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Haiquan Liu,Jianing Rong,Yao Song,Guoqiong Shen,Wen Gu,Xin Liu 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2019 NANO Vol.14 No.6
We prepared a series of Gd2O3:Yb,Er,Ca (3/1/x mol.%, x represents the nominal concentration of Ca element including 0, 2, 3, 4, 5, 7, 10) upconversion nanoparticles (UCNPs) with regular morphology via a wet-chemical route. We observed a simultaneous enhancement of upconversion luminescence (UCL) emission of Gd2O3:Yb,Er after Ca2+ ions were doped. When the doping level of Ca2+ reaches its optimal concentrate at 5 mol.%, the red and green emissions increased by 6.3 and 11 times, respectively. The potential application of Gd2O3:Yb,Er,Ca material as a noninvasion optical thermometry based on FIR technique was investigated. The sensing of temperature at both high and room temperatures was realized by choosing different parameters. The absolute temperature sensitivity (S'a) of Gd2O3:Yb,Er nanoparticles at 293K reached 0.0875 K-1, whereas Gd2O3:Yb,Er, 5 mol.%Ca was chosen as sensor of high temperature due to its considerable Sa of 0.0060 K-1 at 573 K. The resultant UCNPs provided a new way for sensitive thermal detection at various target temperatures.
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A LightGBM and XGBoost Learning Method for Postoperative Critical Illness Key Indicators Analysis
( Lei Han ),( Yiziting Zhu ),( Yuwen Chen ),( Guoqiong Huang ),( Bin Yi ) 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.8
Accurate prediction of critical illness is significant for ensuring the lives and health of patients. The selection of indicators affects the real-time capability and accuracy of the prediction for critical illness. However, the diversity and complexity of these indicators make it difficult to find potential connections between them and critical illnesses. For the first time, this study proposes an indicator analysis model to extract key indicators from the preoperative and intraoperative clinical indicators and laboratory results of critical illnesses. In this study, preoperative and intraoperative data of heart failure and respiratory failure are used to verify the model. The proposed model processes the datum and extracts key indicators through four parts. To test the effectiveness of the proposed model, the key indicators are used to predict the two critical illnesses. The classifiers used in the prediction are light gradient boosting machine (LightGBM) and eXtreme Gradient Boosting (XGBoost). The predictive performance using key indicators is better than that using all indicators. In the prediction of heart failure, LightGBM and XGBoost have sensitivities of 0.889 and 0.892, and specificities of 0.939 and 0.937, respectively. For respiratory failure, LightGBM and XGBoost have sensitivities of 0.709 and 0.689, and specificity of 0.936 and 0.940, respectively. The proposed model can effectively analyze the correlation between indicators and postoperative critical illness. The analytical results make it possible to find the key indicators for postoperative critical illnesses. This model is meaningful to assist doctors in extracting key indicators in time and improving the reliability and efficiency of prediction.
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