Widespread implementation of electronic health records has led to the increased use of artificial intelligence(AI)and computer modeling in clinical medicine.The early recognition and treatment of critical illness are ...Widespread implementation of electronic health records has led to the increased use of artificial intelligence(AI)and computer modeling in clinical medicine.The early recognition and treatment of critical illness are central to good outcomes but are made difficult by,among other things,the complexity of the environment and the often non-specific nature of the clinical presentation.Increasingly,AI applications are being proposed as decision supports for busy or distracted clinicians,to address this challenge.Data driven“associative”AI models are built from retrospective data registries with missing data and imprecise timing.Associative AI models lack transparency,often ignore causal mechanisms,and,while potentially useful in improved prognostication,have thus far had limited clinical applicability.To be clinically useful,AI tools need to provide bedside clinicians with actionable knowledge.Explicitly addressing causal mechanisms not only increases validity and replicability of the model,but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care.展开更多
For the case of carbonate reservoir water flooding development, the flow field identification method based on streamline modeling result was proposed. The Ocean for Petrel platform was used to build the plug-in that e...For the case of carbonate reservoir water flooding development, the flow field identification method based on streamline modeling result was proposed. The Ocean for Petrel platform was used to build the plug-in that exported the streamline data, and the subsequent data was processed and clustered through Python programming, to display the flow field with different water flooding efficiencies at different time in the reservoir. We used density peak clustering as primary streamline cluster algorithm, and Silhouette algorithm as the cluster validation algorithm to select reasonable cluster number, and the results of different clustering algorithms were compared. The results showed that the density peak clustering algorithm could provide better identified capacity and higher Silhouette coefficient than K-means, hierachical clustering and spectral clustering algorithms when clustering coefficients are the same. Based on the results of streamline clustering method, the reservoir engineers can easily identify the flow area with quantification treatment, the inefficient water injection channels and area with developing potential in reservoirs can be identified. Meanwhile, streamlines between the same injector and producer can be subdivided to describe driving capacity distribution in water phase, providing useful information for the decision making of water flooding optimization, well pattern adjustment and deep profile modification.展开更多
The planning,design,operation,control and scientific research of power systems all require a variety of simulation analysis.Thus power grid simulation analysis is a fundamental supporting technology of large-scale pow...The planning,design,operation,control and scientific research of power systems all require a variety of simulation analysis.Thus power grid simulation analysis is a fundamental supporting technology of large-scale power grids.In power grid simulation analysis,in addition to simulation calculations,there are many links for analysis and decision-making,relying on specialists.The introduction of advanced artificial intelligence technology provides a new method to improve the efficiency and accuracy of power grid simulation analysis.Nevertheless,the research of the related artificial intelligence technologies face a great deal of new challenges due to the complexity of the largescale power grid simulation data,including massive volumes,high dimensionality,strong coupling and complex correlations.Also a great deal of knowledge and experience need to be integrated in the process of analysis.In order to deal with these challenges,based on the existing works,this paper focuses on the core scientific problem of artificial intelligence analysis and decision making related to the massive simulation results of large-scale power grids,and proposes an artificial intelligence analysis method framework for large-scale power grids based on digital simulation,which includes the power grid simulation analysis knowledge model with application method,the power grid simulation knowledge mining method and the artificial intelligence models with transfer learning ability of diversified grids as well as analyzing and calculation adjusting for largescale power grid simulation results,etc.This work is expected to open up a new technical approach for large-scale power grid simulation analysis and provide strong technical support for the safe and stable operation of large-scale power grids.展开更多
The utilization of environmentally friendly hydrogen energy requires proton exchange membrane fuel cell de-vices that offer high power output while remaining affordable.However,the current optimization of their key co...The utilization of environmentally friendly hydrogen energy requires proton exchange membrane fuel cell de-vices that offer high power output while remaining affordable.However,the current optimization of their key component,i.e.,the membrane electrode assembly,is still based on intuition-guided,inefficient trial-and-error cycles due to its complexity.Hence,we introduce an innovative,explainable artificial intelligence(AI)tool trained as a reliable assistant for a variable analysis and optimum-value prediction.Among the 8 algorithms considered,the surrogate model built with an artificial neural network achieves high replaceability in the experimentally validated multiphysics simulation(R^(2)=0.99845)and a much lower computational cost.For interpretation,partial dependence plots and the Shapley value method are applied to black-box models to intelligently simulate the impact of each parameter on performance.These methods show that a tradeoff existed in the catalyst layer thickness.The AI-guided optimization suggestions regarding catalyst loading and the ion-omer content are fully supported by the experimental results,and the final product achieves 3.2 times the Pt utilization of commercial products with a time cost orders of magnitude smaller.展开更多
BACKGROUND Minimally invasive techniques for treatment of urinary stones requires expertise,experience and endoscopic skills.Simulators provide a low-stress and low-risk environment while providing a realistic set-up ...BACKGROUND Minimally invasive techniques for treatment of urinary stones requires expertise,experience and endoscopic skills.Simulators provide a low-stress and low-risk environment while providing a realistic set-up and training opportunities.AIM To report the publication trend of‘simulation in urolithiasis’over the last 26 years.METHODS Research of all published papers on“Simulation in Urolithiasis”was performed through PubMed database over the last 26 years,from January 1997 to December 2022.Papers were labelled and divided in three subgroups:(1)Training papers;(2)Clinical simulation application or surgical procedures;and(3)Diagnostic radiology simulation.Each subgroup was then divided into two 13-year time periods to compare and identify the contrast of different decades:period-1(1997-2009)and period-2(2010-2022).RESULTS A total of 168 articles published on the application of simulation in urolithiasis over the last 26 years(training:n=94,surgical procedures:n=66,and radiology:n=8).The overall number of papers published in simulation in urolithiasis was 35 in Period-1 and 129 in Period-2,an increase of+269%(P=0.0002).Each subgroup shows a growing trend of publications from Period-1 to Period-2:training papers+279%(P=0.001),surgical simulations+264%(P=0.0180)and radiological simulations+200%(P=0.2105).CONCLUSION In the last decades there has been a step up of papers regarding training protocols with the aid of various simulation devices,with simulators now a part of training programs.With the development of 3D-printed and high-fidelity models,simulation for surgical procedure planning and patients counseling is also a growing field and this trend will continue to rise in the next few years.展开更多
The distribution of material phases is crucial to determine the composite’s mechanical property.While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite numb...The distribution of material phases is crucial to determine the composite’s mechanical property.While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases,this relationship is difficult to be revealed for complex irregular distributions,preventing design of such material structures to meet certain mechanical requirements.The noticeable developments of artificial intelligence(AI)algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures.It is intriguing how these tools can assist composite design.Here,we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading.We find that generative AI,enabled through fine-tuned Low Rank Adaptation models,can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution.The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness,fracture and robustness of the material with one model,and such has to be done by several different experimental or simulation tests.This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.展开更多
文摘Widespread implementation of electronic health records has led to the increased use of artificial intelligence(AI)and computer modeling in clinical medicine.The early recognition and treatment of critical illness are central to good outcomes but are made difficult by,among other things,the complexity of the environment and the often non-specific nature of the clinical presentation.Increasingly,AI applications are being proposed as decision supports for busy or distracted clinicians,to address this challenge.Data driven“associative”AI models are built from retrospective data registries with missing data and imprecise timing.Associative AI models lack transparency,often ignore causal mechanisms,and,while potentially useful in improved prognostication,have thus far had limited clinical applicability.To be clinically useful,AI tools need to provide bedside clinicians with actionable knowledge.Explicitly addressing causal mechanisms not only increases validity and replicability of the model,but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care.
基金Supported by the the CNPC Science and Technology Innovation Fund Program(2017D-5007-0202)
文摘For the case of carbonate reservoir water flooding development, the flow field identification method based on streamline modeling result was proposed. The Ocean for Petrel platform was used to build the plug-in that exported the streamline data, and the subsequent data was processed and clustered through Python programming, to display the flow field with different water flooding efficiencies at different time in the reservoir. We used density peak clustering as primary streamline cluster algorithm, and Silhouette algorithm as the cluster validation algorithm to select reasonable cluster number, and the results of different clustering algorithms were compared. The results showed that the density peak clustering algorithm could provide better identified capacity and higher Silhouette coefficient than K-means, hierachical clustering and spectral clustering algorithms when clustering coefficients are the same. Based on the results of streamline clustering method, the reservoir engineers can easily identify the flow area with quantification treatment, the inefficient water injection channels and area with developing potential in reservoirs can be identified. Meanwhile, streamlines between the same injector and producer can be subdivided to describe driving capacity distribution in water phase, providing useful information for the decision making of water flooding optimization, well pattern adjustment and deep profile modification.
基金This work was supported by the National Natural Science Foundation of China(No:U1866602).
文摘The planning,design,operation,control and scientific research of power systems all require a variety of simulation analysis.Thus power grid simulation analysis is a fundamental supporting technology of large-scale power grids.In power grid simulation analysis,in addition to simulation calculations,there are many links for analysis and decision-making,relying on specialists.The introduction of advanced artificial intelligence technology provides a new method to improve the efficiency and accuracy of power grid simulation analysis.Nevertheless,the research of the related artificial intelligence technologies face a great deal of new challenges due to the complexity of the largescale power grid simulation data,including massive volumes,high dimensionality,strong coupling and complex correlations.Also a great deal of knowledge and experience need to be integrated in the process of analysis.In order to deal with these challenges,based on the existing works,this paper focuses on the core scientific problem of artificial intelligence analysis and decision making related to the massive simulation results of large-scale power grids,and proposes an artificial intelligence analysis method framework for large-scale power grids based on digital simulation,which includes the power grid simulation analysis knowledge model with application method,the power grid simulation knowledge mining method and the artificial intelligence models with transfer learning ability of diversified grids as well as analyzing and calculation adjusting for largescale power grid simulation results,etc.This work is expected to open up a new technical approach for large-scale power grid simulation analysis and provide strong technical support for the safe and stable operation of large-scale power grids.
基金This work was partially supported by the National Key R&D Plan of China[2019YFB1504503]the National Natural Science Foundation of China[21802069]the Key R&D plan of Zhejiang Province[2020C01006].The database generation from the multiphysics simu-lation model was performed at the High-Performance Computing Center of the Collaborative Innovation Center of Advanced Microstructures,Collaborative Innovation Center of Advanced Microstructures,Nanjing University,Nanjing 210,093,China.
文摘The utilization of environmentally friendly hydrogen energy requires proton exchange membrane fuel cell de-vices that offer high power output while remaining affordable.However,the current optimization of their key component,i.e.,the membrane electrode assembly,is still based on intuition-guided,inefficient trial-and-error cycles due to its complexity.Hence,we introduce an innovative,explainable artificial intelligence(AI)tool trained as a reliable assistant for a variable analysis and optimum-value prediction.Among the 8 algorithms considered,the surrogate model built with an artificial neural network achieves high replaceability in the experimentally validated multiphysics simulation(R^(2)=0.99845)and a much lower computational cost.For interpretation,partial dependence plots and the Shapley value method are applied to black-box models to intelligently simulate the impact of each parameter on performance.These methods show that a tradeoff existed in the catalyst layer thickness.The AI-guided optimization suggestions regarding catalyst loading and the ion-omer content are fully supported by the experimental results,and the final product achieves 3.2 times the Pt utilization of commercial products with a time cost orders of magnitude smaller.
文摘BACKGROUND Minimally invasive techniques for treatment of urinary stones requires expertise,experience and endoscopic skills.Simulators provide a low-stress and low-risk environment while providing a realistic set-up and training opportunities.AIM To report the publication trend of‘simulation in urolithiasis’over the last 26 years.METHODS Research of all published papers on“Simulation in Urolithiasis”was performed through PubMed database over the last 26 years,from January 1997 to December 2022.Papers were labelled and divided in three subgroups:(1)Training papers;(2)Clinical simulation application or surgical procedures;and(3)Diagnostic radiology simulation.Each subgroup was then divided into two 13-year time periods to compare and identify the contrast of different decades:period-1(1997-2009)and period-2(2010-2022).RESULTS A total of 168 articles published on the application of simulation in urolithiasis over the last 26 years(training:n=94,surgical procedures:n=66,and radiology:n=8).The overall number of papers published in simulation in urolithiasis was 35 in Period-1 and 129 in Period-2,an increase of+269%(P=0.0002).Each subgroup shows a growing trend of publications from Period-1 to Period-2:training papers+279%(P=0.001),surgical simulations+264%(P=0.0180)and radiological simulations+200%(P=0.2105).CONCLUSION In the last decades there has been a step up of papers regarding training protocols with the aid of various simulation devices,with simulators now a part of training programs.With the development of 3D-printed and high-fidelity models,simulation for surgical procedure planning and patients counseling is also a growing field and this trend will continue to rise in the next few years.
基金supported by the National Science Foundation CA-REER Grant(Grant No.2145392)the startup funding at Syracuse Uni-versity for supporting the research work.
文摘The distribution of material phases is crucial to determine the composite’s mechanical property.While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases,this relationship is difficult to be revealed for complex irregular distributions,preventing design of such material structures to meet certain mechanical requirements.The noticeable developments of artificial intelligence(AI)algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures.It is intriguing how these tools can assist composite design.Here,we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading.We find that generative AI,enabled through fine-tuned Low Rank Adaptation models,can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution.The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness,fracture and robustness of the material with one model,and such has to be done by several different experimental or simulation tests.This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.
