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In this study, a kinetic model of the heterogeneous photocatalytic degradation of acetaminophen and its main transformation products is presented. Kinetic photocatalytic modeling and photon absorption rate modeling were included. Monte Carlo method was used to model the photon absorption process. Experiments were carried out in a reactor operated in batch mode and TiO2 nanotubes were used as photocatalyst irradiated with 254 nm UVC. Kinetic parameters were estimated from the experiments data by applying a non-linear regression procedure. Intrinsic expressions to the kinetics of acetaminophen degradation and its main transformation products were derived. Model, kinetics and photon absorption formulations and parameters proved to be affordable for describing the photocatalytic degradation of acetaminophen, but improvements should be done for better description of formation and oxidation kinetics of main transformation products. The model should be tested with other pharmaceuticals and emergent pollutants to calibrate it and evaluate its applicability in a wide range of compounds.

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The Biswas-Chatterjee-Sen (BChS) model of opinion dynamics has been studied on three-dimensional Solomon networks by means of extensive Monte Carlo simulations. Finite-size scaling relations for different lattice sizes have been used in order to obtain the relevant quantities of the system in the thermodynamic limit. From the simulation data it is clear that the BChS model undergoes a second-order phase transition. At the transition point, the critical exponents describing the behavior of the order parameter, the corresponding order parameter susceptibility, and the correlation length, have been evaluated. From the values obtained for these critical exponents one can confidently conclude that the BChS model in three dimensions is in a different universality class to the respective model defined on one- and two-dimensional Solomon networks, as well as in a different universality class as the usual Ising model on the same networks.

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In this work we report magnetic properties of the orthorhombic perovskite Lu0.9Sr0.1Cr0.5Fe0.5O3synthesized by a wet chemical method. As in LuCr0.5Fe0.5O3the compound with Sr shows the magnetization reversal phenomenon, but the magnetic order and the compensation temperature occur at higher temperatures. Interestingly, in M vs H curves a hysteresis loop is observed when Cr4+and Cr3+ions coexist as a consequence of the aliovalent substitution of Lu3+by Sr2+in the B sites of the perovskite. To explain this behavior, we performed numerical simulations with a magnetic model for Lu1-xSrxCr0.5Fe0.5O3perovskites withx= 0 andx= 0.1. We found that the ferromagnetic coupling of Fe3+and Cr4+through superexchange interactions (according the empiric Goodenough-Kanamori-Anderson rules) increases the magnetization at high fields and that the presence of ferromagnetic clusters explains the hysteretic behavior found in simulations.

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The widespread presence of arsenic (As) and fluoride (F-) in groundwater poses substantial risks to human health on a global scale. These elements have been identified as the most prevalent geogenic contaminants in groundwater in northern Mexico. Consequently, this study aimed to evaluate the human health and ecological risks associated with the content of As and F- in the Meoqui-Delicias aquifer, which is in one of Mexico's most emblematic irrigation districts. Concentrations of As and F- were measured in 38 groundwater samples using ICP-MS and ion chromatography, respectively. Overall, these elements showed a similar trend across the aquifer, revealing a positive correlation between them and pH. The concentration of As and F- in the groundwater ranged from 5.3 µg/L to 303 µg/L and from 0.5 mg/L to 8.8 mg/L, respectively. Additionally, the levels of As and F- surpassed the established national standards for safe drinking water in 92% and 97% of samples, respectively. Given that groundwater is used for both agricultural purposes and human activities, this study also assessed the associated human health and ecological risks posed by these elements using Monte Carlo simulation and Species Sensitivity Distribution. The findings disclosed a significant noncarcinogenic health risk associated with exposure to As and F-, as well as an unacceptable carcinogenic health risk to As through water consumption for both adults and children. Furthermore, a high ecological risk to aquatic species was identified for F- and high to medium risks for As in the sampling sites. Therefore, the findings in this study provide valuable information for Mexican authorities and international organizations (e.g., WHO) about the adverse effects that any exposure without treatment to groundwater from this region represents for human health.

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A monolayer consisting of two types of particles, with energetically favored alternating stripes of the two components, is studied by Monte Carlo simulations and within a mesoscopic theory. We consider a triangular lattice model and assume short-range attraction and long-range repulsion between particles of the same kind, as well as short-range repulsion and long-range attraction for the cross-interaction. The structural evolution of the model upon increasing temperature is studied for equal chemical potentials of the two species. We determine the structure factor, the chemical potential-density isotherms, the specific heat, and the compressibility, and show how these thermodynamic functions are associated with the spontaneous formation of stripes with varying degrees of order.

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Considerable experimental effort has been aimed at uncovering a reliable way to perform a dosimetric assessment in mixed radiation fields. In fields composed by gammas and neutrons, TLD dosimeters are usually applied to execute such measurements, although there is no consensus on the most favorable strategy to employ them. In this context, TLD-100 measurements within two different core configurations of the IPEN/MB-01 research reactor and Monte Carlo simulations have been used to investigate the behavior of those detectors in multiple mixed radiation fields, deriving a methodology to evaluate the dose deposition in the dosimeter by different gamma and neutron energy spectra and intensities. A surprising outcome is the linear neutron dose response shown by TLD-100 even irradiated by so distinct irradiation fields.

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This paper aims to introduce a novel family of probability distributions by the well-known method of the T-X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan-Quinn information criterion (HQINC), the Cramer-von-Misses (CRMI), and the ANDA (Anderson-Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study.

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One of the challenges in augmentative biological control programs is the definition of releasing strategy for natural enemies, especially when macro-organisms are involved. Important information about the density of insects to be released and frequency of releases usually requires a great number of experiments, which implies time and space that are not always readily available. In order to provide science-based responses for these questions, computational models offer an in silico option to simulate different biocontrol agent releasing scenarios. This allows decision-makers to focus their efforts to more feasible options. The major insect pest in sugarcane crops is the sugarcane borer Diatraea saccharalis, which can be managed using the egg parasitoid Trichogramma galloi. The current strategy consists in releasing 50,000 insects per hectare for each release, in three weekly releases. Here, we present a simulation model to check whether this releasing strategy is optimal against the sugarcane borer. A sensitive analysis revealed that the population of the pest is more affected by the number of releases rather than by the density of parasitoids released. Only the number of releases demonstrated an ability to drive the population curve of the pest towards a negative growth. For example, releasing a total of 600,000 insects per hectare in three releases led to a lower pest control efficacy that releasing only 250,000 insects per hectare in five releases. A higher number of releases covers a wider range of time, increasing the likelihood of releasing parasitoids at the correct time given that the egg stage is short. Based on these results, it is suggested that, if modifications to the releasing strategy are desired, increasing the number of releases from 3 to 5 at weekly intervals is most likely preferable.

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BACKGROUND: Non-melanoma skin cancer is one of the most common types of cancer and one of the main approaches is brachytherapy. For small lesions, the treatment of this cancer with brachytherapy can be done with two commercial applicators, one of these is the Large Field Valencia Applicators (LFVA). PURPOSE: The aim of this study is to test the capabilities of the LFVA to use clinically 60Co sources instead of the 192Ir ones. This study was designed for the same dwell positions and weights for both sources. METHODS: The Penelope Monte Carlo code was used to evaluate dose distribution in a water phantom when a 60Co source is considered. The LFVA design and the optimized dwell weights reported for the case of 192Ir are maintained with the only exception of the dwell weight of the central position, that was increased. 2D dose distributions, field flatness, symmetry and the leakage dose distribution around the applicator were calculated. RESULTS: When comparing the dose distributions of both sources, field flatness and symmetry remain unchanged. The only evident difference is an increase of the penumbra regions for all depths when using the 60Co source. Regarding leakage, the maximum dose within the air volume surrounding the applicator is in the order of 20% of the prescription dose for the 60Co source, but it decreases to less than 5% at about 1 cm distance. CONCLUSIONS: Flatness and symmetry remains unaltered as compared with 192Ir sources, while an increase in leakage has been observed. This proves the feasibility of using the LFVA in a larger range of clinical applications.

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Significance: Continuous-wave functional near-infrared spectroscopy has proved to be a valuable tool for assessing hemodynamic activity in the human brain in a non-invasively and inexpensive way. However, most of the current processing/analysis methods assume the head is a homogeneous medium, and hence do not appropriately correct for the signal coming from the scalp. This effect can be reduced by considering light propagation in a layered model of the human head, being the Monte Carlo (MC) simulations the gold standard to this end. However, this implies large computation times and demanding hardware capabilities. Aim: In this work, we study the feasibility of replacing the homogeneous model and the MC simulations by means of analytical multilayered models, combining in this way, the speed and simplicity of implementation of the former with the robustness and accuracy of the latter. Approach: Oxy- and deoxyhemoglobin (HbO and HbR, respectively) concentration changes were proposed in two different layers of a magnetic resonance imaging (MRI)-based meshed model of the human head, and then these changes were retrieved by means of (i) a typical homogeneous reconstruction and (ii) a theoretical layered reconstruction. Results: Results suggest that the use of analytical models of light propagation in layered models outperforms the results obtained using traditional homogeneous reconstruction algorithms, providing much more accurate results for both, the extra- and the cerebral tissues. We also compare the analytical layered reconstruction with MC-based reconstructions, achieving similar degrees of accuracy, especially in the gray matter layer, but much faster (between 4 and 5 orders of magnitude). Conclusions: We have successfully developed, implemented, and validated a method for retrieving chromophore concentration changes in the human brain, combining the simplicity and speed of the traditional homogeneous reconstruction algorithms with robustness and accuracy much more similar to those provided by MC simulations.

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The present work intends to discuss parameter estimation and statistical analysis in adsorption. The Langmuir and Tóth isotherm models are compared for a set of carbon dioxide adsorption data on 13X zeolite from literature at different temperatures: 303, 323, 373, and 423 K. Statistical analyses were performed under frequentist and Bayesian perspectives. Under the frequentist statistical view, parameters were estimated using Maximum Likelihood estimation (MLE). Statistical analyses of parameters were performed by confidence regions in terms of elliptical approximation and likelihood region, while the evaluation of models was performed by chi-square statistics. The results showed that, for these nonlinear models, the elliptical region offers a poor approximation of the parameter estimates' confidence region, especially for the most correlated parameter pairs. Additionally, the four-parameter Tóth's equation yields less correlated parameters than the three-parameter Langmuir model. From a Bayesian perspective, the Markov chain Monte Carlo (MCMC) technique facilitated the reconstruction of the probability density functions of parameters as well as enabled the propagation of parametric uncertainties in the model responses. Finally, the accurate assessment of experimental uncertainty significantly influences the evaluation of models and their respective parameters.

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BACKGROUND: Uniform random sampling of mass-balanced flux solutions offers an unbiased appraisal of the capabilities of metabolic networks. Unfortunately, it is impossible to avoid thermodynamically infeasible loops in flux samples when using convex samplers on large metabolic models. Current strategies for randomly sampling the non-convex loopless flux space display limited efficiency and lack theoretical guarantees. RESULTS: Here, we present LooplessFluxSampler, an efficient algorithm for exploring the loopless mass-balanced flux solution space of metabolic models, based on an Adaptive Directions Sampling on a Box (ADSB) algorithm. ADSB is rooted in the general Adaptive Direction Sampling (ADS) framework, specifically the Parallel ADS, for which theoretical convergence and irreducibility results are available for sampling from arbitrary distributions. By sampling directions that adapt to the target distribution, ADSB traverses more efficiently the sample space achieving faster mixing than other methods. Importantly, the presented algorithm is guaranteed to target the uniform distribution over convex regions, and it provably converges on the latter distribution over more general (non-convex) regions provided the sample can have full support. CONCLUSIONS: LooplessFluxSampler enables scalable statistical inference of the loopless mass-balanced solution space of large metabolic models. Grounded in a theoretically sound framework, this toolbox provides not only efficient but also reliable results for exploring the properties of the almost surely non-convex loopless flux space. Finally, LooplessFluxSampler includes a Markov Chain diagnostics suite for assessing the quality of the final sample and the performance of the algorithm.

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BACKGROUND: Monte Carlo simulation of radiation transport for medical linear accelerators (linacs) requires accurate knowledge of the geometrical description of the linac head. Since the geometry of Varian TrueBeam machines has not been disclosed, the manufacturer distributes phase-space files of the linac patient-independent part to allow researchers to compute absorbed dose distributions using the Monte Carlo method. This approach limits the possibility of achieving an arbitrarily small statistical uncertainty. This work investigates the use of the geometry of the Varian Clinac 2100, which is included in the Monte Carlo system PRIMO, as a surrogate. METHODS: Energy, radial and angular distributions extracted from the TrueBeam phase space files published by the manufacturer and from phase spaces tallied with PRIMO for the Clinac 2100 were compared for the 6, 8, 10 and 15 MV flattened-filtered beams. Dose distributions in water computed for the two sets of PSFs were compared with the Varian Representative Beam Data (RBD) for square fields with sides ranging from 3 to 30 cm. Output factors were calculated for square fields with sides ranging from 2 to 40 cm. RESULTS: Excellent agreement with the RBD was obtained for the simulations that employed the phase spaces distributed by Varian as well as for those that used the surrogate geometry, reaching in both cases Gamma ([Formula: see text], 2 mm) pass rates larger than [Formula: see text], except for the 15 MV surrogate. This result supports previous investigations that suggest a change in the material composition of the TrueBeam 15 MV flattening filter. In order to get the said agreement, PRIMO simulations were run using enlarged transport parameters to compensate the discrepancies between the actual and surrogate geometries. CONCLUSIONS: This work sustains the claim that the simulation of the 6, 8 and 10 MV flattening-filtered beams of the TrueBeam linac can be performed using the Clinac 2100 model of PRIMO without significant loss of accuracy.

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Gas turbines are thermoelectric plants with various applications, such as large-scale electricity production, petrochemical industry, and steam generation. In order to optimize the operation of a gas turbine, it is necessary to develop system identification models that allow for the development of studies and analyses to increase the system's reliability. Current strategies for modeling complex and non-linear systems can be based on artificial intelligence techniques, using autoregressive neural networks of the NARX and LSTM type. In this context, this work aims to develop a model of a gas turbine capable of estimating the rotation speed of the turbine and simultaneously estimating the uncertainty associated with the estimation. These methodologies are based on artificial neural networks and the Monte Carlo dropout simulation method. The results were obtained from experimental data from a 215 MW gas turbine, getting the best model with a MAPE of 0.02% and an uncertainty associated with the turbine rotation speed of 2.2 RPM.

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This paper analyzes the role of the diffusion coefficient in the movement of analytes that can reversibly react with a selector given a product in the presence of drift. The problem mimics the movement of enantiomers in a capillary electrophoresis experiment. As is well known, the signal in the capillary must be sharp enough to make a good determination of the effective mobility of the analytes being analyzed. The essence of the technique is based on fast interconversion rates. Therefore, the effective diffusion coefficient must be negligible during the experiment. In the present work, an exact expression for both the apparent mobility and the diffusion coefficient is obtained. This is done by writing the rate equations governing the process and solving them using the generating function technique. The effective mobility coincides with the Wren and Rowe equation, whereas the diffusion coefficient allows us to determine the values of the parameters to be taken into account so that this quantity is minimal or close to zero. On the other hand, the numerical solution of the kinetic equations and Monte Carlo simulations allow us to follow the signal in the capillary and to determine its space-time evolution.

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Recently, the pharmaceutical industry has increasingly adopted the Analytical Quality by Design (AQbD) approach for analytical development. To facilitate AQbD approach implementation in the development of chromatographic methods for determining cephalosporin antibiotics, an in silico tool capable of performing virtual DoEs was developed enabling to obtain virtual operable regions of method. To this end, the drugs cephalexin, cefazolin, cefotaxime and ceftriaxone were analyzed using four experimental designs, deriving a DoE-QSRR model and employing Monte Carlo method. The DoE-QSRR model and virtual DoEs were validated using data not used in model's construction, obtaining coefficients of determination of 84.72 % for DoE-QSRR model and over 77 % for virtual DoEs. Virtual MODRs were constructed using data from the virtual DoEs. The virtual MODRs were validated by comparing them with experimental MODRs under various scenarios, with overlap areas reaching values exceeding 84 %. Therefore, the in silico tool was considered suitable for indicating analyte trends under different analytical conditions, being capable of performing virtual DoEs for cephalosporin drugs with sufficient assertiveness to guide analytical development and allow obtaining a MODR capable of providing results of adequate quality.

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BACKGROUND: Although the benefits of breast screening and early diagnosis are known for reducing breast cancer mortality rates, the effects and risks of low radiation doses to the cells in the breast are still ongoing topics of study. PURPOSE: To study specific energy distributions ( f ( z , D g ) $f(z,D_{g})$ ) in cytoplasm and nuclei of cells corresponding to glandular tissue for different x-ray breast imaging modalities. METHODS: A cubic lattice (500 µm length side) containing 4064 spherical cells was irradiated with photons loaded from phase space files with varying glandular voxel doses ( D g $D_{g}$ ). Specific energy distributions were scored for nucleus and cytoplasm compartments using the PENELOPE (v. 2018) + penEasy (v. 2020) Monte Carlo (MC) code. The phase space files, generated in part I of this work, were obtained from MC simulations in a voxelized anthropomorphic phantom corresponding to glandular voxels for different breast imaging modalities, including digital mammography (DM), digital breast tomosynthesis (DBT), contrast enhanced digital mammography (CEDM) and breast CT (BCT). RESULTS: In general, the average specific energy in nuclei is higher than the respective glandular dose scored in the same region, by up to 10%. The specific energy distributions for nucleus and cytoplasm are directly related to the magnitude of the glandular dose in the voxel ( D g $D_{g}$ ), with little dependence on the spatial location. For similar D g $D_{g}$ values, f ( z , D g ) $f(z,D_{g})$ for nuclei is different between DM/DBT and CEDM/BCT, indicating that distinct x-ray spectra play significant roles in f ( z , D g ) $f(z,D_{g})$ . In addition, this behavior is also present when the specific energy distribution ( F g ( z ) $F_{g}(z)$ ) is considered taking into account the GDD in the breast. CONCLUSIONS: Microdosimetry studies are complementary to the traditional macroscopic breast dosimetry based on the mean glandular dose (MGD). For the same MGD, the specific energy distribution in glandular tissue varies between breast imaging modalities, indicating that this effect could be considered for studying the risks of exposing the breast to ionizing radiation.

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BACKGROUND: X-ray breast imaging modalities are commonly employed for breast cancer detection, from screening programs to diagnosis. Thus, dosimetry studies are important for quality control and risk estimation since ionizing radiation is used. PURPOSE: To perform multiscale dosimetry assessments for different breast imaging modalities and for a variety of breast sizes and compositions. The first part of our study is focused on macroscopic scales (down to millimeters). METHODS: Nine anthropomorphic breast phantoms with a voxel resolution of 0.5 mm were computationally generated using the BreastPhantom software, representing three breast sizes with three distinct values of volume glandular fraction (VGF) for each size. Four breast imaging modalities were studied: digital mammography (DM), contrast-enhanced digital mammography (CEDM), digital breast tomosynthesis (DBT) and dedicated breast computed tomography (BCT). Additionally, the impact of tissue elemental compositions from two databases were compared. Monte Carlo (MC) simulations were performed with the MC-GPU code to obtain the 3D glandular dose distribution (GDD) for each case considered with the mean glandular dose (MGD) fixed at 4 mGy (to facilitate comparisons). RESULTS: The GDD within the breast is more uniform for CEDM and BCT compared to DM and DBT. For large breasts and high VGF, the ratio between the minimum/maximum glandular dose to MGD is 0.12/4.02 for DM and 0.46/1.77 for BCT; the corresponding results for a small breast and low VGF are 0.35/1.98 (DM) and 0.63/1.42 (BCT). The elemental compositions of skin, adipose and glandular tissue have a considerable impact on the MGD, with variations up to 30% compared to the baseline. The inclusion of tissues other than glandular and adipose within the breast has a minor impact on MGD, with differences below 2%. Variations in the final compressed breast thickness alter the shape of the GDD, with a higher compression resulting in a more uniform GDD. CONCLUSIONS: For a constant MGD, the GDD varies with imaging modality and breast compression. Elemental tissue compositions are an important factor for obtaining MGD values, being a source of systematic uncertainties in MC simulations and, consequently, in breast dosimetry.

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Abstract Medicines must be subject to physical, chemical, and biological analysis to guarantee their quality, safety, and effectiveness. Despite the efforts to ensure the reliability of analytical results, some uncertainty will always be associated with the measured value, which can lead to false decisions regarding conformity/non-conformity assessment. This work aims to calculate the specific risk of false decisions regarding conformity/non-conformity of acetaminophen oral solution dosage form. The acetaminophen samples from five different manufacturers (A, B, C, D, and E) were subject to an active pharmaceutical ingredient assay, density test, and dose per drop test according to the official compendia. Based on measured values and their respective uncertainties, the risk values were calculated using the Monte Carlo method implemented in an MS Excel spreadsheet. The results for two acetaminophen oral solution samples (C and D) provided an increased total risk value of false acceptance (33.1% and 9.6% for C and D, respectively). On the other hand, the results for the other three acetaminophen samples (A, B, and E) provided a negligible risk of false acceptance (0.004%, 0.025%, and 0.045% for A, B, and E, respectively). This indicates that measurement uncertainty is very relevant when a conformity assessment is carried out, and information on the risks of false decisions is essential to ensure proper decisions.

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This paper characterizes the legislators voting behavior in the Colombian Senate 2010-2014, by implementing a one-dimensional standard Bayesian ideal point estimator via Markov chain Monte Carlo algorithms. Our main goal is to retrieve the political preferences of legislators from their roll-call voting records, which individualizes the electoral behavior of the legislative chamber. Furthermore, we conclude about the nature of the latent trait underlying the deputies voting decisions and the legislators locations in political space. Finally, we also offer several methodological and theoretical tools to guide the analysis of nominal voting data in the context of unbalanced parliaments (multi-party systems), taking as reference the particular case of the Colombian Senate.