Analysis of a semi-empirical leading-edge slat noise prediction model

In this paper semi-empirical formulas are presented that relate macroscopic flow parameters observed in the slat cove and semi-empirical constants proposed in Guo’s semianalytical model. Fourteen slat configurations were simulatedusing the Lattice Boltzmann Method (LBM) implemented in PowerFLOWR commercial software. These results show an elementary relation between the four semiempirical constants proposed in Guo’s slat model and importantflow parameters, e.g., the shear-layer path length and the maximum shear velocity. Consequently, those four semi-empirical constants were rewritten in terms of two empirical constants which values can be derived fromRANS simulations. The proposed noise prediction model is consequently validated against wind tunnel aeroacoustics tests performed with the 30P30N high-lift device model. Experiments performed in the UTwente AeroacousticWind Tunnel at Rec = 1 x 106 and M = 0.15 showed good overall agreement between noise measurements and the proposed slat noise prediction model

O impacto das facções têxteis no Seridó

The present article aims to analyze the importance and the economic and financial impact of garment factions in the Seridó region. Methodology to be applied, through the application of interviews with factional variables. As a result the result reveals that the industry is an important alternative for a generation of income in the region where the investment shortage is launched. Finally, the study concludes that the factions are largely concerned with being idle, with no prospect of work and income, on the margins of the labor market.El presente artículo tiene por objetivo analizar la importancia y el impacto económico-financiero de las facciones de confecciones en la región del Seridó. La metodología utilizada fue cualitativa, a través de la aplicación de entrevistas con colaboradores de esas facciones. Como resultado el estudio revela que esa rama de actividad es una importante alternativa para la generación de ingresos en la referida región, donde es escaso la inversión de la iniciativa privada. Por último, el estudio concluye que el beneficio de las facciones incide en gran parte de la población, que anteriormente se encontraba ociosa y sin perspectiva de trabajo y renta, al margen del mercado de trabajo.O presente artigo tem por objetivo analisar a importância e o impacto econômico-financeiro da facções de confecções na região do Seridó. A metodologia utilizada foi qualitativa, através da aplicação de entrevistas com colaboradores dessas facções. Como resultado o estudo revela que esse ramo de atividade é uma importante alternativa para a geração de renda na referida região, onde é escasso o investimento da iniciativa privada. Por fim, o estudo conclui que o benefício das facções incide em grande parte da população, que anteriormente se encontrava ociosa e sem perspectiva de trabalho e renda, à margem do mercado de trabalho

Tuberculose pulmonar: perfil epidemiológico do sertão Pernambucano, Brasil / Pulmonary tuberculosis: epidemiological profile of sertão Pernambucano, Brazil

Atualmente, observa-se que a tuberculose pulmonar constitui um importante problema de Saúde Pública no mundo, uma vez que esse agravo apresentou, em 2015, 10,4 milhões de casos, dos quais, mais de um milhão de pessoas vieram a óbito. Sob essa perspectiva, o presente artigo tem como objetivo traçar um perfil epidemiológico dos casos de Tuberculose Pulmonar notificados no município de Serra Talhada, entre os anos de 2007 a 2017. Foi realizado um estudo de série histórica observacional do tipo transversal, no intervalo de tempo de 2007 a 2017.  No período investigado o número de casos de tuberculose pulmonar foi de 246 casos, o local que teve a maior prevalência foi Serra Talhada, 287 por 100 mil habitantes. Diante dos dados apresentados, é imprescindível concluir, portanto, que esse estudo corrobora o perfil epidemiológico brasileiro para a Tuberculose Pulmonar, o qual indica variabilidade nos índices de acometimento durante o período analisado

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

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

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