Facing the market economy and global challenge the development ofmanufacturing industry especially casting industry is critical to the national economy. To reformthe traditional casting industry by using computer tech...Facing the market economy and global challenge the development ofmanufacturing industry especially casting industry is critical to the national economy. To reformthe traditional casting industry by using computer technology is one of the hottest researchfrontiers studied by many researchers and engineers. Computer simulation of solidification processof shaped casting can assure the quality of casting, optimize the casting technology, shorten thelead time and therefore decrease the developing and manufacturing cost. Recently, numericalsimulation of mold-filling and solidification processes of shaped casting and prediction ofmicrostructure and property as well are extensively studied and put into application in many castingplants with many successful simulation cases.展开更多
Dear readers,Welcome to the first issue of volume 6 of the Journal of Social Computing!We present six articles that highlight the interplay between artificial intelligence,computational modeling,and research resources...Dear readers,Welcome to the first issue of volume 6 of the Journal of Social Computing!We present six articles that highlight the interplay between artificial intelligence,computational modeling,and research resources in addressing both methodological challenges and real-world societal issues.These papers are grouped into three thematic clusters:(1)AI and computational methods for social and economic research,(2)data-driven insights into real-world social challenges,and(3)research resources for scientific and policy advancements.展开更多
To the Editor:Artificial intelligence(AI)is revolutionizing the biomedical field by enabling advanced data analysis,predictive modeling,and personalized medicine,driving breakthroughs in diagnosis,treatment,and drug d...To the Editor:Artificial intelligence(AI)is revolutionizing the biomedical field by enabling advanced data analysis,predictive modeling,and personalized medicine,driving breakthroughs in diagnosis,treatment,and drug discovery.In pursuit of this goal,researchers are attempting to develop AI-based algorithms and establish models for use in clinical settings.Key challenges in this pursuit include ensuring the models’accuracy and consistency and addressing issues such as the interpretability of AI decisions,integration into existing clinical workflows,and ethical considerations like data privacy.Additionally,the AI model lies in the quality and diversity of training data—robust models require diverse and representative datasets to ensure generalizability across different patient populations,reduce dependence on extensive labeled data,and remain resilient to domain shifts,enabling adaptation to new and unseen cases.Nevertheless,this field continues to grow,especially in image-based AI models for diagnosing diseases,such as cardiovascular diseases.展开更多
Dear Readers,We present six articles in this issue that display a wide range of methods used in social computing.These six articles can be classified into three categories:(1)Theoretical perspectives and formal models...Dear Readers,We present six articles in this issue that display a wide range of methods used in social computing.These six articles can be classified into three categories:(1)Theoretical perspectives and formal models,(2)simulation modeling,and(3)computational models.A theoretical perspective offers deep reflection on social phenomena。展开更多
Modifying solution viscosity is a key functional application of polymers,yet the interplay of molecular chemistry,polymer architecture,and intermolecular interactions makes tailoring precise rheological responses chal...Modifying solution viscosity is a key functional application of polymers,yet the interplay of molecular chemistry,polymer architecture,and intermolecular interactions makes tailoring precise rheological responses challenging.We introduce a computational framework coupling topology-aware generative machine learning,Gaussian process modeling,and multiparticle collision dynamics to design polymers yielding prescribed shear-rate-dependent viscosity profiles.Targeting thirty rheological profiles of varying difficulty,Bayesian optimization identifies polymers that satisfy all lowand most medium-difficulty targets by modifying topology and solvophobicity,with other variables fixed.In these regimes,wefind and explain design degeneracy,where distinct polymers produce nearidentical rheological profiles.However,satisfying high-difficulty targets requires extrapolation beyond the initial constrained design space;this is rationally guided by physical scaling theories.This integrated framework establishes a data-driven yet mechanistic route to rational polymer design.展开更多
This multidisciplinary study integrates structural and cave mapping,3D geological modeling,and Geographical Information System(GIS)analysis to provide constraints of the hydrogeological model for the central Lefka Ori...This multidisciplinary study integrates structural and cave mapping,3D geological modeling,and Geographical Information System(GIS)analysis to provide constraints of the hydrogeological model for the central Lefka Ori Massif.Through 44 km of linear mapping,we discovered the new mid-Miocene Pachnes Thrust(PT)which plays a key role in the central Lefka Ori Massif structural framework.展开更多
Enhanced oil recovery by CO_(2) injection technology(CO_(2)-EOR)plays a crucial role in enhancing oil production and the permanent sequestration of anthropogenic CO_(2) in depleted oil reservoirs.However,the availabil...Enhanced oil recovery by CO_(2) injection technology(CO_(2)-EOR)plays a crucial role in enhancing oil production and the permanent sequestration of anthropogenic CO_(2) in depleted oil reservoirs.However,the availability of CO_(2) in oil field locations and its mobility in contrast with reservoir fluids are prime challenges in CO_(2)-EOR.The cost of CO_(2) and its availability at the oil fields has prompted investigations on efficient injection of CO_(2) at the fields to achieve the best sweep efficiency possible.Injection strategies such as water-alternating-gas(WAG),simultaneous vertical and horizontal WAG,simultaneous water injection into the aquifer and vertical WAG,water and gas injection simultaneously but separately(SS-WAG),and water and gas injection simultaneously but not separately(SNS-WAG)play a significant role,as well as the purity of CO_(2).In this work,the significance of the above criteria was investigated indi-vidually and in combination.The coupled sequence of injection rate,soaking time,WAG ratio,and purity of injected CO_(2) for enhancement of oil production were delineated.A realistic reservoir simulation model conceptualizing the CO_(2)-EOR system with five spot injection patterns was developed by the company CMG.The history-matched model that was developed was used to investigate the sensitivity of the coupled effects to the criteria listed above on oil recovery.Numerical investigations quantitatively emphasized that purity and soaking time of CO_(2) have an inverse effect in the oil production rate and that SNS-WAG resulted in a better oil production rate than SS-WAG.展开更多
基金This project is supported by Natural Science Foundation of China(No.59990470-3)Ford-China R &D Project(No.9715509)and Significant Fundamental Research Project of State Ministry of Science and Technology of China(No.G2000
文摘Facing the market economy and global challenge the development ofmanufacturing industry especially casting industry is critical to the national economy. To reformthe traditional casting industry by using computer technology is one of the hottest researchfrontiers studied by many researchers and engineers. Computer simulation of solidification processof shaped casting can assure the quality of casting, optimize the casting technology, shorten thelead time and therefore decrease the developing and manufacturing cost. Recently, numericalsimulation of mold-filling and solidification processes of shaped casting and prediction ofmicrostructure and property as well are extensively studied and put into application in many castingplants with many successful simulation cases.
文摘Dear readers,Welcome to the first issue of volume 6 of the Journal of Social Computing!We present six articles that highlight the interplay between artificial intelligence,computational modeling,and research resources in addressing both methodological challenges and real-world societal issues.These papers are grouped into three thematic clusters:(1)AI and computational methods for social and economic research,(2)data-driven insights into real-world social challenges,and(3)research resources for scientific and policy advancements.
基金supported by the Sichuan Natural Science Foundation Outstanding Youth Science Foundation(No.2024NSFJQ0053)the National Natural Science Foundation of China(No.82370235)+1 种基金the Tianfu Qingcheng Plan(No.1711)the K-funding of West China Second University Hospital Sichuan University(No.KZ197).
文摘To the Editor:Artificial intelligence(AI)is revolutionizing the biomedical field by enabling advanced data analysis,predictive modeling,and personalized medicine,driving breakthroughs in diagnosis,treatment,and drug discovery.In pursuit of this goal,researchers are attempting to develop AI-based algorithms and establish models for use in clinical settings.Key challenges in this pursuit include ensuring the models’accuracy and consistency and addressing issues such as the interpretability of AI decisions,integration into existing clinical workflows,and ethical considerations like data privacy.Additionally,the AI model lies in the quality and diversity of training data—robust models require diverse and representative datasets to ensure generalizability across different patient populations,reduce dependence on extensive labeled data,and remain resilient to domain shifts,enabling adaptation to new and unseen cases.Nevertheless,this field continues to grow,especially in image-based AI models for diagnosing diseases,such as cardiovascular diseases.
文摘Dear Readers,We present six articles in this issue that display a wide range of methods used in social computing.These six articles can be classified into three categories:(1)Theoretical perspectives and formal models,(2)simulation modeling,and(3)computational models.A theoretical perspective offers deep reflection on social phenomena。
基金supported by the donors of ACS Petroleum Research Fund under Doctoral New Investigator Grant 66706-DNI7Simulations and analyses were performed using resources from Princeton Research Computing at Princeton University, which is a consortium led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology’s Research Computing. These resources include a GPU-based computing cluster purchased with support from the National Science Foundation (Grant No. NSF-MRI: OAC-2320649).
文摘Modifying solution viscosity is a key functional application of polymers,yet the interplay of molecular chemistry,polymer architecture,and intermolecular interactions makes tailoring precise rheological responses challenging.We introduce a computational framework coupling topology-aware generative machine learning,Gaussian process modeling,and multiparticle collision dynamics to design polymers yielding prescribed shear-rate-dependent viscosity profiles.Targeting thirty rheological profiles of varying difficulty,Bayesian optimization identifies polymers that satisfy all lowand most medium-difficulty targets by modifying topology and solvophobicity,with other variables fixed.In these regimes,wefind and explain design degeneracy,where distinct polymers produce nearidentical rheological profiles.However,satisfying high-difficulty targets requires extrapolation beyond the initial constrained design space;this is rationally guided by physical scaling theories.This integrated framework establishes a data-driven yet mechanistic route to rational polymer design.
基金funded by IUGS and UNESCO through the IGCP-715 initiativethe collection of rock samples and topographical data.Special recognition goes to the Sternes Cave Expeditions(2018–2023)the Gourgouthakas Expedition(2022)and the Lion Expeditions(2013–2015)for their substantial contributions.
文摘This multidisciplinary study integrates structural and cave mapping,3D geological modeling,and Geographical Information System(GIS)analysis to provide constraints of the hydrogeological model for the central Lefka Ori Massif.Through 44 km of linear mapping,we discovered the new mid-Miocene Pachnes Thrust(PT)which plays a key role in the central Lefka Ori Massif structural framework.
文摘Enhanced oil recovery by CO_(2) injection technology(CO_(2)-EOR)plays a crucial role in enhancing oil production and the permanent sequestration of anthropogenic CO_(2) in depleted oil reservoirs.However,the availability of CO_(2) in oil field locations and its mobility in contrast with reservoir fluids are prime challenges in CO_(2)-EOR.The cost of CO_(2) and its availability at the oil fields has prompted investigations on efficient injection of CO_(2) at the fields to achieve the best sweep efficiency possible.Injection strategies such as water-alternating-gas(WAG),simultaneous vertical and horizontal WAG,simultaneous water injection into the aquifer and vertical WAG,water and gas injection simultaneously but separately(SS-WAG),and water and gas injection simultaneously but not separately(SNS-WAG)play a significant role,as well as the purity of CO_(2).In this work,the significance of the above criteria was investigated indi-vidually and in combination.The coupled sequence of injection rate,soaking time,WAG ratio,and purity of injected CO_(2) for enhancement of oil production were delineated.A realistic reservoir simulation model conceptualizing the CO_(2)-EOR system with five spot injection patterns was developed by the company CMG.The history-matched model that was developed was used to investigate the sensitivity of the coupled effects to the criteria listed above on oil recovery.Numerical investigations quantitatively emphasized that purity and soaking time of CO_(2) have an inverse effect in the oil production rate and that SNS-WAG resulted in a better oil production rate than SS-WAG.
