Metal-based catalysis,including homogeneous and heterogeneous catalysis,plays a significant role in the modern chemical industry.Heterogeneous catalysis is widely used due to the high efficiency,easy catalyst separati...Metal-based catalysis,including homogeneous and heterogeneous catalysis,plays a significant role in the modern chemical industry.Heterogeneous catalysis is widely used due to the high efficiency,easy catalyst separation and recycling.However,the metal-utilization efficiency for conventional heterogeneous catalysts needs further improvement compared to homogeneous catalyst.To tackle this,the pursing of heterogenizing homogeneous catalysts has always been attractive but challenging.As a recently emerging class of catalytic material,single-atom catalysts(SACs)are expected to bridge homogeneous and heterogeneous catalytic process in organic reactions and have arguably become the most active new frontier in catalysis field.In this review,a brief introduction and development history of single-atom catalysis and SACs involved organic reactions are documented.In addition,recent advances in SACs and their practical applications in organic reactions such as oxidation,reduction,addition,coupling reaction,and other organic reactions are thoroughly reviewed.To understand structure-property relationships of single-atom catalysis in organic reactions,active sites or coordination structure,metal atom-utilization efficiency(e.g.,turnover frequency,TOF calculated based on active metal)and catalytic performance(e.g.,conversion and selectivity)of SACs are comprehensively summarized.Furthermore,the application limitations,development trends,future challenges and perspective of SAC for organic reaction are discussed.展开更多
Purpose–The purpose of this paper is to design a robust tracking algorithm which is suitable for the real-time requirement and solves the mistake labeling issue in the appearance model of trackers with the spare feat...Purpose–The purpose of this paper is to design a robust tracking algorithm which is suitable for the real-time requirement and solves the mistake labeling issue in the appearance model of trackers with the spare features.Design/methodology/approach–This paper proposes a tracker to select the most discriminative randomly projected ferns and integrates a coarse-to-fine search strategy in this framework.First,the authors exploit multiple instance boosting learning to maximize the bag likelihood and select randomly projected fern from feature pool to degrade the effect of mistake labeling.Second,a coarse-to-fine search approach is first integrated into the framework of multiple instance learning(MIL)for less detections.Findings–The quantitative and qualitative experiments demonstrate that the tracker has shown favorable performance in efficiency and effective among the competitors of tracking algorithms.Originality/value–The proposed method selects the feature from the compressive domain by MIL AnyBoost and integrates the coarse-to-fine search strategy first to reduce the burden of detection.This paper designs a tracker with high speed and favorable results which is more suitable for real-time scene.展开更多
基金financially supported by the Key Research and Development Program of Hubei Province(No.2022BAA026)the Major Project of Hubei Provincial Department of Education(No.D20211502)+1 种基金the Open/Innovation Project of Key Laboratory of Novel Biomass-Based Environmental and Energy Materials in Petroleum and Chemical Industry(No.2022BEEA06)support by the Postgraduate Innovation Foundation from Wuhan Institute of Technology(No.CX2021364)。
文摘Metal-based catalysis,including homogeneous and heterogeneous catalysis,plays a significant role in the modern chemical industry.Heterogeneous catalysis is widely used due to the high efficiency,easy catalyst separation and recycling.However,the metal-utilization efficiency for conventional heterogeneous catalysts needs further improvement compared to homogeneous catalyst.To tackle this,the pursing of heterogenizing homogeneous catalysts has always been attractive but challenging.As a recently emerging class of catalytic material,single-atom catalysts(SACs)are expected to bridge homogeneous and heterogeneous catalytic process in organic reactions and have arguably become the most active new frontier in catalysis field.In this review,a brief introduction and development history of single-atom catalysis and SACs involved organic reactions are documented.In addition,recent advances in SACs and their practical applications in organic reactions such as oxidation,reduction,addition,coupling reaction,and other organic reactions are thoroughly reviewed.To understand structure-property relationships of single-atom catalysis in organic reactions,active sites or coordination structure,metal atom-utilization efficiency(e.g.,turnover frequency,TOF calculated based on active metal)and catalytic performance(e.g.,conversion and selectivity)of SACs are comprehensively summarized.Furthermore,the application limitations,development trends,future challenges and perspective of SAC for organic reaction are discussed.
基金This work is supported by the National Natural Science Foundation of China under Grant No.61571345the Fundamental Research Funds for the Central Universities under Grant No.K5051203005the National Natural Science Foundation of China under Grant No.6150110247.
文摘Purpose–The purpose of this paper is to design a robust tracking algorithm which is suitable for the real-time requirement and solves the mistake labeling issue in the appearance model of trackers with the spare features.Design/methodology/approach–This paper proposes a tracker to select the most discriminative randomly projected ferns and integrates a coarse-to-fine search strategy in this framework.First,the authors exploit multiple instance boosting learning to maximize the bag likelihood and select randomly projected fern from feature pool to degrade the effect of mistake labeling.Second,a coarse-to-fine search approach is first integrated into the framework of multiple instance learning(MIL)for less detections.Findings–The quantitative and qualitative experiments demonstrate that the tracker has shown favorable performance in efficiency and effective among the competitors of tracking algorithms.Originality/value–The proposed method selects the feature from the compressive domain by MIL AnyBoost and integrates the coarse-to-fine search strategy first to reduce the burden of detection.This paper designs a tracker with high speed and favorable results which is more suitable for real-time scene.
