The single crystals of Nd_(0.5)Pr_(0.5)FeO_(3)were successfully grown by optical floating zone method.Room temperature x-ray diffraction and Laue photograph declared the homogeneity and high quality of the crystal.The...The single crystals of Nd_(0.5)Pr_(0.5)FeO_(3)were successfully grown by optical floating zone method.Room temperature x-ray diffraction and Laue photograph declared the homogeneity and high quality of the crystal.The significant magnetic anisotropy and multiple magnetic transitions illustrate the complex magnetic structure.At high temperatures(T>66 K),it shows the typical characteristics ofΓ_(4)(G_(x),A_(y),F_(z))state.With the decrease of the temperature,it undergoes a first-order spin reorientation transition fromΓ_(4)(G_(x),A_(y),F_(z))toΓ_(2)(F_(x),C_(y),G_(z))state in the temperature window of 45-66 K under an applied magnetic field of 0.01 T.As the temperature drops to~17 K,a new magnetic interaction mechanism works,which results in a further enhancement of magnetization.The T-H phase diagram of Nd_(0.5)Pr_(0.5)FeO_(3)single crystal was finally constructed.展开更多
Membrane technologies are becoming increasingly versatile and helpful today for sustainable development.Machine Learning(ML),an essential branch of artificial intelligence(AI),has substantially impacted the research an...Membrane technologies are becoming increasingly versatile and helpful today for sustainable development.Machine Learning(ML),an essential branch of artificial intelligence(AI),has substantially impacted the research and development norm of new materials for energy and environment.This review provides an overview and perspectives on ML methodologies and their applications in membrane design and dis-covery.A brief overview of membrane technologies isfirst provided with the current bottlenecks and potential solutions.Through an appli-cations-based perspective of AI-aided membrane design and discovery,we further show how ML strategies are applied to the membrane discovery cycle(including membrane material design,membrane application,membrane process design,and knowledge extraction),in various membrane systems,ranging from gas,liquid,and fuel cell separation membranes.Furthermore,the best practices of integrating ML methods and specific application targets in membrane design and discovery are presented with an ideal paradigm proposed.The challenges to be addressed and prospects of AI applications in membrane discovery are also highlighted in the end.展开更多
Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainti...Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.展开更多
The importance of genetic factors in substance addiction has long been established.The rationale for this work is that understanding of the function of addiction genes and delineation of the key molecular pathways of ...The importance of genetic factors in substance addiction has long been established.The rationale for this work is that understanding of the function of addiction genes and delineation of the key molecular pathways of these genes would enhance the development of novel therapeutic targets and biomarkers that could be used in the prevention and management of substance abuse.Over the past few years,there has been a substantial increase in the number of genetic studies conducted on addiction in China;these studies have primarily focused on heroin,alcohol,and nicotine dependence.Most studies of candidate genes have concentrated on the dopamine,opioid,and serotonin systems.A number of genes associated with substance abuse in Caucasians are also risk factors in Chinese,but several novel genes and genetic risk factors associated with substance abuse in Chinese subjects have also been identified.This paper reviews the genetic studies of substance abuse performed by Chinese researchers.Genotypes and alleles related to addictive behavior in Chinese individuals are discussed and the contributions of Chinese researchers to the international corpus of knowledge about the genetic understanding of substance abuse are described.展开更多
基金the National Natural Science Foundation of China(Grant Nos.12074242 and 51862032)the Ministry of Science and Higher Education of Russia(theme“Spin”No.AAAA-A-18-118020290104-2)the Government of the Russian Federation(Grant No.02.A03.21.0006)。
文摘The single crystals of Nd_(0.5)Pr_(0.5)FeO_(3)were successfully grown by optical floating zone method.Room temperature x-ray diffraction and Laue photograph declared the homogeneity and high quality of the crystal.The significant magnetic anisotropy and multiple magnetic transitions illustrate the complex magnetic structure.At high temperatures(T>66 K),it shows the typical characteristics ofΓ_(4)(G_(x),A_(y),F_(z))state.With the decrease of the temperature,it undergoes a first-order spin reorientation transition fromΓ_(4)(G_(x),A_(y),F_(z))toΓ_(2)(F_(x),C_(y),G_(z))state in the temperature window of 45-66 K under an applied magnetic field of 0.01 T.As the temperature drops to~17 K,a new magnetic interaction mechanism works,which results in a further enhancement of magnetization.The T-H phase diagram of Nd_(0.5)Pr_(0.5)FeO_(3)single crystal was finally constructed.
基金This work is supported by the National Key R&D Program of China(No.2022ZD0117501)the Singapore RIE2020 Advanced Manufacturing and Engineering Programmatic Grant by the Agency for Science,Technology and Research(A*STAR)under grant no.A1898b0043Tsinghua University Initiative Scientific Research Program and Low Carbon En-ergy Research Funding Initiative by A*STAR under grant number A-8000182-00-00.
文摘Membrane technologies are becoming increasingly versatile and helpful today for sustainable development.Machine Learning(ML),an essential branch of artificial intelligence(AI),has substantially impacted the research and development norm of new materials for energy and environment.This review provides an overview and perspectives on ML methodologies and their applications in membrane design and dis-covery.A brief overview of membrane technologies isfirst provided with the current bottlenecks and potential solutions.Through an appli-cations-based perspective of AI-aided membrane design and discovery,we further show how ML strategies are applied to the membrane discovery cycle(including membrane material design,membrane application,membrane process design,and knowledge extraction),in various membrane systems,ranging from gas,liquid,and fuel cell separation membranes.Furthermore,the best practices of integrating ML methods and specific application targets in membrane design and discovery are presented with an ideal paradigm proposed.The challenges to be addressed and prospects of AI applications in membrane discovery are also highlighted in the end.
基金supported by the National Key Research and Development Project of China(2018YFE0122200).
文摘Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.
基金provided by the National Research Program of China(No.2009CB522007)the Natural Science Foundation of China(No.91132719)
文摘The importance of genetic factors in substance addiction has long been established.The rationale for this work is that understanding of the function of addiction genes and delineation of the key molecular pathways of these genes would enhance the development of novel therapeutic targets and biomarkers that could be used in the prevention and management of substance abuse.Over the past few years,there has been a substantial increase in the number of genetic studies conducted on addiction in China;these studies have primarily focused on heroin,alcohol,and nicotine dependence.Most studies of candidate genes have concentrated on the dopamine,opioid,and serotonin systems.A number of genes associated with substance abuse in Caucasians are also risk factors in Chinese,but several novel genes and genetic risk factors associated with substance abuse in Chinese subjects have also been identified.This paper reviews the genetic studies of substance abuse performed by Chinese researchers.Genotypes and alleles related to addictive behavior in Chinese individuals are discussed and the contributions of Chinese researchers to the international corpus of knowledge about the genetic understanding of substance abuse are described.
