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Objetivo: Caracterizar o perfil epidemiológico dos nascidos vivos do estado do Piauí, no período de 2007 a 2017 e evidenciar sua importância para o planejamento em saúde. Métodos: Trata-se de um estudo transversal, com enfoque na análise dos dados sobre nascidos vivos no Piauí, notificados por meio da Declaração de Nascido Vivo (DNV) no Sistema de Informações sobre Nascidos Vivos (SINASC). Resultados: Foram registrados 543.420 nascidos vivos, sendo a maioria do sexo masculino (51,7%), raça parda (82,4%), com duração da gestação ≥ 37 semanas (91,3%), nascidos em ambiente hospitalar (95,5%), de parto vaginal (51,4%), com peso ao nascer de 3000 a 3999g (65,7%) e sem anomalias congênitas (95,5%). Parcela expressiva das mães tinham de 20 a 34 anos (69,3%), 8 a 11 anos de estudo (45,0%), eram casadas (37,7%) e realizaram sete ou mais consultas de pré-natal (50,5%). Conclusão: A análise desse indicador é um excelente instrumento para conhecer a situação de saúde, subsidiando o planejamento e direcionamento das ações de saúde materno-infantil, visando à qualidade da assistência e a melhoria dos indicadores de saúde do estado. Reitera-se ainda a necessidade de sensibilização dos profissionais quanto à importância do correto preenchimento dos dados contidos na DNV.

O uso da Inteligência Artificial vem apresentando crescimento acelerado dado à sua utilização na solução de problemas em diversos domínios de aplicação. Este sucesso é resultado da convergência entre grande quantidade de dados, computação de alto desempenho e precisão dos algoritmos de Aprendizado de Máquina (AM). Mesmo com a relevância dos algoritmos de AM, pouco se sabe sobre seus requisitos computacionais e consumo energético, o que tornou-se tarefa importante para alcançar uma computação mais ecológica. O objetivo deste trabalho é avaliar o consumo de energia dos algoritmos de íArvore de Decisão, a fim de identificar os hotspots de energia dos mesmos. E ainda, investigar a emissão de CO2 equivalente associada aos algoritmos.

The pandemic of the new COVID-19 has raised many questions to a very connected society as to how to best respond to such a challenge at this current time. The best response so far is to call people for following the instructions from the World Health Organisation (WHO) as a way of reducing the spread of the virus and thus relieving the health system, striving to avoid a collapse. This work studies the spread of positive opinion on adhering to social distancing based on network topology and metrics using a network-oriented model for social contagion. It is shown that interventions based on social network measurements can be used to boost the spread of positive opinion about adhering to these measures. It is also shown that our model accounts for the relevance the health authorities have on encouraging people to partake in social distancing voluntarily.

Atualmente a Inteligencia Artificial (IA) é uma das forças mais transformadoras do nosso tempo, com resultados surpreendentes. Esses resultados se devem, em grande parte, ao uso de alta capacidade computacional oferecida pelos ambientes de HPC, os quais ao mesmo tempo requerem muita energia para seu funcionamento. Além disso, o consumo de energia é responsável pela emissão de gases de efeito estufa, entre os quais o CO2 é o mais expressivo. Neste trabalho é avaliado o impacto do treinamento de diferentes algoritmos de IA no consumo energético e na emissão de CO2 equivalente entre diferentes arquiteturas computacionais (ARM, GPU e X86).

The emergence of COVID-19 in China, in December of 2019 led to a local epidemic that rapidly spread to multiple countries in the world, including Brazil. Nowadays, there is an accelerated search to understand the dynamics of the spread of the disease and evaluate the effectiveness of intervention measures. Given these special socioeconomic conditions surrounding Brazil, using the predictive models developed for other countries can make a very incomplete picture of the epidemic, since these differences could result in different patterns in low income settings. The aim of this work is to simulate interventions and understand the impact to reduce the spread of COVID-19 considering the socioeconomic conditions of Brazil. With this purpose we use an agent- based model (ABM), a subarea of the Artificial Intelligence, as it allows us to treat each individual in a personalized manner, as well as the environment of which they are part. The simulations have heterogeneous populations, considering different age groups, socioeconomic differences and number of members per family, contacts and movements intra and inter the sub-populations (favelas and non-favelas), numbers of Intensive Care Unit (ICU) and study different scenarios to show how the interventions can influence the spread of the virus in the population of simulated environments.

This study aims to analyze the efficiency of additional carbides in the high-energy milling of duplex stainless steel chips. At the end of the life of a mechanical component, it is discarded in deposits or recast. Chips originating from machining processes are raw materials for products obtained through powder metallurgy. To obtain the duplex structure for a stainless steel, rolled and annealing are necessary subsequent steps, making this cycle costly for the industry, and its disposal leads to environmental problems. Components produced by powder metallurgy from corrosion-resistant alloys are considered advantageous. The addition of carbides aims to increase the efficiency of the milling process and improve the mechanical properties of the steels produced by powder metallurgy. The granulometric analysis and the scanning electron microscopy were used in the characterization and identification of the particle size. Results indicated that the milling with the addition of carbides showed a higher efficiency in the milling process. The particle size obtained with the addition of TiC and NbC in the milling of UNS S 31803 steel chips was 98 and 85 times smaller than the original chip.

GPUs has been widely used in scientific computing, as by offering exceptional performance as by power-efficient hardware. Its position established in high-performance and scientific computing communities has increased the urgency of understanding the power cost of GPU usage in accurate measurements. For this, the use of internal sensors are extremely important. In this work, we employ the GPU sensors to obtain high-resolution power profiles of real and benchmark applications. We wrote our own tools to query the sensors of two NVIDIA GPUs from different generations and compare the accuracy of them. Also, we compare the power profile of GPU with CPU using IPMItool.