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Remote sensors from the Satellite or Aircrafts are generated by huge volume of data which can utilize for impending signification if collected data aggregated effectively incorporates by insight information. Data is collection from simple to hybrid devices, which are continuously working for technology around us and communicate with each other. These devices are transferring huge amounts of real time data daily. The transaction added to the synchronized inaccessible sensing data that is retrieving the useful information in the proficient way of classification in the direction of the severe computational challenges, analyze, the assortment, and accumulate, where gathered data is inaccessible. The real time sensing devices will continuously export data. In this work, we will implement the big data analytics on remote sensing datasets. We utilized BEST software for header analysis of the datasets and retrieving the full resolution image from the dataset. Then retrieved image is divided into smaller blocks for applying statistical. By applying certain rules and conditions in the form of algorithm, determine the land and sea blocks of image dataset. Our end results are proficiently analyzing real-time remote sensing utilizing the land beacon structure. Finally, a comprehensive investigation of the remotely intelligence earth beacon massive information for earth and ocean space are available by utilizing- Hadoop.
Remote sensors from the Satellite or Aircrafts are generated by huge volume of data which can utilize for impending signification if collected data aggregated effectively incorporates by insight information. Data is collection from simple to hybrid devices, which are continuously working for technology around us and communicate with each other. These devices are transferring huge amounts of real time data daily. The transaction added to the synchronized inaccessible sensing data that is retrieving the useful information in the proficient way of classification in the direction of the severe computational challenges, analyze, the assortment, and accumulate, where gathered data is inaccessible. The real time sensing devices will continuously export data. In this work, we will implement the big data analytics on remote sensing datasets. We utilized BEST software for header analysis of the datasets and retrieving the full resolution image from the dataset. Then retrieved image is divided into smaller blocks for applying statistical. By applying certain rules and conditions in the form of algorithm, determine the land and sea blocks of image dataset. Our end results are proficiently analyzing real-time remote sensing utilizing the land beacon structure. Finally, a comprehensive investigation of the remotely intelligence earth beacon massive information for earth and ocean space are available by utilizing- Hadoop.
As proteínas são bastante utilizadas na indústria alimentícia para o melhoramento da textura dos produtos e são de extrema importância para a nutrição humana. Esse estudo avaliou o conteúdo de proteína total de três derivados lácteos diferentes: bebida láctea, requeijão cremoso e cream cheese, de três marcas comerciais distintas, e comparou com a rotulagem nutricional dos produtos. Todas as marcas diferiram entre si estatisticamente, com nível de significância de 95% pelo teste de Tukey. Apenas uma amostra de bebida láctea diferenciou significativamente das demais (p<0,05), enquanto que para o requeijão cremoso, as três marcas diferiram do valor descrito no rótulo e no cream cheese houve diferença nas marcas C1 e C3 (p > 0,05). Somente a marca C3 do cream cheese ficou fora dos padrões de rotulagem, as demais marcas apresentaram diferença significativa, porém estavam em conformidade com legislação brasileira vigente. Como o cream cheese não possui uma legislação brasileira específica para o conteúdo de proteína, os valores são bastante variáveis. Entretanto, em relação à rotulagem nutricional, a ANVISA preconiza que o valor real não deve exceder a 20% do valor informado. Sendo assim, é importante a fiscalização dos produtos lácteos no tocante à informação nutricional para que o consumidor seja devidamente orientado. Palavras-chave: Bebida Láctea. Rotulagem Nutricional. Cream Cheese. Abstract Proteins are widely used in the food industry for improving the texture of products and are of utmost importance for human nutrition. This study evaluated the total protein content of three different dairy products: dairy drink, cream cheese and cream cheese, from three different brands, and compared it with the nutritional labeling of the products. All the marks differed statistically, with a level of significance of 95% by the Tukey test. Only one sample of milk beverage differed significantly from the others (p <0.05), whereas for cream cheese, the three brands differed from the value described on the label and cream cheese showed differences in the marks C1 and C3 (p> 0, 05). Only the C3 brand of cream cheese was outside the labeling standards, the other brands presented significant difference, but were in compliance with current Brazilian legislation. As cream cheese does not have specific Brazilian legislation for protein content, the values are quite variable. However, in relation to nutritional labeling, ANVISA recommends that the actual value should not exceed 20% of the reported value. Therefore, it is important to monitor dairy products with regard to nutritional information for the consumer to be properly oriented. Keywords: Milk Beverage. Nutrition Facts. Cream Cheese.
RESUMO O cultivo da soja exige um alto nível de qualidade em relação às sementes empregadas, além de ser muito dependente da condição hídrica. Dentro dessa sistemática, este estudo avaliou o impacto do déficit hídrico na germinação de sementes na cultura de soja. Foram avaliados 12 tratamentos, com 4 repetições em cada, totalizando 50 sementes de soja por repetição, em que se comparou o rendimento das sementes tratadas industrialmente com standak top 200 ml a cada 100 kg de sementes (piraclostrobina 25g/l + tiofanato metílico 225g/l + fipronil 713g/l), com fungicidas em comparação com as sementes não tratadas. Os resultados obtidos evidenciaram que a ausência de água até 14 dias não impactou na produtividade e rendimento produtivo das plântulas. No entanto, após esse período, a ausência de umidade influenciou na qualidade fisiológica das sementes, comprometendo o stand final. As análises feitas acerca da influência da temperatura e umidade foram pouco significativas. Outra conclusão deste experimento foi que as sementes tratadas obtiveram um resultado aquém do obtido pelas sementes não tratadas. Pressupõe-se, a partir das análises feitas, que o tratamento empregado nas sementes tratadas industrialmente não tenha sido realizado de forma adequada, causando fitotoxidez na semente ou injúrias causadas no momento da inserção da mistura que comprometeram a qualidade fisiológica e química da semente tratada e, por essa razão, seu rendimento não sobrepujou o obtido pelas sementes não tratadas.
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