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- 저자 : Ning Yilin (검색결과 2 건)
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Qin Yilin,Liao Wei,Lan Tu,Li Fengzhen,Li Feize,Yang Jijun,Liao Jiali,Yang Yuanyou,Liu Ning 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.12
Hydroxyurea (HU) is a novel salt-free reductant used potentially for the separation of U/Pu in the advanced PUREX process. In this work, the radiation stability of HU were systematically investigated in solution by examining the effects of the type of rays (a, b, and g irradiations), the absorbed dose (10 e50 kGy), and the HNO3 concentration (0e3 mol L1 ). The influence degree on HU radiolysis rates followed the order of the absorbed dose > the ray type > the HNO3 concentration, but the latter two had moderate effects on HU radiolysis products where NH4 þ and NO2 were found to be the most abundant ones, suggesting that the differences of a, b, and g rays should be considered in the study of irradiation effects. The radiolysis mechanism was explored using density functional theory (DFT) calculations, and it proposed the dominant radiolysis paths of HU, indicating that the radiolysis of HU was mainly a free radical reaction among $H, eaq e , H2O, intermediates, and the radiolytic free radical fragments of HU. The results reported here provide valuable insights into the mechanistic understanding of HU radiolysis under a, b, and g irradiations and reliable data support for the application of HU in the reprocessing of spent fuel.
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Explainable artificial intelligence in emergency medicine: an overview
Okada Yohei,Ning Yilin,Ong Marcus Eng Hock 대한응급의학회 2023 Clinical and Experimental Emergency Medicine Vol.10 No.4
Artificial intelligence (AI) and machine learning (ML) have potential to revolutionize emergency medical care by enhancing triage systems, improving diagnostic accuracy, refining prognostication, and optimizing various aspects of clinical care. However, as clinicians often lack AI expertise, they might perceive AI as a “black box,” leading to trust issues. To address this, “explainable AI,” which teaches AI functionalities to end-users, is important. This review presents the definitions, importance, and role of explainable AI, as well as potential challenges in emergency medicine. First, we introduce the terms explainability, interpretability, and transparency of AI models. These terms sound similar but have different roles in discussion of AI. Second, we indicate that explainable AI is required in clinical settings for reasons of justification, control, improvement, and discovery and provide examples. Third, we describe three major categories of explainability: pre-modeling explainability, interpretable models, and post-modeling explainability and present examples (especially for post-modeling explainability), such as visualization, simplification, text justification, and feature relevance. Last, we show the challenges of implementing AI and ML models in clinical settings and highlight the importance of collaboration between clinicians, developers, and researchers. This paper summarizes the concept of “explainable AI” for emergency medicine clinicians. This review may help clinicians understand explainable AI in emergency contexts.
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