APLICAÇÃO DE REDES NEURAIS ARTIFICIAIS PARA PREVISÃO DO NÍVEL DE ÁGUA SUBTERRÂNEA EM POÇO DE MONITORAMENTO NA BACIA SEDIMENTAR DO ARARIPE, CEARÁ: Application of artificial neural networks for groundwater level forcasting on monitoring well in Araripe Sedimentary Basin, Ceara

Groundwater level forecasting is essential for water availability. Formalisms such as Artificial Neural Networks (ANN) are broadly used to time series modeling and forecasting. The aim of this paper was to evaluate the application of ANN models for forecasting groundwater level of one well installed at Medium Aquifer System in Araripe sedimentary basin, Ceará. Feedforward ANN models with one hidden layer were applied. By using the time series past values as model inputs, the optimal network architecture was researched, with respect to the number of nodes on input and hidden layers. The ANN models were applied according to single and combined modeling approaches, through linear combination of forecasts, such as Simple Average and Simple Median. The model performances were measured and compared by well-known statistics metrics, such as RMSE and R². Results highlights the ANN good performance, with RMSE = 0,032 m and R² = 0,99. Single models outperformed combinations. This research showed how ANN are efficient models for forecasting groundwater level, even on complex systems and with a few input variables, representing a tool with large applicability on groundwater resources management.A previsão dos níveis de água subterrânea é fundamental para a avaliação da disponibilidade hídrica. Formalismos como Redes Neurais Artificiais (RNA) são amplamente utilizados na modelagem e previsão de séries temporais. O objetivo do presente trabalho foi avaliar a aplicação de modelos RNAs para previsão dos níveis de água em um poço instalado no Sistema Aquífero Médio, na bacia sedimentar do Araripe, Ceará. Foram utilizadas RNA feedforward com uma camada oculta. Utilizando os valores passados da série como entradas nos modelos, investigou-se a melhor arquitetura das redes quanto ao número de nós nas camadas de entrada e oculta. Os modelos foram aplicados segundo abordagens de modelagem individual e combinadas, através da combinação linear de preditores, como a Média Simples e a Mediana Simples. Seus desempenhos foram avaliados segundo métricas estatísticas conhecidas, como o RMSE e o R². Os resultados obtidos evidenciam o bom desempenho das RNAs, com RMSE = 0,032 m e R² = 0,99. Os modelos individuais superaram as combinações. O presente trabalho mostrou como RNAs são modelos eficientes para a previsão do nível da água subterrânea, mesmo em sistemas complexos e com poucas variáveis de entrada, constituindo uma ferramenta com grande aplicabilidade para gestão dos recursos hídricos subterrâneos

Studying the Performance of Cognitive Models in Time Series Forecasting

Cognitive models have been paramount for modeling phenomena for which empirical data are unavailable, scarce, or only partially relevant. These approaches are based on methods dedicated to preparing experts and then to elicit their opinions about the variables that describe the phenomena under study. In time series forecasting exercises, elicitation processes seek to obtain accurate estimates, overcoming human heuristic biases, while being less time consuming. This paper aims to compare the performance of cognitive and mathematical time series predictors, regarding accuracy. The results are based on the comparison of predictors of the cognitive and mathematical models for several time series from the M3-Competition. From the results, one can see that cognitive models are, at least, as accurate as ARIMA models predictions

ESTRATIFICAÇÃO DE RISCO PARA PÉ DIABÉTICO NUMA POPULAÇÃO DE IDOSOS ACOMPANHADOS NA ATENÇÃO PRIMÁRIA

Objetivo: avaliar a estratificação de risco para pé diabético numa população de idosos acompanhados na atenção primária. Método: estudo transversal, analítico, com abordagem quantitativa. Realizaram-se visitas domiciliares a 254 idosos para avaliação neurológica (sensibilidade protetora plantar e sintomas neuropáticos), dermatológica e vascular (pulsos e índice tornozelo braquial). Resultados: parcela substancial (95,3%) dos participantes referiu algum sintoma neuropático, sobretudo fadiga (67,4%). A maioria apresentava risco para pé diabético (64,1%), com predomínio do grau 1 (43,7%); eram tabagistas (71,9%), apresentavam comorbidade osteomuscular (57,8%) e já tinham sofrido um AVC (75%). As pessoas com grau de risco 2 e 3 tinham entre 10-19 desde o diagnóstico da doença (78,1%). Conclusão: boa parte da amostra apresentava algum grau de risco para pé diabético, sobretudo do grau 1, e presença de comorbidade osteomuscular.Descritores: Idoso. Pé Diabético. Atenção Primária à Saúde

Study of association models for determining the growth of the fleet of motor vehicles in the Metropolitan Region of Cariri, Ceará

The quality of life underlying the modern society can be attributed to several factors, among them, the technological and economic development experienced in recent years. Durable consumer goods are part of this modern society, such as automobiles. However, because most automobiles are powered by the combustion of fossil fuels, the emission of greenhouse gases is a worrisome environmental problem. The objective of this article is to analyze Gross Domestic Product (GDP) data, population and SELIC rate (SELIC stands for Special System of Settlement and Custody) in the period from 2001 to 2020 to evaluate the impact on the number of vehicles in the Cariri Metropolitan Region (RMC), using multivariate models. It was verified that the fleet of the RMC experienced an increase of 561.45% in the last 20 years. Three prediction models were tested and the conclusion was reached that for the next 20 years it is not sustainable to maintain the same growth already experienced, in a linear manner. Instead, the ideal is to adopt a model with growth forecast with a logarithmic function, i. e. with a stationary tendency in the long time. In a society where over 50% of vehicles are more than 10 years old, it is essential that public managers, the private initiative, the academic-scientific environment and society adopt sustainable practices and consider future scenarios to make decisions in order to preserve the environment and to ensure everyone\u27s quality of life

A list of land plants of Parque Nacional do Caparaó, Brazil, highlights the presence of sampling gaps within this protected area

Brazilian protected areas are essential for plant conservation in the Atlantic Forest domain, one of the 36 global biodiversity hotspots. A major challenge for improving conservation actions is to know the plant richness, protected by these areas. Online databases offer an accessible way to build plant species lists and to provide relevant information about biodiversity. A list of land plants of “Parque Nacional do Caparaó” (PNC) was previously built using online databases and published on the website "Catálogo de Plantas das Unidades de Conservação do Brasil." Here, we provide and discuss additional information about plant species richness, endemism and conservation in the PNC that could not be included in the List. We documented 1,791 species of land plants as occurring in PNC, of which 63 are cited as threatened (CR, EN or VU) by the Brazilian National Red List, seven as data deficient (DD) and five as priorities for conservation. Fifity-one species were possible new ocurrences for ES and MG states

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt

Método adaptativo de Markov Chain Monte Carlo para manipulação de modelos Bayesianos

Ao longo dos anos, modelos Bayesianos vêm recebendo atenção especial da academia e em aplicações principalmente por possibilitarem uma combinação matemática entre corpos de evidência subjetiva e empírica. A metodologia de integração de Monte Carlo via cadeias de Markov é uma das principais classes de algoritmos para computar estimativas marginais a partir de modelos Bayesianos. Entre os métodos de integração de Monte Carlo via cadeias de Markov, o algoritmo de Metropolis-Hastings merece destaque. Em resumo, para o conjunto de d variáveis (ou componentes) do modelo Bayesiano, X = (X1, X2, , Xd), tal algoritmo elabora uma cadeia de Markov onde cada estado visitado é uma realização de X, x = (x1, x2, , xd), amostrada das distribuições de probabilidades condicionais das variáveis do modelo, f(xi| x1, x2, , xi-1, xi+1, , xd). Quando a simulação é governada por distribuições cuja amostragem direta é viável, o algoritmo de Metropolis-Hastings converge para o método de Gibbs e técnicas de redução de variância tais como Rao-Blackwellization podem ser adotadas. Caso contrário, diante de distribuições cuja amostragem direta é inviável, Rao-Blackwellization é possível a partir do método de griddy-Gibbs, que recorre a funções aproximadas. Esta tese propõe uma variante de griddy-Gibbs que pode ser também classificada como uma extensão do algoritmo de Metropolis-Hastings (diferentemente do método de griddy-Gibbs tradicional que descarta a possibilidade de se rejeitar os valores amostrados ao longo das simulações). Além disso, algoritmos de integração numérica adaptativos e técnicas de agrupamento, tais como o método adaptativo de Simpson e centroidal Voronoi tessellations, são adotados. Casos de estudo apontam o algoritmo proposto como uma boa alternativa a métodos existentes, promovendo estimativas mais precisas sob um menor consumo de recursos computacionais em muitas situaçõe