Characterization of an extrapolation chamber for low-energy X-rays: Experimental and Monte Carlo preliminary results

The extrapolation chamber is a parallel-plate ionization chamber that allows variation of its air-cavity volume. In this work, an experimental study and MCNP-4C Monte Carlo code simulations of an ionization chamber designed and constructed at the Calibration Laboratory at IFEN to be used as a secondary dosimetry standard for low-energy X-rays are reported. The results obtained were within the international recommendations, and the simulations showed that the components of the extrapolation chamber may influence its response up to 11.0%. (C) 2011 Elsevier Ltd. All rights reserved.

Monte Carlo Semi-Empirical Model for Si(Li) X-Ray Detector: Differences between Nominal and Fitted Parameters.

A detailed characterization of a X-ray Si(Li) detector was performed to obtain the energy dependence of efficiency in the photon energy range of 6.4 - 59.5 keV. which was measured and reproduced by Monte Carlo (MC) simulations. Significant discrepancies between MC and experimental values were found when lhe manufacturer parameters of lhe detector were used in lhe simulation. A complete Computerized Tomagraphy (CT) detector scan allowed to find the correct crystal dimensions and position inside the capsule. The computed efficiencies with the resulting detector model differed with the measured values no more than 10% in most of the energy range.

Optimization of x-ray spectra in digital mammography through Monte Carlo simulations

In this work, a Monte Carlo code was used to investigate the performance of different x-ray spectra in digital mammography, through a figure of merit (FOM), defined as FOM = CNR2/(D) over bar (g), with CNR being the contrast-to-noise ratio in image and (D) over bar (g) being the average glandular dose. The FOM was studied for breasts with different thicknesses t (2 cm <= t <= 8 cm) and glandular contents (25%, 50% and 75% glandularity). The anode/filter combinations evaluated were those traditionally employed in mammography (Mo/Mo, Mo/Rh, Rh/Rh), and a W anode combined with Al or K-edge filters (Zr, Mo, Rh, Pd, Ag, Cd, Sn), for tube potentials between 22 and 34 kVp. Results show that the W anode combined with K-edge filters provides higher values of FOM for all breast thicknesses investigated. Nevertheless, the most suitable filter and tube potential depend on the breast thickness, and for t >= 6 cm, they also depend on breast glandularity. Particularly for thick and dense breasts, a W anode combined with K-edge filters can greatly improve the digital technique, with the values of FOM up to 200% greater than that obtained with the anode/filter combinations and tube potentials traditionally employed in mammography. For breasts with t < 4 cm, a general good performance was obtained with the W anode combined with 60 mu m of the Mo filter at 24-25 kVp, while 60 mu m of the Pd filter provided a general good performance at 24-26 kVp for t = 4 cm, and at 28-30 and 29-31 kVp for t = 6 and 8 cm, respectively.

Quantum Monte Carlo study of small aluminum clusters Al-n (n=2-13)

Using fixed node diffusion quantum Monte Carlo (FN-DMC) simulations and density functional theory (DFT) within the generalized gradient approximations, we calculate the total energies of the relaxed and unrelaxed neutral, cationic, and anionic aluminum clusters, Al-n (n = 1-13). From the obtained total energies, we extract the ionization potential and electron detachment energy and compare with previous theoretical and experimental results. Our results for the electronic properties from both the FN-DMC and DFT calculations are in reasonably good agreement with the available experimental data. A comparison between the FN-DMC and DFT results reveals that their differences are a few tenths of electron volt for both the ionization potential and the electron detachment energy. We also observe two distinct behaviors in the electron correlation contribution to the total energies from smaller to larger clusters, which could be assigned to the structural transition of the clusters from planar to three-dimensional occurring at n = 4 to 5.

Generalized Metropolis dynamics with a generalized master equation: An approach for time-independent and time-dependent Monte Carlo simulations of generalized spin systems

The extension of Boltzmann-Gibbs thermostatistics, proposed by Tsallis, introduces an additional parameter q to the inverse temperature beta. Here, we show that a previously introduced generalized Metropolis dynamics to evolve spin models is not local and does not obey the detailed energy balance. In this dynamics, locality is only retrieved for q = 1, which corresponds to the standard Metropolis algorithm. Nonlocality implies very time-consuming computer calculations, since the energy of the whole system must be reevaluated when a single spin is flipped. To circumvent this costly calculation, we propose a generalized master equation, which gives rise to a local generalized Metropolis dynamics that obeys the detailed energy balance. To compare the different critical values obtained with other generalized dynamics, we perform Monte Carlo simulations in equilibrium for the Ising model. By using short-time nonequilibrium numerical simulations, we also calculate for this model the critical temperature and the static and dynamical critical exponents as functions of q. Even for q not equal 1, we show that suitable time-evolving power laws can be found for each initial condition. Our numerical experiments corroborate the literature results when we use nonlocal dynamics, showing that short-time parameter determination works also in this case. However, the dynamics governed by the new master equation leads to different results for critical temperatures and also the critical exponents affecting universality classes. We further propose a simple algorithm to optimize modeling the time evolution with a power law, considering in a log-log plot two successive refinements.

Distribution and molecular characterization of hepatitis C virus (HCV) genotypes in patients with chronic infection from Pernambuco State, Brazil

Hepatitis C virus (HCV) is a public health problem throughout the world and 3% of the world population is infected with this virus. It is estimated that 3-4 millions individuals are being infected every year. It has been estimated that around 1.5% of Brazilian population is anti-HCV positive and the Northeast region showed the highest prevalence in Brazil. The aim of this study was to characterize HCV genotypes circulating in Pernambuco State (PE), Brazil, located in the Northeast region of the country. This study included 85 anti-HCV positive patients followed up between 2004 and 2011. For genotyping, a 380bp fragment of HCV RNA in the NS5B region was amplified by nested PCR. Phylogenetic analysis was conducted using Bayesian Markov chain Monte Carlo simulation (MCMC) using BEAST v.1.5.3. From 85 samples, 63 (74.1%) positive to NS5B fragment were successfully sequenced. Subtype 1b was the most prevalent in this population (42-66.7%), followed by 3a (16-25.4%), 1a (4-6.3%) and 2b (1-1.6%). Twelve (63.1%) and seven (36.9%) patients with HCV and schistosomiasis were infected with subtypes 1b and 3a, respectively. Brazil is a large country with many different population backgrounds; a large variation in the frequencies of HCV genotypes is predictable throughout its territory. This study reports HCV genotypes from Pernambuco State where subtype 1b was found to be the most prevalent. Phylogenetic analysis suggests the presence of the different HCV strains circulating within this population. (C) 2012 Elsevier B.V. All rights reserved.

Response functions of Si(Li), SDD and CdTe detectors for mammographic x-ray spectroscopy

In this work, the energy response functions of Si(Li), SDD and CdTe detectors were studied in the mammographic energy range through Monte Carlo simulation. The code was modified to take into account carrier transport effects and the finite detector energy resolution. The results obtained show that all detectors exhibit good energy response at low energies. The most important corrections for each detector were discussed, and the corrected mammographic x-ray spectra obtained with each one were compared. Results showed that all detectors provided similar corrected spectra, and, therefore, they could be used to accurate mammographic x-ray spectroscopy. Nevertheless, the SDD is particularly suitable for clinic mammographic x-ray spectroscopy due to the easier correction procedure and portability. (C) 2011 Elsevier Ltd. All rights reserved.

THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

Risk assessment of PM2.5 to child residents in Brazilian Amazon region with biofuel production

Background: Exposure to fine fractions of particulate matter (PM2.5) is associated with increased hospital admissions and mortality for respiratory and cardiovascular disease in children and the elderly. This study aims to estimate the toxicological risk of PM2.5 from biomass burning in children and adolescents between the age of 6 and 14 in Tangara da Serra, a municipality of Subequatorial Brazilian Amazon. Methods: Risk assessment methodology was applied to estimate the risk quotient in two scenarios of exposure according to local seasonality. The potential dose of PM2.5 was estimated using the Monte Carlo simulation, stratifying the population by age, gender, asthma and Body Mass Index (BMI). Results: Male asthmatic children under the age of 8 at normal body rate had the highest risk quotient among the subgroups. The general potential average dose of PM2.5 was 1.95 mu g/kg.day (95% CI: 1.62 - 2.27) during the dry scenario and 0.32 mu g/kg. day (95% CI: 0.29 - 0.34) in the rainy scenario. During the dry season, children and adolescents showed a toxicological risk to PM2.5 of 2.07 mu g/kg. day (95% CI: 1.85 - 2.30). Conclusions: Children and adolescents living in the Subequatorial Brazilian Amazon region were exposed to high levels of PM2.5 resulting in toxicological risk for this multi-pollutant. The toxicological risk quotients of children in this region were comparable or higher to children living in metropolitan regions with PM2.5 air pollution above the recommended limits to human health.

The Truncated Inflated Beta Distribution

The study of proportions is a common topic in many fields of study. The standard beta distribution or the inflated beta distribution may be a reasonable choice to fit a proportion in most situations. However, they do not fit well variables that do not assume values in the open interval (0, c), 0 < c < 1. For these variables, the authors introduce the truncated inflated beta distribution (TBEINF). This proposed distribution is a mixture of the beta distribution bounded in the open interval (c, 1) and the trinomial distribution. The authors present the moments of the distribution, its scoring vector, and Fisher information matrix, and discuss estimation of its parameters. The properties of the suggested estimators are studied using Monte Carlo simulation. In addition, the authors present an application of the TBEINF distribution for unemployment insurance data.

Distribution of hepatitis c virus (hcv) genotypes in patients with chronic infection from Rondônia, Brazil

Abstract Background Hepatitis C virus (HCV) is an important human pathogen affecting around 3% of the human population. In Brazil, it is estimated that there are approximately 2 to 3 million HCV chronic carriers. There are few reports of HCV prevalence in Rondônia State (RO), but it was estimated in 9.7% from 1999 to 2005. The aim of this study was to characterize HCV genotypes in 58 chronic HCV infected patients from Porto Velho, Rondônia (RO), Brazil. Methods A fragment of 380 bp of NS5B region was amplified by nested PCR for genotyping analysis. Viral sequences were characterized by phylogenetic analysis using reference sequences obtained from the GenBank (n = 173). Sequences were aligned using Muscle software and edited in the SE-AL software. Phylogenetic analyses were conducted using Bayesian Markov chain Monte Carlo simulation (MCMC) to obtain the MCC tree using BEAST v.1.5.3. Results From 58 anti-HCV positive samples, 22 were positive to the NS5B fragment and successfully sequenced. Genotype 1b was the most prevalent in this population (50%), followed by 1a (27.2%), 2b (13.6%) and 3a (9.0%). Conclusions This study is the first report of HCV genotypes from Rondônia State and subtype 1b was found to be the most prevalent. This subtype is mostly found among people who have a previous history of blood transfusion but more detailed studies with a larger number of patients are necessary to understand the HCV dynamics in the population of Rondônia State, Brazil.

Probabilistic failure modelling of reinforced concrete structures subjected to chloride penetration

Structural durability is an important criterion that must be evaluated for every type of structure. Concerning reinforced concrete members, chloride diffusion process is widely used to evaluate durability, especially when these structures are constructed in aggressive atmospheres. The chloride ingress triggers the corrosion of reinforcements; therefore, by modelling this phenomenon, the corrosion process can be better evaluated as well as the structural durability. The corrosion begins when a threshold level of chloride concentration is reached at the steel bars of reinforcements. Despite the robustness of several models proposed in literature, deterministic approaches fail to predict accurately the corrosion time initiation due the inherent randomness observed in this process. In this regard, structural durability can be more realistically represented using probabilistic approaches. This paper addresses the analyses of probabilistic corrosion time initiation in reinforced concrete structures exposed to chloride penetration. The chloride penetration is modelled using the Fick's diffusion law. This law simulates the chloride diffusion process considering time-dependent effects. The probability of failure is calculated using Monte Carlo simulation and the first order reliability method, with a direct coupling approach. Some examples are considered in order to study these phenomena. Moreover, a simplified method is proposed to determine optimal values for concrete cover.

Economic analysis for oregano under irrigation considering economic risk factors

The oregano is a plant, rich in essential oil and very used as spice in the preparation of foods. The objective of this paper was to analyze the viability of irrigation for oregano in Presidente Prudente, São Paulo state, Brazil, including economic risk factors, their effect on irrigation total cost, as well as the different pumping kinds. The Monte Carlo simulation was utilized to study the economic factors: fixed cost, labor, maintenance, pumping and water. The use of irrigation for the oregano in the region of Presidente Prudente is indicated because of its economic feasibility and the reduced risks. The average values of the benefit/cost for all water depths tested were higher than 1, indicating viability. The use of irrigation promoted lower risks compared to the non irrigated crop. The micro irrigation system presented greater sensitivity to changes of prices of the equipment associated to the variation of the useful life of the system. The oregano selling price was the most important factor involved in annual net profit. The water cost was the factor of lesser influence on the total cost. Due to the characteristic of high drip irrigation frequency there was no difference between the tariffs based in use hour of electric energy classified as green and blue, which are characterized by applying different rates on the energy consumption and demand according to the hours of day and times of the year. For the studied region it was recommended drip irrigation water management of oregano with the daily application of 100% of pan evaporation Class A using electric motor with tariffs blue or green.

Modeling random corrosion processes via polynomial chaos expansion

Polynomial Chaos Expansion (PCE) is widely recognized as a flexible tool to represent different types of random variables/processes. However, applications to real, experimental data are still limited. In this article, PCE is used to represent the random time-evolution of metal corrosion growth in marine environments. The PCE coefficients are determined in order to represent data of 45 corrosion coupons tested by Jeffrey and Melchers (2001) at Taylors Beach, Australia. Accuracy of the representation and possibilities for model extrapolation are considered in the study. Results show that reasonably accurate smooth representations of the corrosion process can be obtained. The representation is not better because a smooth model is used to represent non-smooth corrosion data. Random corrosion leads to time-variant reliability problems, due to resistance degradation over time. Time variant reliability problems are not trivial to solve, especially under random process loading. Two example problems are solved herein, showing how the developed PCE representations can be employed in reliability analysis of structures subject to marine corrosion. Monte Carlo Simulation is used to solve the resulting time-variant reliability problems. However, an accurate and more computationally efficient solution is also presented.

Reliability algorithms applied to reinforced concrete structures durability assessment

This paper addresses the analysis of probabilistic corrosion time initiation in reinforced concrete structures exposed to ions chloride penetration. Structural durability is an important criterion which must be evaluated in every type of structure, especially when these structures are constructed in aggressive atmospheres. Considering reinforced concrete members, chloride diffusion process is widely used to evaluate the durability. Therefore, at modelling this phenomenon, corrosion of reinforcements can be better estimated and prevented. These processes begin when a threshold level of chlorides concentration is reached at the steel bars of reinforcements. Despite the robustness of several models proposed in the literature, deterministic approaches fail to predict accurately the corrosion time initiation due to the inherently randomness observed in this process. In this regard, the durability can be more realistically represented using probabilistic approaches. A probabilistic analysis of ions chloride penetration is presented in this paper. The ions chloride penetration is simulated using the Fick's second law of diffusion. This law represents the chloride diffusion process, considering time dependent effects. The probability of failure is calculated using Monte Carlo simulation and the First Order Reliability Method (FORM) with a direct coupling approach. Some examples are considered in order to study these phenomena and a simplified method is proposed to determine optimal values for concrete cover.