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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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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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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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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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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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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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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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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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