Aplicação de diodos emissores de luz em ambientes hiperbáricos

Em 06/12/2016: Manutenção do Indeferimento. Em 09/08/2016: Indeferimento. Indeferido o pedido por não atender aos requisitos legais, conforme parecer técnico.Não concedidaA inovação ora proposta refere-se à aplicação do diodo emissor de luz, doravante denominado LED (Light-Emitting Diode), em ambientes hiperbáricos secos ou ambientes hiperbáricos molhados. Nesses ambientes o LED pode ser aplicado para iluminar, sinalizar ou como parte integrante de mostradores numéricos de caracteres ou gráficos. Sendo também proposta a aplicação dos LEDs em ambientes hiperbáricos sem estarem protegidos por vasos de pressão. Os LEDs podem ficar em contato direto com o fluido do ambiente líquido ou gasoso, ou protegidos do meio ambiente por um recipiente transparente ou translúcido, fino e leve

Processo para produção de resinas à base de poli (2,5-furanodicarboxilato de etileno), resinas à base de poli (2,5-furanodicarboxilato de etileno) e uso das referidas resinas

DepositadaA presente invenção refere-se a um processo para produção de resinas à base de poli (2,5-furanodicarboxilato de etileno) na presença de etilenoglicol, a resinas à base de poli (2,5-furanodicarboxilato de etileno), bem como ao uso das referidas resinas na fabricação de filmes, embalagens, fibras e peças extrusadas

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