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In this paper, we presented a mathematical model for tuberculosis with treatment for latent tuberculosis cases and incorporated social implementations based on the impact they will have on tuberculosis incidence, cure, and recovery. We incorporated two variables containing the accumulated deaths and active cases into the model in order to study the incidence and mortality rate per year with the data reported by the model. Our objective is to study the impact of social program implementations and therapies on latent tuberculosis in particular the use of once-weekly isoniazid-rifapentine for 12 weeks (3HP). The computational experimentation was performed with data from Brazil and for model calibration, we used the Markov Chain Monte Carlo method (MCMC) with a Bayesian approach. We studied the effect of increasing the coverage of social programs, the Bolsa Familia Programme (BFP) and the Family Health Strategy (FHS) and the implementation of the 3HP as a substitution therapy for two rates of diagnosis and treatment of latent at 1% and 5%. Based of the data obtained by the model in the period 2023-2035, the FHS reported better results than BFP in the case of social implementations and 3HP with a higher rate of diagnosis and treatment of latent in the reduction of incidence and mortality rate and in cases and deaths avoided. With the objective of linking the social and biomedical implementations, we constructed two different scenarios with the rate of diagnosis and treatment. We verified with results reported by the model that with the social implementations studied and the 3HP with the highest rate of diagnosis and treatment of latent, the best results were obtained in comparison with the other independent and joint implementations. A reduction of the incidence by 36.54% with respect to the model with the current strategies and coverage was achieved, and a greater number of cases and deaths from tuberculosis was avoided., (© 2024. The Author(s), under exclusive licence to the Society for Mathematical Biology.)

In this paper, we propose a new cure rate frailty regression model based on a two-parameter weighted Lindley distribution. The weighted Lindley distribution has attractive properties such as flexibility on its probability density function, Laplace transform function on closed-form, among others. An advantage of proposed model is the possibility to jointly model the heterogeneity among patients by their frailties and the presence of a cured fraction of them. To make the model parameters identifiable, we consider a reparameterized version of the weighted Lindley distribution with unit mean as frailty distribution. The proposed model is very flexible in sense that has some traditional cure rate models as special cases. The statistical inference for the model's parameters is discussed in detail using the maximum likelihood estimation under random right-censoring. Further, we present a Monte Carlo simulation study to verify the maximum likelihood estimators' behavior assuming different sample sizes and censoring proportions. Finally, the new model describes the lifetime of 22,148 patients with stomach cancer, obtained from the Fundação Oncocentro de São Paulo, Brazil. [ABSTRACT FROM AUTHOR]

Mark–recapture sampling schemes are conventional approaches for population size (N) estimation. In this paper, we mainly focus on providing fixed-length confidence interval estimation methodologies for N under a mark–recapture–mark sampling scheme, where, during the resampling phase, non-marked items are marked before they are released back in the population. Using a Monte Carlo method, the interval estimates for N are obtained through a purely sequential procedure with an adaptive stopping rule. Such an adaptive decision criterion enables the user to "learn" with the subsequent marked and newly tagged items. The method is then compared with a recently developed accelerated sequential procedure in terms of coverage probability and expected number of captured items during the resampling stage. To illustrate, we explain how the proposed procedure could be applied to estimate the number of infected COVID-19 individuals in a near-closed population. In addition, we present a numeric application inspired on the problem of estimating the population size of endangered monkeys of the Atlantic forest in Brazil. [ABSTRACT FROM AUTHOR]

Source coding maps elements from an information source to a sequence of alphabetic symbols. Then, the source symbols can be recovered exactly from the binary units. In this paper, we derive an approach that includes information variation in the source coding. The approach is more realistic than its standard version. We employ the Shannon entropy for coding the sequences of a source. Our approach is also helpful for short sequences when the central limit theorem does not apply. We rely on a quantifier of the information variation as a source. This quantifier corresponds to the second central moment of a random variable that measures the information content of a source symbol; that is, considering the standard deviation. An interpretation of typical sequences is also provided through this approach. We show how to use a binary memoryless source as an example. In addition, Monte Carlo simulation studies are conducted to evaluate the performance of our approach. We apply this approach to two real datasets related to purity and wheat prices in Brazil. [ABSTRACT FROM AUTHOR]

Purpose: This paper evaluates the effects of the operation of a 5-MW wind turbine in non-ideal conditions on the fatigue of its main components considering the wind resource in two locations; one in the northeast of Brazil and another on the North Sea, in Europe. Methods: The open-source software for wind turbine simulation OpenFAST is used for numerical simulations. Representative parameters of non-ideal operational situations are chosen and probabilistic models are proposed for the associated uncertainties. Afterward, propagation of uncertainties through Monte Carlo simulations is carried out; and finally, supported by a global sensitivity analysis based on Sobol indices, situations that should be primarily treated are identified. Results: This investigation quantifies the impact of (uncertain) non-ideal conditions acting conjointly on the fatigue damage of the wind turbine's main components. Damage equivalent loads (DEL) for yaw and bending moments at the tower top, in addition to the bending moments at the tower base, stand out as the most critical loads, with higher risks of failure over the wind turbine's service life. Finally, the sensitivity analysis shows that uncertainty related to deviation in the pitch angle of the blades is the most influential uncertain input. Conclusion: Uncertainty propagation is of great importance for the evaluation of non-ideal conditions consequences on fatigue damage of wind turbines. Additionally, global sensitivity analysis allows the identification of the main parameters affecting the system response. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to calculating the levelized cost of hydrogen (LCOH) using a stochastic model under an uncertainty and risk perspective. The research problem is associated with how to integrate the concept of financial risk to assess investments in small-scale green hydrogen production for more robust results, resolving issues related to underlying economic and technical factors, often overlooked, that may impact the production of green hydrogen in Brazil and Germany. The approach is applied to an investment project at two localities: Itajubá, Brazil, and Cologne, Germany, to investigate the underlying economic and technical factors that may impact the production of green hydrogen. In this stochastic approach, the Value at Risk (VaR) is calculated with a confidence level of 95% for 5000 LCOH values for each city, generated by the Monte Carlo Simulation (MCS). MCS results show that a correlation between assumptions (uncertainties) significantly affects LCOH with more favorable conditions to produce green hydrogen in Cologne. The risk analysis results show a significant difference concerning the deterministic approach when implementing the risk measure with VaR-LCOH of US$8.39 for Itajubá and VaR-LCOH of US$8.21 for Cologne. • Green hydrogen Investments are evaluated techno-economically in Brazil and Germany. • A Monte Carlo approach under uncertainty and risk conditions is implemented. • A stochastic LCOH based on VaR-LCOH metric is proposed to evaluate the risk. • The different nuances that the assumptions assume in calculating LCOH are discussed. • The risk analysis shows a difference regarding the deterministic approach. [ABSTRACT FROM AUTHOR]

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• Out-of-sample prediction of extreme precipitation using nonstationary models. • Significant trends in data of extreme precipitation are insufficient to warrant the selection of a nonstationary model. • Large prediction uncertainty reduces the support for nonstationary models of extreme precipitation. • (Non)stationary model selection should explicitly account for model complexity. Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes. [ABSTRACT FROM AUTHOR]

Brazil has a vast reserve of natural forests that belong to the state; hence, the state is responsible for supplying native wood to the market. However, the management in public forests has only begun recently; thus, much research is needed to know about its profitability. This study analyzed the financial feasibility of public forest management by addressing the following aspects: state concession, federal concession, and community forest management. We used the Net Present Value, Annual Equivalent Value, and Average Cost of Production methods to assess financial feasibility and applied the Monte Carlo simulation to estimate the probability of occurrence of NPV values. Community forest management showed profitability and a low occurrence of positive NPV values. The initial subsidies and the absence of royalty payments contributed to their good financial performance. The state and federal forest concessions showed financial unfeasibility and a low probability of profitability, which were justified by a low harvested volume of forest management, a high number of hollow individuals with small dimensions, poor volume estimates, and a high occurrence of species with low commercial values. Financial viability is a critical aspect of public forest management, and the achievement of good results depends on the consideration of several factors and aspects of public forests. Characteristics such as species composition, the distance between the managed areas, and the place of consumption must be considered. Areas that are not financially viable in the current period can be considered for future management and maintenance. [ABSTRACT FROM AUTHOR]

Understanding how climatic variables impact the reference evapotranspiration (ETo) is essential for water resource management, especially considering potential fluctuations due to climate change. Therefore, we used the Sobol' method to analyze the spatiotemporal variations of Penman–Monteith ETo sensitivity to the climatic variables: downward solar radiation, relative humidity, maximum and minimum air temperature, and wind speed. The Sobol' indices variances were estimated by Monte Carlo integration, with sample limits set to the 2.5th and 97.5th percentiles of the daily data of 33 automatic weather stations located in the state of Mato Grosso, Brazil. The results of the Sobol' analysis indicate considerable spatiotemporal variations in the sensitivity of ETo to climatic variables and their interactions. The dominant climatic variable responsible for ETo fluctuations in Mato Grosso is incident solar radiation (53% to 93% of annual total sensitivity—Stot), which has a more significant impact in humid environments (70% to 90% of Stot), as observed in the areas of the Amazon biome in the state. Air relative humidity and wind speed have higher sensitivity indices during the dry season in the Cerrado biome (savanna) areas in Mato Grosso (20% and 30% of the Stot, respectively). Our findings show that changes in solar radiation, relative humidity, and wind speed are the main driving forces that impact the reference evapotranspiration. [ABSTRACT FROM AUTHOR]

Investments in mechanized wood harvesting operations are characterized by demanding capital and presenting uncertainties associated with productive and economic factors. The economic analysis based on the Monte Carlo method promotes accurate results by measuring the risks of these investments. Thus, we analyzed whether the mechanized wood harvesting operation consisting of self-propelled forest machines under conditions of uncertainty is economically viable, applying the Monte Carlo method for risk management. With the observance of wood harvesting in a forest planted with Eucalyptus, technical-economic coefficients were obtained for felling, extraction, and wood bundle processing operations. The time horizon was six years, with the application of the investment decision rules net present value, modified internal rate of return, and payback period discounted at the opportunity cost rate by the weighted average cost of capital. The results of the investment decision rules indicated the economic viability of the mechanized wood harvesting operation and, based on the stochastic simulation, it was possible to consider the investment as low risk for capital holders. Therefore, the mechanized wood harvesting operation consisting of self-propelled forest machines, under conditions of uncertainty, is economically viable. [ABSTRACT FROM AUTHOR]

Biomass gasification is the thermochemical conversion of biomass mainly into syngas, and sugarcane bagasse (SCB) is its main feedstock in Brazil. However, the seasonal production of SCB influences biomass supply. This paper analyzed, via multivariate analysis, gasification datasets of 12 Brazilian feedstocks obtained from simulation to determine possible substitutes for SCB, optimal gasification conditions for all biomasses aiming at FT synthesis downstream, and key parameters for several syngas applications. A validated simulation in Aspen Plus™ of a fluidized bed gasifier generated one dataset with fixed gasification conditions used in hierarchical cluster analysis (HCA) and principal component analysis (PCA), and another with varying temperatures and S/B to obtain regression models via partial least squares (PLS) for optimization and Monte Carlo simulations. HCA and optimization using PLS models showed that sugarcane straw, energy cane, energy cane bagasse, and corn straw have similar gasification performances as SCB, being possible substitutes. Independent thermochemical plants based on eucalyptus, or corn and soybean straws are also suggested. Lignin is mostly useful for heat and power applications. PCA and HCA highlighted important correlations between inputs and outputs. Monte Carlo simulations suggested biomasses of lower C and higher O contents to obtain syngas suitable for FT synthesis and the opposite for applications of higher syngas heating values. [Display omitted] • Multivariate analysis of datasets from simulation of 12 feedstocks in a CFB gasifier. • Biomass C correlated to CO and CH 4 vol%, syngas HHV, gasifier heat duty, and CGE. • Low C and high O contents in feedstock produce high H 2 vol% and H 2 /CO in syngas. • Sugarcane bagasse, straw, and energy cane have similar performance in BtL plants. • 2G lignin is a promising feedstock in gasification for heat and power applications. [ABSTRACT FROM AUTHOR]

In 2020, 77% of malaria cases in the Americas were concentrated in Venezuela, Brazil, and Colombia. These countries are characterized by a heterogeneous malaria landscape and malaria hotspots. Furthermore, the political unrest in Venezuela has led to significant cross-border population movement. Hence, the aim of this study was to describe spatial patterns and identify significant climatic drivers of malaria transmission along the Venezuela-Brazil-Guyana border, focusing on Bolivar state, Venezuela and Roraima state, Brazil. Malaria case data, stratified by species from 2016 to 2018, were obtained from the Brazilian Malaria Epidemiology Surveillance Information System, the Guyana Vector Borne Diseases Program, the Venezuelan Ministry of Health, and civil society organizations. Spatial autocorrelation in malaria incidence was explored using Getis-Ord (Gi*) statistics. A Poisson regression model was developed with a conditional autoregressive prior structure and posterior parameters were estimated using the Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. There were 685,498 malaria cases during the study period. Plasmodium vivax was the predominant species (71.7%, 490,861). Malaria hotspots were located in eight municipalities along the Venezuela and Guyana international borders with Brazil. Plasmodium falciparum increased by 2.6% (95% credible interval [CrI] 2.1%, 2.8%) for one meter increase in altitude, decreased by 1.6% (95% CrI 1.5%, 2.3%) and 0.9% (95% CrI 0.7%, 2.4%) per 1 cm increase in 6-month lagged precipitation and each 1 °C increase of minimum temperature without lag. Each 1 °C increase of 1-month lagged maximum temperature increased P. falciparum by 0.6% (95% CrI 0.4%, 1.9%). P. vivax cases increased by 1.5% (95% CrI 1.3%, 1.6%) for one meter increase in altitude and decreased by 1.1% (95% CrI 1.0%, 1.2%) and 7.3% (95% CrI 6.7%, 9.7%) for each 1 cm increase of precipitation lagged at 6-months and 1 °C increase in minimum temperature lagged at 6-months. Each 1°C increase of two-month lagged maximum temperature increased P. vivax by 1.5% (95% CrI 0.6%, 7.1%). There was no significant residual spatial clustering after accounting for climatic covariates. Malaria hotspots were located along the Venezuela and Guyana international border with Roraima state, Brazil. In addition to population movement, climatic variables were important drivers of malaria transmission in these areas. [ABSTRACT FROM AUTHOR]

This article analyzed the cost of capital under risk conditions for thermoelectric plants in Brazil, applying the Capital Asset Pricing Model—CAPM and the Weighted Average Capital Cost—WACC. To estimate the local CAPM, we used information from the Electric Energy Index—IEE of publicly traded companies in the electricity sector in Brazil and for the global CAPM, we observed the companies associated with the Edison Electric Institute—EEI, listed on the New York Stock Exchange—NYSE and at the National Association of Securities Dealers Automated Quotations—NASDAQ—USA. The risk conditions for capital costs were represented by Monte Carlo simulation using, as a basis, the WACC of a fuel oil thermoelectric plant and the local and global CAPM. The main results show that the IEE and EEI companies obtained a positive average daily return. Due to the Brazil risk, under deterministic conditions, the local WACC (11.13% p.a.) was more attractive to investors when compared to the global WACC (10.32% p.a.) and the regulatory WACC of 10.55% p.a., established by the National Electric Energy Agency—ANEEL. The most risk-sensitive input variables were: unleveraged beta, net debt and equity. Under risk conditions observed by the market from the point of view of Brazilian companies, the chances of the WACC of the fuel oil thermoelectric plant being 11.1% p.a. was 68.30% and from a global perspective, the chance of WACC being 10.32% p.a. was 99.51%. It is concluded that the cost of capital under risk conditions provides a more realistic view of decision-making for privately held companies. [ABSTRACT FROM AUTHOR]

This work has been developed in a large steel industry in Brazil, which produces railway and industrial components, and whose aim was to reduce casting defects. Usually, in industrial processes, identifying the causes of defects and their control are relatively complex activities, due to the many variables involved. In this context, the production processes of seven products, involving 38 process variables (inputs and outputs), have been evaluated adopting a new and innovative procedure. Initially, using a Weighted Goal Programming ‐ Multiple Criteria Data Envelopment Analysis (WGP‐MCDEA) model, we identified the most relevant input and output variables, and the studied company validated the results. Next, using the multiple regression technique, empirical functions were constructed for two response variables chosen by the company – number of external cracks and number of internal cracks. Then, to model the real processes adequately, we introduced the occurrence of uncertainty on the coefficients of these functions, considering them as random variables, according to triangular probability functions. Finally, applying the optimizer Optquest, optimization via Monte Carlo simulation (OvMCS) was performed, and with the Ordinary Least Square technique, we obtained the best fit for the two response variables. Specialists from the company validated the proposed procedure. They found that the values of input and output variables obtained by OvMSC, as well as the values of the response variables, belonged to the database available in the ERP system of the company. These results showed that the procedure proposed herein provided feasible and useful solutions to improve the industrial processes under study. [ABSTRACT FROM AUTHOR]

Models with distal outcomes have been commonly used to evaluate the effect of categorical latent variables on an observed dependent variable, which can be binary, counting, or continuous. Several approaches have been recently proposed for modeling continuous distal outcomes. Some recent strategies consider simultaneous modeling of the latent class and its effect on the distal response through the use of the Bayes theorem applied to latent class analysis (LCA) with covariates or through the incorporation of the measurement errors from LCA directly in the estimation procedure for the parameters of the structural submodel. Classify-analyze approaches have also been used for this purpose for several years, but simulation studies had shown the attenuation of their estimates. More recently, Bayesian LCA and other Bayesian approaches for latent variable modeling were proposed and made available in statistical software. We propose four alternative strategies using Bayesian estimation for the structural parameters in the mixture models with distal outcomes. We also consider extensions to allow control for observed covariates in the structural submodel for the distal outcome. Monte Carlo simulation studies were conducted to evaluate the properties of the proposed methods in finite samples. Illustration of these methodologies is carried out with the analysis of the data from the 2006 ENADE (National Student Performance Exam) in Brazil. The simulation results show that the Bayesian Simultaneous method leads to a substantial bias reduction when estimating the effects of the latent variable on the distal outcome. [ABSTRACT FROM AUTHOR]

Recently, unit-Lindley distribution and its associated regression models have been developed as an alternative to Beta regression model for which continuous outcome in the unit interval (0 , 1). Proportion data usually occur in clinical trials, economics and social studies with hierarchical structures. In this study, unit-Lindley mixed-effect model is proposed and the appropriate likelihood analysis methods for parameter estimation are investigated. In the case of clustered or longitudinal proportion data in mixed-effect models, the full-likelihood function does not have a closed form. Parameter estimations of unit-Lindley mixed-effect model are obtained with Laplace and adaptive Gaussian quadrature approximation methods in this study. We analyzed a dataset on the proportion of households with insufficient water supply and sewage with some sociodemographic variables in the cities of Brazil by using unit-Lindley mixed-effect model including a random intercept as federative states of Brazil. Analysis results indicate that the proposed unit-Lindley mixed-effect model provides better fit than unit-Lindley regression model and beta mixed model. Also, in the simulation study the accuracy of the estimates of approximation methods are evaluated and compared via Monte Carlo simulation study in terms of bias and mean square error. [ABSTRACT FROM AUTHOR]

The present work proposes a Monte Carlo Simulation (MCS) to obtain availability projections for Hydroelectric Power Plants (HPP), based mainly on regulatory aspects involving the Availability Factor (AFA). The main purpose of the simulation is to generate scenarios to obtain statistics for risk analysis and decision-making in relation to the HPP. The proposed methodology consists of two steps, firstly, the optimization of the maintenance schedule of the hydroelectric plant is carried out, in order to allocate the mandatory maintenance in the simulation horizon. Then, for the MCS, scenarios of forced shutdowns of the Generating Units (GU) will be generated, which directly influence the operation and, consequently, the availability of the HPP. The scenarios will be inserted into an operation optimization model, which considers the impact of forced shutdown samples on the MCS. The proposed modeling was applied using real data from the Santo Antônio HPP, which is one of the largest hydroelectric plants in Brazil. [ABSTRACT FROM AUTHOR]