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您的检索式：作者名="Youyang Yang"
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| 1 | “Closing the chasm”-guidelines bridge the gap from evidence to implementation显示文摘Since the beginning of the Coronavirus Disease 2019(COVID-19)pandemic,over 9000 articles related to COVID-19 have been released in print by the end of December 2020.1 A majority of these articles were commentaries,several hundred were observational studies,55 were systematic reviews,and 4 were randomized controlled trials.The American College of Rheumatology has published updated clinical guidance for the management of multisystem inflammatory syndrome in children associated with SARS-CoV-2,the most recent version in April 2021.2,3 Additionally,practice guidelines for pediatric specialties in the setting of COVID-19 have also been published,including pulmonology,anesthesiology,infectious disease,orthopedics,and anticoagulation.4-8 The rapid development and dissemination of these guidelines during the pandemic has been unprecedented.Many of these guidelines are based primarily on consensus statements rather than randomized control trials,which have been the gold standard of evidence-based medicine.In this commentary,we discuss the role of practice guidelines from credible sources that are based on incrementally emerging evidence to guide bedside practice,until they can be updated with more robust data from meta-analyses of randomized trials. | Youyang Yang Nilesh M.Mehta | 2021 | Pediatric Investigation2021，5，3： | 0 |
| 2 | Divide and conquer: Machine learning accelerated design of lead-free solder alloys with high strength and high ductility显示文摘The attainment of both high strength and high ductility is always the goal for structure materials,because the two properties generally are mutually competing,called strength-ductility trade-off.Nowadays,the data-driven paradigm combined with expert domain knowledge provides the state-of-the-art methodology to design and discovery for structure materials with high strength and high ductility.To enhance both strength and ductility,a joint feature is proposed here to be the product of strength multiplying ductility.The strategy of“divide and conquer”is developed to solve the contradictory problem,that material experimental data of mechanical behaviors are,in general,small in size and big in noise,while the design space is huge,by a newly developed data preprocessing algorithm,named the Tree-Classifier for Gaussian Process Regression(TCGPR).The TCGPR effectively divides an original dataset in a huge design space into three appropriate sub-domains and then three Machine Learning(ML)models conquer the three sub-domains,achieving significantly improved prediction accuracy and generality.After that the Bayesian sampling is applied to design next experiments by balancing exploitation and exploration.Finally,the experiment results confirm the ML predictions,exhibiting novel lead-free solder alloys with high strength high ductility.Various material characterizations were also conducted to explore the mechanism of high strength and high ductility of the alloys. | Qinghua Wei Bin Cao Hao Yuan Youyang Chen Kangdong You Shuting Yu Tixin Yang Ziqiang Dong Tong-Yi Zhang | 2023 | npj Computational Materials2023，，1： | 0 |
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