Redes neurais, metodologias de agrupamento e combinação de previsores aplicados a previsão de vazões naturais

Orientador: Fernando Gomide, Rosangela BalliniDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Planejamento de sistemas hidroeletricos possui um alto grau de complexidade e dificuldade, uma vez que involve caracteristicas de produção não lineares e depende de muitas variaveis. Um das variaveis chave e a vazão natural. Os valores de vazões devem ser previstos com acuracia, uma vez que esses valores influenciam significativamente na produção de energia. Atualmente, no setor de geração hidroeletrica, a previsão de vazões e baseada na metodologia de Box & Jenkins. Este trabalho propõe um modelo de previsão baseado em agrupamento nebuloso como alternativa para a previsão de vazões naturais medias mensais. O modelo utiliza o algoritmo de agrupamento fuzzy c-means para explorar a estrutura dos dados historicos, e procedimentos de mediana e reconhecimento de padrões para capturar similaridades na tendencia das series. Ainda, este trabalho sugere um modelo que combina previsões geradas por um conjunto de m'etodos individuais de previsão, de uma maneira simples, mas efetiva. Utiliza-se, como combinador, uma rede neural treinada com o algoritmo do gradiente. O objetivo e combinar as previsões geradas por diferentes modelos na tentativa de capturar as contribuições das caracteristicas de previão mais importantes de cada previsor individual. Esse metodo tambem e aplicado a previsão de series de vazões naturais medias mensais escolhendo-se, como modelos individuais, aqueles que obtiveram melhor desempenho para uma dada serie. Resultados experimentais com dados reais de vazão sugerem que o modelo preditivo aseado em agrupamento nebuloso obtem um desempenho superior, quando comparado com a metodologia atual de previsão de vazões adotada pelo setor hidroeletrico, e, ainda, com uma rede neural nebulosa, um modelo não linear. Alem disso, o modelo de combinação alcança um desempenho superior que os modelos de previsão individuais, pois apresentam erros de previsão menoresAbstract: In addition, this work suggests a linear approach to combine forecasts generated by a set of individual forecasting models in a simple and effective way. We use, as a combiner, a neural network trained with the gradient descent algorithm. The aim is to combine the forecasts generated by the different forecasting models as an attempt to capture the contributions of the most important prediction features of each individual model at each prediction step. The approach is also used for streamflow time series prediction choosing, as individual forecasting models, the most promising predictive methods. Experimental results with actual data suggest that the predictive clustering approach performs globally better than the current streamflow forecasting methodology adopted by many hydroelectric systems worldwide, and a fuzzy neural network, a nonlinear prediction model. The combination approach, with lower prediction errors, performs better than each of the individual forecasting modelsMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Measuring tropical rainforest resilience under non-Gaussian disturbances

The Amazon rainforest is considered one of the Earth's tipping elements and may lose stability under ongoing climate change. Recently a decrease in tropical rainforest resilience has been identified globally from remotely sensed vegetation data. However, the underlying theory assumes a Gaussian distribution of forest disturbances, which is different from most observed forest stressors such as fires, deforestation, or windthrow. Those stressors often occur in power-law-like distributions and can be approximated by $\alpha$-stable L\'evy noise. Here, we show that classical critical slowing down indicators to measure changes in forest resilience are robust under such power-law disturbances. To assess the robustness of critical slowing down indicators, we simulate pulse-like perturbations in an adapted and conceptual model of a tropical rainforest. We find few missed early warnings and few false alarms are achievable simultaneously if the following steps are carried out carefully: First, the model must be known to resolve the timescales of the perturbation. Second, perturbations need to be filtered according to their absolute temporal autocorrelation. Third, critical slowing down has to be assessed using the non-parametric Kendall-$\tau$ slope. These prerequisites allow for an increase in the sensitivity of early warning signals. Hence, our findings imply improved reliability of the interpretation of empirically estimated rainforest resilience through critical slowing down indicators

Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests

A variety of modelling studies have suggested tree rooting depth as a key variable to explain evapotranspiration rates, productivity and the geographical distribution of evergreen forests in tropical South America. However, none of those studies have acknowledged resource investment, timing and physical constraints of tree rooting depth within a competitive environment, undermining the ecological realism of their results. Here, we present an approach of implementing variable rooting strategies and dynamic root growth into the LPJmL4.0 (Lund-Potsdam-Jena managed Land) dynamic global vegetation model (DGVM) and apply it to tropical and sub-tropical South America under contemporary climate conditions. We show how competing rooting strategies which underlie the trade-off between above- and below-ground carbon investment lead to more realistic simulation of intra-annual productivity and evapotranspiration and consequently of forest cover and spatial biomass distribution. We find that climate and soil depth determine a spatially heterogeneous pattern of mean rooting depth and below-ground biomass across the study region. Our findings support the hypothesis that the ability of evergreen trees to adjust their rooting systems to seasonally dry climates is crucial to explaining the current dominance, productivity and evapotranspiration of evergreen forests in tropical South America.Fil: Sakschewski, Boris. Potsdam Institute for Climate Impact Research; AlemaniaFil: Von Bloh, Werner. Humboldt-Universität zu Berlin; AlemaniaFil: Drüke, Markus. Humboldt-Universität zu Berlin; AlemaniaFil: Sörensson, Anna. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Ruscica, Romina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Langerwisch, Fanny. Universitat Potsdam; AlemaniaFil: Billing, Maik. Universidade Federal de Santa Catarina; BrasilFil: Bereswill, Sarah. Universidade Estadual de Campinas; BrasilFil: Hirota, Marina. Potsdam Institute for Climate Impact Research; AlemaniaFil: Oliveira, Rafael Silva. Potsdam Institute for Climate Impact Research; AlemaniaFil: Heinke, Jens. Potsdam Institute for Climate Impact Research; AlemaniaFil: Thonicke, Kirsten. Potsdam Institute for Climate Impact Research; Alemani

Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests

A variety of modelling studies have suggested tree rooting depth as a key variable to explain evapotranspiration rates, productivity and the geographical distribution of evergreen forests in tropical South America. However, none of those studies have acknowledged resource investment, timing and physical constraints of tree rooting depth within a competitive environment, undermining the ecological realism of their results. Here, we present an approach of implementing variable rooting strategies and dynamic root growth into the LPJmL4.0 (Lund-Potsdam-Jena managed Land) dynamic global vegetation model (DGVM) and apply it to tropical and sub-tropical South America under contemporary climate conditions. We show how competing rooting strategies which underlie the trade-off between above- and below-ground carbon investment lead to more realistic simulation of intra-annual productivity and evapotranspiration and consequently of forest cover and spatial biomass distribution. We find that climate and soil depth determine a spatially heterogeneous pattern of mean rooting depth and below-ground biomass across the study region. Our findings support the hypothesis that the ability of evergreen trees to adjust their rooting systems to seasonally dry climates is crucial to explaining the current dominance, productivity and evapotranspiration of evergreen forests in tropical South America

Network dynamics of drought-induced tipping cascades in the Amazon rainforest

Tipping elements are nonlinear subsystems of the Earth system that can potentially abruptly and irreversibly shift if environmental change occurs. Among these tipping elements is the Amazon rainforest, which is threatened by anthropogenic activities and increasingly frequent droughts. Here, we assess how extreme deviations from climatological rainfall regimes may cause local forest-savanna transitions that cascade through the coupled forest-climate system. We develop a dynamical network model to uncover the role of atmospheric moisture recycling in such tipping cascades. We account for the heterogeneity in critical thresholds of the forest caused by adaptation to local climatic conditions. Our results reveal that, despite this adaptation, increased dry-season intensity may trigger tipping events particularly in the southeastern Amazon. Moisture recycling is responsible for one-fourth of the tipping events. If the rate of climate change exceeds the adaptive capacity of some parts of the forest, secondary effects through moisture recycling may exceed this capacity in other regions, increasing the overall risk of tipping across the Amazon rainforest

Measuring tropical rainforest resilience under non-Gaussian disturbances

The Amazon rainforest is considered one of the Earth’s tipping elements and may lose stability under ongoing climate change. Recently a decrease in tropical rainforest resilience has been identified globally from remotely sensed vegetation data. However, the underlying theory assumes a Gaussian distribution of forest disturbances, which is different from most observed forest stressors such as fires, deforestation, or windthrow. Those stressors often occur in power-law-like distributions and can be approximated by α-stable Lévy noise. Here, we show that classical critical slowing down (CSD) indicators to measure changes in forest resilience are robust under such power-law disturbances. To assess the robustness of CSD indicators, we simulate pulse-like perturbations in an adapted and conceptual model of a tropical rainforest. We find few missed early warnings and few false alarms are achievable simultaneously if the following steps are carried out carefully: first, the model must be known to resolve the timescales of the perturbation. Second, perturbations need to be filtered according to their absolute temporal autocorrelation. Third, CSD has to be assessed using the non-parametric Kendall-τ slope. These prerequisites allow for an increase in the sensitivity of early warning signals. Hence, our findings imply improved reliability of the interpretation of empirically estimated rainforest resilience through CSD indicators

A social-ecological approach to identify and quantify biodiversity tipping points in South America’s seasonal dry ecosystems

ropical dry forests and savannas harbour unique biodiversity and provide critical ES, yet they are under severe pressure globally. We need to improve our understanding of how and when this pressure provokes tipping points in biodiversity and the associated social-ecological systems. We propose an approach to investigate how drivers leading to natural vegetation decline trigger biodiversity tipping and illustrate it using the example of the Dry Diagonal in South America, an understudied deforestation frontier. The Dry Diagonal represents the largest continuous area of dry forests and savannas in South America, extending over three million km² across Argentina, Bolivia, Brazil, and Paraguay. Natural vegetation in the Dry Diagonal has been undergoing large-scale transformations for the past 30 years due to massive agricultural expansion and intensification. Many signs indicate that natural vegetation decline has reached critical levels. Major research gaps prevail, however, in our understanding of how these transformations affect the unique and rich biodiversity of the Dry Diagonal, and how this affects the ecological integrity and the provisioning of ES that are critical both for local livelihoods and commercial agriculture.Fil: Thonicke, Kirsten. Institute for Climate Impact Research ; AlemaniaFil: Langerwisch, Fanny. Institute for Climate Impact Research ; Alemania. Czech University of Life Sciences Prague; República ChecaFil: Baumann, Matthias. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; AlemaniaFil: Leitão, Pedro J.. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; AlemaniaFil: Václavík, Tomáš. Helmholtz Centre for Environmental Research; Alemania. Palacký University Olomouc; República ChecaFil: Alencar, Anne. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Simões, Margareth. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); BrasilFil: Scheiter, Simon. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); Brasil. Universidade Federal do Rio de Janeiro; BrasilFil: Langan, Liam. Senckenberg Biodiversity and Climate Research Centre; AlemaniaFil: Bustamante, Mercedes. Universidade do Brasília; BrasilFil: Gasparri, Nestor Ignacio. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Hirota, Marina. Universidade Federal de Santa Catarina; Brasil. Universidade Estadual de Campinas; BrasilFil: Börner, Jan. Universitat Bonn; AlemaniaFil: Rajao, Raoni. Universidade Federal de Minas Gerais; BrasilFil: Soares Filho, Britaldo. Universidade Federal de Minas Gerais; BrasilFil: Yanosky, Alberto. Consejo Nacional de Ciencia y Tecnología; ParaguayFil: Ochoa Quinteiro, José Manuel. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt; ColombiaFil: Seghezzo, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; ArgentinaFil: Conti, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: de la Vega Leiner, Anne Cristina. Universität Greifswald; Alemani

Recurrent droughts increase risk of cascading tipping events by outpacing adaptive capacities in the Amazon rainforest

Tipping elements are nonlinear subsystems of the Earth system that have the potential to abruptly shift to another state if environmental change occurs close to a critical threshold with large consequences for human societies and ecosystems. Among these tipping elements may be the Amazon rainforest, which has been undergoing intensive anthropogenic activities and increasingly frequent droughts. Here, we assess how extreme deviations fromclimatological rainfall regimes may cause local forest collapse that cascades through the coupled forest-climate system. We develop a conceptual dynamic network model to isolate and uncover the role of atmospheric moisture recycling in such tipping cascades. We account for heterogeneity in critical thresholds of the forest caused by adaptation to local climatic conditions. Our results reveal that, despite this adaptation, a future climate characterized by permanent drought conditions could trigger a transition to an open canopy state particularly in the southern Amazon.Theloss of atmospheric moisture recycling contributes to one-third of the tipping events.Thus, by exceeding local thresholds in forest adaptive capacity, local climate change impacts may propagate to other regions of the Amazon basin, causing a risk of forest shifts even in regions where critical thresholds have not been crossed locally

Chronic allergen challenge induces bronchial mast cell accumulation in BALB/c but not C57BL/6 mice and is independent of IL-9

As genetically engineered mutant mice deficient in single genes are usually generated on a C57BL/6 background, to study mast cell trafficking in mutant mice, we initially investigated whether mast cells accumulated in bronchi in C57BL/6 mice challenged with OVA allergen acutely or chronically for 1 to 3 months. The total number of bronchial mast cells were quantitated using toluidine blue staining in airways of different sizes, i.e. , small (<90 µm), medium (90–155 µm), or large (>150 µm) airways. Non-OVA challenged and acute OVA challenged mice (C57BL/6 and BALB/c) had no detectable bronchial mast cells. Chronic OVA challenge in BALB/c mice for 1 or 3 months induced a significant increase in the number of bronchial mast cells in small-, medium-, and large-sized airways but minimal change in the number of bronchial mast cells in C57BL/6 mice. Both BALB/c and C57BL/6 mice developed significant lung eosinophilia following acute or chronic OVA challenge. Studies of IL-9-deficient mice on a BALB/c background demonstrated a significant increase in the number of bronchial mast cells in IL-9-deficient mice suggesting that IL-9 was not required for the bronchial accumulation of mast cells. Overall, these studies demonstrate that the chronic OVA challenge protocol we have utilized in BALB/c mice provides a model to study the mechanism of bronchial mast cell accumulation and that bronchial mast cell accumulation in chronic OVA challenged mice is independent of IL-9 in this model

PATOLOGIA DO QUADRIL DAS CRIANÇAS: UMA REVISÃO INTEGRATIVA

Many of the pathologies that affect the hip in adults have their origins in childhood, making the early diagnosis of these conditions through specific pediatric orthopedic evaluation extremely important. Research shows that late diagnoses are associated with a considerable increase in the number of sequelae. This article consists of an integrative review, which aims to analyze and discuss the main hip pathologies in children, in order to expand the knowledge of students and professionals in the area about the subject in question. The work consists of an integrative literature review, in which a basic, qualitative, exploratory and bibliographic research was carried out in the databases. Hip pathology in children is a field of medical study and treatment that encompasses a variety of conditions and problems that affect the hip joint in younger individuals. These conditions can range from congenital problems to disorders acquired over time. It is critical to understand that healthy hip development is crucial for children's mobility and quality of life, and any issues in this area must be addressed with care and attention. In summary, hip pathologies in children encompass a variety of conditions, from developmental dysplasia of the hip to proximal epiphysis of the femur. Early diagnosis and adequate treatment are essential to avoid complications and long-term sequelae.Muitas das patologias que afetam o quadril de adultos têm suas origens na infância, tornando o diagnóstico precoce dessas condições por meio de avaliação ortopédica pediátrica específica de extrema importância. Pesquisas demonstram que diagnósticos tardios estão associados a um aumento considerável no número de sequelas. O presente artigo consiste em uma revisão integrativa, no qual tem como objetivo analisar e discutir acerca das principais patologias de quadril das crianças, no intuito de ampliar os conhecimentos de estudantes e profissionais da área acerca do tema em questão. O trabalho consiste em uma revisão de literatura do tipo integrativa, na qual foi realizada uma pesquisa dos tipos básica, qualitativa, exploratória e bibliográfica, nas bases de dados. A patologia do quadril em crianças é um campo de estudo e tratamento médico que abrange uma variedade de condições e problemas que afetam a articulação do quadril em indivíduos mais jovens. Essas condições podem variar desde problemas congênitos até distúrbios adquiridos ao longo do tempo. É fundamental entender que o desenvolvimento saudável do quadril é crucial para a mobilidade e a qualidade de vida das crianças e qualquer problema nessa área deve ser abordado com cuidado e atenção. Em suma, as patologias do quadril em crianças abrangem uma variedade de condições, desde a displasia do desenvolvimento do quadril até a epifisiólise proximal do fêmur. O diagnóstico precoce e o tratamento adequado são fundamentais para evitar complicações e sequelas a longo prazo