The primary motivation for this study is the recent growth and increased interest in artificial intelligence(AI).Despite the widespread recognition of its critical importance,a discernible scientific gap persists with...The primary motivation for this study is the recent growth and increased interest in artificial intelligence(AI).Despite the widespread recognition of its critical importance,a discernible scientific gap persists within the extant scholarly discourse,particularly concerning exhaustive systematic reviews of AI in the aviation industry.This gap spurred a meticulous analysis of 1,213 articles from the Web of Science(WoS)core database for bibliometric knowledge mapping.This analysis highlights China as the primary contributor to publications,with the Nanjing University of Finance and Economics as the leading institution in paper contributions.Lecture Notes in Artificial Intelligence and the IEEE AIAA Digital Avionics System Conference are the leading journals within this domain.This bibliometric research underscores the key focus on air traffic management,human factors,environmental ini-tiatives,training,logistics,flight operations,and safety through co-occurrence and co-citation analyses.A chro-nological examination of keywords reveals a central research trajectory centered on machine learning,models,deep learning,and the impact of automation on human performance in aviation.Burst keyword analysis identifies the leading-edge research on AI within predictive models,unmanned aerial vehicles,object detection,and con-volutional neural networks.The primary objective is to bridge this knowledge gap and gain comprehensive in-sights into AI in the aviation sector.This study delineates the scholarly terrain of AI in aviation using a bibliometric methodology to facilitate this exploration.The results illuminate the current state of research,thereby enhancing academic understanding of developments within this critical domain.Finally,a new con-ceptual framework was constructed based on the primary elements identified in the literature.This framework can assist emerging researchers in identifying the fundamental dimensions of AI in the aviation industry.展开更多
Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the l...Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the lack of a scientific foundation.Herein,we present a robust,generalizable,yet intelligent polymer discovery framework,which synergizes diverse capabilities,including the in situ burning analyzer,virtual reaction generator,and material genomic model,to achieve results that surpass the sum of individual parts.Notably,the high-throughput analyzer created for the first time,grounded in multiple spectroscopic principles,enables in situ capturing of massive combustion intermediates;then,the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information;further,the proposed feature engineering tool,which embedded both polymer hierarchical structures and massive intermediate data,develops the generalizable genomic model with excellent universality(adapting over 20 kinds of polymers)and high accuracy(88.8%),succeeding discovering series of novel polymers.This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.展开更多
Integrated River Basin Management(IRBM)has been a long discussed way to sustainably manage water and land resources;yet,very few examples of effective IRBM are found because there is a lack of sufficient scientific su...Integrated River Basin Management(IRBM)has been a long discussed way to sustainably manage water and land resources;yet,very few examples of effective IRBM are found because there is a lack of sufficient scientific support,as well as institutional accommodation,to successfully implement it.This paper overviews the major challenges with IRBM,the promising scientific approaches for the implementation of IRBM,and the areas of needed research,with considerable issues and experiences from China.It is expected that novel research will draw together disparate disciplines into an integrated scientific framework,upon which better modeling tools,stakeholder involvement,and decision-making support can be built.Cutting-edge new technologies will bring ideas of IRBM forward to theory and finally to practice.The paper will prompt scientists to undertake research to fill in the gaps in the current IRBM knowledge base and provide practitioners guidance on how to incorporate scientifically based information within the IRBM decision process.展开更多
文摘The primary motivation for this study is the recent growth and increased interest in artificial intelligence(AI).Despite the widespread recognition of its critical importance,a discernible scientific gap persists within the extant scholarly discourse,particularly concerning exhaustive systematic reviews of AI in the aviation industry.This gap spurred a meticulous analysis of 1,213 articles from the Web of Science(WoS)core database for bibliometric knowledge mapping.This analysis highlights China as the primary contributor to publications,with the Nanjing University of Finance and Economics as the leading institution in paper contributions.Lecture Notes in Artificial Intelligence and the IEEE AIAA Digital Avionics System Conference are the leading journals within this domain.This bibliometric research underscores the key focus on air traffic management,human factors,environmental ini-tiatives,training,logistics,flight operations,and safety through co-occurrence and co-citation analyses.A chro-nological examination of keywords reveals a central research trajectory centered on machine learning,models,deep learning,and the impact of automation on human performance in aviation.Burst keyword analysis identifies the leading-edge research on AI within predictive models,unmanned aerial vehicles,object detection,and con-volutional neural networks.The primary objective is to bridge this knowledge gap and gain comprehensive in-sights into AI in the aviation sector.This study delineates the scholarly terrain of AI in aviation using a bibliometric methodology to facilitate this exploration.The results illuminate the current state of research,thereby enhancing academic understanding of developments within this critical domain.Finally,a new con-ceptual framework was constructed based on the primary elements identified in the literature.This framework can assist emerging researchers in identifying the fundamental dimensions of AI in the aviation industry.
基金supported by the National Natural Science Foundation of China(51991351,51827803,52103122,and 22375138)the Institutional Research Fund from Sichuan University(no.2021SCUNL201)the Fundamental Research Funds for the Central Universities,and the 111 project(B20001).
文摘Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the lack of a scientific foundation.Herein,we present a robust,generalizable,yet intelligent polymer discovery framework,which synergizes diverse capabilities,including the in situ burning analyzer,virtual reaction generator,and material genomic model,to achieve results that surpass the sum of individual parts.Notably,the high-throughput analyzer created for the first time,grounded in multiple spectroscopic principles,enables in situ capturing of massive combustion intermediates;then,the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information;further,the proposed feature engineering tool,which embedded both polymer hierarchical structures and massive intermediate data,develops the generalizable genomic model with excellent universality(adapting over 20 kinds of polymers)and high accuracy(88.8%),succeeding discovering series of novel polymers.This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.
基金supported by U.S.National Science Foundation(Grant No.CBET-0747276)
文摘Integrated River Basin Management(IRBM)has been a long discussed way to sustainably manage water and land resources;yet,very few examples of effective IRBM are found because there is a lack of sufficient scientific support,as well as institutional accommodation,to successfully implement it.This paper overviews the major challenges with IRBM,the promising scientific approaches for the implementation of IRBM,and the areas of needed research,with considerable issues and experiences from China.It is expected that novel research will draw together disparate disciplines into an integrated scientific framework,upon which better modeling tools,stakeholder involvement,and decision-making support can be built.Cutting-edge new technologies will bring ideas of IRBM forward to theory and finally to practice.The paper will prompt scientists to undertake research to fill in the gaps in the current IRBM knowledge base and provide practitioners guidance on how to incorporate scientifically based information within the IRBM decision process.
