A study of excavated material on the Gagnoa-Guéyo-Bamenadou road project in southwest Côte d’Ivoire was carried out using electrical resistivity tomography with a dipole-dipole configuration. This study aim...A study of excavated material on the Gagnoa-Guéyo-Bamenadou road project in southwest Côte d’Ivoire was carried out using electrical resistivity tomography with a dipole-dipole configuration. This study aimed to determine the nature and volume of the studied cuts. Based on the cumulative distances of the longitudinal sections of the road alignment superimposed on the tomographic profiles, a cumulative volume of 104681 m3 of material was determined. This volume comprises 88557 m3 of soft cuts and 16,124 m3 of rocky cuts, which can be reused in specific embankment zones. This work may, therefore be useful in the characterization of cuts in a preliminary design study, in order to anticipate changes during the road’s development and asphalting.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
The water content of cut flowers is a significant factor in their post-harvest quality.In this study,we examine the efficacy of silver nanoparticles(NS)on the longevity of cut gladiolus,with a focus on water state and...The water content of cut flowers is a significant factor in their post-harvest quality.In this study,we examine the efficacy of silver nanoparticles(NS)on the longevity of cut gladiolus,with a focus on water state and distribution.We used Low-field nuclear magnetic resonance(LF-NMR)technology to identify three water fractions with different transverse relaxation times(T2)values:bound water T21(<10 ms),intermediate immobilized water T22(10-100 ms),and the slowest component free water T23(>10 ms).During the opening process,T23increased at stages 2 and 3 and then decreased,T22 decreased slowly,and T21 remained unchanged.Free water values were consistently higher than bound water and immobilized water and reached their maximum from stage 2 until stage 4,when the petals were extended and began to wilt.The vascular bundles responsible for transporting water had higher water content,as detected by proton density-weighted magnetic resonance imaging(MRI).Bound water and free water with NS pretreatments in bracts were initially lower but then two days later the signal amplitude of each water state exceeded those of the control,indicating that the treatment enhanced the water-holding capacity over time.Furthermore,NS pretreatments reduced the free water mobility of the cut flowers and inhibited stem decay.Additionally,we found that NS can enter the stem and are primarily transported upward along the xylem with water using scanning electron microscopy(SEM)and energy-dispersive X-ray spectroscopy(EDS)technology.Overall,our findings indicate that NS pretreatment reduces free water in gladiolus cut flowers,enhancing their water retention and prolonging their vase life.展开更多
Traditional meteorological downscaling methods face limitations due to the complex distribution of meteorological variables,which can lead to unstable forecasting results,especially in extreme scenarios.To overcome th...Traditional meteorological downscaling methods face limitations due to the complex distribution of meteorological variables,which can lead to unstable forecasting results,especially in extreme scenarios.To overcome this issue,we propose a convolutional graph neural network(CGNN)model,which we enhance with multilayer feature fusion and a squeeze-and-excitation block.Additionally,we introduce a spatially balanced mean squared error(SBMSE)loss function to address the imbalanced distribution and spatial variability of meteorological variables.The CGNN is capable of extracting essential spatial features and aggregating them from a global perspective,thereby improving the accuracy of prediction and enhancing the model's generalization ability.Based on the experimental results,CGNN has certain advantages in terms of bias distribution,exhibiting a smaller variance.When it comes to precipitation,both UNet and AE also demonstrate relatively small biases.As for temperature,AE and CNNdense perform outstandingly during the winter.The time correlation coefficients show an improvement of at least 10%at daily and monthly scales for both temperature and precipitation.Furthermore,the SBMSE loss function displays an advantage over existing loss functions in predicting the98th percentile and identifying areas where extreme events occur.However,the SBMSE tends to overestimate the distribution of extreme precipitation,which may be due to the theoretical assumptions about the posterior distribution of data that partially limit the effectiveness of the loss function.In future work,we will further optimize the SBMSE to improve prediction accuracy.展开更多
文摘A study of excavated material on the Gagnoa-Guéyo-Bamenadou road project in southwest Côte d’Ivoire was carried out using electrical resistivity tomography with a dipole-dipole configuration. This study aimed to determine the nature and volume of the studied cuts. Based on the cumulative distances of the longitudinal sections of the road alignment superimposed on the tomographic profiles, a cumulative volume of 104681 m3 of material was determined. This volume comprises 88557 m3 of soft cuts and 16,124 m3 of rocky cuts, which can be reused in specific embankment zones. This work may, therefore be useful in the characterization of cuts in a preliminary design study, in order to anticipate changes during the road’s development and asphalting.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
基金financially supported by the Natural Science Foundation of Guangdong Province(Grant Nos.2023A1515030023,2022B0202110003,2021TQ06N115,2020B121201008)the Special Fund for Scientific Innovation Strategy-Construction of High Level Academy of Agriculture Science(R2023PY-JG025)。
文摘The water content of cut flowers is a significant factor in their post-harvest quality.In this study,we examine the efficacy of silver nanoparticles(NS)on the longevity of cut gladiolus,with a focus on water state and distribution.We used Low-field nuclear magnetic resonance(LF-NMR)technology to identify three water fractions with different transverse relaxation times(T2)values:bound water T21(<10 ms),intermediate immobilized water T22(10-100 ms),and the slowest component free water T23(>10 ms).During the opening process,T23increased at stages 2 and 3 and then decreased,T22 decreased slowly,and T21 remained unchanged.Free water values were consistently higher than bound water and immobilized water and reached their maximum from stage 2 until stage 4,when the petals were extended and began to wilt.The vascular bundles responsible for transporting water had higher water content,as detected by proton density-weighted magnetic resonance imaging(MRI).Bound water and free water with NS pretreatments in bracts were initially lower but then two days later the signal amplitude of each water state exceeded those of the control,indicating that the treatment enhanced the water-holding capacity over time.Furthermore,NS pretreatments reduced the free water mobility of the cut flowers and inhibited stem decay.Additionally,we found that NS can enter the stem and are primarily transported upward along the xylem with water using scanning electron microscopy(SEM)and energy-dispersive X-ray spectroscopy(EDS)technology.Overall,our findings indicate that NS pretreatment reduces free water in gladiolus cut flowers,enhancing their water retention and prolonging their vase life.
基金partially funded by the National Natural Science Foundation of China(U2142205)the Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)+1 种基金the Special Fund for Forecasters of China Meteorological Administration(CMAYBY2020-094)the Graduate Student Research and Innovation Program of Central South University(2023ZZTS0347)。
文摘Traditional meteorological downscaling methods face limitations due to the complex distribution of meteorological variables,which can lead to unstable forecasting results,especially in extreme scenarios.To overcome this issue,we propose a convolutional graph neural network(CGNN)model,which we enhance with multilayer feature fusion and a squeeze-and-excitation block.Additionally,we introduce a spatially balanced mean squared error(SBMSE)loss function to address the imbalanced distribution and spatial variability of meteorological variables.The CGNN is capable of extracting essential spatial features and aggregating them from a global perspective,thereby improving the accuracy of prediction and enhancing the model's generalization ability.Based on the experimental results,CGNN has certain advantages in terms of bias distribution,exhibiting a smaller variance.When it comes to precipitation,both UNet and AE also demonstrate relatively small biases.As for temperature,AE and CNNdense perform outstandingly during the winter.The time correlation coefficients show an improvement of at least 10%at daily and monthly scales for both temperature and precipitation.Furthermore,the SBMSE loss function displays an advantage over existing loss functions in predicting the98th percentile and identifying areas where extreme events occur.However,the SBMSE tends to overestimate the distribution of extreme precipitation,which may be due to the theoretical assumptions about the posterior distribution of data that partially limit the effectiveness of the loss function.In future work,we will further optimize the SBMSE to improve prediction accuracy.
