The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title]最新文献

Applications of simulation codes based on Monte Carlo method for Radiotherapy 基于蒙特卡罗方法的仿真代码在放射治疗中的应用

The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title] Pub Date : 2022-01-28 DOI: 10.5772/intechopen.101323

Iury Mergen Knoll, A. Quevedo, M. Salomón Alva Sánchez

{"title":"Applications of simulation codes based on Monte Carlo method for Radiotherapy","authors":"Iury Mergen Knoll, A. Quevedo, M. Salomón Alva Sánchez","doi":"10.5772/intechopen.101323","DOIUrl":"https://doi.org/10.5772/intechopen.101323","url":null,"abstract":"Monte Carlo simulations have been applied to determine and study different parameters that are challenged in experimental measurements, due to its capability in simulating the radiation transport with a probability distribution to interact with electrosferic electrons and some cases with the nucleus from an arbitrary material, which such particle track or history can carry out physical quantities providing data from a studied or investigating quantities. For this reason, simulation codes, based on Monte Carlo, have been proposed. The codes currently available are MNCP, EGSnrc, Geant, FLUKA, PENELOPE, as well as GAMOS and TOPAS. These simulation codes have become a tool for dose and dose distributions, essentially, but also for other applications such as design clinical, tool for commissioning of an accelerator linear, shielding, radiation protection, some radiobiologic aspect, treatment planning systems, prediction of data from results of simulation scenarios. In this chapter will be present some applications for radiotherapy procedures with use, specifically, megavoltage x-rays and electrons beams, in scenarios with homogeneous and anatomical phantoms for determining dose, dose distribution, as well dosimetric parameters through the PENELOPE and TOPAS code.","PeriodicalId":308418,"journal":{"name":"The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title]","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124136275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Markov Chain Monte Carlo in a Dynamical System of Information Theoretic Particles 信息论粒子动力系统中的马尔可夫链蒙特卡罗

The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title] Pub Date : 2021-11-04 DOI: 10.5772/intechopen.100428

T. Ogunfunmi, M. Deb

{"title":"Markov Chain Monte Carlo in a Dynamical System of Information Theoretic Particles","authors":"T. Ogunfunmi, M. Deb","doi":"10.5772/intechopen.100428","DOIUrl":"https://doi.org/10.5772/intechopen.100428","url":null,"abstract":"In Bayesian learning, the posterior probability density of a model parameter is estimated from the likelihood function and the prior probability of the parameter. The posterior probability density estimate is refined as more evidence becomes available. However, any non-trivial Bayesian model requires the computation of an intractable integral to obtain the probability density function (PDF) of the evidence. Markov Chain Monte Carlo (MCMC) is a well-known algorithm that solves this problem by directly generating the samples of the posterior distribution without computing this intractable integral. We present a novel perspective of the MCMC algorithm which views the samples of a probability distribution as a dynamical system of Information Theoretic particles in an Information Theoretic field. As our algorithm probes this field with a test particle, it is subjected to Information Forces from other Information Theoretic particles in this field. We use Information Theoretic Learning (ITL) techniques based on Rényi’s α-Entropy function to derive an equation for the gradient of the Information Potential energy of the dynamical system of Information Theoretic particles. Using this equation, we compute the Hamiltonian of the dynamical system from the Information Potential energy and the kinetic energy. The Hamiltonian is used to generate the Markovian state trajectories of the system.","PeriodicalId":308418,"journal":{"name":"The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title]","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127878127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Monte Carlo and Medical Physics 蒙特卡洛和医学物理

The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title] Pub Date : 2021-09-23 DOI: 10.5772/intechopen.100121

Omaima Essaad Belhaj, H. Boukhal, E. Chakir

引用次数: 0

Flooding Fragility Model Development Using Bayesian Regression 利用贝叶斯回归建立洪水易损性模型

The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title] Pub Date : 2021-08-23 DOI: 10.5772/intechopen.99556

A. Wells, C. Pope

{"title":"Flooding Fragility Model Development Using Bayesian Regression","authors":"A. Wells, C. Pope","doi":"10.5772/intechopen.99556","DOIUrl":"https://doi.org/10.5772/intechopen.99556","url":null,"abstract":"Traditional component pass/fail design analysis and testing protocol drives excessively conservative operating limits and setpoints as well as unnecessarily large margins of safety. Component performance testing coupled with failure probability model development can support selection of more flexible operating limits and setpoints as well as softening defense-in-depth elements. This chapter discuses the process of Bayesian regression fragility model development using Markov Chain Monte Carlo methods and model checking protocol using three types of Bayesian p-values. The chapter also discusses application of the model development and testing techniques through component flooding performance experiments associated with industrial steel doors being subjected to a rising water scenario. These component tests yield the necessary data for fragility model development while providing insight into development of testing protocol that will yield meaningful data for fragility model development. Finally, the chapter discusses development and selection of a fragility model for industrial steel door performance when subjected to a water-rising scenario.","PeriodicalId":308418,"journal":{"name":"The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title]","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132749953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Paradigm of Complex Probability and Isaac Newton’s Classical Mechanics: On the Foundation of Statistical Physics 复概率范式与牛顿经典力学——以统计物理学为基础

The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title] Pub Date : 2021-07-26 DOI: 10.5772/intechopen.98341

Abdo Abou Jaoude

{"title":"The Paradigm of Complex Probability and Isaac Newton’s Classical Mechanics: On the Foundation of Statistical Physics","authors":"Abdo Abou Jaoude","doi":"10.5772/intechopen.98341","DOIUrl":"https://doi.org/10.5772/intechopen.98341","url":null,"abstract":"The concept of mathematical probability was established in 1933 by Andrey Nikolaevich Kolmogorov by defining a system of five axioms. This system can be enhanced to encompass the imaginary numbers set after the addition of three novel axioms. As a result, any random experiment can be executed in the complex probabilities set C which is the sum of the real probabilities set R and the imaginary probabilities set M. We aim here to incorporate supplementary imaginary dimensions to the random experiment occurring in the “real” laboratory in R and therefore to compute all the probabilities in the sets R, M, and C. Accordingly, the probability in the whole set C = R + M is constantly equivalent to one independently of the distribution of the input random variable in R, and subsequently the output of the stochastic experiment in R can be determined absolutely in C. This is the consequence of the fact that the probability in C is computed after the subtraction of the chaotic factor from the degree of our knowledge of the nondeterministic experiment. We will apply this innovative paradigm to Isaac Newton’s classical mechanics and to prove as well in an original way an important property at the foundation of statistical physics.","PeriodicalId":308418,"journal":{"name":"The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title]","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116949555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Reliability and Comparison of Some GEANT4-DNA Processes and Models for Proton Transportation: An Ultra-Thin Layer Study 质子输运的一些GEANT4-DNA过程和模型的可靠性和比较:超薄层研究

The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title] Pub Date : 2021-07-22 DOI: 10.5772/INTECHOPEN.98753

G. Hoff, R. Thomaz, L. I. Gutierres, Sven Muller, V. Fanti, E. Streck, R. Papaléo

{"title":"Reliability and Comparison of Some GEANT4-DNA Processes and Models for Proton Transportation: An Ultra-Thin Layer Study","authors":"G. Hoff, R. Thomaz, L. I. Gutierres, Sven Muller, V. Fanti, E. Streck, R. Papaléo","doi":"10.5772/INTECHOPEN.98753","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.98753","url":null,"abstract":"This chapter presents a specific reliability study of some GEANT4-DNA (version 10.02.p01) processes and models for proton transportation considering ultra-thin layers (UTL). The Monte Carlo radiation transport validation is fundamental to guarantee the simulation results accuracy. However, sometimes this is impossible due to the lack of experimental data and, it is then that the reliability evaluation takes an important role. Geant4-DNA runs in an energy range that makes impossible, nowadays, to perform a proper microscopic validation (cross-sections and dynamic diffusion parameters) and allows very limited macroscopic reliability. The chemical damage cross-sections reliability (experiment versus simulation) is a way to verify the consistency of the simulation results which is presented for 2 MeV incident protons beam on PMMA and PVC UTL. A comparison among different Geant4-DNA physics lists for incident protons beams from 2 to 20 MeV, interacting with homogeneous water UTL (2 to 200 nm) was performed. This comparison was evaluated for standard and five other optional physics lists considering radial and depth profiles of deposited energy as well as number of interactions and stopping power of the incident particle.","PeriodicalId":308418,"journal":{"name":"The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title]","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132563677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Paradigm of Complex Probability and Thomas Bayes’ Theorem 复杂概率范式与托马斯·贝叶斯定理

The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title] Pub Date : 2021-07-14 DOI: 10.5772/intechopen.98340

Abdo Abou Jaoude

{"title":"The Paradigm of Complex Probability and Thomas Bayes’ Theorem","authors":"Abdo Abou Jaoude","doi":"10.5772/intechopen.98340","DOIUrl":"https://doi.org/10.5772/intechopen.98340","url":null,"abstract":"The mathematical probability concept was set forth by Andrey Nikolaevich Kolmogorov in 1933 by laying down a five-axioms system. This scheme can be improved to embody the set of imaginary numbers after adding three new axioms. Accordingly, any stochastic phenomenon can be performed in the set C of complex probabilities which is the summation of the set R of real probabilities and the set M of imaginary probabilities. Our objective now is to encompass complementary imaginary dimensions to the stochastic phenomenon taking place in the “real” laboratory in R and as a consequence to gauge in the sets R, M, and C all the corresponding probabilities. Hence, the probability in the entire set C = R + M is incessantly equal to one independently of all the probabilities of the input stochastic variable distribution in R, and subsequently the output of the random phenomenon in R can be evaluated totally in C. This is due to the fact that the probability in C is calculated after the elimination and subtraction of the chaotic factor from the degree of our knowledge of the nondeterministic phenomenon. We will apply this novel paradigm to the classical Bayes’ theorem in probability theory.","PeriodicalId":308418,"journal":{"name":"The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title]","volume":"s1-5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127194218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2