The paper presents a novel approach for uncertainty propagation in multicomponent two-phase displacements. This approach originates from and extends the frozen streamlines (FROST) distributionmethod for two-phase Buc...
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The paper presents a novel approach for uncertainty propagation in multicomponent two-phase displacements. This approach originates from and extends the frozen streamlines (FROST) distributionmethod for two-phase Buckley-Leverett displacements described in Ibrahima et al. (Transp Porous Media 109(1):81-107, 2015). The developed FROST method for multicomponent systems relies on the analytical solutions of gas-oil displacements provided by the theory of gas injection processes. With examples considering a stochastic porosity field in 1-D, we apply the developed framework to uncertainty propagation in three- and four-component systems to obtain the probabilitydistribution of different compositions in space and time and verify that the results are in excellent agreement with the reference analytical Monte Carlo simulations.
Quantitative predictions of FtsZ protein polymerization are essential for understanding the self regulating mechanisms in biochemical systems. Due to structural complexity and parametric uncertainty, existing kinetic ...
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Quantitative predictions of FtsZ protein polymerization are essential for understanding the self regulating mechanisms in biochemical systems. Due to structural complexity and parametric uncertainty, existing kinetic models remain incomplete and their predictions error-prone. To address such challenges, we perform probabilistic uncertainty quantification and global sensitivity analysis of the concentrations of various protein species predicted with a recent FtsZ protein polymerization model. Our results yield a ranked list of modeling shortcomings that can be improved in order to develop more accurate predictions and more realistic representations of key mechanisms of such biochemical systems and their response to changes in internal or external conditions. Our conclusions and improvement recommendations can be extended to other kinetics models. (C) 2019 Elsevier Ltd. All rights reserved.
Cyber physical internet of things systems (CPIoTs), taking advantages of cyber physical systems, have been considered as a promising technology to provide better interaction and interoperability among various machines...
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Cyber physical internet of things systems (CPIoTs), taking advantages of cyber physical systems, have been considered as a promising technology to provide better interaction and interoperability among various machines. However, the development of CPIoTs suffers severely from big data. In this context, fog computing is proposed to handle the big data bottleneck of CPIoTs. In this study, the authors focus on the joint optimisation of the communication resources and computation resources in fog computing-based CPIoTs to maximise the overall system energy efficiency, in which multiple fog nodes and end users are taken into consideration. Moreover, since the channel estimation error will become serious with the expanding scale, the imperfect channel state information is considered in this study. The formulated optimisation problem is a mixed integer non-linear problem which is indeed non-deterministic polynomial hard, hence a probability distribution method is proposed to reformulate the problem into a non-probability form, and the resource allocation algorithm based on Dinkelbach algorithm and Lagrange duality approach is adopted to tackle the problem efficiently. The simulation results confirm the effectiveness of the proposed scheme, especially when the scales are enormous.
Blind deconvolution is a method of recovering transmitted signals from only received signals. The probability distribution method is one of the blind deconvolution methods. This method has two problems: it has slower ...
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Blind deconvolution is a method of recovering transmitted signals from only received signals. The probability distribution method is one of the blind deconvolution methods. This method has two problems: it has slower convergence and its reliability is lower. In this paper, we propose a new algorithm for solving these two problems. The proposed algorithm is as follows. (l) It is based on the adaptive processing with each sample. (2) Kurtosis is adaptively estimated by recovered signals with each sample. (3) Cost function is decided by kurtosis. (4) Transmitted signals are recovered by received signals using decided cost function on the sample time. We confirm the validity of the new algorithm by computer simulation. (c) 2007 Wiley Periodicals, Inc.
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