scholarly journals Pharmacodynamic Modeling ofIn VitroActivity of Marbofloxacin againstEscherichia coliStrains

2010 ◽  
Vol 55 (2) ◽  
pp. 756-761 ◽  
Author(s):  
M. Andraud ◽  
C. Chauvin ◽  
P. Sanders ◽  
M. Laurentie
Keyword(s):  
Bactericidal Activity ◽  
Growth Conditions ◽  
Major Effect ◽  
Least Square ◽  
Pharmacodynamic Modeling ◽  
Maximum Effect ◽  
Model Parameters ◽  
Delayed Effect ◽  
Fitting Procedures ◽  
Highly Correlated

ABSTRACTA mathematical pharmacodynamic model was developed to describe the bactericidal activity of marbofloxacin againstEscherichia colistrains with reduced susceptibility levels (determined using MICs) under optimal and intestinal growth conditions. Model parameters were estimated using nonlinear least-square curve-fitting procedures for eachE. colistrain. Parameters related to bactericidal activity were subsequently analyzed using a maximum-effect (Emax) model adapted to account for a direct and a delayed effect. While net growth rates did not vary significantly with strain susceptibility, culture medium had a major effect. The bactericidal activity of marbofloxacin was closely associated with the concentration and the duration of exposure of the bacteria to the antimicrobial agent. The value of the concentration inducing a half-maximum effect (C50) was highly correlated with MIC values (R2= 0.87 andR2= 0.94 under intestinal and optimal conditions, respectively). Our model reproduced the time-kill kinetics with good accuracy (R2of >0.90) and helped explain observed regrowth.

Pharmacodynamics and the Bactericidal Activity of Bedaquiline in Pulmonary Tuberculosis

2021 ◽  
Author(s):  
Michael A. Lyons
Keyword(s):  
Bactericidal Activity ◽  
Maximum Effect ◽  
Model Parameters ◽  
Adult Males ◽  
Dose Selection ◽  
Once Daily ◽  
Pulmonary Tb ◽  
Time To Onset ◽  
Ongoing Phase ◽  
Selection Of

Bedaquiline is a diarylquinoline antimycobacterial drug and a key component of several regimens in clinical development for treatment of tuberculosis (TB), but with ongoing phase 3 trials that include assessment of simplified dosing. A pharmacokinetic-pharmacodynamic model of bedaquiline Mycobacterium tuberculosis killing kinetics in adults with pulmonary TB was developed to inform dose selection of bedaquiline-containing regimens. The model parameters were estimated with data from the 14-day early bactericidal activity (EBA) study TMC207-CL001 conducted in Cape Town, South Africa. The study included 60 adult males and females with drug-susceptible pulmonary TB, who were administered bedaquiline with loading doses on the first two days followed by once daily 100 mg, 200 mg, 300 mg, or 400 mg. The modeling results included expected values (mean±SD) for a maximum drug kill rate constant equal to 0.23±0.03 log 10 CFU/mL sputum/day, a half-maximum effect plasma concentration equal to 1.6±0.3 mg/L, and an average time to onset of activity equal to 40±7 h. Model simulations showed once daily 200 mg, 300 mg, and 400 mg (without loading doses) attained 40%, 50%, and 60%, respectively, of an expected maximum 14-day EBA equal to 0.18 log 10 CFU/mL/day, or 10 h/day assessed by liquid culture time to positivity (TTP). Additional simulations illustrated efficacy outcomes during eight weeks of treatment with the recommended and alternative dosages. The results demonstrate a general mathematical and statistical approach to analysis of EBA studies with broad application to TB regimen development.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

scholarly journals New Lindley Half Cauchy Distribution: Theory and Applications

2020 ◽  
Vol 9 (4) ◽  
pp. 1-7
Keyword(s):  
Maximum Likelihood ◽  
Real Data ◽  
Cauchy Distribution ◽  
Least Square ◽  
Cumulative Distribution ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Model Parameters ◽  
Data Set ◽  
Von Mises

In this paper, we have defined a new two-parameter new Lindley half Cauchy (NLHC) distribution using Lindley-G family of distribution which accommodates increasing, decreasing and a variety of monotone failure rates. The statistical properties of the proposed distribution such as probability density function, cumulative distribution function, quantile, the measure of skewness and kurtosis are presented. We have briefly described the three well-known estimation methods namely maximum likelihood estimators (MLE), least-square (LSE) and Cramer-Von-Mises (CVM) methods. All the computations are performed in R software. By using the maximum likelihood method, we have constructed the asymptotic confidence interval for the model parameters. We verify empirically the potentiality of the new distribution in modeling a real data set.

scholarly journals Biopolymer Drill-in Fluid Performance for Different Rheological Models using Statistical Characterisation

2019 ◽  
Vol 19 (1) ◽  
pp. 86-92
Author(s):  
M. Owusu ◽  
H. Osei
Keyword(s):  
Rheological Model ◽  
Rheological Behaviour ◽  
Primary Objective ◽  
Least Square ◽  
Model Parameters ◽  
Drilling Fluids ◽  
Cleaning Efficiency ◽  
Rheological Models ◽  
Least Square Regression ◽  
Best Fit

Appropriate selection of rheological models is important for hydraulic calculations of pressure loss prediction and hole cleaning efficiency of drilling fluids. Power law, Bingham-Plastic and Herschel-Bulkley models are the conventional fluid models used in the oilfield. However, there are other models that have been proposed in literature which are under/or not utilized in the petroleum industry. The primary objective of this paper is to recommend a rheological model that best-fits the rheological behaviour of xanthan gum-based biopolymer drill-in fluids for hydraulic evaluations. Ten rheological models were evaluated in this study. These rheological models have been posed deterministically and due to the unrealistic nature have been replaced by statistical models, by adding an error (disturbance) term and making suitable assumptions about them. Rheological model parameters were estimated by least-square regression method. Models like Sisko and modified Sisko which are not conventional models in oil industry gave a good fit. Modified Sisko model which is a four parameter rheological model was selected as the best-fit model since it produced the least residual mean square of 0.61 Ibf2/100ft4. There is 95% certainty that the true best-fit curve lies within the confidence band of this function of interest. Keywords: Biopolymer; Least-Square Regression; Residual Mean Squares; Rheologram

Examination of Correlation Between Arousal Level and Auditory Environmental Change Using Rock and Classic Music

2017 ◽  
Author(s):  
Kentaro Miyago ◽  
Kenyu Uehara ◽  
Takashi Saito
Keyword(s):  
Visual Cortex ◽  
Frontal Lobe ◽  
Traffic Accidents ◽  
Evaluation Method ◽  
Duffing Oscillator ◽  
Least Square Method ◽  
Arousal Level ◽  
Least Square ◽  
Model Parameters ◽  
Eeg Data

Recently, traffic accidents due to drowsy driving, operation mistake in the power plant by drowsiness and decrease arousal in employment during work have been attracted as problems. To avoid such an accident, arousal level could be quantitatively evaluated in real time. We suggested that the one of the parameters of Duffing oscillator parameters is related to the conventional arousal level using the EEG frequency component. However, in this examination, effects on the EEG from visual and active behavior were considered, but those from hearing also need to be investigated. In this paper, we performed the experiment in the musical environment using rock and classic music to investigate the model parameters for effect of the auditory stimulation, and acquired EEG data in Visual cortex and Frontal lobe. The acquired EEG data was used to identify the model parameters, which were identified solving the inverse problem by Least Square method. Results of investigating correlation between conventional arousal revel and model parameter shows a significant correlation in case of the auditory environmental situation. Moreover, Visual cortex is better than Frontal lobe as a measurement point in this evaluation method.

scholarly journals Polynomial Load Model Development for Analysing Residential Electric Energy Use Behaviour

2018 ◽  
Vol 218 ◽  
pp. 01007 ◽  
Author(s):  
Erwin Nashrullah ◽  
Abdul Halim
Keyword(s):  
Energy Use ◽  
Mean Squared Error ◽  
Median Filter ◽  
Energy System ◽  
Least Square ◽  
Model Parameters ◽  
Data Sampling ◽  
Data Set ◽  
System Designer ◽  
Load Model

Analysing and simulating the dynamic behaviour of home power system as a part of community-based energy system needs load model of either aggregate or dis-aggregate power use. Moreover, in the context of home energy efficiency, development of specific and accurate residential load model can help system designer to develop a tool for reducing energy consumption effectively. In this paper, a new method for developing two types of residential polynomial load model is presented. In the research, computation technique of model parameters is provided based on median filter and least square estimation and implemented by MATLAB. We use AMPDs data set, which have 1-minute data sampling, to show the effectiveness of proposed method. After simulation is carried out, the performance evaluation of model is provided through exploring root mean-squared error between original data and model output. From simulation results, it could be concluded that proposed model is enough for helping system designer to analyse home power energy use.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

scholarly journals Nuclear data adjustment using Bayesian inference, diagnostics for model fit and influence of model parameters

2020 ◽  
Vol 239 ◽  
pp. 13003
Author(s):  
D. Kumar ◽  
S. B. Alam ◽  
H. Sjöstrand ◽  
J.M. Palau ◽  
C. De Saint Jean
Keyword(s):  
Bayesian Inference ◽  
Degrees Of Freedom ◽  
Nuclear Data ◽  
Information Criteria ◽  
Least Square ◽  
Model Fit ◽  
Adjustment Process ◽  
Model Parameters ◽  
Test Case ◽  
Bayesian Approximation

The mathematical models used for nuclear data evaluations contain a large number of theoretical parameters that are usually uncertain. These parameters can be calibrated (or improved) by the information collected from integral/differential experiments. The Bayesian inference technique is used to utilize measurements for data assimilation. The Bayesian approximation is based on the least-square or Monte-Carlo approaches. In this process, the model parameters are optimized. In the adjustment process, it is essential to include the analysis related to the influence of model parameters on the adjusted data. In this work, some statistical indicators such as the concept of Cook’s distance; Akaike, Bayesian and deviance information criteria; effective degrees of freedom are developed within the CONRAD platform. Further, these indicators are applied to a test case of 155Gd to evaluate and compare the influence of resonance parameters.

Robust Weighted Least Squares Estimation of Regression Parameter in the Presence of Outliers and Heteroscedastic Errors

2014 ◽  
Vol 71 (1) ◽  
Author(s):  
Bello Abdulkadir Rasheed ◽  
Robiah Adnan ◽  
Seyed Ehsan Saffari ◽  
Kafi Dano Pati
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Robust Regression ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Least Square ◽  
Model Parameters ◽  
Constant Variance ◽  
Regression Parameters ◽  
M Estimation

In a linear regression model, the ordinary least squares (OLS) method is considered the best method to estimate the regression parameters if the assumptions are met. However, if the data does not satisfy the underlying assumptions, the results will be misleading. The violation for the assumption of constant variance in the least squares regression is caused by the presence of outliers and heteroscedasticity in the data. This assumption of constant variance (homoscedasticity) is very important in linear regression in which the least squares estimators enjoy the property of minimum variance. Therefor e robust regression method is required to handle the problem of outlier in the data. However, this research will use the weighted least square techniques to estimate the parameter of regression coefficients when the assumption of error variance is violated in the data. Estimation of WLS is the same as carrying out the OLS in a transformed variables procedure. The WLS can easily be affected by outliers. To remedy this, We have suggested a strong technique for the estimation of regression parameters in the existence of heteroscedasticity and outliers. Here we apply the robust regression of M-estimation using iterative reweighted least squares (IRWLS) of Huber and Tukey Bisquare function and resistance regression estimator of least trimmed squares to estimating the model parameters of state-wide crime of united states in 1993. The outcomes from the study indicate the estimators obtained from the M-estimation techniques and the least trimmed method are more effective compared with those obtained from the OLS.

Microvascular exchange and interstitial volume regulation in the rat: model validation

1988 ◽  
Vol 254 (2) ◽  
pp. H384-H399 ◽  
Author(s):  
J. L. Bert ◽  
B. D. Bowen ◽  
R. K. Reed
Keyword(s):  
Experimental Data ◽  
Osmotic Pressure ◽  
Plasma Proteins ◽  
Venous Pressure ◽  
Colloid Osmotic Pressure ◽  
Model Parameters ◽  
Transport Parameters ◽  
Interstitial Fluid Volume ◽  
Protein Tracers ◽  
Fitting Procedures

A dynamic mathematical model is formulated and used to describe the distribution and transport of fluid and plasma proteins between the circulation, interstitial space of skin and muscle, and the lymphatics in the rat. Two descriptions of transcapillary exchange are investigated: a homoporous "Starling model" and a heteroporous "plasma leak model." Parameters used in the two hypothetical transport mechanisms are determined based on statistical fitting procedures between simulation predictions and selected experimental data. These data consist of interstitial fluid volume and colloid osmotic pressure measurements as a function of venous pressure for muscle and interstitial colloid osmotic pressure vs. venous pressure for skin. The values determined for the transport parameters compare well with data in the literature. The fully determined model is used to simulate steady-state conditions of hypoproteinemia, overhydration, and dehydration, as well as the dynamic response to changes in venous pressure and intravascularly administered protein tracers. Comparisons between the simulation predictions and experimental data for these various perturbations are made. The plasma leak model appears to provide a better description of microvascular exchange.

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