Coronavirus disease 2019(COVID-19)is an infectious respiratory disease caused by the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which has infected 972,303 people and caused 50,322 deaths all over the ...Coronavirus disease 2019(COVID-19)is an infectious respiratory disease caused by the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which has infected 972,303 people and caused 50,322 deaths all over the world according to the latest WHO report.[1]As a highly contagious disease,COVID-19 has killed more people than severe acute respiratory syndrome(SARS)and middle east respiratory syndrome(MERS)combined,despite an relatively low case-fatality rate.[2,3]Although it mainly attacks respiratory system,other systems including cardiovascular system are also influenced by COVID-19.Acute cardiac injury(ACI)is also one of the noteworthy issues which researchers have noticed in several studies.[4–7] .展开更多
Climate change is a controversial topic of debate, especially in the US, where many do not believe in anthropogenic climate change. Because its consequences are predicted to be dire, such as a mass ocean extinction an...Climate change is a controversial topic of debate, especially in the US, where many do not believe in anthropogenic climate change. Because its consequences are predicted to be dire, such as a mass ocean extinction and frequent extreme weather events, it is important to learn what causes the warming in order to better combat it. In this study, the first challenge dwells on how to construct reliable statistical models based on massive climate data of 800,000 years and accurately capture the relationship between temperature and potential factors such as concentrations of carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4). We compared the performance several mainstream machine learning algorithms on our data, which includes linear regression, lasso, support vector regression and random forest, to build the state of the art model to verify the warming of the earth and identifying factors contributing the global warming. We found that random forest outperforms other algorithms to create accurate climate models which use features including concentrations of different greenhouse gases to precisely forecast global atmosphere. The other challenges in identifying factor importance can be met by the feature of ensemble tree-based random forest algorithm. It was found that CO2 is the largest contributor to temperature change, followed by CH4, then by N2O. They all had some sorts of impact, though, meaning their release into the atmosphere should all be controlled to help restrain temperature increase, and help prevent climate change’s potential ramifications.展开更多
基金supported by the Chinese Cardiovascular Association-V.G.fund(2017-CCA-VG-042).
文摘Coronavirus disease 2019(COVID-19)is an infectious respiratory disease caused by the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which has infected 972,303 people and caused 50,322 deaths all over the world according to the latest WHO report.[1]As a highly contagious disease,COVID-19 has killed more people than severe acute respiratory syndrome(SARS)and middle east respiratory syndrome(MERS)combined,despite an relatively low case-fatality rate.[2,3]Although it mainly attacks respiratory system,other systems including cardiovascular system are also influenced by COVID-19.Acute cardiac injury(ACI)is also one of the noteworthy issues which researchers have noticed in several studies.[4–7] .
文摘Climate change is a controversial topic of debate, especially in the US, where many do not believe in anthropogenic climate change. Because its consequences are predicted to be dire, such as a mass ocean extinction and frequent extreme weather events, it is important to learn what causes the warming in order to better combat it. In this study, the first challenge dwells on how to construct reliable statistical models based on massive climate data of 800,000 years and accurately capture the relationship between temperature and potential factors such as concentrations of carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4). We compared the performance several mainstream machine learning algorithms on our data, which includes linear regression, lasso, support vector regression and random forest, to build the state of the art model to verify the warming of the earth and identifying factors contributing the global warming. We found that random forest outperforms other algorithms to create accurate climate models which use features including concentrations of different greenhouse gases to precisely forecast global atmosphere. The other challenges in identifying factor importance can be met by the feature of ensemble tree-based random forest algorithm. It was found that CO2 is the largest contributor to temperature change, followed by CH4, then by N2O. They all had some sorts of impact, though, meaning their release into the atmosphere should all be controlled to help restrain temperature increase, and help prevent climate change’s potential ramifications.
