QoE over-the-top multimédia em redes sem fios

One of the goals of an operator is to improve the Quality of Experience (QoE) of a client in networks where Over-the-top (OTT) content is being delivered. The appearance of services like YouTube, Netflix or Twitch, where in the first case it contains more than 300 hours of video per minute in the platform, brings issues to the managed data networks that already exist, as well as challenges to fix them. Video traffic corresponds to 75% of the whole transmitted data on the Internet. This way, not only the Internet did become the ’de facto’ video transmission path, but also the general data traffic continues to exponentially increase, due to the desire to consume more content. This thesis presents two model proposals and architecture that aim to improve the users’ quality of experience, by predicting the amount of video in advance liable of being prefetched, as a way to optimize the delivery efficiency where the quality of service cannot be guaranteed. The prefetch is done in the clients’ closest cache server. For that, an Analytic Hierarchy Process (AHP) is used, where through a subjective method of attribute comparison, and from the application of a weighted function on the measured quality of service metrics, the amount of prefetch is achieved. Besides this method, artificial intelligence techniques are also taken into account. With neural networks, there is an attempt of selflearning with the behavior of OTT networks with more than 14.000 hours of video consumption under different quality conditions, to try to estimate the experience felt and maximize it, without the normal service delivery degradation. At last, both methods are evaluated and a proof of concept is made with users in a high speed train.Um dos objetivos de um operador é melhorar a qualidade de experiência do cliente em redes onde existem conteúdos Over-the-top (OTT) a serem entregues. O aparecimento de serviços como o YouTube, Netflix ou Twitch, onde no primeiro caso são carregadas mais de 300 horas de vídeo por minuto na plataforma, vem trazer problemas às redes de dados geridas que já existiam, assim como desafios para os resolver. O tráfego de vídeo corresponde a 75% de todos os dados transmitidos na Internet. Assim, não só a Internet se tornou o meio de transmissão de vídeo ’de facto’, como o tráfego de dados em geral continua a crescer exponencialmente, proveniente do desejo de consumir mais conteúdos. Esta tese apresenta duas propostas de modelos e arquitetura que pretendem melhorar a qualidade de experiência do utilizador, ao prever a quantidade de vídeo em avanço passível de ser précarregado, de forma a optimizar a eficiência de entrega das redes onde a qualidade de serviço não é possível de ser garantida. O pré-carregamento dos conteúdos é feito no servidor de cache mais próximo do cliente. Para tal, é utilizado um processo analítico hierárquico (AHP), onde através de um método subjetivo de comparação de atributos, e da aplicação de uma função de valores ponderados nas medições das métricas de qualidade de serviço, é obtida a quantidade a pré-carregar. Além deste método, é também proposta uma abordagem com técnicas de inteligência artificial. Através de redes neurais, há uma tentativa de auto-aprendizagem do comportamento das redes OTT com mais de 14.000 horas de consumo de vídeo sobre diferentes condições de qualidade, para se tentar estimar a experiência sentida e maximizar a mesma, sem degradação da entrega de serviço normal. No final, ambos os métodos propostos são avaliados num cenário de utilizadores num comboio a alta velocidade.Mestrado em Engenharia de Computadores e Telemátic

Essential trace elements status in portuguese pregnant women and their association with maternal and neonatal outcomes: A prospective study from the IoMum Cohort

Cobalt (Co), copper (Cu), manganese (Mn), molybdenum (Mo), and zinc (Zn) are essential trace elements (ETEs) important in cellular chemical reactions and antioxidant defense. Ingestion of ETEs during pregnancy is crucial but their role in specific pregnancy outcomes is largely unknown. This study aimed to quantify urinary levels of these ETEs in pregnancy and to evaluate their role in pregnancy health. First trimester pregnant women of Porto and Lisbon regions provided a urine sample, and sociodemographic and lifestyle data. Clinical data were obtained from clinical records. Urinary ETEs were quantified by inductively coupled plasma mass spectrometry (ICP-MS). Our results show that having urinary Zn levels above the 50th percentile (P50) increases the risk of pre-eclampsia (PE). On the other hand, urinary Zn levels above the P50 decreased the risk of being born with head circumference small for gestational age but it increased the risk having length small for gestational age at birth. This study may provide valuable information for public health policies related to prenatal nutrition, while informing future efforts to de-fine urinary reference intervals for ETEs in pregnant womeninfo:eu-repo/semantics/publishedVersio

Predictors of cardiac involvement in idiopathic inflammatory myopathies

Copyright © 2023 Bandeira, Dourado, Melo, Martins, Fraga, Ferraro, Saraiva, Sousa, Parente, Soares, Correia, Almeida, Dinis, Pinto, Oliveira Pinheiro, Rato, Beirão, Samões, Santos, Mazeda, Chícharo, Faria, Neto, Lourenço, Brites, Rodrigues, Silva-Dinis, Dias, Araújo, Martins, Couto, Valido, Santos, Barreira, Fonseca and Campanilho-Marques. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Objectives: Idiopathic inflammatory myopathies (IIM) are a group of rare disorders that can affect the heart. This work aimed to find predictors of cardiac involvement in IIM. Methods: Multicenter, open cohort study, including patients registered in the IIM module of the Rheumatic Diseases Portuguese Register (Reuma.pt/Myositis) until January 2022. Patients without cardiac involvement information were excluded. Myo(peri)carditis, dilated cardiomyopathy, conduction abnormalities, and/or premature coronary artery disease were considered. Results: 230 patients were included, 163 (70.9%) of whom were females. Thirteen patients (5.7%) had cardiac involvement. Compared with IIM patients without cardiac involvement, these patients had a lower bilateral manual muscle testing score (MMT) at the peak of muscle weakness [108.0 ± 55.0 vs 147.5 ± 22.0, p=0.008] and more frequently had oesophageal [6/12 (50.0%) vs 33/207 (15.9%), p=0.009] and lung [10/13 (76.9%) vs 68/216 (31.5%), p=0.001] involvements. Anti-SRP antibodies were more commonly identified in patients with cardiac involvement [3/11 (27.3%) vs 9/174 (5.2%), p=0.026]. In the multivariate analysis, positivity for anti-SRP antibodies (OR 104.3, 95% CI: 2.5-4277.8, p=0.014) was a predictor of cardiac involvement, regardless of sex, ethnicity, age at diagnosis, and lung involvement. Sensitivity analysis confirmed these results. Conclusion: Anti-SRP antibodies were predictors of cardiac involvement in our cohort of IIM patients, irrespective of demographical characteristics and lung involvement. We suggest considering frequent screening for heart involvement in anti-SRP-positive IIM patients.info:eu-repo/semantics/publishedVersio

COOPEDU IV — Cooperação e Educação de Qualidade

O quarto Congresso Internacional de Cooperação e Educação-IV COOPEDU, organizado pelo Centro de Estudos Internacionais (CEI) do Instituto Universitário de Lisboa e pela Escola Superior de Educação e Ciências Sociais do Instituto Politécnico de Leiria decorreu nos dias 8 e 9 de novembro de 2018, subordinado à temática Cooperação e Educação de Qualidade. Este congresso insere-se numa linha de continuidade de intervenção por parte das duas instituições organizadoras e dos elementos coordenadores e este ano beneficiou do financiamento do Instituto Camões, obtido através de um procedimento concursal, que nos permitiu contar com a participação presencial de elementos dos Países Africanos de Língua Portuguesa, fortemente implicados nas problemáticas da Educação e da Formação. Contou também com a participação do Instituto Camões e da Fundação Calouste Gulbenkian, entidades que sistematizaram a sua intervenção nos domínios da cooperação na área da educação nos últimos anos. A opção pela temática da qualidade pareceu aos organizadores pertinente e actual. Com efeito os sistemas educativos dos países que constituem a Comunidade de países de língua portuguesa têm implementado várias reformas mas em vários domínios mantem-se a insatisfação de responsáveis políticos, pedagogos, técnicos sociais face aos resultados obtidos. Aliás o caminho de procura da Qualidade é interminável porque vai a par da aposta na exigência e na promoção da cidadania e responsabilidade social. As comunicações que agora se publicam estão organizadas em dois eixos: o das Políticas da Educação e Formação e o das dimensões em que se traduzem essas políticas. Neste último eixo encontramos fios condutores para agregarmos as comunicações apresentadas

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

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Iodine knowledge is associated with iodine status in Portuguese pregnant women: results from the IoMum cohort study

Lack of knowledge about iodine has been suggested as a risk factor for iodine deficiency in pregnant women, but no studies have addressed this issue in Portugal. So, the aim of this study was to investigate iodine knowledge among Portuguese pregnant women and its association with iodine status. IoMum, a prospective observational study, included 485 pregnant women recruited at Centro Hospitalar e Universitário de S. João, Porto, between the 10th and 13th gestational weeks. Partial scores for knowledge on iodine importance, on iodine food sources or on iodised salt were obtained through the application of a structured questionnaire. Then, a total iodine knowledge score was calculated and grouped into low, medium and high knowledge categories. Urinary iodine concentration (UIC) was measured in spot urine samples by inductively coupled plasma MS. Of the pregnant women, 54 % correctly recognised iodine as important to neurocognitive development, 32 % were unable to identify any iodine-rich food and 71 % presented lack of knowledge regarding iodised salt. Of the women, 61 % had a medium total score of iodine knowledge. Knowledge on iodine importance during pregnancy was positively associated with iodine supplementation and also with UIC. Nevertheless, median UIC in women who correctly recognised the importance of iodine was below the cut-off for adequacy in pregnancy (150 µg/l). In conclusion, knowledge on iodine importance is positively associated with iodine status. Despite this, recognising iodine importance during pregnancy may not be sufficient to ensure iodine adequacy. Literacy-promoting actions are urgently needed to improve iodine status in pregnancy.info:eu-repo/semantics/publishedVersio