Design of a Robust Coordinated Power Oscillation Damper for Use with Large-Scale Wind Energy System Connected to a Multimachine Power System by Paul Kabiru, Keren Kaberere, Stanley Kamau

“…Probabilistic analysis established on Monte Carlo simulation is then carried out to prove the robustness of the coordinated design strategy under diverse operating conditions. …”
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Simulating the Emergence and Survival of Mutations Using a Self Regulating Multitype Branching Processes by Charles J. Mode, Towfique Raj, Candace K. Sleeman

“…During the lifespan of Fisher, computer technology had not advanced to a point at which it became an effective tool for simulating the phenomenon of the emergence and survival of mutations, but given the wide availability of personal desktop and laptop computers, it is now possible and financially feasible for investigators to perform Monte Carlo Simulation experiments. In this paper all computer simulation experiments were carried out within a framework of self regulating multitype branching processes, which are part of a stochastic working paradigm. …”
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Loan classification using a feed-forward neural network by U. I. Behunkou

“…The impact of data normalization on the final result was analyzed, the influence of the number of units in the hidden layer on the outcome was evaluated using a two-stage method and the Monte Carlo method, the effect of balanced data use was determined, the optimal threshold value for output layer of the neural network under investigation was calculated, the optimal activation function for the hidden layer units was found, the effect of increasing input indicators through missing values imputation and the use of polynomials of varying degrees was studied and the redundancy in the existing set of input indicators was analyzed.Conclusion. …”
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Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation by Joseph P. Yurko, Jacopo Buongiorno, Robert Youngblood

“…Use of a fast emulator makes the calibration processes used here with Markov Chain Monte Carlo (MCMC) sampling feasible. This work uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. …”
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Bayesian Inference for Zero-Modified Power Series Regression Models by Katiane S. Conceição, Marinho G. Andrade, Victor Hugo Lachos, Nalini Ravishanker

“…This paper proposes a Bayesian estimation procedure based on the stochastic gradient Hamiltonian Monte Carlo algorithm, effectively addressing many challenges associated with estimating the model parameters. …”
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METROLOGICAL SUPPORT OF DOSIMETRY GAMMA-RAY WITH ENERGY TO 10 MEV FOR RADIATION PROTECTION DEVICES by D. I. Komar, R. V. Lukashevich, V. D. Guzov, S. A. Kutsen

“…The aim of this work was to determine the metrological characteristics of capture gamma-ray fields from titanium target and nickel target obtained at the AT140 Neutron Calibration Facility to provide dosimetry up to 10 MeV.We have chosen energy intervals in which we can calibrate dosimetry devices taking into account the accompanying generation of gamma-ray neutrons by the fast neutron source 238PuBe, the capture radiation of collimator materials and capture radiation from targets.We measured air kerma rate with the aid of the reference AT5350 dosimeter with the ionization chamber TM32002. Using the Monte-Carlo simulation, we obtained the energy distribution of the air kerma rate for targets. …”
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Precursor sticking coefficient determination from indented deposits fabricated by electron beam induced deposition by Alexander Kuprava, Michael Huth

“…A fast simulation approach for focused electron beam induced deposition (FEBID) numerically solves the diffusion–reaction equation (continuum model) of the precursor surface on the growing nanostructure in conjunction with a Monte Carlo simulation for electron transport in the growing deposit. …”
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Performance analysis of generalized orthogonal code index modulation system by Lina BIAN, Huizhen FENG, Xiaoping JIN, Weizhong LOU, Chongxiao QU

“…In order to improve the transmission efficiency of the code index modulation (CIM) system, a generalized orthogonal code index modulation (GQCIM) system with lower complexity was proposed, which was a single input single output (SISO) system.CIM system used spreading codes and constellation symbols to transmit information, but only activated two spreading code indexes and one modulation symbol.GQCIM system overcame the limitation of only activating one modulation symbol at a time in a novel way, at the same time, made full use of the orthogonality of modulation symbols, increased the spreading code index to transmit more additional information bits, and improved the transmission efficiency of the system.In addition, the theoretical performance of GQCIM system was analyzed and derived the upper bound of bit error rate performance.Then the performance of GQCIM was verified by Monte Carlo simulation, and the theoretical and simulation performance of GQCIM system was found to be consistent.And under the same transmission efficiency, the results show that the performance of GQCIM system is better than the same orthogonal modulation system, such as generalized code index modulation (GCIM) system, CIM system, code index modulation aided quadrature spatial modulation (CIM-QSM) system, code index modulation aided spatial modulation (CIM-SM) system, pulse index modulation (PIM) system.…”
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Performance Evaluation of Car Cargo Scanner Systems for Determining Linear Attenuation Coefficients of Aluminum Metal Sheets: Modeling by Amir Mohammad Beigzadeh, Mojtaba Askari

“…For this purpose, radiographic images of aluminum sheets with different thicknesses were first extracted using modeling. Mcnpx monte Carlo code was used to model the system. The obtained images were processed using the algorithm written with MATLAB. …”
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Site-wise dynamic defects in a non-conserving exclusion process by Nikhil Bhatia, Arvind Kumar Gupta

“…Using a mean-field approach, we obtain an analytical expression for both particle and defect density and validate them using Monte Carlo simulation. The study investigates the steady-state characteristics of the system, including phase transitions, analysis of boundary layers, and phase diagrams. …”
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Damage Evaluation of Bridge Hanger Based on Bayesian Inference: Analytical Model by Yang Ding, Jing-liang Dong, Tong-lin Yang, Zhong-ping Wang, Shuang-xi Zhou, Yong-qi Wei, An-ming She

“…In order to solve the complex analytical expressions in damage evaluation model, the Metropolis-Hastings (MH) sampling of Markov chain Monte Carlo (MCMC) method was used. Three case studies are adopted to demonstrate the effect of the initial value and the applicability of the proposed model. …”
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Nonlinear Programming to Determine Best Weighted Coefficient of Balanced LINEX Loss Function Based on Lower Record Values by Fuad S. Al-Duais, Mohammed Alhagyan

“…Comparisons are made between Bayesian estimators (SE, BSE, LINEX, and BLINEX) and maximum likelihood estimator via Monte Carlo simulation. The evaluation was done based on absolute bias and mean square errors. …”
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DSMC Simulation and Experimental Validation of Shock Interaction in Hypersonic Low Density Flow by Hong Xiao, Yuhe Shang, Di Wu

“…Direct simulation Monte Carlo (DSMC) of shock interaction in hypersonic low density flow is developed. …”
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Explaining Unwanted Radial Oscillations in Single-Bubble Sonoluminescence by Mohamad Mansouri, Farough Molaei, Majid Vaezzadeh

“…The authors use the Monte Carlo method to calculate the number of released electrons and demonstrate that the amplitude and frequency of these oscillations can be reduced by manipulating the electron density and energy distribution. …”
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Study on Tensile Properties of Nanoreinforced Epoxy Polymer: Macroscopic Experiments and Nanoscale FEM Simulation Prediction by Zhenqing Wang, Fang Liu, Wenyan Liang, Limin Zhou

“…Three analytical models were used to obtain the effective elastic modulus of nanoparticle-reinforced composites. Based on Monte-Carlo method, the special program for the automatic generation of 2D random distribution particles without overlapping was developed for nanocomposite modeling. …”
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Short‐Term Prediction of the Dst Index and Estimation of Efficient Uncertainty Using a Hybrid Deep Learning Network by Ruyao Wang, Jianhui Wang, Tuo Liang, Huixiong Zhang

“…We also employed the Monte Carlo (MC) dropout technique to estimate the uncertainty and provide the prediction interval by introducing a recalibration factor. …”
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A Bounded Lifetime Distribution Specified by a Trigonometric Function: Properties, Regression Model, and Applications by Simon A. Ogumeyo, Festus C. Opone, Abdul Ghaniyyu Abubakari, Jacob C. Ehiwario

“…The behavior of the estimators obtained from the estimation methods are investigated using Monte Carlo simulation studies. The results show that the estimation methods are asymptotically efficient and consistent. …”
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Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy by Byoungjoon Ahn, Hyun-Sik Jeong, Keun-Young Kim, Kwan Yun

“…Utilizing neural ordinary differential equations alongside Monte-Carlo integration, we develop a method tailored for continuous training functions to extract the general isotropic bulk metric from entanglement entropy data. …”
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Cauchy–Logistic Unit Distribution: Properties and Application in Modeling Data Extremes by Vladica S. Stojanović, Tanja Jovanović Spasojević, Radica Bojičić, Brankica Pažun, Zlatko Langović

“…In addition, a quantile-based parameter estimation procedure, an asymptotic analysis of the thus obtained estimators, and their Monte Carlo simulation study are conducted. Finally, the application of the proposed distribution in stochastic modeling of some real-world data with emphasized extreme values is provided.…”
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Thermodynamics of modified Chaplygin-Jacobi gas and modified Chaplygin-Abel gas: Stability analysis and observational constraints by Banadipa Chakraborty, Tamal Mukhopadhyay, Debojyoti Mondal, Ujjal Debnath

“…We then perform observational analysis using CC+BAO and Pantheon+SH0ES datasets to impose constraints on our model parameters using the Markov Chain Monte Carlo (MCMC) process.…”
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