The Experts below are selected from a list of 80895 Experts worldwide ranked by ideXlab platform
Camille Hamon - One of the best experts on this subject based on the ideXlab platform.
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a Stochastic optimal power flow problem with stability constraints part i approximating the stability boundary
IEEE Transactions on Power Systems, 2013Co-Authors: Camille Hamon, Magnus Perninge, Lennart SoderAbstract:Stochastic optimal power flow can provide the system operator with adequate strategies for controlling the power flow to maintain secure operation under Stochastic Parameter variations. One limitation of Stochastic optimal power flow has been that only line flows have been used as security constraints. In many systems voltage stability and small-signal stability also play an important role in constraining the operation. In this paper we aim to extend the Stochastic optimal power flow problem to include constraints for voltage stability as well as small-signal stability. This is done by approximating the voltage stability and small-signal stability constraint boundaries with second-order approximations in Parameter space. Then we refine methods from mathematical finance to be able to estimate the probability of violating the constraints. In this first part of the paper, we derive second-order approximations of stability boundaries in Parameter space. In the second part, the approximations will be used to solve a Stochastic optimal power flow problem.
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a Stochastic optimal power flow problem with stability constraints part ii the optimization problem
IEEE Transactions on Power Systems, 2013Co-Authors: Magnus Perninge, Camille HamonAbstract:Stochastic optimal power flow can provide the system operator with adequate strategies for controlling the power flow to maintain secure operation under Stochastic Parameter variations. One limitation of Stochastic optimal power flow has been that only limits on line flows have been used as stability constraints. In many systems voltage stability and small-signal stability also play an important role in constraining the operation. In this paper we aim to extend the Stochastic optimal power flow problem to include constraints for voltage stability as well as small-signal stability. This is done by approximating the voltage stability and small-signal stability constraint surfaces with second-order approximations in Parameter space. Then we refine methods from mathematical finance to be able to estimate the probability of violating the constraints. In this, the second part of the paper, we look at how Cornish-Fisher expansion combined with a method of excluding sets that are counted twice, can be used to estimate the probability of violating the stability constraints. We then show in a numerical example how this leads to an efficient solution method for the Stochastic optimal power flow problem.
Amy Brock - One of the best experts on this subject based on the ideXlab platform.
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cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an allee effect
PLOS Biology, 2019Co-Authors: Kaitlyn E Johnson, Grant R Howard, William Mo, Michael Strasser, Ernesto A B F Lima, Sui Huang, Amy BrockAbstract:Most models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations to account for observed slowing of growth rate only at higher densities due to limited resources such as space and nutrients. However, recent preclinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale positively with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models, however, tumor growth data are limited by the lower limit of detection (i.e., a measurable lesion) and confounding variables, such as tumor microenvironment, and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a Stochastic modeling framework to disentangle expected deviations due to small population size Stochastic effects from cooperative growth and use the moment approach for Stochastic Parameter estimation to calibrate the observed growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells increases with cell number in the regime of small population size. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging and relapsed tumors.
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cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an allee effect
bioRxiv, 2019Co-Authors: Kaitlyn E Johnson, Grant R Howard, William Mo, Michael Strasser, Ernesto A B F Lima, Sui Huang, Amy BrockAbstract:Abstract Models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations from exponential growth only at higher densities due to limited resources such as space and nutrients. However, recent pre-clinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models however, tumor growth data is limited by the lower limit of detection (i.e. a measurable lesion) and confounding variables, such as tumor microenvironment and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a Stochastic modeling framework to consider the small number of cells in this low-density regime and use the moment approach for Stochastic Parameter estimation to calibrate the Stochastic growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells depends on cell number. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging tumors and relapse. Author Summary The growth kinetics of cancer cells at very low cell densities are of utmost clinical importance as the ability of a small number of newly transformed or surviving cells to grow exponentially and thus, to “take off” underlies tumor formation and relapse after treatment. Mathematical models of Stochastic tumor cell growth typically assume a Stochastic birth-death process of cells impacted by limited nutrients and space when cells reach high density, resulting in the widely accepted logistic growth model. Here we present an in-depth investigation of alternate growth models adopted from ecology to describe potential deviations from a simple cell autonomous birth-death model at low cell densities. We show that our Stochastic modeling framework is robust and can be used to identify the underlying structure of Stochastic growth trajectories from both simulated and experimental data taken from a controlled in vitro setting in which we can capture data from the relevant low cell density regime. This work suggests that the assumption of cell autonomous proliferation via a constant exponential growth rate at low cell densities may not be appropriate for all cancer cell growth dynamics. Consideration of cooperative behavior amongst tumor cells in this regime is critical for elucidating strategies for controlling tumor cell growth.
Elizabeth Buckinghamjeffery - One of the best experts on this subject based on the ideXlab platform.
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spatio temporal modelling of leishmania infantum infection among domestic dogs a simulation study and sensitivity analysis applied to rural brazil
Parasites & Vectors, 2019Co-Authors: Elizabeth Buckinghamjeffery, Edward M Hill, Samik Datta, Erin Dilger, Orin CourtenayAbstract:The parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease. Leishmania infantum parasites are transmitted between hosts during blood-feeding by infected female phlebotomine sand flies. With a principal reservoir host of L. infantum being domestic dogs, limiting prevalence in this reservoir may result in a reduced risk of infection for the human population. To this end, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. One way this can be achieved is through the use of mathematical models. We have developed a Stochastic, spatial, individual-based mechanistic model of L. infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with Parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fitted distributions for these entities to existing survey data. To identify the model Parameters of highest importance, we performed a Stochastic Parameter sensitivity analysis of the prevalence of infection among dogs to the model Parameters. We computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The Stochastic Parameter sensitivity analysis determined prevalence of L. infantum infection in dogs to be most strongly affected by the sand fly associated Parameters and the proportion of immigrant dogs already infected with L. infantum parasites. Establishing the model Parameters with the highest sensitivity of average L. infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions.
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spatio temporal modelling of leishmania infantum infection among domestic dogs a simulation study and sensitivity analysis applied to rural brazil
bioRxiv, 2018Co-Authors: Elizabeth Buckinghamjeffery, Edward M Hill, Samik Datta, Erin Dilger, Orin CourtenayAbstract:Background: The parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease. Leishmania infantum parasites are transmitted between hosts during blood feeding by infected female phlebotomine sand flies. With domestic dogs being a principal reservoir host of Leishmania infantum, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. The intention being that limiting prevalence in this reservoir will result in a reduced risk of infection for the human population. One way this can be achieved is through the use of mathematical models. Methods: We have developed a Stochastic, spatial, individual-based mechanistic model of Leishmania infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with Parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fit distributions for these to existing survey data. To identify the model Parameters of highest importance, we performed a Stochastic Parameter sensitivity analysis of the prevalence of infection among dogs to the model Parameters. Results: We computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The Stochastic Parameter sensitivity analysis determined prevalence of Leishmania infantum infection in dogs to be most strongly affected by the sand fly associated Parameters and the proportion of immigrant dogs already infected with Leishmania infantum parasites. Conclusions: Establishing the model Parameters with the highest sensitivity of average Leishmania infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions.
Shuli Sun - One of the best experts on this subject based on the ideXlab platform.
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optimal linear recursive estimators for Stochastic uncertain systems with time correlated additive noises and packet dropout compensations
Signal Processing, 2020Co-Authors: Shuli SunAbstract:Abstract This paper is concerned with the state estimation problem over a packet-dropping network for Stochastic uncertain systems with time-correlated additive noises. Additive process and measurement noises are both depicted by first-order Gauss-Markov processes. Stochastic Parameter uncertainties are described by correlated white multiplicative noises. When a sensor measurement transmitted over networks is lost, its predictor is used for compensation. The optimal linear recursive full-order state filter, predictor and smoother are proposed under the linear minimum variance (LMV) criterion by an innovation analysis approach. They are calculated in terms of the filter of the product of the multiplicative noise and state, estimators of the process and measurement noises, and cross-covariance matrices for the state and/or noises. The steady-state estimators are also studied. A sufficient condition for convergence of optimal linear estimators is given. The simulation results verify the effectiveness of the proposed algorithms.
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optimal sequential fusion estimation with Stochastic Parameter perturbations fading measurements and correlated noises
IEEE Transactions on Signal Processing, 2018Co-Authors: Honglei Lin, Shuli SunAbstract:This paper focuses on the linear optimal recursive sequential fusion filter design for multisensor systems subject to Stochastic Parameter perturbations, fading measurements, and correlated noises. The Stochastic Parameter perturbations existing in the state model are described by white multiplicative noises. The fading measurement phenomena for different sensors are described by independent random variables with known statistical properties. Moreover, the measurement noises of different sensors are correlated with each other and also correlated with the system noise at the same time step. First, a model equivalent to the original system is established by transferring the multiplicative noises into the additive noises. Then, based on the equivalent model and an innovation analysis method, a sequential fusion filter in the linear minimum variance sense is proposed to solve the linear optimal state estimation problem in real time according to the arriving order of measurements from different sensors. Finally, the equivalence on estimation accuracy of the proposed sequential fusion filter and the centralized fusion filter is strictly proven, which shows the optimality of the proposed sequential fusion algorithm. Moreover, the proposed sequential fusion filter has a reduced computational burden. Compared with the distributed matrix-weighted fusion filter, the computation of cross-covariance matrices is avoided and the estimation accuracy is improved. Finally, a simulation example verifies the effectiveness of the proposed sequential fusion filtering algorithm.
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fusion predictors for multisensor Stochastic uncertain systems with missing measurements and unknown measurement disturbances
IEEE Sensors Journal, 2015Co-Authors: Chongyan Pang, Shuli SunAbstract:This paper addresses the information fusion state estimation problem for multisensor Stochastic uncertain systems with missing measurements and unknown measurement disturbances. The missing measurements of sensors are described by Bernoulli distributed random variables. Measurements of sensors are subject to external disturbances whose any prior information is unknown. Stochastic Parameter uncertainties of systems are depicted by multiplicative noises. For such complex systems with multiple sensors, the Kalman-like centralized fusion and distributed fusion state one-step predictors (i.e., prior filters) independent of unknown measurement disturbances are designed based on the linear unbiased minimum variance criterion, respectively. Estimation error cross-covariance matrices between any two local predictors are derived. Their steady-state properties are analyzed. The sufficient conditions for the existence of the steady-state predictors are given. Two simulation examples show the effectiveness of the proposed algorithms.
Orin Courtenay - One of the best experts on this subject based on the ideXlab platform.
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spatio temporal modelling of leishmania infantum infection among domestic dogs a simulation study and sensitivity analysis applied to rural brazil
Parasites & Vectors, 2019Co-Authors: Elizabeth Buckinghamjeffery, Edward M Hill, Samik Datta, Erin Dilger, Orin CourtenayAbstract:The parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease. Leishmania infantum parasites are transmitted between hosts during blood-feeding by infected female phlebotomine sand flies. With a principal reservoir host of L. infantum being domestic dogs, limiting prevalence in this reservoir may result in a reduced risk of infection for the human population. To this end, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. One way this can be achieved is through the use of mathematical models. We have developed a Stochastic, spatial, individual-based mechanistic model of L. infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with Parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fitted distributions for these entities to existing survey data. To identify the model Parameters of highest importance, we performed a Stochastic Parameter sensitivity analysis of the prevalence of infection among dogs to the model Parameters. We computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The Stochastic Parameter sensitivity analysis determined prevalence of L. infantum infection in dogs to be most strongly affected by the sand fly associated Parameters and the proportion of immigrant dogs already infected with L. infantum parasites. Establishing the model Parameters with the highest sensitivity of average L. infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions.
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spatio temporal modelling of leishmania infantum infection among domestic dogs a simulation study and sensitivity analysis applied to rural brazil
bioRxiv, 2018Co-Authors: Elizabeth Buckinghamjeffery, Edward M Hill, Samik Datta, Erin Dilger, Orin CourtenayAbstract:Background: The parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease. Leishmania infantum parasites are transmitted between hosts during blood feeding by infected female phlebotomine sand flies. With domestic dogs being a principal reservoir host of Leishmania infantum, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. The intention being that limiting prevalence in this reservoir will result in a reduced risk of infection for the human population. One way this can be achieved is through the use of mathematical models. Methods: We have developed a Stochastic, spatial, individual-based mechanistic model of Leishmania infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with Parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fit distributions for these to existing survey data. To identify the model Parameters of highest importance, we performed a Stochastic Parameter sensitivity analysis of the prevalence of infection among dogs to the model Parameters. Results: We computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The Stochastic Parameter sensitivity analysis determined prevalence of Leishmania infantum infection in dogs to be most strongly affected by the sand fly associated Parameters and the proportion of immigrant dogs already infected with Leishmania infantum parasites. Conclusions: Establishing the model Parameters with the highest sensitivity of average Leishmania infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions.
