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RISS 인기검색어
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- 저자 : Wang Jiashuo (검색결과 2 건)
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Zhou Qing-Qing,Wang Jiashuo,Tang Wen,Hu Zhang-Chun,Xia Zi-Yi,Xue-Song Li,Zhang Rongguo,Yin Xindao,Zhang Bing,Zhang Hong 대한영상의학회 2020 Korean Journal of Radiology Vol.21 No.7
Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists’ workload.
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Core@shell MOFs derived Co2P/CoP@NPGC as a highly-active bifunctional electrocatalyst for ORR/OER
Weijia Gong,Hongyu Zhang,Liuyang Zhou,Ya Yang,Jiashuo Wang,Heng Liang 한국공업화학회 2022 Journal of Industrial and Engineering Chemistry Vol.106 No.-
In this study, Co2P/CoP hybrid nanoparticles (NPs) imbedded on the surface of core–shell metal–organicframeworks (MOFs) derived three-dimensional N, P co-doped graphitized carbon (Co2P/CoP@NPGC) areprepared via direct pyrolysis of P-containing MOF precursors. P dopant dosage is tailored to adjust activesites and crystalline phases of Co2P/CoP@NPGC. The active Co2P and CoP NPs and the synergistic effectfrom the Co-Nx/C and Co-P/C active sites and porous NPGC make the dominant contributions to theORR/OER. For ORR, the half-wave potential of Co2P/CoP@NPGC-1 is 0.93 V, which is superior to that ofPt/C (E1/2 = 0.875 V). As for OER, Co2P/CoP@NPGC-1 displays a lower overpotential (ƞ = 340 mV) comparedto RuO2 (ƞ = 380 mV, at 10 mA cm2). The Co2P@CoOOH heterojunction guarantees intrinsic conductivity. Furthermore, doping with N and P can modify the surface electronic structure of catalyst to lower theenergy of oxygen adsorption and dissociation, which are beneficial to enhance the ORR and OER activity. Additionally, its bifunctional activity parameter (DE) for ORR and OER is only 0.64 V, which is lower thanthat of Pt/C and RuO2 (0.76 V). Therefore, this work proposes a new sight into constructing a competitivecore–shell MOFs derived electrocatalyst for ORR/OER.
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