The Experts below are selected from a list of 35157 Experts worldwide ranked by ideXlab platform
Geoffrey J Goodhill - One of the best experts on this subject based on the ideXlab platform.
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retraction a mathematical Model explains saturating axon guidance responses to molecular gradients
eLife, 2018Co-Authors: Huyen Nguyen, Peter Dayan, Zac Pujic, Justin J Cooperwhite, Geoffrey J GoodhillAbstract:Correct wiring is crucial for the proper functioning of the nervous system. Molecular gradients provide critical signals to guide growth cones, which are the motile tips of developing axons, to their targets. However, in vitro, growth cones trace highly stochastic trajectories, and exactly how molecular gradients bias their movement is unclear. Here, we introduce a mathematical Model based on persistence, bias, and noise to describe this behaviour, constrained directly by measurements of the detailed statistics of growth cone movements in both attractive and repulsive gradients in a microfluidic device. This Model provides a mathematical explanation for why average axon turning angles in gradients in vitro saturate very rapidly with time at relatively small values. This work introduces the most Accurate Predictive Model of growth cone trajectories to date, and deepens our understanding of axon guidance events both in vitro and in vivo.
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a mathematical Model explains saturating axon guidance responses to molecular gradients
eLife, 2016Co-Authors: Huyen Nguyen, Peter Dayan, Zac Pujic, Justin J Cooperwhite, Geoffrey J GoodhillAbstract:Correct wiring is crucial for the proper functioning of the nervous system. Molecular gradients provide critical signals to guide growth cones, which are the motile tips of developing axons, to their targets. However, in vitro, growth cones trace highly stochastic trajectories, and exactly how molecular gradients bias their movement is unclear. Here, we introduce a mathematical Model based on persistence, bias, and noise to describe this behaviour, constrained directly by measurements of the detailed statistics of growth cone movements in both attractive and repulsive gradients in a microfluidic device. This Model provides a mathematical explanation for why average axon turning angles in gradients in vitro saturate very rapidly with time at relatively small values. This work introduces the most Accurate Predictive Model of growth cone trajectories to date, and deepens our understanding of axon guidance events both in vitro and in vivo.
J.c. Álvarez Antón - One of the best experts on this subject based on the ideXlab platform.
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Battery State-of-Charge Estimator Using the MARS Technique
IEEE Transactions on Power Electronics, 2013Co-Authors: J.c. Álvarez Antón, P.j. García Nieto, Francisco Javier De Cos Juez, Cecilio Blanco Viejo, Fernando Las-heras, Nieves Roqueñí GutiérrezAbstract:State of charge (SOC) is the equivalent of a fuel gauge for a battery pack in an electric vehicle. Determining the state of charge is thus particularly important for electric vehicles (EVs), hybrid EVs, or portable devices. The aim of this innovative study is to estimate the SOC of a high-capacity lithium iron phosphate (LiFePO4) battery cell from an experimental dataset obtained in the University of Oviedo Battery Laboratory using the multivariate adaptive regression splines (MARS) technique. An Accurate Predictive Model able to forecast the SOC in the short term is obtained and it is a first step using the MARS technique to estimate the SOC of batteries. The agreement of the MARS Model with the experimental dataset confirmed the goodness of fit for a limited range of SOC (25-90% SOC) and for a simple dynamic data profile [constant-current (CC) constant-voltage charge-CC discharge].
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Battery state-of-charge estimator using the SVM technique
Applied Mathematical Modelling, 2013Co-Authors: J.c. Álvarez Antón, P.j. García Nieto, Francisco Javier De Cos Juez, F. Sánchez Lasheras, M. Gonzalez Vega, M.n. Roqueñí GutiérrezAbstract:Abstract State-of-charge (SOC) is the equivalent of a fuel gauge for a battery pack in an electric vehicle. Determining the state-of-charge becomes an important issue in all battery applications including electric vehicles (EV), hybrid electric vehicles (HEV) or portable devices. The aim of this innovative study is to estimate the SOC of a high capacity lithium iron phosphate (LiFePO 4 ) battery cell from an experimental data-set obtained in the University of Oviedo Battery Laboratory (UOB Lab) using support vector machine (SVM) approach. The SOC of a battery cannot be measured directly and must be estimated from measurable battery parameters such as current, voltage or temperature. An Accurate Predictive Model able to forecast the SOC in the short term is obtained. The agreement of the SVM Model with the experimental data-set confirmed its good performance.
M.n. Roqueñí Gutiérrez - One of the best experts on this subject based on the ideXlab platform.
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Battery state-of-charge estimator using the SVM technique
Applied Mathematical Modelling, 2013Co-Authors: J.c. Álvarez Antón, P.j. García Nieto, Francisco Javier De Cos Juez, F. Sánchez Lasheras, M. Gonzalez Vega, M.n. Roqueñí GutiérrezAbstract:Abstract State-of-charge (SOC) is the equivalent of a fuel gauge for a battery pack in an electric vehicle. Determining the state-of-charge becomes an important issue in all battery applications including electric vehicles (EV), hybrid electric vehicles (HEV) or portable devices. The aim of this innovative study is to estimate the SOC of a high capacity lithium iron phosphate (LiFePO 4 ) battery cell from an experimental data-set obtained in the University of Oviedo Battery Laboratory (UOB Lab) using support vector machine (SVM) approach. The SOC of a battery cannot be measured directly and must be estimated from measurable battery parameters such as current, voltage or temperature. An Accurate Predictive Model able to forecast the SOC in the short term is obtained. The agreement of the SVM Model with the experimental data-set confirmed its good performance.
Huyen Nguyen - One of the best experts on this subject based on the ideXlab platform.
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retraction a mathematical Model explains saturating axon guidance responses to molecular gradients
eLife, 2018Co-Authors: Huyen Nguyen, Peter Dayan, Zac Pujic, Justin J Cooperwhite, Geoffrey J GoodhillAbstract:Correct wiring is crucial for the proper functioning of the nervous system. Molecular gradients provide critical signals to guide growth cones, which are the motile tips of developing axons, to their targets. However, in vitro, growth cones trace highly stochastic trajectories, and exactly how molecular gradients bias their movement is unclear. Here, we introduce a mathematical Model based on persistence, bias, and noise to describe this behaviour, constrained directly by measurements of the detailed statistics of growth cone movements in both attractive and repulsive gradients in a microfluidic device. This Model provides a mathematical explanation for why average axon turning angles in gradients in vitro saturate very rapidly with time at relatively small values. This work introduces the most Accurate Predictive Model of growth cone trajectories to date, and deepens our understanding of axon guidance events both in vitro and in vivo.
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a mathematical Model explains saturating axon guidance responses to molecular gradients
eLife, 2016Co-Authors: Huyen Nguyen, Peter Dayan, Zac Pujic, Justin J Cooperwhite, Geoffrey J GoodhillAbstract:Correct wiring is crucial for the proper functioning of the nervous system. Molecular gradients provide critical signals to guide growth cones, which are the motile tips of developing axons, to their targets. However, in vitro, growth cones trace highly stochastic trajectories, and exactly how molecular gradients bias their movement is unclear. Here, we introduce a mathematical Model based on persistence, bias, and noise to describe this behaviour, constrained directly by measurements of the detailed statistics of growth cone movements in both attractive and repulsive gradients in a microfluidic device. This Model provides a mathematical explanation for why average axon turning angles in gradients in vitro saturate very rapidly with time at relatively small values. This work introduces the most Accurate Predictive Model of growth cone trajectories to date, and deepens our understanding of axon guidance events both in vitro and in vivo.
P.j. García Nieto - One of the best experts on this subject based on the ideXlab platform.
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Battery State-of-Charge Estimator Using the MARS Technique
IEEE Transactions on Power Electronics, 2013Co-Authors: J.c. Álvarez Antón, P.j. García Nieto, Francisco Javier De Cos Juez, Cecilio Blanco Viejo, Fernando Las-heras, Nieves Roqueñí GutiérrezAbstract:State of charge (SOC) is the equivalent of a fuel gauge for a battery pack in an electric vehicle. Determining the state of charge is thus particularly important for electric vehicles (EVs), hybrid EVs, or portable devices. The aim of this innovative study is to estimate the SOC of a high-capacity lithium iron phosphate (LiFePO4) battery cell from an experimental dataset obtained in the University of Oviedo Battery Laboratory using the multivariate adaptive regression splines (MARS) technique. An Accurate Predictive Model able to forecast the SOC in the short term is obtained and it is a first step using the MARS technique to estimate the SOC of batteries. The agreement of the MARS Model with the experimental dataset confirmed the goodness of fit for a limited range of SOC (25-90% SOC) and for a simple dynamic data profile [constant-current (CC) constant-voltage charge-CC discharge].
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Battery state-of-charge estimator using the SVM technique
Applied Mathematical Modelling, 2013Co-Authors: J.c. Álvarez Antón, P.j. García Nieto, Francisco Javier De Cos Juez, F. Sánchez Lasheras, M. Gonzalez Vega, M.n. Roqueñí GutiérrezAbstract:Abstract State-of-charge (SOC) is the equivalent of a fuel gauge for a battery pack in an electric vehicle. Determining the state-of-charge becomes an important issue in all battery applications including electric vehicles (EV), hybrid electric vehicles (HEV) or portable devices. The aim of this innovative study is to estimate the SOC of a high capacity lithium iron phosphate (LiFePO 4 ) battery cell from an experimental data-set obtained in the University of Oviedo Battery Laboratory (UOB Lab) using support vector machine (SVM) approach. The SOC of a battery cannot be measured directly and must be estimated from measurable battery parameters such as current, voltage or temperature. An Accurate Predictive Model able to forecast the SOC in the short term is obtained. The agreement of the SVM Model with the experimental data-set confirmed its good performance.
