Pub Date : 2022-01-28DOI: 10.5772/intechopen.101323
Iury Mergen Knoll, A. Quevedo, M. Salomón Alva Sánchez
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.
{"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}
Pub Date : 2021-11-04DOI: 10.5772/intechopen.100428
T. Ogunfunmi, M. Deb
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.
{"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}
Pub Date : 2021-09-23DOI: 10.5772/intechopen.100121
Omaima Essaad Belhaj, H. Boukhal, E. Chakir
The different codes based on the Monte Carlo method, allows to make simulations in the field of medical physics, so the determination of all the magnitudes of radiation protection namely the absorbed dose, the kerma, the equivalent dose, and effective, what guarantees the good planning of the experiment in order to minimize the degrees of exposure to ionizing radiation, and to strengthen the radiation protection of patients and workers in clinical environment as well as to respect the 3 principles of radiation protection ALARA (As Low As Reasonably Achievable) and which are based on: -Justification of the practice -Optimization of radiation protection -Limitation of exposure.
{"title":"Monte Carlo and Medical Physics","authors":"Omaima Essaad Belhaj, H. Boukhal, E. Chakir","doi":"10.5772/intechopen.100121","DOIUrl":"https://doi.org/10.5772/intechopen.100121","url":null,"abstract":"The different codes based on the Monte Carlo method, allows to make simulations in the field of medical physics, so the determination of all the magnitudes of radiation protection namely the absorbed dose, the kerma, the equivalent dose, and effective, what guarantees the good planning of the experiment in order to minimize the degrees of exposure to ionizing radiation, and to strengthen the radiation protection of patients and workers in clinical environment as well as to respect the 3 principles of radiation protection ALARA (As Low As Reasonably Achievable) and which are based on: -Justification of the practice -Optimization of radiation protection -Limitation of exposure.","PeriodicalId":308418,"journal":{"name":"The Monte Carlo Methods - Recent Advances, New Perspectives and Applications [Working Title]","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116649958","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}
Pub Date : 2021-08-23DOI: 10.5772/intechopen.99556
A. Wells, C. Pope
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.
{"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}
Pub Date : 2021-07-26DOI: 10.5772/intechopen.98341
Abdo Abou Jaoude
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.
数学概率的概念是在1933年由Andrey Nikolaevich Kolmogorov通过定义一个由五个公理组成的系统而建立的。该系统可以扩展到包含三个新公理后的虚数集合。因此，任何随机实验都可以在复概率集C中进行，复概率集C是实概率集R和虚概率集M的和。我们在这里的目标是将补充虚维加入到R“实”实验室中发生的随机实验中，从而计算集合R、M和C中的所有概率。整个集合C = R + M中的概率始终等于一个独立于R中输入随机变量分布的概率，因此R中随机实验的输出可以绝对地在C中确定。这是在我们对不确定性实验的了解程度中减去混沌因素后计算C中的概率的结果。我们将把这个创新的范例应用到艾萨克·牛顿的经典力学中，并以一种新颖的方式证明统计物理学基础上的一个重要性质。
{"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}
Pub Date : 2021-07-22DOI: 10.5772/INTECHOPEN.98753
G. Hoff, R. Thomaz, L. I. Gutierres, Sven Muller, V. Fanti, E. Streck, R. Papaléo
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.
{"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}
Pub Date : 2021-07-14DOI: 10.5772/intechopen.98340
Abdo Abou Jaoude
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.
数学上的概率概念是由Andrey Nikolaevich Kolmogorov在1933年通过建立一个五公理系统提出的。在加入三个新公理后，可以将该方案改进为虚数集的体现。因此，任何随机现象都可以在复概率集合C中进行，即实概率集合R和虚概率集合M的和。我们现在的目标是将互补的虚维包含到R中“真实”实验室中发生的随机现象中，从而在集合R、M和C中测量所有相应的概率。因此，整个集合C = R + M中的概率不断地等于1，独立于R中输入随机变量分布的所有概率，随后R中随机现象的输出可以完全在C中评估。这是因为C中的概率是在我们对不确定性现象的了解程度中消除并减去混沌因素后计算的。我们将把这种新的范例应用于概率论中的经典贝叶斯定理。
{"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}
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