The Experts below are selected from a list of 168459 Experts worldwide ranked by ideXlab platform
Christopher J L Murray - One of the best experts on this subject based on the ideXlab platform.
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Efficiency of health care production in low resource settings a monte carlo simulation to compare the performance of data envelopment analysis stochastic distance functions and an ensemble model
PLOS ONE, 2016Co-Authors: Laura Di Giorgio, Abraham D Flaxman, Mark Moses, Nancy Fullman, Michael Hanlon, Ruben Conner, Alexandra Wollum, Christopher J L MurrayAbstract:Low-resource countries can greatly benefit from even small increases in Efficiency of health service provision, supporting a strong case to measure and pursue Efficiency improvement in low- and middle-income countries (LMICs). However, the knowledge base concerning Efficiency Measurement remains scarce for these contexts. This study shows that current estimation approaches may not be well suited to measure technical Efficiency in LMICs and offers an alternative approach for Efficiency Measurement in these settings. We developed a simulation environment which reproduces the characteristics of health service production in LMICs, and evaluated the performance of Data Envelopment Analysis (DEA) and Stochastic Distance Function (SDF) for assessing Efficiency. We found that an ensemble approach (ENS) combining Efficiency estimates from a restricted version of DEA (rDEA) and restricted SDF (rSDF) is the preferable method across a range of scenarios. This is the first study to analyze Efficiency Measurement in a simulation setting for LMICs. Our findings aim to heighten the validity and reliability of Efficiency analyses in LMICs, and thus inform policy dialogues about improving the Efficiency of health service production in these settings.
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Efficiency of health care production in low resource settings a monte carlo simulation to compare the performance of data envelopment analysis stochastic distance functions and an ensemble model
PLOS ONE, 2016Co-Authors: Laura Di Giorgio, Abraham D Flaxman, Mark Moses, Nancy Fullman, Michael Hanlon, Ruben Conner, Alexandra Wollum, Christopher J L MurrayAbstract:Low-resource countries can greatly benefit from even small increases in Efficiency of health service provision, supporting a strong case to measure and pursue Efficiency improvement in low- and middle-income countries (LMICs). However, the knowledge base concerning Efficiency Measurement remains scarce for these contexts. This study shows that current estimation approaches may not be well suited to measure technical Efficiency in LMICs and offers an alternative approach for Efficiency Measurement in these settings. We developed a simulation environment which reproduces the characteristics of health service production in LMICs, and evaluated the performance of Data Envelopment Analysis (DEA) and Stochastic Distance Function (SDF) for assessing Efficiency. We found that an ensemble approach (ENS) combining Efficiency estimates from a restricted version of DEA (rDEA) and restricted SDF (rSDF) is the preferable method across a range of scenarios. This is the first study to analyze Efficiency Measurement in a simulation setting for LMICs. Our findings aim to heighten the validity and reliability of Efficiency analyses in LMICs, and thus inform policy dialogues about improving the Efficiency of health service production in these settings.
Hao Qian - One of the best experts on this subject based on the ideXlab platform.
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design of high Efficiency bidirectional dc dc converter and high precision Efficiency Measurement
IEEE Transactions on Power Electronics, 2010Co-Authors: Wensong Yu, Hao QianAbstract:This paper first introduces the design of an ultrahigh Efficiency 50-kW bidirectional dc-dc converter at zero-voltage-switching operation, and then, a high-precision Efficiency Measurement method using a regenerative approach. The ultrahigh Efficiency bidirectional dc-dc converter is achieved with 1) the use of CoolMOS as the main switch under zero-voltage soft switching condition; 2) multiple-phase legs for current sharing to reduce the conduction loss; and 3) coupling inductors between each two-phase legs to reduce the core loss. Two identical hardware prototypes were designed, fabricated, and tested for performance evaluation. In order to precisely measure the converter Efficiency, the two identical bidirectional dc-dc converters are tested with one as the device under test and the other as the regenerative unit. With the use of ± 0.5% current shunt and regenerative Measurement, the relative Efficiency error stays below ±0.025%. Measured Efficiency with load from 20% to 100% consistently shows above 97.50%. At the 50 kW full-load condition, the Efficiency is 99.05% with ±0.01% Efficiency relative error.
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design of high Efficiency bidirectional dc dc converter and high precision Efficiency Measurement
Conference of the Industrial Electronics Society, 2008Co-Authors: Wensong Yu, Hao QianAbstract:This paper first introduces the design of an ultra high-Efficiency 50 kW bidirectional DC-DC converter at ZVS operation and then a high-precision Efficiency Measurement method using regenerative approach. The ultra high-Efficiency bidirectional DC-DC converter is achieved with (1) the use of CoolMOS as the main switch under zero-voltage soft switching condition (2) multiple phase legs for current sharing to reduce the conduction loss, and (3) coupling inductors between each two phase legs to reduce the core loss. Two identical hardware prototypes were designed, fabricated and tested for performance evaluation. In order to precisely measure the converter Efficiency, the two identical bidirectional DC-DC converters are tested with one as the device under test (DUT) and the other as the regenerative unit. With the use of plusmn0.5% current shunt and regenerative Measurement, the relative Efficiency error stays below plusmn0.025%. Measured Efficiency from 20% to 100% load consistently show above 97.50% and peaks at 99.05%.
Laura Di Giorgio - One of the best experts on this subject based on the ideXlab platform.
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Efficiency of health care production in low resource settings a monte carlo simulation to compare the performance of data envelopment analysis stochastic distance functions and an ensemble model
PLOS ONE, 2016Co-Authors: Laura Di Giorgio, Abraham D Flaxman, Mark Moses, Nancy Fullman, Michael Hanlon, Ruben Conner, Alexandra Wollum, Christopher J L MurrayAbstract:Low-resource countries can greatly benefit from even small increases in Efficiency of health service provision, supporting a strong case to measure and pursue Efficiency improvement in low- and middle-income countries (LMICs). However, the knowledge base concerning Efficiency Measurement remains scarce for these contexts. This study shows that current estimation approaches may not be well suited to measure technical Efficiency in LMICs and offers an alternative approach for Efficiency Measurement in these settings. We developed a simulation environment which reproduces the characteristics of health service production in LMICs, and evaluated the performance of Data Envelopment Analysis (DEA) and Stochastic Distance Function (SDF) for assessing Efficiency. We found that an ensemble approach (ENS) combining Efficiency estimates from a restricted version of DEA (rDEA) and restricted SDF (rSDF) is the preferable method across a range of scenarios. This is the first study to analyze Efficiency Measurement in a simulation setting for LMICs. Our findings aim to heighten the validity and reliability of Efficiency analyses in LMICs, and thus inform policy dialogues about improving the Efficiency of health service production in these settings.
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Efficiency of health care production in low resource settings a monte carlo simulation to compare the performance of data envelopment analysis stochastic distance functions and an ensemble model
PLOS ONE, 2016Co-Authors: Laura Di Giorgio, Abraham D Flaxman, Mark Moses, Nancy Fullman, Michael Hanlon, Ruben Conner, Alexandra Wollum, Christopher J L MurrayAbstract:Low-resource countries can greatly benefit from even small increases in Efficiency of health service provision, supporting a strong case to measure and pursue Efficiency improvement in low- and middle-income countries (LMICs). However, the knowledge base concerning Efficiency Measurement remains scarce for these contexts. This study shows that current estimation approaches may not be well suited to measure technical Efficiency in LMICs and offers an alternative approach for Efficiency Measurement in these settings. We developed a simulation environment which reproduces the characteristics of health service production in LMICs, and evaluated the performance of Data Envelopment Analysis (DEA) and Stochastic Distance Function (SDF) for assessing Efficiency. We found that an ensemble approach (ENS) combining Efficiency estimates from a restricted version of DEA (rDEA) and restricted SDF (rSDF) is the preferable method across a range of scenarios. This is the first study to analyze Efficiency Measurement in a simulation setting for LMICs. Our findings aim to heighten the validity and reliability of Efficiency analyses in LMICs, and thus inform policy dialogues about improving the Efficiency of health service production in these settings.
Wensong Yu - One of the best experts on this subject based on the ideXlab platform.
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design of high Efficiency bidirectional dc dc converter and high precision Efficiency Measurement
IEEE Transactions on Power Electronics, 2010Co-Authors: Wensong Yu, Hao QianAbstract:This paper first introduces the design of an ultrahigh Efficiency 50-kW bidirectional dc-dc converter at zero-voltage-switching operation, and then, a high-precision Efficiency Measurement method using a regenerative approach. The ultrahigh Efficiency bidirectional dc-dc converter is achieved with 1) the use of CoolMOS as the main switch under zero-voltage soft switching condition; 2) multiple-phase legs for current sharing to reduce the conduction loss; and 3) coupling inductors between each two-phase legs to reduce the core loss. Two identical hardware prototypes were designed, fabricated, and tested for performance evaluation. In order to precisely measure the converter Efficiency, the two identical bidirectional dc-dc converters are tested with one as the device under test and the other as the regenerative unit. With the use of ± 0.5% current shunt and regenerative Measurement, the relative Efficiency error stays below ±0.025%. Measured Efficiency with load from 20% to 100% consistently shows above 97.50%. At the 50 kW full-load condition, the Efficiency is 99.05% with ±0.01% Efficiency relative error.
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design of high Efficiency bidirectional dc dc converter and high precision Efficiency Measurement
Conference of the Industrial Electronics Society, 2008Co-Authors: Wensong Yu, Hao QianAbstract:This paper first introduces the design of an ultra high-Efficiency 50 kW bidirectional DC-DC converter at ZVS operation and then a high-precision Efficiency Measurement method using regenerative approach. The ultra high-Efficiency bidirectional DC-DC converter is achieved with (1) the use of CoolMOS as the main switch under zero-voltage soft switching condition (2) multiple phase legs for current sharing to reduce the conduction loss, and (3) coupling inductors between each two phase legs to reduce the core loss. Two identical hardware prototypes were designed, fabricated and tested for performance evaluation. In order to precisely measure the converter Efficiency, the two identical bidirectional DC-DC converters are tested with one as the device under test (DUT) and the other as the regenerative unit. With the use of plusmn0.5% current shunt and regenerative Measurement, the relative Efficiency error stays below plusmn0.025%. Measured Efficiency from 20% to 100% load consistently show above 97.50% and peaks at 99.05%.
Michael Hanlon - One of the best experts on this subject based on the ideXlab platform.
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Efficiency of health care production in low resource settings a monte carlo simulation to compare the performance of data envelopment analysis stochastic distance functions and an ensemble model
PLOS ONE, 2016Co-Authors: Laura Di Giorgio, Abraham D Flaxman, Mark Moses, Nancy Fullman, Michael Hanlon, Ruben Conner, Alexandra Wollum, Christopher J L MurrayAbstract:Low-resource countries can greatly benefit from even small increases in Efficiency of health service provision, supporting a strong case to measure and pursue Efficiency improvement in low- and middle-income countries (LMICs). However, the knowledge base concerning Efficiency Measurement remains scarce for these contexts. This study shows that current estimation approaches may not be well suited to measure technical Efficiency in LMICs and offers an alternative approach for Efficiency Measurement in these settings. We developed a simulation environment which reproduces the characteristics of health service production in LMICs, and evaluated the performance of Data Envelopment Analysis (DEA) and Stochastic Distance Function (SDF) for assessing Efficiency. We found that an ensemble approach (ENS) combining Efficiency estimates from a restricted version of DEA (rDEA) and restricted SDF (rSDF) is the preferable method across a range of scenarios. This is the first study to analyze Efficiency Measurement in a simulation setting for LMICs. Our findings aim to heighten the validity and reliability of Efficiency analyses in LMICs, and thus inform policy dialogues about improving the Efficiency of health service production in these settings.
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Efficiency of health care production in low resource settings a monte carlo simulation to compare the performance of data envelopment analysis stochastic distance functions and an ensemble model
PLOS ONE, 2016Co-Authors: Laura Di Giorgio, Abraham D Flaxman, Mark Moses, Nancy Fullman, Michael Hanlon, Ruben Conner, Alexandra Wollum, Christopher J L MurrayAbstract:Low-resource countries can greatly benefit from even small increases in Efficiency of health service provision, supporting a strong case to measure and pursue Efficiency improvement in low- and middle-income countries (LMICs). However, the knowledge base concerning Efficiency Measurement remains scarce for these contexts. This study shows that current estimation approaches may not be well suited to measure technical Efficiency in LMICs and offers an alternative approach for Efficiency Measurement in these settings. We developed a simulation environment which reproduces the characteristics of health service production in LMICs, and evaluated the performance of Data Envelopment Analysis (DEA) and Stochastic Distance Function (SDF) for assessing Efficiency. We found that an ensemble approach (ENS) combining Efficiency estimates from a restricted version of DEA (rDEA) and restricted SDF (rSDF) is the preferable method across a range of scenarios. This is the first study to analyze Efficiency Measurement in a simulation setting for LMICs. Our findings aim to heighten the validity and reliability of Efficiency analyses in LMICs, and thus inform policy dialogues about improving the Efficiency of health service production in these settings.
