Performability Evaluation of Voice Services in Converged Networks

In the last years, the transmission of voice services in converged networks has experienced a huge growth. However, there are still some questions considering the ability of these networks to deliver voice services with acceptable quality. In this paper, we applied analytical modeling and simulation to analyze the quality of voice services using a new index, called MOS a , which considers jointly the MOS index and the availability of the subjacent infrastructure. We consider the influence of different CODECs (G.711 and G.729), queuing policies (Priority Queuing and Custom Queuing), and the warm standby redundancy mechanism. Our goal is to analyze the quality of these services by taking into account overloading conditions in different  architectures/scenarios. These scenarios were constructed using the modeling mechanisms Reliability Block Diagram and Stochastic Petri Nets in addition to a discrete event simulator. Experimental results indicate that the G.711 CODEC has a higher sensitivity both in terms of data traffic volume and allocated network resources in relation to the G.729 CODEC

Análise e Diagnóstico da Qualidade da Água de um Residencial Localizado em Belém do Pará

Atualmente, a região metropolitana de Belém, assim como várias capitais do Brasil, sofre com o crescimento populacional e urbanização desordenadas, fato que gera consequências desfavoráveis ao meio ambiente, pois ocorre a falta de serviços de saneamento básico. A carência de saneamento causa diarreias e inúmeras doenças como por exemplo: febre tifoide, cólera, a amebíase, a giardíase, a ascaridíase, a metahemoglobinemia (baby blue syndrome) e o câncer gástrico. Entre os anos de 2016 a 2019 foram coletadas amostras da água de um poço residencial, localizado no bairro de Val de Cans, no município de Belém-PA, para analisar o comportamento do nitrogênio amoniacal, nitrito, nitrato e outros parâmetros físico-químicos e microbiológicos indicadores de contaminação antrópica. Após as análises das amostras, observou-se que os parâmetros pH e nitrogênio amoniacal não atendem às condições e padrões estabelecidos pelas regulamentações pertinentes e vigentes, sendo considerada imprópria para o consumo humano

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

Diminishing benefits of urban living for children and adolescents’ growth and development

Optimal growth and development in childhood and adolescence is crucial for lifelong health and well-being1–6. Here we used data from 2,325 population-based studies, with measurements of height and weight from 71 million participants, to report the height and body-mass index (BMI) of children and adolescents aged 5–19 years on the basis of rural and urban place of residence in 200 countries and territories from 1990 to 2020. In 1990, children and adolescents residing in cities were taller than their rural counterparts in all but a few high-income countries. By 2020, the urban height advantage became smaller in most countries, and in many high-income western countries it reversed into a small urban-based disadvantage. The exception was for boys in most countries in sub-Saharan Africa and in some countries in Oceania, south Asia and the region of central Asia, Middle East and north Africa. In these countries, successive cohorts of boys from rural places either did not gain height or possibly became shorter, and hence fell further behind their urban peers. The difference between the age-standardized mean BMI of children in urban and rural areas was <1.1 kg m–2 in the vast majority of countries. Within this small range, BMI increased slightly more in cities than in rural areas, except in south Asia, sub-Saharan Africa and some countries in central and eastern Europe. Our results show that in much of the world, the growth and developmental advantages of living in cities have diminished in the twenty-first century, whereas in much of sub-Saharan Africa they have amplified

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