In the present work, a series of Pt-based catalysts, alloyed with a second metal, i.e., Re, Sn, Er, La, and Y, and supported on activated carbon, ordered mesoporous carbon, N-doped mesoporous carbon or reduced graphen...In the present work, a series of Pt-based catalysts, alloyed with a second metal, i.e., Re, Sn, Er, La, and Y, and supported on activated carbon, ordered mesoporous carbon, N-doped mesoporous carbon or reduced graphene oxide(rGO), have been developed for selective hydrogenation of cinnamaldehyde to cinnamylalcohol. Re and rGO were proved to be the most favorable metal dopant and catalyst support, respectively. Pt_(50) Re_(50)/rGO showed the highest cinnamylalcohol selectivity of 89% with 94% conversion of cinnamaldehyde at the reaction conditions of 120 °C, 2.0 MPaH_2 and 4 h.展开更多
In this paper,a nonlinear time-fractional coupled diffusion system is solved by using a mixed finite element(MFE)method in space combined with L1-approximation and implicit second-order backward difference scheme in t...In this paper,a nonlinear time-fractional coupled diffusion system is solved by using a mixed finite element(MFE)method in space combined with L1-approximation and implicit second-order backward difference scheme in time.The stability for nonlinear fully discrete finite element scheme is analyzed and a priori error estimates are derived.Finally,some numerical tests are shown to verify our theoretical analysis.展开更多
Cell segmentation and counting play a very important role in the medical field.The diagnosis of many diseases relies heavily on the kind and number of cells in the blood.convolution neural network achieves encouraging...Cell segmentation and counting play a very important role in the medical field.The diagnosis of many diseases relies heavily on the kind and number of cells in the blood.convolution neural network achieves encouraging results on image segmentation.However,this data-driven method requires a large number of annotations and can be a time-consuming and expensive process,prone to human error.In this paper,we present a novel frame to segment and count cells without too many manually annotated cell images.Before training.we generated the cell image labels on single-kind cell images using traditional algorithms.These images were then used to form the train set with the label.Different train sets composed of different kinds of cell images are presented to the segmentation model to update its parameters.Finally,the pretrained U-Net model is transferred to segment the mixed cell images using a small dataset of manually labeled mixed cell images.To better evaluate the efectiveness of the proposed method,we design and train a new automatic cell segmentation and count framework.The test results and analyses show that the segmentation and count performance of the framework trained by the proposed method equal the model trained by large amounts of annotated mixed cell images.展开更多
基金Supported by the National Natural Science Foundation of China(21476211)the Zhejiang Provincial Natural Science Foundation of China(LY16B060004 and LY18B060016)
文摘In the present work, a series of Pt-based catalysts, alloyed with a second metal, i.e., Re, Sn, Er, La, and Y, and supported on activated carbon, ordered mesoporous carbon, N-doped mesoporous carbon or reduced graphene oxide(rGO), have been developed for selective hydrogenation of cinnamaldehyde to cinnamylalcohol. Re and rGO were proved to be the most favorable metal dopant and catalyst support, respectively. Pt_(50) Re_(50)/rGO showed the highest cinnamylalcohol selectivity of 89% with 94% conversion of cinnamaldehyde at the reaction conditions of 120 °C, 2.0 MPaH_2 and 4 h.
基金the National Natural Science Fund(11661058,11301258,11361035)the Natural Science Fund of Inner Mongolia Autonomous Region(2016MS0102,2015MS0101)+1 种基金the Scientific Research Projection of Higher Schools of Inner Mongolia(NJZZ12011)the National Undergraduate Innovative Training Project(201510126026).
文摘In this paper,a nonlinear time-fractional coupled diffusion system is solved by using a mixed finite element(MFE)method in space combined with L1-approximation and implicit second-order backward difference scheme in time.The stability for nonlinear fully discrete finite element scheme is analyzed and a priori error estimates are derived.Finally,some numerical tests are shown to verify our theoretical analysis.
基金support from the National Key R&D Program of China(No.2019YFB1309700)the Bejing Nova Program of Science and Technology under Grant No.Z19100001119003.
文摘Cell segmentation and counting play a very important role in the medical field.The diagnosis of many diseases relies heavily on the kind and number of cells in the blood.convolution neural network achieves encouraging results on image segmentation.However,this data-driven method requires a large number of annotations and can be a time-consuming and expensive process,prone to human error.In this paper,we present a novel frame to segment and count cells without too many manually annotated cell images.Before training.we generated the cell image labels on single-kind cell images using traditional algorithms.These images were then used to form the train set with the label.Different train sets composed of different kinds of cell images are presented to the segmentation model to update its parameters.Finally,the pretrained U-Net model is transferred to segment the mixed cell images using a small dataset of manually labeled mixed cell images.To better evaluate the efectiveness of the proposed method,we design and train a new automatic cell segmentation and count framework.The test results and analyses show that the segmentation and count performance of the framework trained by the proposed method equal the model trained by large amounts of annotated mixed cell images.
