Efficiency comparison of Markov Chain Monte Carlo simulation with subset simulation (MCMC/ss) to standard Monte Carlo Simulation (sMC) for extreme event scenarios

Standard Monte Carlo (sMC) simulation models have been widely used in AEC industry research to address system uncertainties. Although the benefits of probabilistic simulation analyses over deterministic methods are well documented, the sMC simulation technique is quite sensitive to the probability distributions of the input variables. This phenomenon becomes highly pronounced when the region of interest within the joint probability distribution (a function of the input variables) is small. In such cases, the standard Monte Carlo approach is often impractical from a computational standpoint. In this paper, a comparative analysis of standard Monte Carlo simulation to Markov Chain Monte Carlo with subset simulation (MCMC/ss) is presented. The MCMC/ss technique constitutes a more complex simulation method (relative to sMC), wherein a structured sampling algorithm is employed in place of completely randomized sampling. Consequently, gains in computational efficiency can be made. The two simulation methods are compared via theoretical case studies.

Exploring young student's functional thinking (Exploración del pensamiento funcional de estudiantes jóvenes)

The Early Years Generalizing Project (EYGP) involves Australian years 1 to 4 (age 5 to 9) students and investigates how they grasp and express generalizations. This paper focuses on data collected from 6 Year 1 students in an exploratory study within a clinical interview setting that required students to identify function rules. Preliminary findings suggest that the use of gestures (both by students and interviewers), self-talk (by students), and concrete acting out, assisted students to reach generalizations and to begin to express these generalities. It also appears that as students became aware of the structure, their use of gestures and selftalk tended to decrease.

Monte Carlo simulations of small field output factors in solid phantoms

Introduction Total scatter factor (or output factor) in megavoltage photon dosimetry is a measure of relative dose relating a certain field size to a reference field size. The use of solid phantoms has been well established for output factor measurements, however to date these phantoms have not been tested with small fields. In this work, we evaluate the water equivalency of a number of solid phantoms for small field output factor measurements using the EGSnrc Monte Carlo code. Methods The following small square field sizes were simulated using BEAMnrc: 5, 6, 7, 8, 10 and 30 mm. Each simulated phantom geometry was created in DOSXYZnrc and consisted of a silicon diode (of length and width 1.5 mm and depth 0.5 mm) submersed in the phantom at a depth of 5 g/cm2. The source-to-detector distance was 100 cm for all simulations. The dose was scored in a single voxel at the location of the diode. Interaction probabilities and radiation transport parameters for each material were created using custom PEGS4 files. Results A comparison of the resultant output factors in the solid phantoms, compared to the same factors in a water phantom are shown in Fig. 1. The statistical uncertainty in each point was less than or equal to 0.4 %. The results in Fig. 1 show that the density of the phantoms affected the output factor results, with higher density materials (such as PMMA) resulting in higher output factors. Additionally, it was also calculated that scaling the depth for equivalent path length had negligible effect on the output factor results at these field sizes. Discussion and conclusions Electron stopping power and photon mass energy absorption change minimally with small field size [1]. Also, it can be seen from Fig. 1 that the difference from water decreases with increasing field size. Therefore, the most likely cause for the observed discrepancies in output factors is differing electron disequilibrium as a function of phantom density. When measuring small field output factors in a solid phantom, it is important that the density is very close to that of water.

Monte Carlo dosimetry for stereotactic radiosurgery

Stereotactic radiosurgery (SRS) treatments for brain cancers require small and precisely shaped photon beams. These beams can be generated by fitting a linear accelerator with a micro-multileaf collimator (mMLC) such as the BrainLAB m3, which offers greater flexibility for field shaping than standard SRS cone collimators

Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

This article presents the field applications and validations for the controlled Monte Carlo data generation scheme. This scheme was previously derived to assist the Mahalanobis squared distance–based damage identification method to cope with data-shortage problems which often cause inadequate data multinormality and unreliable identification outcome. To do so, real-vibration datasets from two actual civil engineering structures with such data (and identification) problems are selected as the test objects which are then shown to be in need of enhancement to consolidate their conditions. By utilizing the robust probability measures of the data condition indices in controlled Monte Carlo data generation and statistical sensitivity analysis of the Mahalanobis squared distance computational system, well-conditioned synthetic data generated by an optimal controlled Monte Carlo data generation configurations can be unbiasedly evaluated against those generated by other set-ups and against the original data. The analysis results reconfirm that controlled Monte Carlo data generation is able to overcome the shortage of observations, improve the data multinormality and enhance the reliability of the Mahalanobis squared distance–based damage identification method particularly with respect to false-positive errors. The results also highlight the dynamic structure of controlled Monte Carlo data generation that makes this scheme well adaptive to any type of input data with any (original) distributional condition.

Angular distribution of carbon ion flux in a nanotube array during the plasma process by the Monte Carlo technique

Angular distribution of microscopic ion fluxes around nanotubes arranged into a dense ordered pattern on the surface of the substrate is studied by means of multiscale numerical simulation. The Monte Carlo technique was used to show that the ion current density is distributed nonuniformly around the carbon nanotubes arranged into a dense rectangular array. The nonuniformity factor of the ion current flux reaches 7 in dense (5× 1018 m-3) plasmas for a nanotube radius of 25 nm, and tends to 1 at plasma densities below 1× 1017 m-3. The results obtained suggest that the local density of carbon adatoms on the nanotube side surface, at areas facing the adjacent nanotubes of the pattern, can be high enough to lead to the additional wall formation and thus cause the single- to multiwall structural transition, and other as yet unexplained nanoscience phenomena.

Assessment on the effect of pollution abatement on environmental efficiency with Markov chain Monte Carlo simulation

Both environmental economists and policy makers have shown a great deal of interest in the effect of pollution abatement on environmental efficiency. In line with the modern resources available, however, no contribution is brought to the environmental economics field with the Markov chain Monte Carlo (MCMC) application, which enables simulation from a distribution of a Markov chain and simulating from the chain until it approaches equilibrium. The probability density functions gained prominence with the advantages over classical statistical methods in its simultaneous inference and incorporation of any prior information on all model parameters. This paper concentrated on this point with the application of MCMC to the database of China, the largest developing country with rapid economic growth and serious environmental pollution in recent years. The variables cover the economic output and pollution abatement cost from the year 1992 to 2003. We test the causal direction between pollution abatement cost and environmental efficiency with MCMC simulation. We found that the pollution abatement cost causes an increase in environmental efficiency through the algorithm application, which makes it conceivable that the environmental policy makers should make more substantial measures to reduce pollution in the near future.

A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design

A computationally efficient sequential Monte Carlo algorithm is proposed for the sequential design of experiments for the collection of block data described by mixed effects models. The difficulty in applying a sequential Monte Carlo algorithm in such settings is the need to evaluate the observed data likelihood, which is typically intractable for all but linear Gaussian models. To overcome this difficulty, we propose to unbiasedly estimate the likelihood, and perform inference and make decisions based on an exact-approximate algorithm. Two estimates are proposed: using Quasi Monte Carlo methods and using the Laplace approximation with importance sampling. Both of these approaches can be computationally expensive, so we propose exploiting parallel computational architectures to ensure designs can be derived in a timely manner. We also extend our approach to allow for model uncertainty. This research is motivated by important pharmacological studies related to the treatment of critically ill patients.

Transdimensional sequential Monte Carlo using variational Bayes — SMCVB

A new transdimensional Sequential Monte Carlo (SMC) algorithm called SM- CVB is proposed. In an SMC approach, a weighted sample of particles is generated from a sequence of probability distributions which ‘converge’ to the target distribution of interest, in this case a Bayesian posterior distri- bution. The approach is based on the use of variational Bayes to propose new particles at each iteration of the SMCVB algorithm in order to target the posterior more efficiently. The variational-Bayes-generated proposals are not limited to a fixed dimension. This means that the weighted particle sets that arise can have varying dimensions thereby allowing us the option to also estimate an appropriate dimension for the model. This novel algorithm is outlined within the context of finite mixture model estimation. This pro- vides a less computationally demanding alternative to using reversible jump Markov chain Monte Carlo kernels within an SMC approach. We illustrate these ideas in a simulated data analysis and in applications.

A Monte-Carlo tree search in argumentation

Monte-Carlo Tree Search (MCTS) is a heuristic to search in large trees. We apply it to argumentative puzzles where MCTS pursues the best argumentation with respect to a set of arguments to be argued. To make our ideas as widely applicable as possible, we integrate MCTS to an abstract setting for argumentation where the content of arguments is left unspecified. Experimental results show the pertinence of this integration for learning argumentations by comparing it with a basic reinforcement learning.

Contribución de la fuerza y el dolor en la función del paciente con artrosis trapecio metacarpiana. Estudio transversal

Objetivo Establecer una posible relación entre la fuerza (Jamar), el dolor (EVA) y la capacidad funcional referida por el paciente (DASH) determinando en qué grado influyen unas en otras. Estudio observacional transversal analítico. Participantes Muestra de 72 pacientes que presentaban una artrosis trapecio metacarpiana grado 2-3 de Eaton. Los pacientes fueron reclutados cuando acudían a la Unidad de Cirugía de mano. Método Se realizaron mediciones de fuerza de agarre, pinza, valoración del dolor y funcionalidad, y se establecieron las correlaciones entre cada una de ellas. Resultados El modelo más significativo para la función (R2 =0.83) incluye la variable dolor y la fuerza. Pero es la fuerza punta contra punta la que presenta una mayor correlación con el cuestionario DASH (B-estandarizado: –57). Respecto al dolor, influye en todas las mediciones de fuerza realizadas con el dinamómetro, siendo también la fuerza de la pinza punta contra punta la que presenta una mayor correlación. Conclusiones Los hallazgos corroboran que existe una correlación significativa entre la función referida por el paciente y variables que podemos medir en consulta, como la fuerza del puño y la pinza. Pero también esta correlación es significativa entre las variables función y dolor entre sí, pero es la pinza punta contra punta la que presenta una mayor asociación con el cuestionario DASH. Abstract in English Objective To assess the relationship between muscle strength (Jama), and pain (VAS) levels with hand function (DASH) in patients with trapeziometarcapal osteoarthritis. Cross-sectional study. Participants Sample of 72 patients with osteoarthritis stage 2-3 (Eaton) and trapeziometacarpal osteoarthritis. Patients were recruited when they came to the Hand Surgery Unit. Method Grip strength, pinch, pain and hand function were measured, and correlation and regression coefficients between them were obtained. Results For function, the most significant model (R2 = 0.83) included pain and strength. But it is tip to tip pinch force which has a stronger relationship with DASH (Standardized B: –57) questionnaire. Pain also influenced strength measured with the dynamometer but it was tip to tip pinch force that was the most affected. Conclusions Findings confirm that there is a significant correlation between function referred by the patient and variables that can be measured in the clinic such as grip strength and pinch. The correlation between pain intensity and function was also significant, but tip to tip pinch strength had the greatest impact on the function.

Etapas del cambio en personas inactivas tras un programa de promoción de la actividad física en Atención Primaria

Objetivo El objetivo del estudio fue evaluar las Etapas del cambio en relación con la actividad física y el estado de salud general entre personas que participaron en un Programa de promoción de la actividad física (PPAF) de 12 semanas frente a un grupo control. Diseño Ensayo clínico aleatorizado. Participantes Noventa y ocho personas inactivas de ambos sexos con una edad media de 62,82 años procedentes de 2 centros de Atención Primaria del Distrito Sanitario Costa del Sol. Intervención Un PPAF organizado en grupos y siguiendo los criterios del Colegio Americano de Medicina del Deporte, 2 sesiones semanales de 60 min durante 12 semanas. Mediciones principales La variable principal de resultado fue resistencia al cambio en relación con la actividad física. La variable secundaria fue el estado de salud general (componentes físicos y mentales), determinado con el cuestionario de salud general SF12. Resultados Se encontraron diferencias significativas en las etapas del cambio a favor del grupo experimental (p < 0,05). No se encontraron diferencias estadísticamente significativas entre grupos después de la intervención en el estado general de salud. Conclusión Las etapas del cambio se modificaron en las personas inactivas que realizaron el PPAF en Atención Primaria. Futuros estudios son necesarios para identificar qué factores del entorno de los participantes influyen en la resistencia al cambio de la actividad física. Abstract Objective This study has aimed to evaluate the stages of change in relation to physical activity and overall health status among persons who participated in a 12-week Physical activity promotion program (PAPP) compared to a control group. Design Randomized clinical trial. Participants The study included 98 inactive persons of both sexes with a mean age of 62.82 years from 2 of Primary Care Centers of the Malaga Health Care District. Interventions A PAPP organized in groups according to the American College of Sports Medicine criteria including two weekly sessions of 60 minutes each for 3 months. Main measures The primary outcome was to assess resistance to change in relation to physical activity. The secondary variable was overall health (physical and mental components) determined with the SF12 general health questionnaire. Results Significant differences were found in the stages of change (P<.05). There were no significant differences found in general health status improvement in regards to the initial assessment. Conclusion The stages of change were modified in the inactive persons who carried out the PAPP in Primary Care. Future studies are needed to identify which environmental factors influence the resistance to change in physical activity of the participants.

Adolescent sexual behavioral intentions measurement: A gender approach in the Colombian Caribbean region [Medición de la Intención en la Actividad Sexual en Adolescentes: una Aproximación de Acuerdo al Género del Caribe Colombiano]

The study of 1777 male and female adolescent students of 11-19 years in the Colombian Caribbean had two objectives: development and validation of two reproductive health intention scales and analyze gender differences. The pilot of the scale consisted of 8 items and was reduced to 6, to check the reliability and validity using factor analysis and principal components with VARIMAX rotation yielded two factors: Intention and Intention Risk Protection, explained between 69.4% and 70% respectively. In the male Protection Intent (M = 3.87 and SD = 1.29) and risk (M = 2.56 and SD = 1.18) obtained an alpha between 0.74 and 0.86, and in Protection of Intent to female (M = 3.49 and SD = 1.35) and risk (M = 1.50 and SD = 0.89) ranged between 0.78 and 086. In conclusion, the reliability and structural stability are adequate and there are gender differences in the scales.

Monte Carlo analysis of a puzzle game

When a puzzle game is created, its design parameters must be chosen to allow solvable and interesting challenges to be created for the player. We investigate the use of random sampling as a computationally inexpensive means of automated game analysis, to evaluate the BoxOff family of puzzle games. This analysis reveals useful insights into the game, such as the surprising fact that almost 100% of randomly generated challenges have a solution, but less than 10% will be solved using strictly random play, validating the inventor’s design choices. We show the 1D game to be trivial and the 3D game to be viable.