A Comprehensive Modeling Approach for Crop Yield Forecasts using AI-based Methods and Crop Simulation Models

Numerous solutions for yield estimation are either based on data-driven models, or on crop-simulation models (CSMs). Researchers tend to build data-driven models using nationwide crop information databases provided by agencies such as the USDA. On the opposite side of the spectrum, CSMs require fine data that may be hard to generalize from a handful of fields. In this paper, we propose a comprehensive approach for yield forecasting that combines data-driven solutions, crop simulation models, and model surrogates to support multiple user-profiles and needs when dealing with crop management decision-making. To achieve this goal, we have developed a solution to calibrate CSMs at scale, a surrogate model of a CSM assuring faster execution, and a neural network-based approach that performs efficient risk assessment in such settings. Our data-driven modeling approach outperforms previous works with yield correlation predictions close to 91\%. The crop simulation modeling architecture achieved 6% error; the proposed crop simulation model surrogate performs predictions almost 100 times faster than the adopted crop simulator with similar accuracy levels

Cartografia e diplomacia: usos geopolíticos da informação toponímica (1750-1850)

O artigo explora dimensões geopolíticas da toponímia, registradas em documentos cartográficos, desde as reformas empreendidas pelo consulado pombalino em meados do século XVIII, até às primeiras décadas do século XIX, em meio ao processo de afirmação do Estado imperial pós-colonial.This paper explores the geopolitical dimensions of toponymy as registered in cartographic documents dating from the reforms pushed through by the consulate of Marquis of Pombal in the mid 18th century to the early decades of the 19th century, as the post-colonial imperial State established itself

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

Telehealth network of Minas Gerais and its contributions to universality, equality and integrality in the brazilian Unified Health System (Sistema Único de Saúde – SUS): an empirical study

Made available in DSpace on 2017-01-27T17:23:29Z (GMT). No. of bitstreams: 2 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) 8.pdf: 226464 bytes, checksum: 7befc9c12b944a2b2c5248acf6f5bd81 (MD5) Previous issue date: 2013Universidade Federal de Minas Gerais. Faculdade de Medicina. Departamento de Clínica Médica. Belo Horizonte, MG, Brasil / Universidade Federal de Minas Gerais. Hospital das Clínicas. Centro de Telessaúde. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Hospital das Clínicas. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Faculdade de Medicina. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Faculdade de Medicina. Belo Horizonte, MG, Brasil.Pontifícia Universidade Católica de Minas Gerais. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Hospital das Clínicas. Centro de Telessaúde. Belo Horizonte, MG, Brasil.Centro Universitário Newton Paiva. Belo Horizonte, MG, Brasil / Universidade Federal de Minas Gerais. Hospital das Clínicas. Centro de Telessaúde. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Hospital das Clínicas. Centro de Telessaúde. Belo Horizonte, MG, Brasil / Asociación Iberoamericana de Telesalud y Telemedicina. Aurora, CO, USA.Universidade Federal de Minas Gerais. Escola de Engenharia. Departamento de Engenharia Metalúrgica e de Materiais. Belo Horizonte, MG, Brasil / Universidade Federal de Minas Gerais. Hospital das Clínicas. Centro de Telessaúde. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Faculdade de Medicina. Departamento de Clínica Médica. Belo Horizonte, MG, Brasil / Universidade Federal de Minas Gerais. Hospital das Clínicas. Centro de Telessaúde. Belo Horizonte, MG, Brasil.No Brasil, há desigualdade no acesso a serviços de saúde especializados, principalmente em municípios remotos. A telessaúde surgiu como estratégia para fornecer suporte aos profissionais de saúde da Atenção Primária desses municípios. O objetivo deste estudo é relatar a experiência exitosa da Rede de Teleassistência de Minas Gerais (RTMG), ressaltando como o serviço contribui para atingir os princípios doutrinários do SUS. A metodologia é relato da experiência, estudo observacional retrospectivo com relação à avaliação das teleconsultorias e avaliação de custo-efetividade. Em 2005, recursos públicos do governo do estado e de agências de fomento à pesquisa financiaram a criação da RTMG, com o objetivo de conectar hospitais de seis universidades públicas à Atenção Primária de municípios remotos. Em 2006, 82 municípios eram atendidos. Várias expansões foram realizadas e, desde 2012, o serviço atende 660 municípios. Até fevereiro de 2013, 1.165.410 eletrocardiogramas e 48.680 teleconsultorias foram realizados (média de 6,1 atividades/município/semana). As teleconsultorias evitaram potenciais encaminhamentos em 80%. O Retorno sobre Investimento foi de R$3,75 para cada R$ investido. Concluindo a RTMG colabora para se atingir no sistema público de saúde de Minas Gerais os pressupostos de universalidade, equidade e integralidade, além de contribuir com a melhora da qualidade do cuidado.Brazilian have unequal access to specialized health care services, especially in remote municipalities. Telehealth is a strategy to support primary health care professionals in such municipalities. The objective for this study was to report a successful experience from the Telehealth Network of Minas Gerais (Rede de Teleassistência de Minas Gerais - RTMG), which highlights the contribution of this service to the Brazilian Unified Health System principles (Sistema Único de Saúde – SUS). The methodology used includes empirical observations, which compose a retrospective observational study that evaluates teleconsultations and costeffectiveness. In 2005, public funds from the state government and research development agencies financed RTMG construction, which was intended to connect hospitals from six public universities to primary health caregivers in remote municipalities. In 2006, 82 municipalities were served. The service was expanded several times, and it has reached 660 municipalities since 2012. As of February 2013, 1,165,410 electrocardiograms and 48,680 teleconsultations had been conducted (6.1 activities/municipality/week mean). The teleconsultations averted potential referrals for specialized health care services by 80%. The return on investment was R$3.75 for every R$ invested. In conclusion, the RTMG facilitates universality, equality and integrality in the Minas Gerais public health care system as well as contributes to improved care quality