Methaemoglobin and Carboxyhaemoglobin Formation in Human Blood Due to NOx and CO Exposure: A Mathematical Model

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Ankit Biyani ◽  
Raj K. Vyas ◽  
Kailash Singh ◽  
Akhilendra B. Gupta ◽  
Sangeeta Vyas ◽  
...  
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Exponential Model ◽  
Invasive Technique ◽  
Model Parameters ◽  
Liquefied Petroleum Gas ◽  
Non Invasive ◽  
Proposed Model ◽  
Co Concentration ◽  
Linear Regression Technique

Combustion of Liquefied Petroleum Gas (LPG) in household kitchens results in the production of several harmful gases including Carbon Monoxide (CO) and Nitrogen Oxides (NOx). In the present work, an attempt has been made to investigate the effect of such gases on the percent oxygen saturation of blood and a nonlinear exponential model has been proposed based on reaction kinetics to quantify the amount of carboxyhaemoglobin and methaemoglobin formed in blood due to such exposures using a non-invasive technique. The model parameters have been estimated by using the experimental data obtained from exposure of the individuals to different concentrations of CO and NOx. Non-linear regression technique has been used in MATLAB® to optimise the objective function. The model has been validated for %COHb concentration in blood and corresponding data for ambient CO concentration reported in literature. The proposed model can be used to find the concentration of methaemoglobin and carboxyhaemoglobin formed in the blood for low exposures.

Modeling study of solid-particle erosion with consideration of particle velocity dependent model parameters

2016 ◽  
Vol 07 (03) ◽  
pp. 1650026 ◽  
Author(s):  
Shijie Qian ◽  
Kuiying Chen ◽  
Rong Liu ◽  
Ming Liang
Keyword(s):  
Experimental Data ◽  
Solid Particle ◽  
Particle Velocity ◽  
Target Material ◽  
Model Parameters ◽  
Particle Impact ◽  
Erosion Rates ◽  
Proposed Model ◽  
Dependent Model ◽  
Predicted Model

An advanced erosion model that correlates two model parameters—the energies required to remove unit mass of target material during cutting wear and deformation wear, respectively, with particle velocity, particle size and density, as well as target material properties, is proposed. This model is capable of predicting the erosion rates for a material under solid-particle impact over a specific range of particle velocity at the impingement angle between [Formula: see text] and [Formula: see text], provided that the experimental data of erosion rate for the material at a particle velocity within this range and at impingement angles between [Formula: see text] and [Formula: see text] are available. The proposed model is applied on three distinct types of materials: aluminum, perspex and graphite, to investigate the dependence behavior of the model parameters on particle velocity for ductile and brittle materials. The predicted model parameters obtained from the model are validated by the experimental data of aluminum plate under Al2O3 particle impact. The significance and limitation of the model are discussed; possible improvements on the model are suggested.

Model-Based EEG Analysis: Proposal and Verification of Mathematical Model for Application to Neurotechnology

2019 ◽  
Author(s):  
Kenyu Uehara ◽  
Takashi Saito
Keyword(s):  
Mathematical Model ◽  
Binary Classification ◽  
Least Square ◽  
Mental Condition ◽  
Support Vector ◽  
Model Parameters ◽  
Eeg Analysis ◽  
Analysis Method ◽  
Model Based ◽  
Proposed Model

Abstract We have modeled dynamics of EEG with one degree of freedom nonlinear oscillator and examined the relationship between mental state of humans and model parameters simulating behavior of EEG. At the IMECE conference last year, Our analysis method identified model parameters sequentially so as to match the waveform of experimental EEG data of the alpha band using one second running window. Results of temporal variation of model parameters suggested that the mental condition such as degree of concentration could be directly observed from the dynamics of EEG signal. The method of identifying the model parameters in accordance with the EEG waveform is effective in examining the dynamics of EEG strictly, but it is not suitable for practical use because the analysis (parameter identification) takes a long time. Therefore, the purpose of this study is to test the proposed model-based analysis method for general application as a neurotechnology. The mathematical model used in neuroscience was improved for practical use, and the test was conducted with the cooperation of four subjects. model parameters were experimentally identified approximately every one second by using least square method. We solved a binary classification problem of model parameters using Support Vector Machine. Results show that our proposed model-based EEG analysis is able to discriminate concentration states in various tasks with an accuracy of over 80%.

scholarly journals Shubnikov–de Haas Oscillations in Semiconductors at the Microwave-Radiation Absorption

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
G. Gulyamov ◽  
U. I. Erkaboev ◽  
A. G. Gulyamov
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Electromagnetic Field ◽  
Microwave Radiation ◽  
Three Dimensional ◽  
Radiation Absorption ◽  
Narrow Gap ◽  
Proposed Model ◽  
Different Temperatures ◽  
Shubnikov De Haas Oscillations

Mathematical models for the Shubnikov-de Haas oscillations in semiconductors are obtained at the microwave-radiation absorption and its temperature dependence. Three-dimensional image of microwave magnetoabsorption oscillations in narrow-gap semiconductors is established. Using a mathematical model, the oscillations of the microwave magnetoabsorption are considered for different values of the electromagnetic field. The results of calculations are compared with experimental data. The proposed model explains the experimental results in HgSe at different temperatures.

A mathematical model of metabolic insulin signaling pathways

2002 ◽  
Vol 283 (5) ◽  
pp. E1084-E1101 ◽  
Author(s):  
Ahmad R. Sedaghat ◽  
Arthur Sherman ◽  
Michael J. Quon
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Insulin Receptor ◽  
Signaling Pathways ◽  
Insulin Signaling ◽  
Molecular Mechanisms ◽  
Glucose Transporter ◽  
Insulin Dose ◽  
Model Parameters ◽  
Insight Into

We develop a mathematical model that explicitly represents many of the known signaling components mediating translocation of the insulin-responsive glucose transporter GLUT4 to gain insight into the complexities of metabolic insulin signaling pathways. A novel mechanistic model of postreceptor events including phosphorylation of insulin receptor substrate-1, activation of phosphatidylinositol 3-kinase, and subsequent activation of downstream kinases Akt and protein kinase C-ζ is coupled with previously validated subsystem models of insulin receptor binding, receptor recycling, and GLUT4 translocation. A system of differential equations is defined by the structure of the model. Rate constants and model parameters are constrained by published experimental data. Model simulations of insulin dose-response experiments agree with published experimental data and also generate expected qualitative behaviors such as sequential signal amplification and increased sensitivity of downstream components. We examined the consequences of incorporating feedback pathways as well as representing pathological conditions, such as increased levels of protein tyrosine phosphatases, to illustrate the utility of our model for exploring molecular mechanisms. We conclude that mathematical modeling of signal transduction pathways is a useful approach for gaining insight into the complexities of metabolic insulin signaling.

scholarly journals Experimental studies of iron transformations kinetics and autocatalysis during its physicochemical removal from underground water

2021 ◽  
Author(s):  
S. Yu Martynov ◽  
V. L. Poliakov
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Experimental Studies ◽  
Underground Water ◽  
Model Parameters ◽  
Kinetic Coefficients ◽  
Transfer Equations ◽  
The Mathematical Model ◽  
Autocatalytic Effect ◽  
Forms Of Iron

Abstract The mathematical model of physicochemical iron removal from groundwater was developed. It consists of three interrelated compartments. The results of the experimental research provide information in support of the first two compartments of the mathematical model. The dependencies for the concentrations of the adsorbed ferrous iron and deposited hydroxide concentrations are obtained as a result of the exact solution of the system of the mass transfer equations for two forms of iron in relation to the inlet surface of the bed. An analysis of the experimental data of the dynamics of the deposit accumulation in a small bed sample was made, using a special application that allowed to select the values of the kinetic coefficients and other model parameters based on these dependencies. We evaluated the autocatalytic effect on the dynamics of iron ferrous and ferric forms. The verification of the mathematical model was carried out involving the experimental data obtained under laboratory and industrial conditions.

scholarly journals Mathematical Model of Kinetics of Antigens Accumulation in the Process of Periodical Submerged Cultivation of Vibrio cholerae 569В Inaba with Limitation as Regards Carbonic Substrate

2014 ◽  
pp. 96-99
Author(s):  
A. V. Komissarov ◽  
A. K. Nikiforov ◽  
S. N. Zadokhin ◽  
S. A. Eremin ◽  
O. A. Volokh ◽  
...  
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Model Comparison ◽  
Initial Conditions ◽  
Accumulation Rate ◽  
Submerged Cultivation ◽  
Maximum Output ◽  
Proposed Model ◽  
Cultivation Process ◽  
Kinetics Of

Presented is mathematical model of kinetics of the process of O-antigen and cholera toxin synthesis during periodical submerged cultivation of V. cholerae 569В Inaba with limitation as regards carbonic substrate. The proposed model is based upon analysis of experimental data on V. cholerae 569В Inaba biomass and antigens accumulation, rate of growth and antigens release, and glucose utilization. Using Mathcad 15.0 software calculated are coefficients of differential equations entering into the mathematical model. Comparison of predicted and experimental data demonstrates that relative error of determination of concentrations of the synthesized substances, glucose and cholera vibrio is between 5 and 20 %. The proposed model permits to determine maximum output of final products and specify the parameters of cultivation process performance at different initial conditions.

scholarly journals Classical and Bayesian Inference of a Mixture of Bivariate Exponentiated Exponential Model

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Refah Alotaibi ◽  
Mervat Khalifa ◽  
Ehab M. Almetwally ◽  
Indranil Ghosh ◽  
Rezk. H.
Keyword(s):  
Exponential Model ◽  
Gaussian Copula ◽  
Estimation Methods ◽  
Model Parameters ◽  
Reliability Engineering ◽  
Bivariate Model ◽  
Conditional Moments ◽  
Bayesian Paradigm ◽  
Proposed Model ◽  
Two Parameters

Exponentiated exponential (EE) model has been used effectively in reliability, engineering, biomedical, social sciences, and other applications. In this study, we introduce a new bivariate mixture EE model with two parameters assuming two cases, independent and dependent random variables. We develop a bivariate mixture starting from two EE models assuming two cases, two independent and two dependent EE models. We study some useful statistical properties of this distribution, such as marginals and conditional distributions and product moments and conditional moments. In addition, we study a dependent case, a new mixture of the bivariate model based on EE distribution marginal with two parameters and with a bivariate Gaussian copula. Different methods of estimation for the model parameters are used both under the classical and under the Bayesian paradigm. Some simulation studies are presented to verify the performance of the estimation methods of the proposed model. To illustrate the flexibility of the proposed model, a real dataset is reanalyzed.

scholarly journals A Non-Invasive Procedure for Estimating the Exponential Model Parameters of Bypass Diodes in Photovoltaic Modules

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 303 ◽  
Author(s):  
Jeisson Vélez-Sánchez ◽  
Juan Bastidas-Rodríguez ◽  
Carlos Ramos-Paja ◽  
Daniel González Montoya ◽  
Luz Trejos-Grisales
Keyword(s):  
Invasive Procedure ◽  
Exponential Model ◽  
Model Parameters ◽  
Estimation Errors ◽  
Photovoltaic Modules ◽  
Non Invasive ◽  
Pv Module ◽  
Current Voltage ◽  
Pv Modules ◽  
Two Parameters

Bypass diodes (BDs) present in photovoltaic (PV) modules are represented by the exponential model, which requires two parameters: the inverse-saturation current ( I s a t , d b ) and the ideality factor ( η d b ). However, it is difficult to estimate those parameters since the terminals of the BDs are not isolated, hence there is only access to the series connection of the module BDs. This problem must be addressed since inaccurate BDs parameters could produce errors in the reproduction of the current-voltage (I-V) curves of commercial PV modules, which lead to wrong predictions of the power production. This paper proposes a non-invasive procedure to estimate I s a t , d b and η d b of the bypass diodes present in a PV module using two experimental I-V curves. One I-V curve is measured completely covering the submodule of the module whose BD will be parameterized; while the other I-V curve is measured without any shadow on the module. From those curves, the I-V curve of the BD is estimated and I s a t , d b and η d b are calculated by solving a system of two nonlinear equations. The proposed procedure is validated through simulations and experimental results considering a commercial PV module formed by three submodules, where the estimation errors in the reproduction of the BD I-V curve are less than 1% in the simulations and less than 10% in the experiments.

Parameter Identification for HAPC System in Hot Tandem Mill

2010 ◽  
Vol 154-155 ◽  
pp. 781-786
Author(s):  
Xu Li ◽  
Wen Xue Zhang ◽  
Dian Hua Zhang ◽  
Dan Yan
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Parameter Identification ◽  
Actual Data ◽  
Automation System ◽  
Model Parameters ◽  
Simulation Data ◽  
Matlab Software ◽  
Identification Method ◽  
Curve Fit

Under condition that exact values of model parameters can not be calculated accurately in hot tandem mill system and change with the time passing, model parameters are identified by adopting identification method based on the parameter model and sampling the datum on site; Basic automation system is used for the sampling of actual data, MATLAB software is adopted for curve fit. By comparing the experimental data and simulation data, the consequence of simulation testifies the accuracy of identified mathematical model.

VLE Determination of Carbon Dioxide Loaded Aqueous Alkanolamine Mixtures Using Modified Kent Eisenberg Model

2017 ◽  
Vol 231 (11-12) ◽  
Author(s):  
Humbul Suleman ◽  
Abdulhalim Shah Maulud ◽  
Zakaria Man
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide ◽  
Model Parameters ◽  
Thermodynamic Models ◽  
Carbon Dioxide Absorption ◽  
Proposed Model ◽  
Wide Range ◽  
Experimental Values ◽  
Good Agreement

AbstractA computationally simple thermodynamic framework has been presented to correlate the vapour-liquid equilibria of carbon dioxide absorption in five representative types of alkanolamine mixtures. The proposed model is an extension of modified Kent Eisenberg model for the carbon dioxide loaded aqueous alkanolamine mixtures. The model parameters are regressed on a large experimental data pool of carbon dioxide solubility in aqueous alkanolamine mixtures. The model is applicable to a wide range of temperature (298–393 K), pressure (0.1–6000 kPa) and alkanolamine concentration (0.3–5 M). The correlated results are compared to the experimental values and found to be in good agreement with the average deviations ranging between 6% and 20%. The model results are comparable to other thermodynamic models.

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