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| 1 | Pediatric reference intervals in China(PRINCE):design and rationale for a large,multicenter collaborative cross-sectional study显示文摘There is a lack of accurate pediatric reference intervals(RIs) in China, with most commonly used RIs established without consideration of the effect of age and gender. The Pediatric Reference Intervals in China(PRINCE) project aims to establish and verify pediatric RIs for 31 common laboratory measurands.The project will be a large, multicenter cross-sectional study:14,490 healthy children and adolescents aged up to 19 years will be surveyed by 10 children's hospitals and one pediatric department of a university hospital. To evaluate the feasibility and efficiency of the study methods, 602 children were surveyed in the pilot phase of the PRINCE study in April 2017: it found that some measurands were distinctly age dependent and that there were differences between values for males and females. The results of the pilot study affirmed the necessity of the PRINCE project for Chinese pediatrics. The pilot also indicated potential difficulties in the full survey, e.g., difficulties in recruiting children aged under 3 years and insufficient collection of blood samples from infants. The operation of the PRINCE project has been modified based on the findings in the pilot study toward improving the validity of the PRINCE project and promoting its openness and transparency. | Xin Ni Wenqi Song Xiaoxia Peng Ying Shen Yaguang Peng Qiliang Li Yan Wang Lixin Hu Yanying Cai Hong Shang Min Zhao Hong Jiang Yaoguo Huang Runqing Mu Wenxiang Chen Mingting Peng Chuanbao Zhang Jie Zeng Chenbin Li Hongling Yang Yongmei Jiang Jin Xu Guixia Li Hongbing Chen Yun Xiang Sancheng Cao Zhenxin Guo Dapeng Chen | 2018 | Science Bulletin2018，63，24： | 12 |
| 2 | Fast object detection based on selective visual attention显示文摘 | Guo Mingwei Zhao Yuzhou Zhang Chenbin | 2014 | Neurocomputing2014，144，： | 1 |
| 3 | A New Hybrid Machine Learning Model for Short-Term Climate Prediction by Performing Classification Prediction and Regression Prediction Simultaneously显示文摘Machine learning methods are effective tools for improving short-term climate prediction.However,commonly used methods often carry out classification and regression prediction modeling separately and independently.Such a single modeling approach may obtain inconsistent prediction results in classification and regression and thus may not meet the needs of practical applications well.To address this issue,this study proposes a selective Naive Bayes ensemble model(SENB-EM)by introducing causal effect and voting strategy on Naive Bayes.The new model can not only screen effective predictors but also perform classification and regression prediction simultaneously.After being applied to the area prediction of summer western North Pacific subtropical high(WNPSH)from 2008 to 2021,it is found that the accuracy classification score(a metric to assess the overall classification prediction accuracy)and the time correlation coefficient(TCC)of SENB-EM can reach 1.0 and 0.81,respectively.After integrating the results of different models[including multiple linear regression ensemble model(MLR-EM),SENB-EM,and Chinese Multimodel Ensemble Prediction System(CMME)used by National Climate Center(NCC)]for 2017-2021,the TCC of the ensemble results of SENB-EM and CMME can reach 0.92(the highest result among them).This indicates that the prediction results of the summer WNPSH area provided by SENB-EM have a high reference value for the real-time prediction.It is worth noting that,except for the numerical prediction results,the SENB-EM model can also give the range of numerical prediction intervals and predictions for anomalous degrees of the WNPSH area,thus providing more reference information for meteorological forecasters.Overall,as a new hybrid machine learning model,the SENB-EM has a good prediction ability;the approach of performing classification prediction and regression prediction simultaneously through integration is informative to short-term climate prediction. | Deqian LI Shujuan HU Jinyuan GUO Kai WANG Chenbin GAO Siyi WANG Wenping HE | 2022 | Journal of Meteorological Research2022，36，6： | 0 |
| 4 | Holocene temperature variation recorded by branched glycerol dialkyl glycerol tetraethers in a loess-paleosol sequence from the north-eastern Tibetan Plateau显示文摘Reconstructing Holocene temperature evolution is important for understanding present temperature variations and for predicting future climate change,in the context of global warming.The evolution of Holocene global temperature remains disputed,due to differences between proxy reconstructions and model simulations,a discrepancy known as the῾Holocene temperature conundrum᾽.More reliable and quantitative terrestrial temperature records are needed to resolve the spatial heterogeneity of existing records.In this study,based on the analysis of branched glycerol dialkyl glycerol tetraethers(brGDGTs)from a loess-paleosol sequence from the Ganjia Basin in the north-eastern Tibetan Plateau(NETP),we quantitatively reconstructed the mean annual air temperature(MAAT)over the past 12 ka.The MAAT reconstruction shows that the temperature remained low during the early Holocene(12−8 ka),followed by a rapid warming at around 8 ka.From 8 to 4 ka,the MAAT record reached its highest level,followed by a cooling trend from the late Holocene(4−0 ka).The variability of the reconstructed MAAT is consistent with trends of annual temperature records from the Tibetan Plateau(TP)during the Holocene.We attribute the relatively low temperatures during the early Holocene to the existence of ice sheets at high-latitude regions in the Northern Hemisphere and the weaker annual mean insolation at 35°N.During the mid to late Holocene,the long-term cooling trend in the annual temperature record was primarily driven by declining summer insolation.This study provides key geological evidence for clarifying Holocene temperature change in the TP. | Tianxiao WANG Duo WU Tao WANG Lin CHEN Shilong GUO Youmo LI Chenbin ZHANG | 2023 | Frontiers of Earth Science2023，17，4： | 0 |
| 5 | Economic stability analysis of blue carbon cooperation in the South China sea region using evolutionary game model with Weber's law显示文摘The political environment of the South China Sea Region(SCSR)has gradually stabilized,such that regional cooperation in the preservation of marine resources seems realistic.Blue carbon international cooperation is an important solution to the problem of global warming,which has a large number of economic and political attributes.As a region that has incredibly abundant blue carbon resources,further cooperation among SCSR governments would present the opportunity to establish meaningful economic and environmental protections that would promote peaceful blue carbon development of this region.To examine the feasibility of such an undertaking,we leverage the imitator's dynamic game as a research method and introduce Weber's law to examine the subjective psychological factors(i.e.,biases)of participants in qualifying the economic stability of blue carbon cooperation in the SCSR.The results suggest that the economic stability of blue carbon cooperation correlates to Weber's coefficient and the income produced by the different strategies.Based on these findings,we discussed policy recommendations to promote the sustainable economic development of SCSR. | Changping Zhao Xiaojiang Xu Mengru Liu Yu Gong Chenbin Guo | 2020 | Chinese Journal of Population,Resources and Environment2020，18，3： | 0 |
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