The weather conditions on rice yield in the State of São Paulo, Brazil

O objetivo deste trabalho foi medir o efeito das condições do tempo sobre a produtividade agrícola do arroz (Oryza sativa L.) em São Paulo. Utilizou-se a análise de regressão para estimar as relações entre a produtividade da cultura e as deficiências hídricas mensais. Os resultados sugerem grande sensibilidade do arroz à deficiência de água nos meses de janeiro, fevereiro e março. Alguns dos modelos ajustados fornecem previsões de rendimento melhores do que os levantamentos de campo, segundo os critérios de seleção adotados. The objective of this paper was to measure the effects of weather conditions on rice yield in the State of São Paulo, Brazil. Regression analysis was used to estimate the relation between crop yield and monthly hydric deficiencies. The results suggest great sensibility of rice to water deficiencies in January, February and March. Some of the adjusted models supply better yield forecasts than the field surveys, according to the adopted selection criteria

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

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

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI <18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For schoolaged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference) and obesity (BMI >2 SD above the median). Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesit

Numerical Simulation of Recirculating Flow and Physical Model of Slag–Metal Behavior in an RH Reactor: Application to Desulfurization

Computational fluid dynamics (CFD) techniques and a 1:7.5 physical model of an RH degasser have been used to evaluate the flow of gas and metal inside an RH reactor for vacuum degassing of liquid steel. The effect of gas injection on the gas spatial distribution, steel circulation rate and flow field inside the ladle, snorkels and vacuum chamber have been assessed. N-pentane oil was employed to evaluate the average residence time as well as the slag droplet size distribution. The predicted radial gas distribution and liquid circulation rate have been validated against experimental data from a physical model. The results with incorporation of the virtual mass force coefficient of 0.25 and the turbulence dispersion force showed better predictions of gas distribution in the up-snorkel as well as circulation rate. Full-scale simulations were performed, and the predicted circulation rate was significantly affected by the argon bubble expansion. Data from these simulations were used to analyze the degree of desulfurization performed by the addition of desulfurizing agents inside the vacuum chamber. A model of the kinetics of desulfurization based on the results from the physical model and CFD simulation and on slag dispersion inside liquid steel yields degrees of desulfurization similar to the industrial trials reported in the literature

Desempenho de bezerros da raça Holandesa alimentados com proteína de soja sólida ou líquida Performance of Holstein calves fed soybean meal protein in solid or liquid form

Avaliaram-se os efeitos da substituição de metade do total de proteína da dieta na forma sólida (farelo de soja) por proteína na forma líquida (leite de soja), com relações sólidos:líquidos de 100,0:0%; 87,5:12,5%; e 75,0:25,0%. Utilizaram-se 24 bezerros machos da raça Holandesa com 60 dias de idade, distribuídos em delineamento em blocos ao acaso, com três dietas à base de concentrado (80%) e feno de capim coast-cross (Cynodon dactylon) (20%), balanceadas para nitrogênio e energia. Nas dietas com partes líquidas, o intuito foi manter o reflexo de formação da goteira esofagiana no período experimental de 10 meses, no qual os animais foram mantidos em confinamento, sendo abatidos ao atingirem pesos corporais superiores a 400 kg. Em alguns dos animais, dotados de cânulas de rúmen, foi possível detectar neste órgão apenas pequenas quantidades do leite de soja ingerido, confirmando sucesso na formação da goteira esofagiana. Os ganhos de peso diários apresentaram redução linear (1,399; 1,341; e 1,191 kg) à medida que foram fornecidas maiores quantidades de leite de soja. As conversões de matéria seca e proteína bruta em ganhos de peso, durante os últimos 60 dias experimentais, pioraram conforme aumentaram as quantidades de proteína na forma líquida. O fornecimento de proteína na forma líquida na dieta não altera os rendimentos de carcaça em bezerros holandeses.<br>The effects were assessed of substituting half the total diet protein in solid form (soybean meal) with liquid form (soybean milk) at solid:liquid ratios of: 100.0:0% L; 87.5:12.5% L; 75.0:25.0% L. Twenty-four Holstein breed 60-d male calf steers were assigned to a randomized block design, with three concentrate (80%) diets and 20% coast-cross (Cynodon dactylon) hay, balanced for nitrogen and energy. In the diets with liquid parts, the intention was to keep the functional reflex of the esophageal groove throughout the 10 months of the experimental period, in which the animals were kept in a feedlot until slaughter at body weights higher than 400 kg. In some of the animals, with rumen canulas, only small quantities of ingested soybean milk were detected in the rumen contents, indicating success in esophageal groove formation. Daily weight gains were linearly reduced (1.399, 1.341 and 1.191 kg) as the treatments supplied larger quantities of soybean milk. Dry matter and crude protein conversions in weight gains, in the last 60 days of the experimental period, declined as the proportions of liquid feed increased. Supplying protein in liquid form in the diet did not alter carcass yield in Holstein steer

Seminário de Dissertação (2024)

Página da disciplina de Seminário de Dissertação (MPPP, UFPE, 2022) Lista de participantes == https://docs.google.com/spreadsheets/d/1mrULe1y04yPxHUBaF50jhaM1OY8QYJ3zva4N4yvm198/edit#gid=