Surface hydrogen storage facilities are limited and costly,making subsurface hydrogen storage in geological formations a more viable alternative due to its substantial capacity,safety,and economic feasibility.This met...Surface hydrogen storage facilities are limited and costly,making subsurface hydrogen storage in geological formations a more viable alternative due to its substantial capacity,safety,and economic feasibility.This method is essential for large-scale hydrogen storage to support renewable energy integration,fuel cell technologies,and other applications aimed at mitigating global climate change.This review examines underground hydrogen storage(UHS)in geological formations,focusing on recent experiments,modeling and simulations,and field applications.Geological formations such as depleted oil reservoirs,salt caverns,and depleted natural gas reservoirs are identified as favorable candidates due to minimal interactions with hydrogen,leading to low hydrogen loss.Globally,80%of UHS projects utilize depleted natural gas and oil reservoirs,with over 50%focused on depleted natural gas and oil condensate reservoirs due to cost-effective existing infrastructure.Among storage options,salt caverns are the most advantageous,offering self-healing properties,low caprock permeability,large storage capacity,rapid injection and withdrawal rates,and low contamination risk.Additionally,hydrogen produced from coal is the cheapest option,costing 1.2e2 USD/kg,whereas hydrogen from renewable sources,such as water,is the most expensive at 3e13 USD/kg.Despite its higher cost,green hydrogen from water,characterized by low carbon emissions,requires further research to reduce production costs.This review highlights critical research gaps,challenges,and policy recommendations to advance UHS technologies,ensuring their role in combating climate change.展开更多
(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognitio...(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognition.(Methods)521 subjects yield 1164 slice images via the slice level selection method.All the 1164 slice images comprise four categories:COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control.Our method firstly introduced an improved multiple-way data augmentation.Secondly,an n-conv rankbased average pooling module(NRAPM)was proposed in which rank-based pooling—particularly,rank-based average pooling(RAP)—was employed to avoid overfitting.Third,a novel DRAPNet was proposed based on NRAPM and inspired by the VGGnetwork.Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis.(Results)Our DRAPNet achieved a micro-averaged F1 score of 95.49%by 10 runs over the test set.The sensitivities of the four classes were 95.44%,96.07%,94.41%,and 96.07%,respectively.The precisions of four classes were 96.45%,95.22%,95.05%,and 95.28%,respectively.The F1 scores of the four classes were 95.94%,95.64%,94.73%,and 95.67%,respectively.Besides,the confusion matrix was given.(Conclusions)The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases.The RAP gives better results than four other methods:strided convolution,l2-norm pooling,average pooling,and max pooling.展开更多
The Pressure-Volume-Temperature(PVT)properties of crude oil are typically determined through laboratory analysis during the early phases of exploration and fielddevelopment.However,due to extensive data required,time-...The Pressure-Volume-Temperature(PVT)properties of crude oil are typically determined through laboratory analysis during the early phases of exploration and fielddevelopment.However,due to extensive data required,time-consuming nature,and high costs,laboratory methods are often not preferred.Machine learning,with its efficiencyand rapid convergence,has emerged as a promising alternative for PVT properties estimation.This study employs the modified particle swarm optimization-based group method of data handling(PSO-GMDH)to develop predictive models for estimating both the oil formation volume factor(OFVF)and bubble point pressure(P_(b)).Data from the Mpyo oil fieldin Uganda were used to create the models.The input parameters included solution gas-oil ratio(R_(s)),oil American Petroleum Institute gravity(API),specificgravity(SG),and reservoir temperature(T).The results demonstrated that PSO-GMDH outperformed backpropagation neural networks(BPNN)and radial basis function neural networks(RBFNN),achieving higher correlation coefficientsand lower prediction errors during training and testing.For OFVF prediction,PSO-GMDH yielded a correlation coefficient(R)of 0.9979(training)and 0.9876(testing),with corresponding root mean square error(RMSE)values of 0.0021 and 0.0099,and mean absolute error(MAE)values of 0.00055 and 0.00256,respectively.For P_(b)prediction,R was 0.9994(training)and 0.9876(testing),with RMSE values of 6.08 and 8.26,and MAE values of 1.35 and 2.63.The study also revealed that R_(s)significantlyimpacts OFVF and P_(b)predictions compared to other input parameters.The models followed physical laws and remained stable,demonstrating that PSO-GMDH is a robust and efficientmethod for predicting OFVF and P_(b),offering a time and cost-effective alternative.展开更多
(Aim)COVID-19 is an ongoing infectious disease.It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021.Traditional computer vision methods have achieved promising results on the automatic s...(Aim)COVID-19 is an ongoing infectious disease.It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021.Traditional computer vision methods have achieved promising results on the automatic smart diagnosis.(Method)This study aims to propose a novel deep learning method that can obtain better performance.We use the pseudo-Zernike moment(PZM),derived from Zernike moment,as the extracted features.Two settings are introducing:(i)image plane over unit circle;and(ii)image plane inside the unit circle.Afterward,we use a deep-stacked sparse autoencoder(DSSAE)as the classifier.Besides,multiple-way data augmentation is chosen to overcome overfitting.The multiple-way data augmentation is based on Gaussian noise,salt-and-pepper noise,speckle noise,horizontal and vertical shear,rotation,Gamma correction,random translation and scaling.(Results)10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06%±1.54%,a specificity of 92.56%±1.06%,a precision of 92.53%±1.03%,and an accuracy of 92.31%±1.08%.Its F1 score,MCC,and FMI arrive at 92.29%±1.10%,84.64%±2.15%,and 92.29%±1.10%,respectively.The AUC of our model is 0.9576.(Conclusion)We demonstrate“image plane over unit circle”can get better results than“image plane inside a unit circle.”Besides,this proposed PZM-DSSAE model is better than eight state-of-the-art approaches.展开更多
A coupled solid-fluid FE-model for partially saturated soils,characterized by modeling the soil as a three-phase material consisting of a deformable soil skeleton and the fluid phases water and air,is reviewed briefly...A coupled solid-fluid FE-model for partially saturated soils,characterized by modeling the soil as a three-phase material consisting of a deformable soil skeleton and the fluid phases water and air,is reviewed briefly.As a constitutive model for the soil skeleton,the well-known Barcelona Basic model(BBM)is employed,which is formulated in terms of net stress and matric suction.For the BBM,a computationally efficient return mapping algorithm is proposed,which only requires the solution of a scalar nonlinear equation at the integration point level.The coupled FE-model is applied to the coupled transient numerical simulation of the water flow and the deformations and stresses in an embankment dam.展开更多
基金the Chinese Scholarship Council for their support(Grant No.2022GXZ005733).
文摘Surface hydrogen storage facilities are limited and costly,making subsurface hydrogen storage in geological formations a more viable alternative due to its substantial capacity,safety,and economic feasibility.This method is essential for large-scale hydrogen storage to support renewable energy integration,fuel cell technologies,and other applications aimed at mitigating global climate change.This review examines underground hydrogen storage(UHS)in geological formations,focusing on recent experiments,modeling and simulations,and field applications.Geological formations such as depleted oil reservoirs,salt caverns,and depleted natural gas reservoirs are identified as favorable candidates due to minimal interactions with hydrogen,leading to low hydrogen loss.Globally,80%of UHS projects utilize depleted natural gas and oil reservoirs,with over 50%focused on depleted natural gas and oil condensate reservoirs due to cost-effective existing infrastructure.Among storage options,salt caverns are the most advantageous,offering self-healing properties,low caprock permeability,large storage capacity,rapid injection and withdrawal rates,and low contamination risk.Additionally,hydrogen produced from coal is the cheapest option,costing 1.2e2 USD/kg,whereas hydrogen from renewable sources,such as water,is the most expensive at 3e13 USD/kg.Despite its higher cost,green hydrogen from water,characterized by low carbon emissions,requires further research to reduce production costs.This review highlights critical research gaps,challenges,and policy recommendations to advance UHS technologies,ensuring their role in combating climate change.
基金This study is partially supported by the Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)Royal Society International Exchanges Cost Share Award,UK(RP202G0230)+3 种基金Hope Foundation for Cancer Research,UK(RM60G0680)British Heart Foundation Accelerator Award,UKSino-UK Industrial Fund,UK(RP202G0289)Global Challenges Research Fund(GCRF),UK(P202PF11).We thank Dr.Hemil Patel for his help in English correction.
文摘(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognition.(Methods)521 subjects yield 1164 slice images via the slice level selection method.All the 1164 slice images comprise four categories:COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control.Our method firstly introduced an improved multiple-way data augmentation.Secondly,an n-conv rankbased average pooling module(NRAPM)was proposed in which rank-based pooling—particularly,rank-based average pooling(RAP)—was employed to avoid overfitting.Third,a novel DRAPNet was proposed based on NRAPM and inspired by the VGGnetwork.Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis.(Results)Our DRAPNet achieved a micro-averaged F1 score of 95.49%by 10 runs over the test set.The sensitivities of the four classes were 95.44%,96.07%,94.41%,and 96.07%,respectively.The precisions of four classes were 96.45%,95.22%,95.05%,and 95.28%,respectively.The F1 scores of the four classes were 95.94%,95.64%,94.73%,and 95.67%,respectively.Besides,the confusion matrix was given.(Conclusions)The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases.The RAP gives better results than four other methods:strided convolution,l2-norm pooling,average pooling,and max pooling.
基金support from the Chinese Scholarship Council(Grant No.2022GXZ005733)。
文摘The Pressure-Volume-Temperature(PVT)properties of crude oil are typically determined through laboratory analysis during the early phases of exploration and fielddevelopment.However,due to extensive data required,time-consuming nature,and high costs,laboratory methods are often not preferred.Machine learning,with its efficiencyand rapid convergence,has emerged as a promising alternative for PVT properties estimation.This study employs the modified particle swarm optimization-based group method of data handling(PSO-GMDH)to develop predictive models for estimating both the oil formation volume factor(OFVF)and bubble point pressure(P_(b)).Data from the Mpyo oil fieldin Uganda were used to create the models.The input parameters included solution gas-oil ratio(R_(s)),oil American Petroleum Institute gravity(API),specificgravity(SG),and reservoir temperature(T).The results demonstrated that PSO-GMDH outperformed backpropagation neural networks(BPNN)and radial basis function neural networks(RBFNN),achieving higher correlation coefficientsand lower prediction errors during training and testing.For OFVF prediction,PSO-GMDH yielded a correlation coefficient(R)of 0.9979(training)and 0.9876(testing),with corresponding root mean square error(RMSE)values of 0.0021 and 0.0099,and mean absolute error(MAE)values of 0.00055 and 0.00256,respectively.For P_(b)prediction,R was 0.9994(training)and 0.9876(testing),with RMSE values of 6.08 and 8.26,and MAE values of 1.35 and 2.63.The study also revealed that R_(s)significantlyimpacts OFVF and P_(b)predictions compared to other input parameters.The models followed physical laws and remained stable,demonstrating that PSO-GMDH is a robust and efficientmethod for predicting OFVF and P_(b),offering a time and cost-effective alternative.
基金This study was supported by Royal Society International Exchanges Cost Share Award,UK(RP202G0230)Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)+1 种基金Hope Foundation for Cancer Research,UK(RM60G0680)Global Challenges Research Fund(GCRF),UK(P202PF11)。
文摘(Aim)COVID-19 is an ongoing infectious disease.It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021.Traditional computer vision methods have achieved promising results on the automatic smart diagnosis.(Method)This study aims to propose a novel deep learning method that can obtain better performance.We use the pseudo-Zernike moment(PZM),derived from Zernike moment,as the extracted features.Two settings are introducing:(i)image plane over unit circle;and(ii)image plane inside the unit circle.Afterward,we use a deep-stacked sparse autoencoder(DSSAE)as the classifier.Besides,multiple-way data augmentation is chosen to overcome overfitting.The multiple-way data augmentation is based on Gaussian noise,salt-and-pepper noise,speckle noise,horizontal and vertical shear,rotation,Gamma correction,random translation and scaling.(Results)10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06%±1.54%,a specificity of 92.56%±1.06%,a precision of 92.53%±1.03%,and an accuracy of 92.31%±1.08%.Its F1 score,MCC,and FMI arrive at 92.29%±1.10%,84.64%±2.15%,and 92.29%±1.10%,respectively.The AUC of our model is 0.9576.(Conclusion)We demonstrate“image plane over unit circle”can get better results than“image plane inside a unit circle.”Besides,this proposed PZM-DSSAE model is better than eight state-of-the-art approaches.
基金Financial support by a scholarship for young researchers granted by the University of Innsbruck to the second author is gratefully acknowledged.
文摘A coupled solid-fluid FE-model for partially saturated soils,characterized by modeling the soil as a three-phase material consisting of a deformable soil skeleton and the fluid phases water and air,is reviewed briefly.As a constitutive model for the soil skeleton,the well-known Barcelona Basic model(BBM)is employed,which is formulated in terms of net stress and matric suction.For the BBM,a computationally efficient return mapping algorithm is proposed,which only requires the solution of a scalar nonlinear equation at the integration point level.The coupled FE-model is applied to the coupled transient numerical simulation of the water flow and the deformations and stresses in an embankment dam.
