Optimization of filament antennas using the Gauss-Newton method

The project of the Yagi-Uda antenna was optimized using the Gauss-Newton method. The optimization consisted of specifying value interval for directivity, front-to-back ratio and beamwidth and, starting from a pre-defined initial model, the best values for the length and spacing of the elements were determined. For the direct modeling, the method of moments on the integral Pocklington equation was used, which consisted of obtaining the values of directivity, front-to-back ratio and beamwidth from the length and spacing between known elements. The procedure was applied to the synthesis of Yagi-Uda antennas with five and six elements and the results were found to be as good as those obtained in the literature using other optimization methods

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

Arquivos Brasileiros de Cardiologia

p.747-756OBJETIVO: Estimar a prevalência (Pr) da hipertensão arterial (HA) e da sua associação com outros fatores de risco cardiovascular em população fortemente miscigenada. MÉTODOS: Estudo de corte transversal, realizado em amostra populacional de 1.439 adultos e > 20 anos, em Salvador-Brasil. Todos responderam a questionário em domicilio e tiveram medidos: pressão arterial, peso, altura, circunferência da cintura (CC), glicemia e lípidas séricas. O critério para HA foi a média da PAS > 140 e/ou PAD > 90mmHg. Foram estimadas Pr da HA com IC a 95%. As associações foram medidas pelo OR ajustado (ORaj), por análise de regressão. RESULTADOS: A Pr total foi da HA foi 29,9%: 27,4% IC (23,9-31,2) em homens e 31,7%, IC(28,5-34,9) em mulheres. Em negros a Pr foi 31,6% para homens e 41,1% para mulheres. Em brancos foi 25,8% nos homens e 21,1% nas mulheres. A HA apresentou associação significante com idades > 40 anos, sobrepeso/obesidade [ORaj = 2,37(1,57-3,60)] para homens e 1,62(1,02—2,58) para mulheres. Nos homens a HA associou-se à escolaridade elevada e nas mulheres com a cor da pele parda e negra, com obesidade abdominal, ORaj = 2,05 IC(1,31-3,21), diabetes ORaj = 2,16 IC(1,19-3,93) e com a menopausa. CONCLUSÃO: A HA predominou em negros de ambos os sexos, e em mulheres. Excetuando-se o sobrepeso/obesidade, as variáveis que se mantiveram independentemente associadas à HA diferiram entre os sexos. Os resultados sugerem aprofundamento do estudo da HA em negros e necessidade de intervenções educacionais contínuas e de início precoce