Nanoparticle Mass Transfer From Lung Airways to Systemic Regions—Part II: Multi-Compartmental Modeling

2013 ◽  
Vol 135 (12) ◽  
Author(s):  
Arun V. Kolanjiyil ◽  
Clement Kleinstreuer
Keyword(s):  
Mass Transfer ◽  
Model Parameters ◽  
Computationally Efficient ◽  
Nanoparticle Deposition ◽  
Aerosol Dynamics ◽  
Multicompartmental Model ◽  
Quantitative Results ◽  
Species Extrapolation ◽  
Critical Insight ◽  
Lung Airways

This is the second article of a two-part paper, combining high-resolution computer simulation results of inhaled nanoparticle deposition in a human airway model (Kolanjiyil and Kleinstreuer, 2013, “Nanoparticle Mass Transfer From Lung Airways to Systemic Regions—Part I: Whole-Lung Aerosol Dynamics,” ASME J. Biomech. Eng., 135(12), p. 121003) with a new multicompartmental model for insoluble nanoparticle barrier mass transfer into systemic regions. Specifically, it allows for the prediction of temporal nanoparticle accumulation in the blood and lymphatic systems and in organs. The multicompartmental model parameters were determined from experimental retention and clearance data in rat lungs and then the validated model was applied to humans based on pharmacokinetic cross-species extrapolation. This hybrid simulator is a computationally efficient tool to predict the nanoparticle kinetics in the human body. The study provides critical insight into nanomaterial deposition and distribution from the lungs to systemic regions. The quantitative results are useful in diverse fields such as toxicology for exposure-risk analysis of ubiquitous nanomaterial and pharmacology for nanodrug development and targeting.

Nanoparticle Mass Transfer From Lung Airways to Systemic Regions—Part I: Whole-Lung Aerosol Dynamics

2013 ◽  
Vol 135 (12) ◽  
Author(s):  
Arun V. Kolanjiyil ◽  
Clement Kleinstreuer
Keyword(s):  
Mass Transfer ◽  
Respiratory Tract ◽  
Current Model ◽  
Lung Model ◽  
Model Parameters ◽  
Therapeutic Effects ◽  
Human Respiratory Tract ◽  
Nanoparticle Transport ◽  
Multicompartmental Model ◽  
In Series

This is a two-part paper describing inhaled nanoparticle (NP) transport and deposition in a model of a human respiratory tract (Part I) as well as NP-mass transfer across barriers into systemic regions (Part II). Specifically, combining high-resolution computer simulation results of inhaled NP deposition in the human airways (Part I) with a multicompartmental model for NP-mass transfer (Part II) allows for the prediction of temporal NP accumulation in the blood and lymphatic systems as well as in organs. An understanding of nanoparticle transport and deposition in human respiratory airways is of great importance, as exposure to nanomaterial has been found to cause serious lung diseases, while the use of nanodrugs may have superior therapeutic effects. In Part I, the fluid-particle dynamics of a dilute NP suspension was simulated for the entire respiratory tract, assuming steady inhalation and planar airways. Thus, a realistic airway configuration was considered from nose/mouth to generation 3, and then an idealized triple-bifurcation unit was repeated in series and parallel to cover the remaining generations. Using the current model, the deposition of NPs in distinct regions of the lung, namely extrathoracic, bronchial, bronchiolar, and alveolar, was calculated. The region-specific NP-deposition results for the human lung model were used in Part II to determine the multicompartmental model parameters from experimental retention and clearance data in human lungs. The quantitative, experimentally validated results are useful in diverse fields, such as toxicology for exposure-risk analysis of ubiquitous nanomaterial as well as in pharmacology for nanodrug development and targeting.

Nearly Exact Bayesian Estimation of Non-linear No-Arbitrage Term-Structure Models*

2021 ◽  
Author(s):  
Marcello Pericoli ◽  
Marco Taboga
Keyword(s):  
Bayesian Estimation ◽  
Term Structure ◽  
Broad Class ◽  
Model Parameters ◽  
State Variables ◽  
Computationally Efficient ◽  
Macroeconomic Factors ◽  
No Arbitrage ◽  
Term Structure Models ◽  
Non Linear

Abstract We propose a general method for the Bayesian estimation of a very broad class of non-linear no-arbitrage term-structure models. The main innovation we introduce is a computationally efficient method, based on deep learning techniques, for approximating no-arbitrage model-implied bond yields to any desired degree of accuracy. Once the pricing function is approximated, the posterior distribution of model parameters and unobservable state variables can be estimated by standard Markov Chain Monte Carlo methods. As an illustrative example, we apply the proposed techniques to the estimation of a shadow-rate model with a time-varying lower bound and unspanned macroeconomic factors.

scholarly journals Bayesian Inference of Species Trees using Diffusion Models

2020 ◽  
Vol 70 (1) ◽  
pp. 145-161 ◽  
Author(s):  
Marnus Stoltz ◽  
Boris Baeumer ◽  
Remco Bouckaert ◽  
Colin Fox ◽  
Gordon Hiscott ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Numerical Algorithms ◽  
Diffusion Models ◽  
Model Parameters ◽  
Data Sets ◽  
Species Trees ◽  
Computationally Efficient ◽  
Data Set ◽  
Snp Data ◽  
Binary Markers

Abstract We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of data sets containing hundreds or thousands of individuals. The method, which we call Snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements, and accuracy recovering known model parameters. A reanalysis of soybean SNP data demonstrates that the models implemented in Snapp and Snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP data set sampled from 399 fresh water turtles in 41 populations. [Bayesian inference; diffusion models; multi-species coalescent; SNP data; species trees; spectral methods.]

scholarly journals Application of a computationally efficient method to approximate gap model results with a probabilistic approach

2014 ◽  
Vol 7 (1) ◽  
pp. 1535-1600
Author(s):  
M. Scherstjanoi ◽  
J. O. Kaplan ◽  
H. Lischke
Keyword(s):  
Climate Change ◽  
Efficient Method ◽  
Tree Species ◽  
Forest Dynamics ◽  
Probabilistic Approach ◽  
Age Distribution ◽  
Change Scenario ◽  
Model Parameters ◽  
Computationally Efficient ◽  
Tree Species Composition

Abstract. To be able to simulate climate change effects on forest dynamics over the whole of Switzerland, we adapted the second generation DGVM LPJ-GUESS to the Alpine environment. We modified model functions, tuned model parameters, and implemented new tree species to represent the potential natural vegetation of Alpine landscapes. Furthermore, we increased the computational efficiency of the model to enable area-covering simulations in a fine resolution (1 km) sufficient for the complex topography of the Alps, which resulted in more than 32 000 simulation grid cells. To this aim, we applied the recently developed method GAPPARD (Scherstjanoi et al., 2013) to LPJ-GUESS. GAPPARD derives mean output values from a combination of simulation runs without disturbances and a patch age distribution defined by the disturbance frequency. With this computationally efficient method, that increased the model's speed by approximately the factor 8, we were able to faster detect shortcomings of LPJ-GUESS functions and parameters. We used the adapted LPJ-GUESS together with GAPPARD to assess the influence of one climate change scenario on dynamics of tree species composition and biomass throughout the 21st century in Switzerland. To allow for comparison with the original model, we additionally simulated forest dynamics along a north-south-transect through Switzerland. The results from this transect confirmed the high value of the GAPPARD method despite some limitations towards extreme climatic events. It allowed for the first time to obtain area-wide, detailed high resolution LPJ-GUESS simulation results for a large part of the Alpine region.

scholarly journals Modeling of the adsorption kinetics of zinc onto granular activated carbon and natural zeolite

2006 ◽  
Vol 71 (8-9) ◽  
pp. 957-967 ◽  
Author(s):  
Ljiljana Markovska ◽  
Vera Meshko ◽  
Mirko Marinkovski
Keyword(s):  
Mass Transfer ◽  
Activated Carbon ◽  
Simulation Study ◽  
Natural Zeolite ◽  
Granular Activated Carbon ◽  
Model Parameters ◽  
Mass Transfer Resistance ◽  
External Mass Transfer ◽  
Transfer Resistance ◽  
Kinetics Of

The isotherms and kinetics of zinc adsorption from aqueous solution onto granular activated carbon (GAC) and natural zeolite were studied using an agitated batch adsorber. The maximum adsorption capacities of GAC and natural zeolite towards zinc(II) from Langmuir adsorption isotherms were determined using experimental adsorption equilibrium data. The homogeneous solid diffusion model (HSD-model) combined with external mass transfer resistance was applied to fit the experimental kinetic data. The kinetics simulation study was performed using a computer program based on the proposed mathematical model and developed using gPROMS. As the two-mass transfer resistance approach was applied, two model parameters were fitted during the simulation study. External mass transfer and solid phase diffusion coefficients were obtained to predict the kinetic curves for varying initial Zn(II) concentration at constant agitation speed and constant adsorbent mass. For any particular Zn(II) - adsorbent system, k f was constant, except for the lowest initial concentration, while D s was found to increase with increasing initial Zn(II) concentration.

scholarly journals Efficient approximations for stationary single-channel Ca2+ nanodomains across length scales

2020 ◽  
Author(s):  
Y Chen ◽  
C Muratov ◽  
V Matveev
Keyword(s):  
Parameter Space ◽  
Single Channel ◽  
Asymptotic Series ◽  
Rational Functions ◽  
Great Accuracy ◽  
Model Parameters ◽  
Relevant Parameter ◽  
Buffer Concentration ◽  
Computationally Efficient ◽  
Wide Range

ABSTRACTWe consider the stationary solution for the Ca2+ concentration near a point Ca2+ source describing a single-channel Ca2+ nanodomain, in the presence of a single mobile Ca2+ buffer with one-to-one Ca2+ binding. We present computationally efficient approximants that estimate stationary single-channel Ca2+ nanodomains with great accuracy in broad regions of parameter space. The presented approximants have a functional form that combines rational and exponential functions, which is similar to that of the well-known Excess Buffer Approximation and the linear approximation, but with parameters estimated using two novel (to our knowledge) methods. One of the methods involves interpolation between the short-range Taylor series of the buffer concentration and its long-range asymptotic series in inverse powers of distance from the channel. Although this method has already been used to find Padé (rational-function) approximants to single-channel Ca2+ and buffer concentration, extending this method to interpolants combining exponential and rational functions improves accuracy in a significant fraction of the relevant parameter space. A second method is based on the variational approach, and involves a global minimization of an appropriate functional with respect to parameters of the chosen approximations. Extensive parameter sensitivity analysis is presented, comparing these two methods with previously developed approximants. Apart from increased accuracy, the strength of these approximants is that they can be extended to more realistic buffers with multiple binding sites characterized by cooperative Ca2+ binding, such as calmodulin and calretinin.STATEMENT OF SIGNIFICANCEMathematical and computational modeling plays an important role in the study of local Ca2+ signals underlying vesicle exocysosis, muscle contraction and other fundamental physiological processes. Closed-form approximations describing steady-state distribution of Ca2+ in the vicinity of an open Ca2+ channel have proved particularly useful for the qualitative modeling of local Ca2+ signals. We present simple and efficient approximants for the Ca2+ concentration in the presence of a mobile Ca2+ buffer, which achieve great accuracy over a wide range of model parameters. Such approximations provide an efficient method for estimating Ca2+ and buffer concentrations without resorting to numerical simulations, and allow to study the qualitative dependence of nanodomain Ca2+ distribution on the buffer’s Ca2+ binding properties and its diffusivity.

scholarly journals Implementation of Spiegler–Kedem and Steric Hindrance Pore Models for Analyzing Nanofiltration Membrane Performance for Smart Water Production

Membranes ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 78 ◽  
Author(s):  
Remya Nair ◽  
Evgenia Protasova ◽  
Skule Strand ◽  
Torleiv Bilstad
Keyword(s):  
Mass Transfer ◽  
Reflection Coefficient ◽  
Oil Recovery ◽  
Water Permeability ◽  
Structural Parameters ◽  
Pure Water ◽  
Model Parameters ◽  
Water Production ◽  
Solute Permeability ◽  
Smart Water

A predictive model correlating the parameters in the mass transfer-based model Spiegler–Kedem to the pure water permeability is presented in this research, which helps to select porous polyamide membranes for enhanced oil recovery (EOR) applications. Using the experimentally obtained values of flux and rejection, the reflection coefficient σ and solute permeability Ps have been estimated as the mass transfer-based model parameters for individual ions in seawater. The reflection coefficient and solute permeability determined were correlated with the pure water permeability of a membrane, which is related to the structural parameters of a membrane. The novelty of this research is the development of a model that consolidates the various complex mechanisms in the mass transfer of ions through the membrane to an empirical correlation for a given feed concentration and membrane type. These correlations were later used to predict ion rejections of any polyamide membrane with a known pure water permeability and flux with seawater as a feed that aids in the selection of suitable nanofiltration (NF) for smart water production.

Recycling and Extraction of Zinc Ions in Disc-Donut Column Considering Forward Mixing Mass Transfer, Chemical Reaction and Effects of Pulsed and non-Pulsed Condition

2021 ◽  
Author(s):  
Mehdi Asadollahzadeh ◽  
Rezvan Torkaman ◽  
Meisam Torab-Mostaedi ◽  
Mojtaba Saremi
Keyword(s):  
Experimental Data ◽  
Mass Transfer ◽  
Flow Rate ◽  
Positive Impact ◽  
Plug Flow ◽  
Model Parameters ◽  
Zinc Ions ◽  
Zinc Extraction ◽  
Balance Equations ◽  
Transfer Rates

Abstract The current study focuses on the recovery of zinc ions by solvent extraction in the pulsed contactor. The Zn(II) ions from chloride solution were extracted into the organic phase containing D2EHPA extractant. The resulting data were characterized for the relative amount of (a) pulsed and no-pulsed condition; and (b) flow rate of both phases. Based on the mass balance equations for the column performance description, numerical computations of mass transfer in a disc-donut column were conducted and validated the experimental data for zinc extraction. Four different models, such as plug flow, backflow, axial dispersion, and forward mixing were evaluated in this study. The results showed that the intensification of the process with the pulsed condition increased and achieved higher mass transfer rates. The forward mixing model findings based on the curve fitting approach validated well with the experimental data. The results showed that an increase in pulsation intensity, as well as the phase flow rates, have a positive impact on the performance of the extractor, whereas the enhancement of flow rate led to the reduction of the described model parameters for adverse phase.

scholarly journals Joint Maximum Likelihood of Phylogeny and Ancestral States Is Not Consistent

2019 ◽  
Vol 36 (10) ◽  
pp. 2352-2357
Author(s):  
David A Shaw ◽  
Vu C Dinh ◽  
Frederick A Matsen
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Branch Length ◽  
Continuous Model ◽  
Model Parameters ◽  
Infinite Length ◽  
Computationally Efficient ◽  
Ancestral States ◽  
Joint Method

Abstract Maximum likelihood estimation in phylogenetics requires a means of handling unknown ancestral states. Classical maximum likelihood averages over these unknown intermediate states, leading to provably consistent estimation of the topology and continuous model parameters. Recently, a computationally efficient approach has been proposed to jointly maximize over these unknown states and phylogenetic parameters. Although this method of joint maximum likelihood estimation can obtain estimates more quickly, its properties as an estimator are not yet clear. In this article, we show that this method of jointly estimating phylogenetic parameters along with ancestral states is not consistent in general. We find a sizeable region of parameter space that generates data on a four-taxon tree for which this joint method estimates the internal branch length to be exactly zero, even in the limit of infinite-length sequences. More generally, we show that this joint method only estimates branch lengths correctly on a set of measure zero. We show empirically that branch length estimates are systematically biased downward, even for short branches.

The Effect of Model Parameters on CFD Simulation of a Thermosyphon

2019 ◽  
Author(s):  
Huiyu Wang ◽  
D. Keith Walters ◽  
Keisha B. Walters
Keyword(s):  
Mass Transfer ◽  
Phase Change ◽  
Change Process ◽  
Flow Behavior ◽  
Density Ratio ◽  
Model Parameters ◽  
Special Focus ◽  
Transfer Coefficients ◽  
Mass Transfer Coefficients ◽  
Phase Change Process

Abstract Both numerical and experimental studies have previously been carried out to investigate the heat transfer performance of the two-phase closed thermosyphon (TPCT). This paper investigates the performance of a commercially available computational fluid dynamics (CFD) solver (Ansys FLUENT) to predict the complex flow behavior of TPCTs, with special focus on modeling of the mass transfer phase change process. The present study uses four different sets of mass transfer coefficients for condensation and evaporation within a previously documented phase change model to determine their impact on the simulation results. The mass transfer coefficients effectively control the rate of transfer from liquid to vapor phase during evaporation and vice versa during condensation. The choice of coefficients is assumed to represent a balance between numerical accuracy and stability. A baseline simulation is performed for which both the evaporation and condensation coefficients are equal and set to default values. Three additional simulations vary the magnitude of the coefficients and adopt relative values based on density ratio following a recommended method that has been previously found to be effective for these simulations. Initial results show that the case with the highest coefficient of evaporation and coefficient for condensation based on the density ratio is in good agreement with available experimental data of overall thermal resistance of the TPCT., with predictive capability degrading as the values of the coefficients are reduced. Additionally, the 3D CFD models implemented in this study appear to successfully predict the phase change process and vital flow behavior inside the TPCTs, at least in a qualitative sense.

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