The virus SARS-CoV2,which causes the Coronavirus disease COVID-19 has become a pandemic and has spread to every inhabited continent.Given the increasing caseload,there is an urgent need to augment clinical skills in o...The virus SARS-CoV2,which causes the Coronavirus disease COVID-19 has become a pandemic and has spread to every inhabited continent.Given the increasing caseload,there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness.We present a first step towards building an artificial intelligence(AI)framework,with predictive analytics(PA)capabilities applied to real patient data,to provide rapid clinical decision-making support.COVID-19 has presented a pressing need as a)clinicians are still developing clinical acumen given the disease’s novelty,and b)resource limitations in a rapidly expanding pandemic require difficult decisions relating to resource allocation.The objectives of this research are:(1)to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes,and(2)to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation.The predictive models learn from historical data to help predict specifically who will develop acute respiratory distress syndrome(ARDS),a severe outcome in COVID-19.Our experimental results based on two hospitals in Wenzhou,Zhejang,China identify features most predictive of ARDS in COVID-19 initial presentation which would not have stood out to clinicians.A mild increase in elevated alanine aminotransferase(ALT)(a liver enzyme)),a presence of myalgias(body aches),and an increase in hemoglobin,in this order,are the clinical features,on presentation,that are the most predictive.Those two centers’COVID-19 case series symptoms on initial presentation can help predict severe outcomes.Predictive models that learned from historical data of patients from two Chinese hospitals achieved 70%to 80%accuracy in predicting severe cases.展开更多
Side-chain symmetry-breaking strategy plays an important role in developing photovoltaic materials for high-efficiency all-small-molecule organic solar cells(ASM OSCs).However,the power conversion efficiencies(PCEs)of...Side-chain symmetry-breaking strategy plays an important role in developing photovoltaic materials for high-efficiency all-small-molecule organic solar cells(ASM OSCs).However,the power conversion efficiencies(PCEs)of ASM OSCs still lag behind their polymer-based counterparts,which can be attributed to the difficulties in achieving favorable morphology.Herein,two asymmetric porphyrin-based donors named DAPor-DPP and DDPor-DPP were synthesized,presenting stronger intermolecular interaction and closer molecular stacking compared to the symmetric ZnP-TEH.The DAPor-DPP:6TIC blend afforded a favorablemorphologywith nanoscale phase separation and more ordered molecular packing,thus achievingmore efficient charge transportation and suppressed charge recombination.Consequently,the DAPor-DPP:6TIC-based device exhibited superior photovoltaic parameters,yielding a champion PCE of 16.62%higher than that of the DDPor-DPP-based device(14.96%).To our knowledge,16.62%can be ranked as one of the highest PCE values among the binary ASM OSC filed.Thiswork provides a prospective approach to address the challenge ofASM OSCs in improving film morphology and further achieving high efficiency via side-chain symmetry-breaking strategy,exhibiting great potential in constructing efficient ASM OSCs.展开更多
In this Letter, an alternative solution is proposed and demonstrated for simultaneous measurement of axial strain and temperature. This sensor consists of two twisted points on a commercial single mode fiber introduce...In this Letter, an alternative solution is proposed and demonstrated for simultaneous measurement of axial strain and temperature. This sensor consists of two twisted points on a commercial single mode fiber introduced by flame-heated and rotation treatment. The fabrication process modifies the geometrical configuration and refractive index of the fiber. Different cladding modes are excited at the first twisted point, and part of them are coupled back to the fiber core at the second twisted point. Experimental results show distinct sensitivities of 34.9 pm/με with 49.23 pm/℃ and -36.19 pm/με with 62.99 pm/℃ for the two selected destructive interference wavelengths.展开更多
文摘The virus SARS-CoV2,which causes the Coronavirus disease COVID-19 has become a pandemic and has spread to every inhabited continent.Given the increasing caseload,there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness.We present a first step towards building an artificial intelligence(AI)framework,with predictive analytics(PA)capabilities applied to real patient data,to provide rapid clinical decision-making support.COVID-19 has presented a pressing need as a)clinicians are still developing clinical acumen given the disease’s novelty,and b)resource limitations in a rapidly expanding pandemic require difficult decisions relating to resource allocation.The objectives of this research are:(1)to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes,and(2)to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation.The predictive models learn from historical data to help predict specifically who will develop acute respiratory distress syndrome(ARDS),a severe outcome in COVID-19.Our experimental results based on two hospitals in Wenzhou,Zhejang,China identify features most predictive of ARDS in COVID-19 initial presentation which would not have stood out to clinicians.A mild increase in elevated alanine aminotransferase(ALT)(a liver enzyme)),a presence of myalgias(body aches),and an increase in hemoglobin,in this order,are the clinical features,on presentation,that are the most predictive.Those two centers’COVID-19 case series symptoms on initial presentation can help predict severe outcomes.Predictive models that learned from historical data of patients from two Chinese hospitals achieved 70%to 80%accuracy in predicting severe cases.
基金National Key Research and Development Program of China,Grant/Award Number:2022YFB4200400National Natural Science Foundation of China,Grant/Award Numbers:52172048,52103221,22205130,12175298+3 种基金Shandong Provincial Natural Science Foundation,Grant/Award Numbers:ZR2021QB024,ZR2021QB179,ZR2021ZD06,2023HWYQ-026Qingdao New Energy Shandong Laboratory Open Project,Grant/Award Number:QNESL OP 202309Guangdong Natural Science Foundation of China,Grant/Award Numbers:2023A1515012323,2023A1515010943,2022A1515110643,2024A1515010023Fundamental Research Funds of Shandong University。
文摘Side-chain symmetry-breaking strategy plays an important role in developing photovoltaic materials for high-efficiency all-small-molecule organic solar cells(ASM OSCs).However,the power conversion efficiencies(PCEs)of ASM OSCs still lag behind their polymer-based counterparts,which can be attributed to the difficulties in achieving favorable morphology.Herein,two asymmetric porphyrin-based donors named DAPor-DPP and DDPor-DPP were synthesized,presenting stronger intermolecular interaction and closer molecular stacking compared to the symmetric ZnP-TEH.The DAPor-DPP:6TIC blend afforded a favorablemorphologywith nanoscale phase separation and more ordered molecular packing,thus achievingmore efficient charge transportation and suppressed charge recombination.Consequently,the DAPor-DPP:6TIC-based device exhibited superior photovoltaic parameters,yielding a champion PCE of 16.62%higher than that of the DDPor-DPP-based device(14.96%).To our knowledge,16.62%can be ranked as one of the highest PCE values among the binary ASM OSC filed.Thiswork provides a prospective approach to address the challenge ofASM OSCs in improving film morphology and further achieving high efficiency via side-chain symmetry-breaking strategy,exhibiting great potential in constructing efficient ASM OSCs.
基金supported by the National Natural Science Foundation of China(Nos.61775070 and 61275083)the Fundamental Research Funds for the Central Universities(No.2017KFYXJJ032)
文摘In this Letter, an alternative solution is proposed and demonstrated for simultaneous measurement of axial strain and temperature. This sensor consists of two twisted points on a commercial single mode fiber introduced by flame-heated and rotation treatment. The fabrication process modifies the geometrical configuration and refractive index of the fiber. Different cladding modes are excited at the first twisted point, and part of them are coupled back to the fiber core at the second twisted point. Experimental results show distinct sensitivities of 34.9 pm/με with 49.23 pm/℃ and -36.19 pm/με with 62.99 pm/℃ for the two selected destructive interference wavelengths.
