influences of climate change and anthropogenic activities.Here,the key indicators of climate change were assumed to be precipitation and temperature.The main results were:(1)the grassland NDVI in Zhegucuo valley did not reflect a significant temporal change during the last 21 years.The variation of precipitation during the early growing season(GSP)resembled that of NDVI,and the GSP was positively correlated with NDVI.At the pixel level,the partial correlation analysis showed that 37.79%of the pixels depicted a positive relationship between GSP and NDVI,while 11.32%of the pixels showed a negative relationship between temperature during the early growing season(GST)and NDVI.(2)In view of the spatial distribution,the areas mainly controlled by GSP were generally distributed in the southern part,while those affected by GST stood in the eastern part,mainly around the Zhegucuo lake where most population in Cuomei County settled down.(3)Decreasing NDVI trends were mainly occurred in alpine steppe at lower elevations rather than alpine meadow at higher elevations.(4)The residual trend(RESTREND)analysis further indicated that the anthropogenic activities played a more pivotal role in regulating the annual changes of NDVI rather than climate factors in this area.Future studies should pay more attention on climate extremes rather than the simple temporal trends.Also,the influence of human activities on alpine grassland needs to be accessed and fully considered in future sustainable management.
