Mineral structure-stimulated material design has made great success in the development of excellent phosphor materials.Herein,spinel-type oxides Mg_(4)Ga_(1−y)Al_(y)SbO_(8)(MGA_(y)SO)with a double 2:1 ordering of Mg/(...Mineral structure-stimulated material design has made great success in the development of excellent phosphor materials.Herein,spinel-type oxides Mg_(4)Ga_(1−y)Al_(y)SbO_(8)(MGA_(y)SO)with a double 2:1 ordering of Mg/(Ga/Al)and Mg/Sb cations in tetrahedral and octahedral sublattices,respectively,were rationally designed and structurally characterized by combined Rietveld refinements against high-resolution X-ray powder diffraction(XRPD)data and neutron powder diffraction(NPD)data.A joint hybrid density functional theory(DFT)calculation and crystal orbital Hamilton population(COHP)analysis demonstrated that these new spinels are direct semiconductors with band gap values increasing along with the Al^(3+)content due to the lift of anti-bonding states from the Sb_(2)–O pairs.Mn-activated MGSO exhibited dual emissions from multiple green-emitting Mn^(2+)and red-emitting Mn^(4+)activators due to the facile occurrence of Mn^(4+)-to-Mn^(2+)self-reduction,which is inevitable in Mn-doped spinel-type phosphors.This self-reduction can be effectively inhibited by the site-selective Al^(3+)-to-Ga^(3+)substitution in MGA_(y)SO:Mn^(2+/4+),thereby resulting in an accumulation of Mn ions in the octahedrally coordinated sites and a tunable emission colour from green to yellow and then to deep-red.Interestingly,Mn^(2+)green emissions presented excellent anti-thermal quenching(165.4%at 463 K)in a very wide temperature range(303–463 K),whereas severe thermal quenching was observed for the Mn^(4+)red emissions.This distinctive thermal response could be applied in temperature sensing,as demonstrated by a high relative sensitivity(S_(r))of 1.22%K^(-1)at room temperature(303 K),which is superior to many reported optical thermometry materials.Our findings not only offer structural insight into new doubly ordered spinels,but also provide an effective strategy for regulating the valence states of Mn ions for potential application in light-emitting diodes and temperature sensing.展开更多
Three-dimensional skeleton-based action recognition(3D SAR)has gained important attention within the computer vision community,owing to the inherent advantages offered by skeleton data.As a result,a plethora of impres...Three-dimensional skeleton-based action recognition(3D SAR)has gained important attention within the computer vision community,owing to the inherent advantages offered by skeleton data.As a result,a plethora of impressive works,including those based on conventional handcrafted features and learned feature extraction methods,have been conducted over the years.However,prior surveys on action recognition have primarily focused on video or red-green-blue(RGB)data-dominated approaches,with limited coverage of reviews related to skeleton data.Furthermore,despite the extensive application of deep learning methods in this field,there has been a notable absence of research that provides an introductory or comprehensive review from the perspective of deep learning architectures.To address these limitations,this survey first underscores the importance of action recognition and emphasizes the significance of 3-dimensional(3D)skeleton data as a valuable modality.Subsequently,we provide a comprehensive introduction to mainstream action recognition techniques based on 4 fundamental deep architectures,i.e.,recurrent neural networks,convolutional neural networks,graph convolutional network,and Transformers.All methods with the corresponding architectures are then presented in a data-driven manner with detailed discussion.Finally,we offer insights into the current largest 3D skeleton dataset,NTU-RGB+D,and its new edition,NTU-RGB+D 120,along with an overview of several top-performing algorithms on these datasets.To the best of our knowledge,this research represents the first comprehensive discussion of deep learning-based action recognition using 3D skeleton data.展开更多
Supported on available paleomagnetic data,a new Columbia(Nuna)supercontinent reconstruction is proposed based on matching U-Pb-dated 1.79-1.75 Ga Large Igneous Province(LIP)mafic unit fragments and particularly on lin...Supported on available paleomagnetic data,a new Columbia(Nuna)supercontinent reconstruction is proposed based on matching U-Pb-dated 1.79-1.75 Ga Large Igneous Province(LIP)mafic unit fragments and particularly on linking their dykes into radiating systems.Information from the literature is augmented with the herein dated 1762 Ma(U-Pb)Januária dyke swarm from the São Francisco Craton(Brazil).展开更多
基金National Natural Science Foundation of China(no.22271030 and 22171032)Natural Science Foundation of Chongqing(cstc2021jcyj-msxmX0971)。
文摘Mineral structure-stimulated material design has made great success in the development of excellent phosphor materials.Herein,spinel-type oxides Mg_(4)Ga_(1−y)Al_(y)SbO_(8)(MGA_(y)SO)with a double 2:1 ordering of Mg/(Ga/Al)and Mg/Sb cations in tetrahedral and octahedral sublattices,respectively,were rationally designed and structurally characterized by combined Rietveld refinements against high-resolution X-ray powder diffraction(XRPD)data and neutron powder diffraction(NPD)data.A joint hybrid density functional theory(DFT)calculation and crystal orbital Hamilton population(COHP)analysis demonstrated that these new spinels are direct semiconductors with band gap values increasing along with the Al^(3+)content due to the lift of anti-bonding states from the Sb_(2)–O pairs.Mn-activated MGSO exhibited dual emissions from multiple green-emitting Mn^(2+)and red-emitting Mn^(4+)activators due to the facile occurrence of Mn^(4+)-to-Mn^(2+)self-reduction,which is inevitable in Mn-doped spinel-type phosphors.This self-reduction can be effectively inhibited by the site-selective Al^(3+)-to-Ga^(3+)substitution in MGA_(y)SO:Mn^(2+/4+),thereby resulting in an accumulation of Mn ions in the octahedrally coordinated sites and a tunable emission colour from green to yellow and then to deep-red.Interestingly,Mn^(2+)green emissions presented excellent anti-thermal quenching(165.4%at 463 K)in a very wide temperature range(303–463 K),whereas severe thermal quenching was observed for the Mn^(4+)red emissions.This distinctive thermal response could be applied in temperature sensing,as demonstrated by a high relative sensitivity(S_(r))of 1.22%K^(-1)at room temperature(303 K),which is superior to many reported optical thermometry materials.Our findings not only offer structural insight into new doubly ordered spinels,but also provide an effective strategy for regulating the valence states of Mn ions for potential application in light-emitting diodes and temperature sensing.
基金supported by the National Natural Science Foundation of China(No.62203476)the Natural Science Foundation of Shenzhen(No.JCYJ20230807120801002).
文摘Three-dimensional skeleton-based action recognition(3D SAR)has gained important attention within the computer vision community,owing to the inherent advantages offered by skeleton data.As a result,a plethora of impressive works,including those based on conventional handcrafted features and learned feature extraction methods,have been conducted over the years.However,prior surveys on action recognition have primarily focused on video or red-green-blue(RGB)data-dominated approaches,with limited coverage of reviews related to skeleton data.Furthermore,despite the extensive application of deep learning methods in this field,there has been a notable absence of research that provides an introductory or comprehensive review from the perspective of deep learning architectures.To address these limitations,this survey first underscores the importance of action recognition and emphasizes the significance of 3-dimensional(3D)skeleton data as a valuable modality.Subsequently,we provide a comprehensive introduction to mainstream action recognition techniques based on 4 fundamental deep architectures,i.e.,recurrent neural networks,convolutional neural networks,graph convolutional network,and Transformers.All methods with the corresponding architectures are then presented in a data-driven manner with detailed discussion.Finally,we offer insights into the current largest 3D skeleton dataset,NTU-RGB+D,and its new edition,NTU-RGB+D 120,along with an overview of several top-performing algorithms on these datasets.To the best of our knowledge,this research represents the first comprehensive discussion of deep learning-based action recognition using 3D skeleton data.
基金support of the Brazilian National Council for Scientific and Technological Development(CNPq).
文摘Supported on available paleomagnetic data,a new Columbia(Nuna)supercontinent reconstruction is proposed based on matching U-Pb-dated 1.79-1.75 Ga Large Igneous Province(LIP)mafic unit fragments and particularly on linking their dykes into radiating systems.Information from the literature is augmented with the herein dated 1762 Ma(U-Pb)Januária dyke swarm from the São Francisco Craton(Brazil).
