Kinetic modeling of the photocatalytic degradation of acetaminophen and its main transformation products.

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.

Arsenic and fluoride in groundwater triggering a high risk: Probabilistic results using Monte Carlo simulation and species sensitivity distribution.

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.

A novel flexible exponent power-X family of distributions with applications to COVID-19 mortality rate in Mexico and Canada.

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.

Relevance of the calculation of the diffusion coefficient in a capillary electrophoresis experiment.

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.

Risk of false conformity decisions due to measurement uncertainty of Active Pharmaceutical Ingredient: A multiparametric evaluation

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.

Characterization of an Ancient Bimetallic Alloy from Moche Civilization (Peru).

The Moche civilization in Peru developed marvelous metallurgy, primarily using alloys of gold, copper and silver, with the most famous of them called Tumbaga, which resembles pure gold after a depletion process on its surface. However, they also created objects with more standard single-layer alloys or gilding. To distinguish between these techniques in a non-destructive manner is essential. Here, we analyzed a thigh protector, composed of two parts, one seemingly in silver and the other seemingly in gold. The sample was analyzed using X-ray fluorescence measurements integrated with Monte Carlo simulation. The results show that the silver part is formed of a silver-based alloy covered in a corrosion layer, while the gold part is made of Tumbaga. Moreover, for the first time, the gold profiles of different Tumbaga gold objects, from the same burial, were compared, allowing us to obtain information about the standardization of their manufacture.

Primary standardization and Monte Carlo modeling of (243Am + 239Np) by means of a 4π(PC)-γ coincidence counting system.

The procedure followed by the Nuclear Metrology Laboratory (LMN) at the IPEN for the primary standardization of a (243Am + 239Np) solution, in secular equilibrium, is described. The measurement was carried out in a 4π(PC) (α,ß)-γ coincidence system. The total activity per unit mass of the solution was determined by the extrapolation technique, using a software coincidence counting systsem. The extrapolation curves were compared with Monte Carlo calculations by means of Code ESQUEMA, used in previous works, which, was improved and applied in order to calculate the alpha, beta, gamma, X-rays and coincidence spectra.

Nonparametric item response models: a comparison on recovering true scores / Modelos de resposta ao item não-paramétricos: comparando a recuperação de escores verdadeiros / Modelos de respuesta al item no-paramétricos: comparando la recuperación de puntuaciones verdaderas

Nonparametric procedures are used to add flexibility to models. Three nonparametric item response models have been proposed, but not directly compared: the Kernel smoothing (KS-IRT); the Davidian-Curve (DC-IRT); and the Bayesian semiparametric Rasch model (SP-Rasch). The main aim of the present study is to compare the performance of these procedures in recovering simulated true scores, using sum scores as benchmarks. The secondary aim is to compare their performances in terms of practical equivalence with real data. Overall, the results show that, apart from the DC-IRT, which is the model that performs the worse, all the other models give results quite similar to those when sum scores are used. These results are followed by a discussion with practical implications and recommendations for future studies.(AU)

Procedimentos não paramétricos são usados para adicionar flexibilidade aos modelos. Três modelos não paramétricos de resposta ao item foram propostos, mas não comparados diretamente: o Kernel smoothing (KS-IRT); a Curva Davidiana (DC-IRT); e o modelo semiparamétrico Rasch Bayesiano (SP-Rasch). O objetivo principal do presente estudo é comparar o desempenho desses procedimentos na recuperação de escores verdadeiros simulados, utilizando escores de soma como benchmarks. O objetivo secundário é comparar seus desempenhos em termos de equivalência prática com dados reais. De forma geral, os resultados mostram que, além do DC-IRT, que é o modelo que apresenta o pior desempenho, todos os outros modelos apresentam resultados bastante semelhantes aos de quando se usam somatórios. Esses resultados são seguidos de uma discussão com implicações práticas e recomendações para estudos futuros.(AU)

Se utilizan procedimientos no paramétricos para agregar flexibilidad a los modelos. Se propusieron tres modelos de respuesta al ítem no paramétricos, pero no se compararon directamente: Kernel smoothing (KS-IRT); la curva davidiana (DC-IRT); y el modelo bayesiano de Rasch semiparamétrico (SP-Rasch). El objetivo principal del presente estudio es comparar el desempeño de estos procedimientos en la recuperación de puntajes verdaderos simulados, utilizando puntajes de suma como puntos de referencia. El objetivo secundario es comparar su desempeño en términos de equivalencia práctica con datos reales. En general, los resultados muestran que, a excepción de DC-IRT, que es el modelo con peor desempeño, todos los otros modelos presentan resultados bastante similares a los obtenidos cuando se utilizan sumatorios. Estos resultados son seguidos por una discusión con implicaciones prácticas y recomendaciones para estudios futuros.(AU)

Unveiling protein-protein interaction potential through Monte Carlo simulation combined with small-angle X-ray scattering.

Protein interactions are investigated under different conditions of lysozyme concentration, temperature and ionic strength by means of in-solution small angle X-Ray scattering (SAXS) experiments and Monte Carlo (MC) simulations. Initially, experimental data were analysed through a Hard-Sphere Double Yukawa (HSDY) model combined with Random Phase Approximation (RPA), a closure relationship commonly used in the literature for monodisperse systems. We realized by means of MC that the HSDY/RPA modelling fails to describe the protein-protein pair potential for moderated and dense systems at low ionic strength, mainly due to inherent distortions of the RPA approximation. Our SAXS/MC results thus show that lysozyme concentrations between 2 (diluted) and 20 mg/mL (not crowded) present similar protein-protein pair potential preserving the values of surface net charge around 7 e, protein diameter of 28 Å, decay range of attractive well potential of 3 Å and a depth of the well potential varying from 1 to 5 kBT depending on temperature and salt addition. Noteworthy, we here propose a novel method to analyse the SAXS data from interacting proteins through MC simulations, which overcomes the deficiencies presented by the use of a closure relationship. Furthermore, this new methodology of combining SAXS with MC simulations gives a step forward to investigate more complex systems as those composed of a mixture of proteins of distinct species presenting different molecular weights (and hence sizes) and surface net charges at low, moderate and very dense systems.

Polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs) in human breast milk from Colombia: A probabilistic risk assessment approach.

Polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), and organochlorine pesticides (OCPs) are ubiquitous environmental contaminants. They were produced in relatively large volumes in the last century and are now subject to long-term monitoring and regulated under the United Nations Stockholm Convention (SC) on persistent organic pollutants (POPs). Though restricted, human exposure is still a concern and in some regions of the globe the information on the health risk is limited. Sixty breast milk samples from nursing mothers were collected between 2014 and 2015, residing in Bogota, Cartagena, and Medellin, which are industrialized cities in Colombia. This is the first comprehensive study to determine the concentrations in breast milk of PBDEs (n = 7), PCBs (n = 29), and OCPs (n = 28) in Colombia. The detection frequency of POPs, including BDE-47, CB-138, CB-153, CB-156, and CB-180, as well as several OCPs such as chloroneb, aldrins, HCHs, DDTs, and heptachlor, was found to be 100% in all samples tested. The mean concentrations of the analyzed legacy POPs were ∑3DDTs (423 ng/g lw) > chloroneb (50.1 ng/g lw) > ∑2permetrins (17.5 ng/g lw) > ∑2aldrins (16.7 ng/g lw) > 29 PCBs (15.04 ng/g lw) > ∑2chlordanes (CHLs) (11.2 ng/g lw) ≈ ∑3endosulfans (11.1 ng/g lw) > ∑2heptachlors (2.43 ng/g lw) > 7PBDEs (2.1 ng/g lw) > ∑4HCHs (0.58 ng/g lw). The results of this study suggest that the concentrations of DDTs were present in breast milk samples from Colombia at levels comparable to those found in previous studies conducted in other countries such as Brazil, Uruguay, Chile, and various Asian countries. The concentrations of PBDE and PCB congeners, as well as many pesticides, were found to be significantly correlated with each other. This suggests that these substances may have similar sources of exposure. The strength of the pair correlation among concentrations of POPs was assessed using Spearman's correlation coefficients, which varied from r = 0.62 for the association between BDE-47 and CB-153, to a high correlation of 0.99 for the correlation between γ-Chlordane and heptachlor. This suggests that these POPs may share similar sources, such as diet. An exposure assessment model obtained by Monte Carlo simulation showed that infants were exposed to low concentrations of POPs with exception of p,p'-DDE and Aldrin, in which 25th, 50th and 95th percentiles were greater than the threshold reference values of non-carcinogenic effects suggested by US-EPA regulations while the 90th percentile of pg TEQ/Kg-bw/day for dl-PCBs was above of the tolerable daily intake (TDI) proposed by the World Health Organization (WHO). Therefore, the health risk of infants exposed to OCPs and dl-PCBs should be exanimated continually through biomonitoring programs in the Colombian population.

Similarity-Based Predictive Models: Sensitivity Analysis and a Biological Application with Multi-Attributes.

Predictive models based on empirical similarity are instrumental in biology and data science, where the premise is to measure the likeness of one observation with others in the same dataset. Biological datasets often encompass data that can be categorized. When using empirical similarity-based predictive models, two strategies for handling categorical covariates exist. The first strategy retains categorical covariates in their original form, applying distance measures and allocating weights to each covariate. In contrast, the second strategy creates binary variables, representing each variable level independently, and computes similarity measures solely through the Euclidean distance. This study performs a sensitivity analysis of these two strategies using computational simulations, and applies the results to a biological context. We use a linear regression model as a reference point, and consider two methods for estimating the model parameters, alongside exponential and fractional inverse similarity functions. The sensitivity is evaluated by determining the coefficient of variation of the parameter estimators across the three models as a measure of relative variability. Our results suggest that the first strategy excels over the second one in effectively dealing with categorical variables, and offers greater parsimony due to the use of fewer parameters.

Economic and financial risks of commercial tilapia cage culture in a neotropical reservoir.

The aim of this study was to analyze the financial and economic risks of tilapia cage culture across different production water volumes (m³). The production water volumes evaluated were 10 to 50 thousand m³ (Small Volume, SV), 51 to 150 thousand m³ (Medium Volume, MV), 151 to 300 thousand m³ (Large Volume, LV), and >301 thousand m³ (Extra-Large Volume, ELV). Productivity and economic data were obtained from a commercial Nile tilapia cage farm with 232 net cages installed in a neotropical reservoir, in Brazil, from 2017 to 2019. Cost and profitability analyses, economic feasibility, and risk and sensitivity analyses were performed using a Monte Carlo simulation. The implementation of commercial tilapia cage farming relies mainly on feed prices. The initial investment demand is proportional to the size of the farms. On the other hand, MV, LV, and ELV tilapia farms showed the lowest financial risks despite the higher investments. These farms presented a medium-low risk at ≈39% probability, whereas the SV farm presented a medium to medium-high risk at 51.17% probability. Thus, fish farms with a production volume above 51 thousand m³ tend to be more profitable and have a ≈36% probability of low financial and economic risk with a Payback period of fewer than 10 years, mainly due to the lower feed costs per mass of fish produced. This study assists investors in choosing a better path toward a more viable and profitable activity.

Monitoring of SARS-CoV-2 RNA in wastewater: A surveillance tool to foresee infection's evolution in the Mexican Caribbean.

The study aims to determine SARS-CoV-2 RNA in sewage of Cancun wastewater treatment plants, the main touristic destination of Mexico, and to estimate the infected persons during the sampling period. SARS-CoV-2 RNA traces were detected in the inlet of the five plants during almost all the sampling months. However, there is no presence of SARS-CoV-2 RNA traces in the effluent of the five WWTPs during the study period. ANOVA analysis showed differences in the concentrations of RNA traces of SARS-CoV-2 between the sample dates, but no differences were found from one WWTP to another. Estimated infected individuals by Markov chain Monte Carlo simulation are higher (between 77% and 91%) than the cases reported by the health authority. Wastewater monitoring and the estimation of infected individuals are a helpful tool, because estimation provides early warning signs on how broadly SARS-CoV-2 is circulating in the city, and led to the authorities to take measures wisely. PRACTITIONER POINTS: There is no presence of SARS-CoV-2 RNA traces in the effluent of the facilities, suggesting the effectiveness of treatment. Surveillance of viral RNA concentrations at treatment plants revealed presence in the influent of five plants Estimated infected individuals by MCMC simulation are higher than cases reported by health authority Environmental surveillance approach in wastewater influent is helpful to identify the clusters and to take informed decisions.

Landscapes of Biochemical Warfare: Spatial Self-Organization Woven from Allelopathic Interactions.

Evidence shows that diversity and spatial distributions of biological communities are largely driven by the race of living organisms in their adaptation to chemicals synthesized by their neighbors. In this report, the emergence of mathematical models on pure spatial self-organization induced by biochemical suppression (allelopathy) and competition between species were investigated through numerical analysis. For both random and patched initial spatial distributions of species, we demonstrate that warfare survivors are self-organized on the landscape in Turing-like patterns driven by diffusive instabilities of allelochemicals. These patterns are simple; either all species coexist at low diffusion rates or are massively extinct, except for a few at high diffusivities, but they are complex and biodiversity-sustained at intermediate diffusion rates. "Defensive alliances" and ecotones seem to be basic mechanisms that sustain great biodiversity in our hybrid cellular automata model. Moreover, species coexistence and extinction exhibit multi-stationarity.

Impact of fibroglandular tissue distribution and breast shape in voxelized breast models for dosimetry in mammography.

Objective.This work proposes to study the impact of different voxelized heterogeneous breast models (gaussian centered - GaussC; gaussian lower - GaussL; and fitted equation patient-based on 3D realistic distribution (Fedonet al2021) - FitPB) for dosimetry in mammography compared to a well-established homogeneous approximation. Influence of breast outer shape also was investigated by comparing semicylindric and anthropomorphic breasts.Approach.By using the PENELOPE (v. 2018) + penEasy (v. 2020) MC code, simulations were performed to evaluate the normalized glandular dose (DgN) and the glandular depth dose (GDD(z)) for different breast characteristics and x-ray beam spectra.Main results.The averageDgNoverestimation caused by homogeneous tissue approximation was 33.0%, with the highest values attributed to GaussLand FitPBmodels, where fibroglandular tissue is concentrated deeper in the breast. The observed variation between anthropomorphic and semicylindrical breast shapes was, on average, 5.6%, legitimizing the latter approximation for breast dosimetry. Thicker breasts and lower energy beams resulted in larger overestimation caused by the homogeneous approach, while variations inDgNvalues among different heterogeneous models were higher for thinner breast and lower energy beams. Moreover, the depth where differences betweenGDD(z) for different breast models became maximum depends on the axial variation of fibroglandular tissue concentration between each model. TheGDD(z) dependence results in a significant variation of the contribution of each breast depth to mean glandular dose (MGD) among the breast models studied.Significance.Intercomparison between different breast models for dosimetry can be useful for estimating more accurateMGDvalues for population-based dosimetry, for exploring the use of 1D gaussian distribution for breast dosimetry, and for understanding the dose distributions inside the fibroglandular tissues, which could be a novel source of information for risk estimations.

Porosity determination of Vienna sausages through digital images analysis.

BACKGROUND: This research aimed to determine the porosity and particle size distribution in canned Vienna-type sausages using digital image analysis (DIA) on photographs captured with a digital camera and applying a Monte Carlo simulation. The methodology determined morphometric parameters (area and Feret diameter) by DIA of transverse and longitudinal sections of canned sausages. Those images were previously contrast enhanced, color threshold adjusted, and binarized. Subsequently, the estimation of the pore volume was carried out from the inverse Gaussian distributions of Feret diameter and area, as well as the porosity, using Monte Carlo simulation. RESULTS: The pores had an average Feret diameter of 0.335 mm and an average area of 0.085 mm2 . The highest estimated bivariate kernel density was presented for the smallest pores (around 0.02 mm2 in area and 0.25 mm in diameter). Simulation average values of pore volume, assumed as a cylinder, and porosity were 1.455 mm3 and 0.737 respectively. The average porosity value was consistent with the value experimentally estimated by the indirect method, in concordance with the definition of porosity, which was 0.715, presenting a mean relative percentage error of 3.08% concerning the estimated experimental value as well. CONCLUSION: This research presents interesting perspectives for the quantitative analysis of the microstructure of food and biological materials through a novel, low-cost, reliable, and fast proposal. Moreover, this is the first study to report the porosity determination in canned sausages by DIA. © 2022 Society of Chemical Industry.

The unit generalized half-normal quantile regression model: formulation, estimation, diagnostics, and numerical applications.

In this paper, we propose and derive a new regression model for response variables defined on the open unit interval. By reparameterizing the unit generalized half-normal distribution, we get the interpretation of its location parameter as being a quantile of the distribution. In addition, we can evaluate effects of the explanatory variables in the conditional quantiles of the response variable as an alternative to the Kumaraswamy quantile regression model. The suitability of our proposal is demonstrated with two simulated examples and two real applications. For such data sets, the obtained fits of the proposed regression model are compared with that provided by a Kumaraswamy regression model.

Health risk assessments of exposure carbon dioxide among communities and children around Tonasa cement plant, Pangkep Regency, Indonesia. Monte Carlo Simulation (MCS) application / Avaliações de risco à saúde na exposição ao dióxido de carbono entre comunidades e crianças ao redor da fábrica de cimento Tonasa, Pangkep Regency, Indonésia. Aplicação da Simulação de Monte Carlo (MCS)

Abstract Chronic exposure to carbon dioxide (CO2) can have a negative influence on one's health and be hazardous to the environment. It could be both directly and indirectly to those communities who are living near the CO2 point sources. This study aimed to investigate the magnitude of CO2 level in ambient air and its spatial distribution which then continued to assess the potential health risks posed by communities living surround the site as well as applied the Monte Carlo Simulation (MCS) approach to predict the risks magnitude among adult and children due to CO2 air pollution from the cement industry activities in Pangkep. This observational analytic study applied health risk assessment due to the CO2 exposure both to adult and children population. To estimate the non carcinogenic risk, study used the Monte Carlo Simulation model with 10,000 iterations to estimate the risk through the inhalation pathway suffered by communities, as well as analyzing the sensitivity level every single health risk parameter. The highest risks for the adults was in station 7 with 7,641 whereas the lowest risks was in station 3 with 1,194, respectively. Furthermore, the highest risks for child was in station 4 with 498 whereas the lowest one was in station 15 with 32, respectively. Those non carcinogenic HQ were exceed the standard for adult but not at risks for children. The results of the Monte Carlo Simulation that assessed the non risks cancer probability with the 5th and 95th percentiles demonstrated that adult population were at value of 0.83 and 1.53 0.83 and 1.53 respectively, that still indicated at low risk for developing adverse health effects among those communities temporarily. However, at the same percentiles children indicated at value of 199 and 388 that indicated at risk for developing adverse health effects among those children. In addition, level of sensitivity analysis result indicated that exposure frequency with (20,9%) for adult and the exposure duration with (25,6%) for children were the most contributing factors to health risks among, respectively. Simulation determines the critical factors with major effects in reducing health risks. The CO2 magnitude not poses risks to adults, by contrast, children are at risk. Thus, limiting exposure frequency and inhalation of CO2 levels in the school for children area are highly demanded.

Resumo A exposição crônica ao dióxido de carbono (CO2) pode ter uma influência negativa sobre a saúde e ser perigosa para o meio ambiente. Pode ser direta ou indiretamente para as comunidades que vivem perto das fontes pontuais de CO2. Este estudo teve como objetivo investigar a magnitude do nível de CO2 no ar ambiente e sua distribuição espacial, que então continuou a avaliar os riscos potenciais à saúde apresentados pelas comunidades que vivem no entorno do local, bem como aplicou a abordagem da Simulação de Monte Carlo (MCS) para prever a magnitude dos riscos entre adultos e crianças devido à poluição do ar por CO2 das atividades da indústria de cimento em Pangkep. Este estudo analítico observacional aplicou a avaliação de risco à saúde devido à exposição ao CO2 tanto na população adulta quanto na infantil. Para estimar o risco não cancerígeno, o estudo utilizou o modelo da Simulação de Monte Carlo com 10.000 iterações para estimar o risco pela via inalatória sofrida pelas comunidades, bem como analisar o nível de sensibilidade de cada parâmetro de risco à saúde. O maior risco para os adultos foi na estação 7 com 7.641 enquanto o menor risco foi na estação 3 com 1.194, respectivamente. Além disso, o maior risco para crianças estava na estação 4 com 498 enquanto o menor estava na estação 15 com 32, respectivamente. Aqueles HQ não cancerígenos excederam o padrão para adultos, mas não representam riscos para crianças. Os resultados da Simulação de Monte Carlo que avaliaram a probabilidade de câncer sem riscos com os percentis 5 e 95 demonstraram que a população adulta estava no valor de 0,83 e 1,53 respectivamente, que ainda indicava baixo risco para desenvolver efeitos adversos à saúde entre essas comunidades temporariamente. No entanto, nos mesmos percentis, as crianças indicaram valores de 199 e 388, que mostraram risco de desenvolver efeitos adversos à saúde entre essas crianças. Além disso, o resultado da análise de nível de sensibilidade indicou que a frequência de exposição com 20,9% para adultos e a duração da exposição com 25,6% para crianças foram os fatores que mais contribuíram para riscos à saúde, respectivamente. A simulação determina os fatores críticos com efeitos importantes na redução dos riscos à saúde. A magnitude do CO2 não oferece riscos aos adultos, ao contrário, as crianças correm risco. Assim, a limitação da frequência de exposição e inalação de níveis de CO2 na área escolar para crianças é altamente exigida.

Opinion dynamics in financial markets via random networks.

We investigate financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize individuals in a financial market according to their trading strategy, namely, whether they are noise traders or fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavior of the market influences the decisions of fundamentalist agents. We introduce a noise parameter, q, to represent the level of anxiety and perceived uncertainty regarding market behavior, enabling the possibility of adrift financial action. We place individuals as nodes in an Erdös-Rényi random graph, where the links represent their social interactions. At any given time, individuals assume one of two possible opinion states ±1 regarding buying or selling an asset. The model exhibits fundamental qualitative and quantitative real-world market features such as the distribution of logarithmic returns with fat tails, clustered volatility, and the long-term correlation of returns. We use Student's t distributions to fit the histograms of logarithmic returns, showing a gradual shift from a leptokurtic to a mesokurtic regime depending on the fraction of fundamentalist agents. Furthermore, we compare our results with those concerning the distribution of the logarithmic returns of several real-world financial indices.

On a log-symmetric quantile tobit model applied to female labor supply data.

The study of female labor supply has been a topic of relevance in the economic literature. Generally, the data are left-censored and the classic tobit model has been extensively used in the modeling strategy. This model, however, assumes normality for the error distribution and is not recommended for data with positive skewness, heavy-tails and heteroscedasticity, as is the case of female labor supply data. Moreover, it is well-known that the quantile regression approach accounts for the influences of different quantiles in the estimated coefficients. We take all these features into account and propose a parametric quantile tobit regression model based on quantile log-symmetric distributions. The proposed method allows one to model data with positive skewness (which is not suitable for the classic tobit model), to study the influence of the quantiles of interest, and to account for heteroscedasticity. The model parameters are estimated by maximum likelihood and a Monte Carlo experiment is performed to evaluate alternative estimators. The new method is applied to two distinct female labor supply data sets. The results indicate that the log-symmetric quantile tobit model fits better the data than the classic tobit model.