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The conversion of native forests into pastures is still a common practice in Brazil. Abandoned pastures have great potential for natural regeneration and therefore could play an important role in meeting the enormous demand for forest restoration. Few studies, however, have investigated the extent to which spatially-structured environmental variables and community structure are correlated with the variation in species abundance of regenerating forests on abandoned pastures. Therefore, we aimed to determine whether environmental and spatial variables were capable of explaining the variation in abundance of woody species on abandoned pastures in the subtropical Atlantic Forest. We systematically distributed 45 sample plots with size and inclusion criteria that changed according to the vegetation layer in three different abandoned pastures. In general, most of the variation in species abundance that our models were able to explain was correlated with spatially-unstructured physical-chemical soil properties. A smaller part of the variation was correlated with spatially-structured soil variables and topography-related variables. An even smaller portion of the variation was spatially-structured but was not correlated with spatially-structured environmental variables. Therefore, our results suggest that the variation in species abundance of regenerating subtropical Atlantic forests on abandoned pastures is more closely related to niche-based processes mediated by environmental variables than to stochastic spatial processes. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has imposed several dilemmas for managers in the public sector, with school reopening being among the most complex decisions. The present article presents a microsimulation model of the pandemic course considering various scenarios within the confines of a classroom in the city of Belo Horizonte, Brazil. For that, a susceptible-infectious-recovered (SIR) model was integrated with a random graph model, associating epidemiological characteristics with sociometric and sociodemographic factors. Social contact rates projected for Brazil in the European POLYMOD project were adapted for the city of Belo Horizonte to simulate the number of contacts among individuals considering a Poisson distribution. The simulation used as reference a group of 20 students and their families. The projected scenarios discriminated three age groups with their respective rate of daily social contacts: 0 to 5 years (0.01), 6 a 14 years (1.80), and 15 to 19 years (0.20). The simulations showed clear differences between these age groups, depending on the initial number of infected individuals and on the use or not of face masks in the school. The results confirm that the absence of adequate mitigation measures entails a considerable increase in transmission in the school setting. [ABSTRACT FROM AUTHOR]

Summary: Brazil has one of the largest varieties of aquatic ecosystems and rich freshwater biodiversity, but these components have constantly been damaged by the expansion of unsustainable activities. Free-living nematodes are an abundant and ubiquitous component of continental benthic communities, occurring in all freshwater habitats, including extreme environments. Despite this, hardly any studies have examined the generic composition of nematodes in different latitudes and the geographic overlap of assemblages. We provide data on nematode genera from six regions in Brazil, over a north-south gradient spanning about 4000 km, encompassing rivers, coastal lakes, and reservoirs with different levels of human impact. Interpolation/extrapolation curves were generated and the zeta diversity was used to assess the overlap of nematode assemblages. Freshwater nematode assemblages comprised 54 families and 132 genera. Mononchidae, Monhysteridae, Chromadoridae, Tobrilidae and Dorylaimidae were the most diverse families. Differences in diversity and high turnover of genera were found among regions, probably related to stochastic processes. Mononchus was the only widely distributed genus. Our results revealed a high biodiversity of free-living freshwater nematodes among the regions. The limited spatial coverage of the data reveals an enormous knowledge gap in a country with 12% of the world's freshwater resources. The lack of spatial patterns, e.g. , latitudinal variation, suggests that freshwater nematode assemblages are primarily structured by the intrinsic properties of habitats. This reinforces the uniqueness of freshwater ecosystems and suggests that the nematode assemblages may be sensitive to environmental disturbances, since the limited distributions of taxa may lead to lower resilience. [ABSTRACT FROM AUTHOR]

We propose a Lévy noise-driven susceptible-exposed-infected-recovered model incorporating media coverage to analyze the outbreak of COVID-19. We conduct a theoretical analysis of the stochastic model by the suitable Lyapunov function, including the existence and uniqueness of the positive solution, the dynamic properties around the disease-free equilibrium and the endemic equilibrium; we deduce a stochastic basic reproduction number R 0 s for the extinction of disease, that is, if R 0 s ≤ 1 , the disease will go to extinction. Particularly, we fit the data from Brazil to predict the trend of the epidemic. Our main findings include the following: (i) stochastic perturbation may affect the dynamic behavior of the disease, and larger noise will be more beneficial to control its spread; (ii) strengthening social isolation, increasing the cure rate and media coverage can effectively control the spread of disease. Our results support the feasible ways of containing the outbreak of the epidemic. [ABSTRACT FROM AUTHOR]

• High plant mortality caused by fire in vegetation islands on rock outcrops. • Dispersal limitation and environmental filtering did not affect species composition. • Seeding and resprouting drove the post-fire regeneration on vegetation islands. • Desiccation-tolerant obligate seeder is a novel post-fire regeneration strategy. • Colonization–persistence trade-offs shape changes on island community assembly. We studied neglected island plant communities on rock outcrops (vegetation islands or patches) that harbor many endemic species. We assessed how the post-fire habitat conditions (factor related to habitat filtering), the patch area and substrate depth (factors related to resource availability and environmental filtering), and the patch distance (spatial factor related to dispersal limitation) shape the plant community attributes, life forms and persistence–reproduction trade-offs in a campo rupestre in the Chapada Diamantina, Brazil. Two years after a wildfire, we sampled 82 vegetation islands in two habitat conditions based on fire disturbance (burned and unburned). We verified high plant mortality caused by wildfire. All succulent and desiccation-tolerant chamaephytes died, but while the desiccation-tolerant chamaephyte recolonized burned vegetation islands by seedling recruitment, the succulent chamaephyte lack regeneration after disturbance, demonstrating their vulnerability to fire disturbances. The post-fire recovery was driven by obligate resprouters (hemicryptophytes and phanerophyte), post-fire colonizers (therophytes and phanerophytes), facultative seeders (phanerophytes), and desiccation-tolerant obligate seeders (chamaephytes). Despite the similar species richness between burned and unburned vegetation islands, the fire-habitat conditions determined changes in species composition and persistence–reproduction trade-offs. We demonstrated that random processes related to the colonization-survival and persistence–reproduction trade-offs can be a strong biotic factor shaping vegetation islands on rocky outcrops beyond environmental filtering and dispersal limitation. Our study is the first to investigate the fire effects on vegetation islands in rock outcrops and reveal the desiccation-tolerant obligate seeder as a novel post-fire regeneration strategy, that has a central role in the vegetation island community assembly. [ABSTRACT FROM AUTHOR]

Species prevalence across the landscape is related to their local abundance, which is a result of deterministic and stochastic processes that select organisms capable of recolonizing sites where they were once extinct, a process known as the rescue effect. The occupancy-frequency distribution (OFD) describes these patterns and has been extensively used to understand organism's distribution but has been poorly tested on microorganisms. In order to test OFD on freshwater bacteria, we collected data from 60 shallow lakes distributed across a wide area in southeastern Brazil, to determine the bacterial operational taxonomic units (OTUs) that were present in all sites (core) and at only one site (satellite). Then, we analyzed the spatial abundance distributions of individual OTUs to understand the influence of local abundances on regional occupancy patterns. Finally, we tested the environmental factors that influenced occupancy and abundance. We found a significant bimodal OFD for freshwater bacteria using both OTUs (97% clustering) and amplicon sequence variants (ASVs, unique sequences), with 13 core OTUs and 1169 satellite OTUs, but only three core ASVs. Core organisms had a bimodal or gamma abundance distribution. The main driver of the core community was pH, while nutrients were key when the core community was excluded and the rest of the community (mild and satellite taxa) was considered. This study demonstrates the close relationship between local environmental conditions and the abundance and dispersion of microorganisms, which shapes their distribution across the landscape. [ABSTRACT FROM AUTHOR]

The search for alternative energy sources has fomented the study of several crops. The macauba palm crop, for instance, has been highlighted because of its particular relevance in Brazil due to its wide distribution across Brazilian territory and its potential for yielding high amounts of oil per cultivated hectare. However, the species is still most commonly harvested via extractivism, which results in low yields. Therefore, we aimed to analyze the dynamic behavior of the fruit-rachilla system when subjected to mechanical vibration to gather baseline information for the subsequent development of macauba harvesting machines. The fruit-rachilla system of the species was modeled for different fruit maturation stages and plant accessions. Natural frequencies and modes of vibration were determined by the stochastic finite element method (FEM), adopting the specific mass and the modulus of elasticity of the system as random variables, which enabled us to compile a dataset of natural frequencies based on the variability of the system properties. The mean values of the natural frequencies obtained in the vibration assays were 26.02 Hz at the green maturation stage and 21.22 Hz at the ripe maturation stage. The mean values of natural frequencies found in the simulation by stochastic FEM, referring to the third mode of vibration, were 26.05 Hz at the green maturation stage and 21.23 Hz at the ripe maturation stage. We concluded that the natural frequencies of the macauba fruit-rachilla system on the basis of different plant accessions showed a decreasing behavior during fruit maturation. The modes of vibration characterized by pendulum displacement did not differ among plant accessions or between fruit maturation stages. [ABSTRACT FROM AUTHOR]

Copyright of RACE- Revista de Administraçâo, Contabilidade e Economia is the property of Revista de Administracao, Contabeis e Economia-RACE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aim: Plant community assembly in tropical rain forest has been shown to be largely governed by stochastic processes, but as arbuscular mycorrhizal (AM) fungi display limited host preference, they may not follow the same stochastic assembly pattern. Here, we determined the relative importance of environmental and spatial drivers responsible for the community assembly process of AM fungi in two types of tropical rain forest: semideciduous rain forest and dense ombrophilous forests. Location: Atlantic rain forest in north‐eastern Brazil, South America. Taxon: Arbuscular mycorrhizal fungi (Glomeromycotina). Methods: We collected root samples from eight protected areas of Atlantic forest along a 700 km transect in north‐eastern Brazil. We measured the relative importance of deterministic and stochastic processes by redundancy analysis (RDA) and variation partitioning in comparison with null expectations using ad hoc generated neutral communities. Furthermore, we accessed species associations from co‐occurrence data, at different scales using a Bayesian approach of Hierarchical Modelling of Species Communities. Results: Overall, the extent to which stochastic and deterministic processes affected community assembly depended on the forest type and the spatial scale. Specifically, we found that abiotic and biotic predictors of AM fungal community assemblages are related to environmental homogeneity in tropical rain forests. Main conclusions: The results of the study show that dynamics in community assembly was clearly different between the two forest types, and that the difference most likely is due to differences in responses to environmental variables. [ABSTRACT FROM AUTHOR]

In recent years there has been increasing attention in patterns of ß-diversity and mechanisms related to variations in species composition. In this study, we evaluated beta diversity patterns of bromeliads growing on cliffs immersed in Atlantic Forest. We hypothesized that the species composition varies according to the spatial scale, inferring that there is a replacement of species influenced mainly by environmental factors. The study was carried out on sandstone cliffs included in contiguous but distinct vegetation formations: Evergreen and Seasonal forests. Twenty-four vertical rocky outcrops were sampled. The spatial variation in species composition was evaluated by two ß-diversity components, turnover and nestedness. Multivariate analysis and variation partitioning were performed to distinguish niche and stochastic processes. We recorded 26 bromeliad species and a significantly higher contribution of turnover explaining beta diversity. Environmental factors affect ß-diversity patterns of Bromeliaceae. However, individually, the environmental predictors do not explain the data variation. Environmental variations spatially structured, and spatial variables determinate the dissimilarity in the composition of bromeliads on cliffs. Thus, our results revealed that both environmental and spatial effects can act together to define the floristic composition of rock-dwelling bromeliad communities. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a setup under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate comprise the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly more straightforward and tractable than semi-Markov models with a similar level of generality. Based on stochastic stability, we derive a sufficient condition for a shrinking epidemic regarding the queuing system's occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil, and assess the effect of different stabilising strategies in the latter setting. Results suggest that the proposed approach can curb the epidemic with various occupation rate levels if the mitigation is timely., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Horizontal Visibility Graphs (HVGs) are a recently developed method to construct networks from time series. The values of the time series are considered as the nodes of the network and are linked to each other if there is no larger value between them, such as they can “see” each other. The network properties reflect the nonlinear dynamics of the time series. For some classes of stochastic processes and for periodic time series, analytical results can be obtained for network-derived quantities such as the degree distribution, the local clustering coefficient distribution, the mean path length, and others. HVGs have the potential to discern between deterministic-chaotic and correlated-stochastic time series. Here, we investigate the sensitivity of the HVG methodology to properties and pre-processing of real-world data, i.e., time series length, the presence of ties, and deseasonalization, using a set of around 150 runoff time series from managed rivers at daily resolution from Brazil with an average length of 65 years. We show that an application of HVGs on real-world time series requires a careful consideration of data pre-processing steps and analysis methodology before robust results and interpretations can be obtained. For example, one recent analysis of the degree distribution of runoff records reported pronounced sub-exponential “long-tailed” behavior of North American rivers, whereas another study of South American rivers showed hyper-exponential “short-tailed” behavior resembling correlated noise. We demonstrate, using the dataset of Brazilian rivers, that these apparently contradictory results can be reconciled by minor differences in data-preprocessing (here: small differences in subtracting the seasonal cycle). Hence, data-preprocessing that is conventional in hydrology (“deseasonalization”) changes long-term correlations and the overall runoff dynamics substantially, and we present empirical consequences and extensive simulations to investigate these issues from a HVG methodological perspective. After carefully accounting for these methodological aspects, the HVG analysis reveals that the river runoff dataset shows indeed complex behavior that appears to stem from a superposition of short-term correlated noise and “long-tailed behaviour,” i.e., highly connected nodes. Moreover, the construction of a dam along a river tends to increase short-term correlations in runoff series. In summary, the present study illustrates the (often substantial) effects of methodological and data-preprocessing choices for the interpretation of river runoff dynamics in the HVG framework and its general applicability for real-world time series. [ABSTRACT FROM AUTHOR]

This paper assesses the economic feasibility of ethanol and higher alcohols production through a thermochemical route in Brazil. This route comprises mainly sugarcane bagasse gasification, syngas cleaning, and synthesis of alcohols. Ethanol production costs and associated risks are evaluated in different scenarios. A case study of a virtual plant to be located adjacent to a sugarcane mill, in the state of São Paulo, is presented with the objective of finding the minimum ethanol selling price (MESP) that makes the plant’s project economically feasible. The analysis utilizes first the traditional discounted cash flow method and then proceeds to use a stochastic approach, which is more suitable to study economic feasibility under uncertainty conditions. In the latter approach, prices of raw materials and products are modelled through a mean reverting stochastic process, and economic feasibility is analysed with the help of Monte Carlo simulations. Results are summarized via a histogram of the MESPs obtained for different simulated price scenarios. Finally, the project’s risk is evaluated by computing the number of instances in which the MESP is greater than the ethanol market price. [ABSTRACT FROM AUTHOR]

Currently, in addition to the search for sustainable development, there is increased pressure for a change in consumption and production of energy patterns in Brazil. In this scenario, net metering is an important mechanism fostering dissemination of small photovoltaic (PV) solar systems. As complementary support to net metering, a tax exemption is currently being offered in some Brazilian states. Thus, the objective of the present study is to analyze the impact of tax exemption on the circulation of goods and services, and the returns and risks of the PV microgeneration project in four cities located in different regions of Brazil: Belém, Petrolina, Uberaba and Uruguaiana. The analysis is performed using the Monte Carlo Simulation (MCS), wherein uncertainties related to the financial and environmental variables are considered. The results of the stochastic economic viability analysis allowed us to conclude that the exemption of tax on circulation of goods and services (ICMS) is fundamental to make PV microgeneration viable in Brazil. In the cities analyzed, PV microgeneration presented economic unfeasibility to the investor when the ICMS is charged. When considering the ICMS exemption policy, the cities of Petrolina and Belém obtained high probabilities of viability. In relation to the risk analysis, PV microgeneration obtained the best results in Petrolina, both in terms of the condition of collection and exemption of ICMS. There is high potential for harness solar throughout the Brazilian territory. However, due to the current evolution stage and the productive chain of the Brazilian PV industry, high costs limit the expansion of this clean energy generation technology. The findings of this study could assist policymakers in evaluating this incentive program, highlighting that this tax exemption directly meets one of the objectives for which the ICMS was created which is to encourage the development of productive sectors, such as PV industry. [ABSTRACT FROM AUTHOR]

We present a framework for incorporating weather uncertainty into electricity demand forecasting when weather patterns cannot be assumed to be stable, such as in climate change scenarios. This is done by first calibrating an econometric model for electricity demand on historical data, and subsequently applying the model to a large number of simulated weather paths, together with projections for the remaining determinants. Simulated weather paths are generated based on output from a global circulation model, using a method that preserves the trend and annual seasonality of the first and second moments, as well as the spatial and serial correlations. The application of the framework is demonstrated by creating long-term, probabilistic electricity demand forecasts for Brazil for the period 2016–2100 that incorporates weather uncertainty for three climate change scenarios. All three scenarios indicate steady growth in annual average electricity demand until reaching a peak of approximately 1071–1200 TWh in 2060, then subsequently a decline, largely reflecting the trajectory of the population projections. The weather uncertainty in all scenarios is significant, with up to 400 TWh separating the 10th and the 90th percentiles, or approximately ±17% relative to the mean. [ABSTRACT FROM AUTHOR]

The introduction of the flex-fuel cars in the Brazilian market in 2003 changed considerably the consumer decision-making process. Prior to this date, it was necessary to choose the automobile type only by gasoline or by ethanol fuel; today it is possible to choose a car type with both fuel options. This flexibility generates economic advantages for his owner, but what are the financial benefits of a flex-fuel car in comparison with a car using only gasoline? Geographically, where is the owner of the benefits from this flexibility located? This article presents an empirical application of the Real Options Theory in the analysis of the flex-fuel car option for five geographic Brazilian regions: Northern, Northeastern, Central-Western, Southeastern and Southern. The regional price differences as well as the consumer preferences of these regions were met. For this purpose, historical fuel prices were considered stochastic and following a Mean Reverting Stochastic process. The prediction and option values were generated by a Monte Carlo simulation. The results indicated that the option embedded on the Brazilian flex-fuel car adds considerable value to the owner in all regions and car models considered, with the Southeastern Region receiving most benefits by the flex option. [ABSTRACT FROM PUBLISHER]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Herein, we propose a fully Bayesian approach to the greenhouse gas emission problem. The goal of this work is to estimate the emission rate of polluting gases from the area flooded by hydroelectric reservoirs. We present models for gas concentration evolution in two ways: first, by proposing them from ordinary differential equation solutions and, second, by using stochastic differential equations with a discretization scheme. Finally, we present techniques to estimate the emission rate for the entire reservoir. In order to carry out the inference, we use the Bayesian framework with Monte Carlo via Markov Chain methods. Discretization schemes over continuous differential equations are used when necessary. These models applied to greenhouse gas emission and Bayesian inference for this purpose are completely new in statistical literature, as far as we know, and contribute to estimate the amount of polluting gases released from hydroelectric reservoirs in Brazil. The proposed models are applied in a real data set and results are presented. [ABSTRACT FROM PUBLISHER]

Conservation of species in agroecosystems has attracted attention. Irrigation channels can improve habitats and offer conditions for freshwater species conservation. Two questions from biodiversity conservation point of view are: 1) Can the irrigated channels maintain a rich diversity of macrophytes, macroinvertebrates and amphibians over the cultivation cycle? 2) Do richness, abundance and composition of aquatic species change over the rice cultivation cycle? For this, a set of four rice field channels was randomly selected in Southern Brazilian wetlands. In each channel, six sample collection events were carried out over the rice cultivation cycle (June 2005 to June 2006). A total of 160 taxa were identified in irrigated channels, including 59 macrophyte species, 91 taxa of macroinvertebrate and 10 amphibian species. The richness and abundance of macrophytes, macroinvertebrates and amphibians did not change significantly over the rice cultivation cycle. However, the species composition of these groups in the irrigation channels varied between uncultivated and cultivated periods. Our results showed that the species diversity found in the irrigation channels, together with the permanence of water enables these man-made aquatic networks to function as important systems that can contribute to the conservation of biodiversity in regions where the wetlands were converted into rice fields. The conservation of the species in agriculture, such as rice field channels, may be an important alternative for biodiversity conservation in Southern Brazil, where more than 90% of wetland systems have already been lost and the remaining ones are still at high risk due to the expansion of rice production. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ambiente e Água is the property of Revista Ambiente e Agua and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de Economia e Sociologia Rural is the property of Sociedade Brasileira de Economia e Sociologia Rural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de Economia e Sociologia Rural is the property of Sociedade Brasileira de Economia e Sociologia Rural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives. We examined the viability and efficacy of a known quantity of exercise in facilitating weight loss among previously sedentary or irregularly active overweight and obese adult women residing in a slum (favela) in Brazil. Methods. In this randomized controlled trial, 156 women were randomized to a control or intervention group (78 in each group). Exercise was supervised, consisting of three 50-minute aerobic sessions each week for 6 months. Results. Ninety-one percent (71) of the participants in the intervention group completed 6 months of the exercise program. At 6 months, women in the treatment group showed significant reduction in weight (mean=-1.69 kg; 95% confidence interval [CI] =-2.36,-1.03) and body mass index (mean=-0.63 kg/m[sup 2]; 95% CI=-0.97, -0.30) compared with controls (P for both <.001). Conclusions. A moderately intense, structured exercise program resulted in modest weight loss in women when sustained for 6 months. (Am J Public Health. 2008;99:76-80. doi:10.2105/AJPH.2007.124495) [ABSTRACT FROM AUTHOR]

This study had the objective of comparing weighted difasics logistic models applied to the study of Hereford females growth curves with three different error structures: independent errors (IE), first-order auto-regressive (AR (1)) and second-order auto-regressive (AR (2)) to weight-age data of 55 females of the Hereford breed, raised in the Bagé region, RS, Brazil, evaluated from birth to 675 days old. The weight and %AR options of model procedure, available in the software Statistical Analysis System (SAS), was used to fit data. The comparison among the models was carried out through the biological interpretation basis of the parameters and in the adjustment of quality measures (adjusted determination coefficient, Durbin-Watson test, residual standard desviation, number of iterations), beyond the Akaike information criteria (AIC) and the F test for model comparison. The models fitted to mean data indicated that the difasic logistic with AR(2) structure was the most efficient to describe the herd growth curve. In the individual fit, none of the models was accepted because they didn't produce consistent estimates. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an empirical investigation of the intraday Brazilian stock market price fluctuations, considering -Gaussian distributions that emerge from a non-extensive statistical mechanics. Our results show that, when price returns are measured over intervals less than one hour, the empirical distributions are well fitted by -Gaussians with exponential damped tails. Scaling behavior is also observed for these microscopic time intervals. We find that the time evolution of the return distributions is according to a super-diffusive -Gaussian stationary process within a nonlinear Fokker–Planck equation. This regime breaks down due to the exponential fall-off of the tails, which in turn, governs the transient dynamics to the long-term macroscopic Gaussian regime. This exponentially damped, non-extensive modeling provides a new framework to investigate the dynamics of other stock markets intraday price fluctuations. [Copyright &y& Elsevier]

Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed. The parameters of the resulting system of differential equations are identifiable. Using these differential equations, we develop a stochastic model for the conditional distribution of current disease incidence given the entire past history of reported cases. We estimate the model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution. We use our model to estimate the transmission rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight American Countries: the United States of America, Brazil, Mexico, Argentina, Chile, Colombia, Peru, and Panama, from January 2020 to May 2021. Our analysis reveals that the fraction of reported cases varies across all countries. For example, the reported incidence fraction for the United States of America varies from 0.3 to 0.6, while for Brazil it varies from 0.2 to 0.4., Competing Interests: The authors have declared that no competing interests exist.

The observed joint distribution of births and child deaths for a cohort of women at a given point in time depends on the number of children that would have been born had the family experienced no deaths, the number of child deaths experienced, and the proportion of these deaths that are replaced by a subsequent birth. In this paper we estimate the parameters of the assumed distributions of these three events using a minimum distance estimation model and data from the 1970 Brazilian census. The parameter estimates are shown to be similar to those obtained previously using a maximum likelihood estimation model. When the data are subdivided according to women's years of schooling, estimates of probability of a child death and mean and variance of children born if no deaths decrease while estimates of probability of replacement of a dead child increase as years of schooling increase. [ABSTRACT FROM AUTHOR]

During the coronavirus disease 2019 (COVID-19) pandemic, many countries opted for strict public health measures, including closing schools. After some time, they have started relaxing some of those restrictions. To avoid overwhelming health systems, predictions for the number of new COVID-19 cases need to be considered when choosing a school reopening strategy. Using a computer simulation based on a stochastic compartmental model that includes a heterogeneous and dynamic network, we analyse different strategies to reopen schools in the São Paulo Metropolitan Area, including one similar to the official reopening plan. Our model allows us to describe different types of relations between people, each type with a different infectiousness. Based on our simulations and model assumptions, our results indicate that reopening schools with all students at once has a big impact on the number of new COVID-19 cases, which could cause a collapse of the health system. On the other hand, our results also show that a controlled school reopening could possibly avoid the collapse of the health system, depending on how people follow sanitary measures. We estimate that postponing the schools' return date for after a vaccine becomes available may save tens of thousands of lives just in the São Paulo Metropolitan Area compared to a controlled reopening considering a worst-case scenario. We also discuss our model constraints and the uncertainty of its parameters.

Worldwide, alien plant invasions have been intensively studied in the past decades, but mechanisms controlling the invasibility of native communities are not fully understood yet. The stochastic niche hypothesis predicts that species-rich plant communities are less prone to alien plant invasions than species-poor communities, which is supported by some but not all field studies, with some very species-rich communities such as the Brazilian Cerrado becoming heavily invaded. However, species-rich communities potentially contain a greater variety of facilitative interactions in resource exploitation than species-poor communities, from which invasive plants might benefit. This alternative hypothetical mechanism might explain why nutrient-poor, species-rich ecosystems are prone to invasion. Here we show that a high species richness both impedes and promotes invasive plants in the Brazilian Cerrado, using structural equation modelling and data from 38 field sites. We found support for the stochastic niche hypothesis through an observed direct negative influence of species richness on abundance of alien invasive species, but an indirect positive effect of species richness on invasive alien plants through soil phosphatase activity that enhances P availability was also found. These field observations were supported with results from a mesocosm experiment. Root phosphatase activity of plants increased with species richness in the mesocosms, which was associated with greater community P and N uptake. The most prominent alien grass species of the region, Melinis minutiflora, benefited most from the higher N and P availability in the species mixtures. Hence, this study provides a novel explanation of why species-richness may sometimes promote rather than impede invasion, and highlights the need to perform facilitation experiments in multi-species communities.

Background: Despite the greater sensitivity of the new dengue clinical classification proposed by the World Health Organization (WHO) in 2009, there is a need for a better definition of warning signs and clinical progression of dengue cases. Classic statistical methods have been used to evaluate risk criteria in dengue patients, however they usually cannot access the complexity of dengue clinical profiles. We propose the use of machine learning as an alternative tool to identify the possible characteristics that could be used to develop a risk criterion for severity in dengue patients., Method: In this study, we analyzed the clinical profiles of 523 confirmed dengue cases using self-organizing maps (SOM) and random forest algorithms to identify clusters of patients with similar patterns., Results: We identified four natural clusters, two with features of dengue without warning signs or mild disease, one that comprises the severe dengue cases and high frequency of warning signs, and another with intermediate characteristics. Age appeared as the key variable for splitting the data into these four clusters although warning signs such as abdominal pain or tenderness, clinical fluid accumulation, mucosal bleeding, lethargy, restlessness, liver enlargement and increased hematocrit associated with a decrease in platelet counts should also be considered to evaluate severity in dengue patients., Conclusions: These findings suggest that age must be the first characteristic to be considered in places where dengue is hyperendemic. Our results show that warning signs should be closely monitored, mainly in children. Further studies exploring these results in a longitudinal approach may help to understand the full spectrum of dengue clinical manifestations.

Background: The parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease. Leishmania infantum parasites are transmitted between hosts during blood-feeding by infected female phlebotomine sand flies. With a principal reservoir host of L. infantum being domestic dogs, limiting prevalence in this reservoir may result in a reduced risk of infection for the human population. To this end, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. One way this can be achieved is through the use of mathematical models., Methods: We have developed a stochastic, spatial, individual-based mechanistic model of L. infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fitted distributions for these entities to existing survey data. To identify the model parameters of highest importance, we performed a stochastic parameter sensitivity analysis of the prevalence of infection among dogs to the model parameters., Results: We computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The stochastic parameter sensitivity analysis determined prevalence of L. infantum infection in dogs to be most strongly affected by the sand fly associated parameters and the proportion of immigrant dogs already infected with L. infantum parasites., Conclusions: Establishing the model parameters with the highest sensitivity of average L. infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions.

There are a huge number of pathogens with multi-component transmission cycles, involving amplifier hosts, vectors or complex pathogen life cycles. These complex systems present challenges in terms of modeling and policy development. A lethal tick-borne infectious disease, the Brazilian Spotted Fever (BSF), is a relevant example of that. The current increase of human cases of BSF has been associated with the presence and expansion of the capybara Hydrochoerus hydrochaeris, amplifier host for the agent Rickettsia rickettsii and primary host for the tick vector Amblyomma sculptum. We introduce a stochastic dynamical model that captures the spatial distribution of capybaras and ticks to gain a better understanding of the spatial spread of the R. rickettsii and potentially predict future epidemic outcomes. We implemented a reaction-diffusion process in which individuals were divided into classes denoting their state with respect to the disease. The model considered bidirectional movements between base and destination locations limited by the carrying capacity of the environment. We performed systematic stochastic simulations and numerical analysis of the model and investigate the impact of potential interventions to mitigate the spatial spread of the disease. The mobility of capybaras and their attached ticks was significantly influenced by the birth rate of capybaras and therefore, disease propagation velocity was higher in places with higher carrying capacity. Some geographical barriers, generated for example by riparian reforesting, can impede the spatial spread of BSF. The results of this work will allow the formulation of public actions focused on the prevention of BSF human cases., Competing Interests: The authors have declared that no competing interests exist.

There is increasing recognition that community assembly theory can offer valuable insights for ecological restoration. We studied community assembly processes following tropical forest restoration efforts, using dung beetles (Scarabaeinae) as a focal taxon to investigate taxonomic and functional patterns of biodiversity recovery. We evaluated the relative importance of the local environment (i.e., canopy cover, understory cover, tree basal area, and soil texture), landscape context (i.e., habitat patch proximity and availability and percentage of surrounding area classified as natural forest or Eucalyptus spp. plantation), and space (i.e., spatial proximity of the study areas to estimate dispersal limitation or unmeasured spatially structured processes) on dung beetle species and functional trait composition across a gradient of 15 restoration sites in Brazilian Atlantic Forest. We also assessed which factors were the primary determinants in the establishment of individual dung beetle functional groups, classified according to size, food relocation habit, diet, and period of flight activity. Both species and functional trait composition were most strongly influenced by the local environment, indicating that assembly was predominantly driven by niche-based processes. Most of the variation explained by space was co-explained by local environment and landscape context, ruling out a strong influence of dispersal limitation and random colonization on assembly following restoration. In addition, nearly all of the variance explained by landscape context was co-explained by local environment, suggesting that arrival and establishment at a site depends on both local and landscape-scale environmental factors. Despite strong evidence for niche-based assembly, a large amount of variation remained unexplained in all models, suggesting that stochastic processes and/or unmeasured environmental variables also play an important role. The relative importance of local environment, landscape context, and space changed considerably when analyzing the assembly mechanisms of each functional group separately. Therefore, to recover distinct functional traits in restoration sites, it may be necessary to manipulate different components of the local environment and surrounding landscape. Overall, this study shows that assembly rules can help to better understand recovery processes, enabling improvement of future restoration efforts., (© 2017 by the Ecological Society of America.)

Sustainability of communities, agriculture, and industry is strongly dependent on an effective storage and supply of water resources. In some regions the economic growth has led to a level of water demand that can only be accomplished through efficient reservoir networks. Such infrastructures are not always planned at larger scale but rather made by farmers according to their local needs of irrigation during droughts. Based on extensive data from the upper Jaguaribe basin, one of the world's largest system of reservoirs, located in the Brazilian semiarid northeast, we reveal that surprisingly it self-organizes into a scale-free network exhibiting also a power-law in the distribution of the lakes and avalanches of discharges. With a new self-organized-criticality-type model we manage to explain the novel critical exponents. Implementing a flow model we are able to reproduce the measured overspill evolution providing a tool for catastrophe mitigation and future planning.

Due to the need for management, control, and monitoring of information in an effient way. The hospital automation has been the object of a number of studies owing to constantly evolving technologies. However, many hospital processes are still manual in private and public hospitals. Thus, the aim of this study is to model and simulate of medical care provided to patients in the Intensive Care Unit (ICU), using stochastic Petri Nets and their possible use in a number of automation processes.

This study is aimed at creating a stochastic model, named Brazilian Climate and Health Model (BCHM), through Poisson regression, in order to predict the occurrence of hospital respiratory admissions (for children under thirteen years of age) as a function of air pollutants, meteorological variables, and thermal comfort indices (effective temperatures, ET). The data used in this study were obtained from the city of São Paulo, Brazil, between 1997 and 2000. The respiratory tract diseases were divided into three categories: URI (Upper Respiratory tract diseases), LRI (Lower Respiratory tract diseases), and IP (Influenza and Pneumonia). The overall results of URI, LRI, and IP show clear correlation with SO₂ and CO, PM₁₀ and O₃, and PM₁₀, respectively, and the ETw4 (Effective Temperature) for all the three disease groups. It is extremely important to warn the government of the most populated city in Brazil about the outcome of this study, providing it with valuable information in order to help it better manage its resources on behalf of the whole population of the city of Sao Paulo, especially those with low incomes.

Simulation models are increasingly used to gain insights regarding the long-term effect of both direct and indirect anthropogenic impacts on natural resources and to devise and evaluate policies that aim to minimize these effects. If the uncertainty from simulation model projections is not adequately quantified and reported, modeling results might be misleading, with potentially serious implications. A method is described, based on a nested simulation design associated with multimodel projections, that allows the partitioning of the overall uncertainty in model projections into a number of different sources of uncertainty: model stochasticity, starting conditions, parameter uncertainty, and uncertainty that originates from the use of key model assumptions. These sources of uncertainty are likely to be present in most simulation models. Using the forest dynamics model SYMFOR as a case study, it is shown that the uncertainty originated from the use of alternate modeling assumptions, a source of uncertainty seldom reported, can be the greatest source of uncertainty, accounting for 66-97% of the overall variance of the mean after 100 years of stand dynamics simulation. This implicitly reveals the great importance of these multimodel projections even when multiple models from independent research groups are not available. Finally, it is suggested that a weighted multimodel average (in which the weights are estimated from the data) might be substantially more precise than a simple multimodel average (equivalent to equal weights for all models) as models that strongly conflict with the data are given greatly reduced or even zero weights. The method of partitioning modeling uncertainty is likely to be useful for other simulation models, allowing for a better estimate of the uncertainty of model projections and allowing researchers to identify which data need to be collected to reduce this uncertainty.

This paper is concerned with stochastic models for the spread of an epidemic among a community of households, in which individuals mix uniformly within households and, in addition, uniformly at a much lower rate within the population at large. This two-level mixing structure has important implications for the threshold behaviour of the epidemic and, consequently, for both the effectiveness of vaccination strategies for controlling an outbreak and the form of optimal vaccination schemes. A brief introduction to optimal vaccination schemes in this setting is provided by presenting a unified treatment of the simplest and most-studied case, viz. the single-type SIR (susceptible -->infective --> removed) epidemic. A reproduction number R*, which determines whether a trace of initial infection can give rise to a major epidemic, is derived and the effect of a vaccination scheme on R* is studied using a general model for vaccine action. In particular, optimal vaccination schemes which reduce R* to its threshold value of one with minimum vaccination coverage are considered. The theory is illustrated by application to data on a variola minor outbreak in São Paulo, which, together with other examples, is used to highlight key issues related to vaccination schemes.

Objective: To evaluate a new method of deriving the reproductive number for vector-borne diseases from the early epidemic curves for vector-borne diseases with incubations in the vectors and in the hosts., Method: We applied the model to several dengue epidemics in different climatic regions of Brazil: Brasilia, Belém, Fortaleza, Boa Vista., Results: The new method leads to higher estimates of the reproductive number than previous models., Conclusion: At present, Aedes aegypti densities, the meeting of more compatible strains of viruses and mosquitoes, may lead to re-emergence of urban yellow fever epidemics.

Schistosoma mansoni egg counts by faecal examination vary considerably and are not very sensitive, so prevalences are underestimated. The distribution of egg counts can adequately be described by a stochastic model which distinguishes variation in counts between persons and variation in repeated counts within a person. Based on this model a pocket chart has been developed which predicts the proportion of individuals harbouring at least 1 S. mansoni worm pair-the 'true prevalence'-from a simple single survey prevalence and geometric mean egg count (using common duplicate 25 mg Kato-Katz smears). The current paper describes the validation of this chart by comparing predicted true prevalences with prevalences observed after 5-7 repeated Kato-Katz faecal examinations (Burundi), by examination of a large quantity of stool using the Visser filter (Brazil) or a selective sedimentation-filtration method (Surinam). Because 5-7 repeated examinations do not suffice to measure all infections, predictions have been made of the cumulative proportion positives over 5-7 surveys-the 'approximate true prevalence'-as well. After dividing the data into age groups, 12 different subsets were considered for validation. In all 12 cases, predicted true prevalences (or approximate true prevalences for the Burundi data) agree well with those observed. The overall agreement depends only slightly on the assumed relationship between worm numbers and mean egg counts, with a good fit for a productivity between 0.8 and 4.4 eggs per gramme faeces (EPG) per worm pair (WP). This interval includes the most plausible value from the literature, i.e. 1.0 EPG/WP, which has been applied in the initial pocket chart. These findings support the validity of the chart to predict true prevalences for a wide range of productivity assumptions, and reinforces the applicability of its underlying stochastic model to describe egg count variation. However, as predictions appear to vary importantly when using only part of the data, it is also concluded that the pocket chart never compensates for limited validity of initial single survey prevalences and geometric means in consequence of small sample sizes.

Data of epidemiologic interest often occur as spatial information during each of several time periods. In most cases data are available from a set of regions or localities which can be viewed as points in a plane. Although contour mapping is useful for displaying these data, the lack of data for all data points in a region may lead to erroneous interpretation. In this paper we use stimulation to investigate the impact of missing data points for contour mapping using two distinct simulated spatial-time distributions for epidemiologic variables. A model for the occurrence of malaria in localities randomly distributed in one region is chosen as the prototype for data generation.