Most studies of coronavirus disease 2019(COVID-19)progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units.Little is known about the r...Most studies of coronavirus disease 2019(COVID-19)progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units.Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community iso-lation,who are either asymptomatic or suffer from only mild to moderate symptoms.Using a multivari-able competing risk survival analysis,we identify several important predictors of progression to severe COVID-19—rather than to recovery—among patients in the largest community isolation center in Wuhan,China from 6 February 2020(when the center opened)to 9 March 2020(when it closed).All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms.We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients(n=1753)in the isolation center.The potential predictors we inves-tigated were the routine patient data collected upon admission to the isolation center:age,sex,respira-tory symptoms,gastrointestinal symptoms,general symptoms,and computed tomography(CT)scan signs.The main outcomes were time to severe COVID-19 or recovery.The factors predicting progression to severe COVID-19 were:male sex(hazard ratio(HR)=1.29,95%confidence interval(CI)1.04–1.58,p=0.018),young and old age,dyspnea(HR=1.58,95%CI 1.24–2.01,p<0.001),and CT signs of ground-glass opacity(HR=1.39,95%CI 1.04–1.86,p=0.024)and infiltrating shadows(HR=1.84,95%CI 1.22–2.78,p=0.004).The risk of progression was found to be lower among patients with nausea or vomiting(HR=0.53,95%CI 0.30–0.96,p=0.036)and headaches(HR=0.54,95%CI 0.29–0.99,p=0.046).Our results suggest that several factors that can be easily measured even in resource-poor set-tings(dyspnea,sex,and age)can be used to identify mild COVID-19 patients who are at increased risk of disease progression.Looking for CT signs of ground-glass opacity and infiltrating shadows may be an affordable option to support triage decisions in resource-rich settings.Common and unspecific symptoms(headaches,nausea,and vomiting)are likely to have led to the identification and subsequent community isolation of COVID-19 patients who were relatively unlikely to deteriorate.Future public health and clinical guidelines should build on this evidence to improve the screening,triage,and monitoring of COVID-19 patients who are asymtomatic or suffer from mild to moderate symptoms.展开更多
Electrical power supply to communities isolated from urban areas is typically complex and expensive. Their geographical situation and the lack of infrastructure and qualified workforce impede the provision of electric...Electrical power supply to communities isolated from urban areas is typically complex and expensive. Their geographical situation and the lack of infrastructure and qualified workforce impede the provision of electricity. Many are the sources of energy available, but few are appropriate or sustainable. Combustion of diesel fuel is a good technical solution; however, it is neither economic nor environmentally tenable. This paper presents the advantages of using biomass as an energy source, along with its potential increase in efficiency when steam is generated in specific circumstances of temperature and pressure.展开更多
In rural territories, the communities use energy sources based on fossil fuels to supply themselves with electricity, which may address two main problems: greenhouse gas emissions and high fuel prices. Hence, there is...In rural territories, the communities use energy sources based on fossil fuels to supply themselves with electricity, which may address two main problems: greenhouse gas emissions and high fuel prices. Hence, there is an opportunity to include renewable resources in the energy mix. This paper develops an optimization model to determine the optimal sizing, the total annual investment cost in renewable generation, and other operating costs of the components of a hybrid microgrid. By running a k-means clustering algorithm on a meteorological dataset of the community under study, the hourly representative values become input parameters in the proposed optimization model. The method for the optimal design of hybrid microgrid is analyzed in six operating scenarios considering:(1) 24-hour continuous power supply;(2) load shedding percentage;(3) diesel power generator(genset) curtailment;(4) the worst meteorological conditions;(5) the use of renewable energy sources including battery energy storage systems(BESSs);and(6) the use of genset. A mathematical programming language(AMPL) tool is used to find solutions of the proposed optimization model. Results show that the total costs of microgrid in the scenarios that cover 100% of the load demand(without considering the scenario with 100% renewables) increase by over 16% compared with the scenario with genset operation limitation. For the designs with power supply restrictions, the total cost of microgrid in the scenario with load shedding is reduced by over 27% compared with that without load shedding.展开更多
With the increasing connection of controllable devices to isolated community microgrid,an economic operation model of isolated community microgrid based on the temperature regulation characteristics of temperature con...With the increasing connection of controllable devices to isolated community microgrid,an economic operation model of isolated community microgrid based on the temperature regulation characteristics of temperature controlling devices composed of wind turbine,micro-gas turbine,energy storage battery and heat pump is proposed.With full consideration of various economic costs,including fuel cost,start-stop cost,energy storage battery depletion expense and penalty for wind curtailment,the model is solved by hybrid particle swarm optimization(HPSO)algorithm.The optimal output of the micro-sources and total operating cost of the system in the scheduling cycle are also obtained.The case study demonstrates that temperature adjustment of temperature controlling devices can adjust the power load indirectly and increase the schedulability of the isolated community microgrid,and reduce the operating cost of the microgrid.展开更多
基金supported by the Alexander von Humboldt Foundation in Germany and the Bill & Melinda Gates Foundation (Project INV-006261)supported by the National Center for Advancing Translational Sciences of the National Institutes of Health (KL2TR003143)+4 种基金supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor awardfunded by the German Federal Ministry of Education and Research, the European Union’s Research and Innovation Programme Horizon 2020the European & Developing Countries Clinical Trials Partnership (EDCTP)supported by the Sino-German Center for Research Promotion (Project C-0048), which is funded by the German Research Foundation (DFG)the National Natural Science Foundation of China (NSFC)
文摘Most studies of coronavirus disease 2019(COVID-19)progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units.Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community iso-lation,who are either asymptomatic or suffer from only mild to moderate symptoms.Using a multivari-able competing risk survival analysis,we identify several important predictors of progression to severe COVID-19—rather than to recovery—among patients in the largest community isolation center in Wuhan,China from 6 February 2020(when the center opened)to 9 March 2020(when it closed).All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms.We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients(n=1753)in the isolation center.The potential predictors we inves-tigated were the routine patient data collected upon admission to the isolation center:age,sex,respira-tory symptoms,gastrointestinal symptoms,general symptoms,and computed tomography(CT)scan signs.The main outcomes were time to severe COVID-19 or recovery.The factors predicting progression to severe COVID-19 were:male sex(hazard ratio(HR)=1.29,95%confidence interval(CI)1.04–1.58,p=0.018),young and old age,dyspnea(HR=1.58,95%CI 1.24–2.01,p<0.001),and CT signs of ground-glass opacity(HR=1.39,95%CI 1.04–1.86,p=0.024)and infiltrating shadows(HR=1.84,95%CI 1.22–2.78,p=0.004).The risk of progression was found to be lower among patients with nausea or vomiting(HR=0.53,95%CI 0.30–0.96,p=0.036)and headaches(HR=0.54,95%CI 0.29–0.99,p=0.046).Our results suggest that several factors that can be easily measured even in resource-poor set-tings(dyspnea,sex,and age)can be used to identify mild COVID-19 patients who are at increased risk of disease progression.Looking for CT signs of ground-glass opacity and infiltrating shadows may be an affordable option to support triage decisions in resource-rich settings.Common and unspecific symptoms(headaches,nausea,and vomiting)are likely to have led to the identification and subsequent community isolation of COVID-19 patients who were relatively unlikely to deteriorate.Future public health and clinical guidelines should build on this evidence to improve the screening,triage,and monitoring of COVID-19 patients who are asymtomatic or suffer from mild to moderate symptoms.
文摘Electrical power supply to communities isolated from urban areas is typically complex and expensive. Their geographical situation and the lack of infrastructure and qualified workforce impede the provision of electricity. Many are the sources of energy available, but few are appropriate or sustainable. Combustion of diesel fuel is a good technical solution; however, it is neither economic nor environmentally tenable. This paper presents the advantages of using biomass as an energy source, along with its potential increase in efficiency when steam is generated in specific circumstances of temperature and pressure.
基金supported in part by SENESCYT and in part by PRESTIGE Research Group,and CERA from ESPOL.
文摘In rural territories, the communities use energy sources based on fossil fuels to supply themselves with electricity, which may address two main problems: greenhouse gas emissions and high fuel prices. Hence, there is an opportunity to include renewable resources in the energy mix. This paper develops an optimization model to determine the optimal sizing, the total annual investment cost in renewable generation, and other operating costs of the components of a hybrid microgrid. By running a k-means clustering algorithm on a meteorological dataset of the community under study, the hourly representative values become input parameters in the proposed optimization model. The method for the optimal design of hybrid microgrid is analyzed in six operating scenarios considering:(1) 24-hour continuous power supply;(2) load shedding percentage;(3) diesel power generator(genset) curtailment;(4) the worst meteorological conditions;(5) the use of renewable energy sources including battery energy storage systems(BESSs);and(6) the use of genset. A mathematical programming language(AMPL) tool is used to find solutions of the proposed optimization model. Results show that the total costs of microgrid in the scenarios that cover 100% of the load demand(without considering the scenario with 100% renewables) increase by over 16% compared with the scenario with genset operation limitation. For the designs with power supply restrictions, the total cost of microgrid in the scenario with load shedding is reduced by over 27% compared with that without load shedding.
基金the National Natural Science Foundation of China under Grant 51677011.
文摘With the increasing connection of controllable devices to isolated community microgrid,an economic operation model of isolated community microgrid based on the temperature regulation characteristics of temperature controlling devices composed of wind turbine,micro-gas turbine,energy storage battery and heat pump is proposed.With full consideration of various economic costs,including fuel cost,start-stop cost,energy storage battery depletion expense and penalty for wind curtailment,the model is solved by hybrid particle swarm optimization(HPSO)algorithm.The optimal output of the micro-sources and total operating cost of the system in the scheduling cycle are also obtained.The case study demonstrates that temperature adjustment of temperature controlling devices can adjust the power load indirectly and increase the schedulability of the isolated community microgrid,and reduce the operating cost of the microgrid.
