Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article presents a framework that includes (1) g...Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article presents a framework that includes (1) generation of porous media samples,(2) computation of permeability via fluid dynamics simulations,(3) training of convolutional neural networks (CNN) with simulated data, and (4) validations against simulations. Comparison of machine learning results and the ground truths suggests excellent predictive performance across a wide range of porosities and pore geometries, especially for those with dilated pores. Owning to such heterogeneity, the permeability cannot be estimated using the conventional Kozeny–Carman approach. Computational time was reduced by several orders of magnitude compared to fluid dynamic simulations. We found that, by including physical parameters that are known to affect permeability into the neural network, the physics-informed CNN generated better results than regular CNN. However, improvements vary with implemented heterogeneity.展开更多
Porous materials present significant advantages for absorbing radioactive isotopes in nuclear waste streams.To improve absorption efficiency in nuclear waste treatment,a thorough understanding of the diffusion-advecti...Porous materials present significant advantages for absorbing radioactive isotopes in nuclear waste streams.To improve absorption efficiency in nuclear waste treatment,a thorough understanding of the diffusion-advection process within porous structures is essential for material design.In this study,we present advancements in the volumetric lattice Boltzmann method(VLBM)for modeling and simulating pore-scale diffusion-advection of radioactive isotopes within geopolymer porous structures.These structures are created using the phase field method(PFM)to precisely control pore architectures.In our VLBM approach,we introduce a concentration field of an isotope seamlessly coupled with the velocity field and solve it by the time evolution of its particle population function.To address the computational intensity inherent in the coupled lattice Boltzmann equations for velocity and concentration fields,we implement graphics processing unit(GPU)parallelization.Validation of the developed model involves examining the flow and diffusion fields in porous structures.Remarkably,good agreement is observed for both the velocity field from VLBM and multiphysics object-oriented simulation environment(MOOSE),and the concentration field from VLBM and the finite difference method(FDM).Furthermore,we investigate the effects of background flow,species diffusivity,and porosity on the diffusion-advection behavior by varying the background flow velocity,diffusion coefficient,and pore volume fraction,respectively.Notably,all three parameters exert an influence on the diffusion-advection process.Increased background flow and diffusivity markedly accelerate the process due to increased advection intensity and enhanced diffusion capability,respectively.Conversely,increasing the porosity has a less significant effect,causing a slight slowdown of the diffusion-advection process due to the expanded pore volume.This comprehensive parametric study provides valuable insights into the kinetics of isotope uptake in porous structures,facilitating the de展开更多
The pore structure images of ore particles located at different heights of leaching column were scanned with X-ray computerized tomography (CT) scanner, the porosity and pore size distribution were calculated and the ...The pore structure images of ore particles located at different heights of leaching column were scanned with X-ray computerized tomography (CT) scanner, the porosity and pore size distribution were calculated and the geometrical shape and connectivity of pores were analyzed based on image process method, and the three dimensional reconstruction of pore structure images was realized. The results show that the porosity of ore particles bed in leaching column is 42.92%, 41.72%, 39.34% at top, middle and bottom zone, respectively. Obviously it has spatial variability and decreases appreciably along the height of the column. The overall average porosity obtained by image processing is 41.33% while the porosity gotten from general measurement method in laboratory is 42.77% showing the results of both methods are consistent well. The pore structure of ore granular media is characterized as a dynamical space network composed of interconnected pore bodies and pore throats. The ratio of throats with equivalent diameter less than 1.91 mm to the total pores is 29.31%, and that of the large pores with equivalent diameter more than 5.73 mm is 2.90%.展开更多
We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion.Public transport is playing an increasingly important role in urban mobility with a nee...We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion.Public transport is playing an increasingly important role in urban mobility with a need to move people and goods efficiently around the city.With such pressures on existing public transportation systems,this paper investigates the opportunities to use social media to more effectively engage with citizens and customers using such services.This research forms a case study of the use of passively collected forms of big data in cities-focusing on Sydney,Australia.Firstly,it examines social media data(Tweets)related to public transport performance.Secondly,it joins this to longitudinal big data-delay information continuously broadcast by the network over a year,thus forming hundreds of millions of data artifacts.Topics,tones,and sentiment are modeled using machine learning and Natural Language Processing(NLP)techniques.These resulting data,and models,are compared to opinions derived from a citizen survey among users.The validity of such data and models versus the intentions of users,in the context of systems that monitor and improve transport performance,are discussed.As such,key recommendations for developing Smart Cities were formed in an applied research context based on these data and techniques.展开更多
The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spr...The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spread data with minimal examination and filters freely.Due to this,the old problem of fake news has resurfaced.It has become an important concern due to its negative impact on the community.To manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)techniques.To perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite effectively.Still,a huge effort is required from the human side to generate the labelled training data.The recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news detection.To unlock social media data,an automatic text classifier is highly helpful in the domain of NLP.The current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)approach.The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news.To attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding process.Besides,the QCLSTM model is utilized for classification.To boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the hyperparameters.The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset.The QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.展开更多
Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our lives.Cyberbullying is a social media network issue,a global crisis affecting the victims a...Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our lives.Cyberbullying is a social media network issue,a global crisis affecting the victims and society as a whole.It results from a misunderstanding regarding freedom of speech.In this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet allocation.In addition,we used accuracy,precision,recall,and F1 score to assess prior classifiers.Results show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.展开更多
文摘Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article presents a framework that includes (1) generation of porous media samples,(2) computation of permeability via fluid dynamics simulations,(3) training of convolutional neural networks (CNN) with simulated data, and (4) validations against simulations. Comparison of machine learning results and the ground truths suggests excellent predictive performance across a wide range of porosities and pore geometries, especially for those with dilated pores. Owning to such heterogeneity, the permeability cannot be estimated using the conventional Kozeny–Carman approach. Computational time was reduced by several orders of magnitude compared to fluid dynamic simulations. We found that, by including physical parameters that are known to affect permeability into the neural network, the physics-informed CNN generated better results than regular CNN. However, improvements vary with implemented heterogeneity.
基金supported as part of the Center for Hierarchical Waste Form Materials,an Energy Frontier Research Center funded by the U.S.Department of Energy,Office of Science,Basic Energy Sciences under Award No.DE-SC0016574.
文摘Porous materials present significant advantages for absorbing radioactive isotopes in nuclear waste streams.To improve absorption efficiency in nuclear waste treatment,a thorough understanding of the diffusion-advection process within porous structures is essential for material design.In this study,we present advancements in the volumetric lattice Boltzmann method(VLBM)for modeling and simulating pore-scale diffusion-advection of radioactive isotopes within geopolymer porous structures.These structures are created using the phase field method(PFM)to precisely control pore architectures.In our VLBM approach,we introduce a concentration field of an isotope seamlessly coupled with the velocity field and solve it by the time evolution of its particle population function.To address the computational intensity inherent in the coupled lattice Boltzmann equations for velocity and concentration fields,we implement graphics processing unit(GPU)parallelization.Validation of the developed model involves examining the flow and diffusion fields in porous structures.Remarkably,good agreement is observed for both the velocity field from VLBM and multiphysics object-oriented simulation environment(MOOSE),and the concentration field from VLBM and the finite difference method(FDM).Furthermore,we investigate the effects of background flow,species diffusivity,and porosity on the diffusion-advection behavior by varying the background flow velocity,diffusion coefficient,and pore volume fraction,respectively.Notably,all three parameters exert an influence on the diffusion-advection process.Increased background flow and diffusivity markedly accelerate the process due to increased advection intensity and enhanced diffusion capability,respectively.Conversely,increasing the porosity has a less significant effect,causing a slight slowdown of the diffusion-advection process due to the expanded pore volume.This comprehensive parametric study provides valuable insights into the kinetics of isotope uptake in porous structures,facilitating the de
基金Project(2004CB619205) supported by the National Key Fundamental Research and Development Program of ChinaProject(50325415) supported by the National Science Fund for Distinguished Young ScholarsProject(50574099) supported by the National Natural Science Foundation of China
文摘The pore structure images of ore particles located at different heights of leaching column were scanned with X-ray computerized tomography (CT) scanner, the porosity and pore size distribution were calculated and the geometrical shape and connectivity of pores were analyzed based on image process method, and the three dimensional reconstruction of pore structure images was realized. The results show that the porosity of ore particles bed in leaching column is 42.92%, 41.72%, 39.34% at top, middle and bottom zone, respectively. Obviously it has spatial variability and decreases appreciably along the height of the column. The overall average porosity obtained by image processing is 41.33% while the porosity gotten from general measurement method in laboratory is 42.77% showing the results of both methods are consistent well. The pore structure of ore granular media is characterized as a dynamical space network composed of interconnected pore bodies and pore throats. The ratio of throats with equivalent diameter less than 1.91 mm to the total pores is 29.31%, and that of the large pores with equivalent diameter more than 5.73 mm is 2.90%.
文摘We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion.Public transport is playing an increasingly important role in urban mobility with a need to move people and goods efficiently around the city.With such pressures on existing public transportation systems,this paper investigates the opportunities to use social media to more effectively engage with citizens and customers using such services.This research forms a case study of the use of passively collected forms of big data in cities-focusing on Sydney,Australia.Firstly,it examines social media data(Tweets)related to public transport performance.Secondly,it joins this to longitudinal big data-delay information continuously broadcast by the network over a year,thus forming hundreds of millions of data artifacts.Topics,tones,and sentiment are modeled using machine learning and Natural Language Processing(NLP)techniques.These resulting data,and models,are compared to opinions derived from a citizen survey among users.The validity of such data and models versus the intentions of users,in the context of systems that monitor and improve transport performance,are discussed.As such,key recommendations for developing Smart Cities were formed in an applied research context based on these data and techniques.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R281)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR41).
文摘The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spread data with minimal examination and filters freely.Due to this,the old problem of fake news has resurfaced.It has become an important concern due to its negative impact on the community.To manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)techniques.To perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite effectively.Still,a huge effort is required from the human side to generate the labelled training data.The recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news detection.To unlock social media data,an automatic text classifier is highly helpful in the domain of NLP.The current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)approach.The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news.To attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding process.Besides,the QCLSTM model is utilized for classification.To boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the hyperparameters.The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset.The QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.
文摘Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our lives.Cyberbullying is a social media network issue,a global crisis affecting the victims and society as a whole.It results from a misunderstanding regarding freedom of speech.In this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet allocation.In addition,we used accuracy,precision,recall,and F1 score to assess prior classifiers.Results show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.
