scholarly journals Determination of the Glutamate—Glutamine Cycling Flux Using Two-Compartment Dynamic Metabolic Modeling is Sensitive to Astroglial Dilution

2008 ◽  
Vol 29 (1) ◽  
pp. 108-118 ◽  
Author(s):  
Jun Shen ◽  
Douglas L Rothman ◽  
Kevin L Behar ◽  
Su Xu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Metabolic Modeling ◽  
Metabolic Model ◽  
Isotopic Dilution ◽  
Resonance Spectroscopy ◽  
Large Loss ◽  
Accuracy And Precision ◽  
Cerebral Glutamine

Over the last decade 13C magnetic resonance spectroscopy (13C MRS) combined with the infusion of [1-13C]glucose has been used to measure the cerebral rate of the glutamate—glutamine cycle ( Vcyc). However, the effect of the astroglial label dilution pathways on the accuracy and precision of the C MRS measurement of Vcyc has not been evaluated or realized. In this report, we use the numerical Monte Carlo method to study the effect of astroglial dilution on the reliability of extracting Vcyc using the neuronal-astroglial two-compartment metabolic model and [1-13C]glucose infusion. The results show that omission of the astroglial dilution flux leads to a large loss in the sensitivity of the glutamine turnover curve to Vcyc. When the measured isotopic dilution of cerebral glutamine is accounted for in the analysis, the value of Vcyc can be precisely and accurately determined.

Monte Carlo method for the reduction of measurement errors in the material parameter estimation with cavities

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ronny Peter ◽  
Luca Bifano ◽  
Gerhard Fischerauer
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Measurement Errors ◽  
Input Data ◽  
Material Parameter ◽  
Cavity Resonator ◽  
Calculation Algorithm ◽  
The Monte Carlo Method ◽  
Electromagnetic Resonances

Abstract The quantitative determination of material parameter distributions in resonant cavities is a relatively new method for the real-time monitoring of chemical processes. For this purpose, electromagnetic resonances of the cavity resonator are used as input data for the reverse calculation (inversion). However, the reverse calculation algorithm is sensitive to disturbances of the input data, which produces measurement errors and tends to diverge, which leads to no measurement result at all. In this work a correction algorithm based on the Monte Carlo method is presented which ensures a convergent behavior of the reverse calculation algorithm.

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

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