Análise de desempenho de um modelo fenológico para a cultura do arroz irrigado no Estado do Rio Grande do Sul

O arroz tem o segundo maior volume de produção em grãos do mundo, sendo o Brasil, o nono maior produtor. O Estado do Rio Grande do Sul, é responsável por aproximadamente 70% de toda a produção nacional de arroz, fato que apresenta grande relevância socioeconômica para o Estado e para o país, uma vez que a agricultura está diretamente ligada a economia. Diversas variáveis meteorológicas exercem influência direta no desenvolvimento da cultura no Estado, como radiação solar, precipitação pluvial e principalmente temperatura. Portanto, o conhecimento de como a cultura responde as diferentes variações meteorológicas no desenvolvimento fenológico é de extrema importância e tem a modelagem como aliada. O objetivo deste trabalho foi avaliar um software computacional que auxilia a determinação das temperaturas cardinais do modelo fenológico utilizado pelo modelo ORYZA2000, auxiliando a calibração dos coeficientes para caracterização das cultivares de arroz irrigado. Pode-se constatar, através dos resultados, que as cultivares analisadas apresentam valores de temperaturas cardinais similares ao longo dos estádios de desenvolvimento abordados e que por vezes subestimam e superestimam as faixas de temperaturas propostas pela literatura. Concluiu-se, que o programa desenvolvido foi capaz de simular corretamente os processos fenológicos existentes na cultura do arroz irrigado e precisar as temperaturas cardinais ideais para duas diferentes cultivares de arroz irrigado, comumente utilizadas na lavoura gaúcha

Variáveis meteorológicas e crescimento de arroz irrigado

A cultura orizícola é de extrema importância para a economia da região sul do Brasil, principalmente para o estado do Rio Grande do Sul/RS, que é o maior produtor nacional de arroz irrigado. Análises durante o desenvolvimento fenológico dos componentes de crescimento das plantas (fontes) de diferentes cultivares e suas interações (feedback) com as variáveis meteorológicas, vem se tornando cada vez mais importantes, pois proporcionam ajustes mais acurados para a indicação ideal da(s) a(s) época(s) de semeadura das culturas em diversos ambientes. O objetivo do trabalho foi analisar o desenvolvimento dos diferentes estádios fenológicos de três cultivares de arroz irrigado: BRS Querência, BRS Pampeira e BRS Pampa CL. Foi conduzido um experimento de campo com três diferentes cultivares e em três épocas de semeaduras, na Estação Experimental da Embrapa Terras Baixas, município de Capão do Leão, RS. As avaliações foram realizadas com amostras de folhas verdes, folhas mortas, colmos e panículas em quatro épocas de coleta, sendo estas, realizadas em um metro linear com quatro repetições e baseadas nos estádios fenológicos de cada cultivar. Pode-se concluir que o predomínio de desenvolvimento das cultivares foi variável e dependente do estádio fenológico em que eram analisadas, as variáveis meteorológicas apresentaram-se favoráveis ao desenvolvimento dos três diferentes tipos de arroz irrigado analisados

A Critical Review of Robustness in Power Grids using Complex Networks Concepts

Complex network theory for analyzing robustness in energy gridsThis paper reviews the most relevant works that have investigated robustness in power grids using Complex Networks (CN) concepts. In this broad field there are two different approaches. The first one is based solely on topological concepts, and uses metrics such as mean path length, clustering coefficient, efficiency and betweenness centrality, among many others. The second, hybrid approach consists of introducing (into the CN framework) some concepts from Electrical Engineering (EE) in the effort of enhancing the topological approach, and uses novel, more efficient electrical metrics such as electrical betweenness, net-ability, and others. There is however a controversy about whether these approaches are able to provide insights into all aspects of real power grids. The CN community argues that the topological approach does not aim to focus on the detailed operation, but to discover the unexpected emergence of collective behavior, while part of the EE community asserts that this leads to an excessive simplification. Beyond this open debate it seems to be no predominant structure (scale-free, small-world) in high-voltage transmission power grids, the vast majority of power grids studied so far. Most of them have in common that they are vulnerable to targeted attacks on the most connected nodes and robust to random failure. In this respect there are only a few works that propose strategies to improve robustness such as intentional islanding, restricted link addition, microgrids and smart grids, for which novel studies suggest that small-world networks seem to be the best topology.This work has been partially supported by the project TIN2014-54583-C2-2-R from the Spanish Ministerial Commission of Science and Technology (MICYT), by the project S2013/ICE-2933 from Comunidad de Madrid and by the project FUTURE GRIDS-2020 from the Basque Government

Collateral, Liquidity and Debt Sustainability

We study Markov‐perfect optimal fiscal policy in an economy with financial frictions and sovereign default in the form endogenously determined haircuts on outstanding debt. Government bonds facilitate tax smoothing but also provide collateral and liquidity services that mitigate financial frictions. A debt Laffer curve exists, which induces the government to issue bonds to a point where marginal debt has negative welfare effects. Debt positions in the order of magnitude of annual output remain sustainable despite the option to default. When default happens, liquidity on the bond market is impaired, which can trigger extended periods of recurrent haircuts

Star Formation and Dynamics in the Galactic Centre

The centre of our Galaxy is one of the most studied and yet enigmatic places in the Universe. At a distance of about 8 kpc from our Sun, the Galactic centre (GC) is the ideal environment to study the extreme processes that take place in the vicinity of a supermassive black hole (SMBH). Despite the hostile environment, several tens of early-type stars populate the central parsec of our Galaxy. A fraction of them lie in a thin ring with mild eccentricity and inner radius ~0.04 pc, while the S-stars, i.e. the ~30 stars closest to the SMBH (<0.04 pc), have randomly oriented and highly eccentric orbits. The formation of such early-type stars has been a puzzle for a long time: molecular clouds should be tidally disrupted by the SMBH before they can fragment into stars. We review the main scenarios proposed to explain the formation and the dynamical evolution of the early-type stars in the GC. In particular, we discuss the most popular in situ scenarios (accretion disc fragmentation and molecular cloud disruption) and migration scenarios (star cluster inspiral and Hills mechanism). We focus on the most pressing challenges that must be faced to shed light on the process of star formation in the vicinity of a SMBH.Comment: 68 pages, 35 figures; invited review chapter, to be published in expanded form in Haardt, F., Gorini, V., Moschella, U. and Treves, A., 'Astrophysical Black Holes'. Lecture Notes in Physics. Springer 201

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Friedrich Hayek and his visits to Chile

F. A. Hayek took two trips to Chile, the first in 1977, the second in 1981. The visits were controversial. On the first trip he met with General Augusto Pinochet, who had led a coup that overthrew Salvador Allende in 1973. During his 1981 visit, Hayek gave interviews that were published in the Chilean newspaper El Mercurio and in which he discussed authoritarian regimes and the problem of unlimited democracy. After each trip, he complained that the western press had painted an unfair picture of the economic situation under the Pinochet regime. Drawing on archival material, interviews, and past research, we provide a full account of this controversial episode in Hayek’s life

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques