Virtual environments promoting interaction

Virtual reality (VR) has been widely researched in the academic environment and is now breaking into the industry. Regular companies do not have access to this technology as a collaboration tool because these solutions usually require specific devices that are not at hand of the common user in offices. There are other collaboration platforms based on video, speech and text, but VR allows users to share the same 3D space. In this 3D space there can be added functionalities or information that in a real-world environment would not be possible, something intrinsic to VR. This dissertation has produced a 3D framework that promotes nonverbal communication. It plays a fundamental role on human interaction and is mostly based on emotion. In the academia, confusion is known to influence learning gains if it is properly managed. We designed a study to evaluate how lexical, syntactic and n-gram features influence perceived confusion and found results (not statistically significant) that point that it is possible to build a machine learning model that can predict the level of confusion based on these features. This model was used to manipulate the script of a given presentation, and user feedback shows a trend that by manipulating these features and theoretically lowering the level of confusion on text not only drops the reported confusion, as it also increases reported sense of presence. Another contribution of this dissertation comes from the intrinsic features of a 3D environment where one can carry actions that in a real world are not possible. We designed an automatic adaption lighting system that reacts to the perceived user’s engagement. This hypothesis was partially refused as the results go against what we hypothesized but do not have statistical significance. Three lines of research may stem from this dissertation. First, there can be more complex features to train the machine learning model such as syntax trees. Also, on an Intelligent Tutoring System this could adjust the avatar’s speech in real-time if fed by a real-time confusion detector. When going for a social scenario, the set of basic emotions is well-adjusted and can enrich them. Facial emotion recognition can extend this effect to the avatar’s body to fuel this synchronization and increase the sense of presence. Finally, we based this dissertation on the premise of using ubiquitous devices, but with the rapid evolution of technology we should consider that new devices will be present on offices. This opens new possibilities for other modalities.A Realidade Virtual (RV) tem sido alvo de investigação extensa na academia e tem vindo a entrar na indústria. Empresas comuns não têm acesso a esta tecnologia como uma ferramenta de colaboração porque estas soluções necessitam de dispositivos específicos que não estão disponíveis para o utilizador comum em escritório. Existem outras plataformas de colaboração baseadas em vídeo, voz e texto, mas a RV permite partilhar o mesmo espaço 3D. Neste espaço podem existir funcionalidades ou informação adicionais que no mundo real não seria possível, algo intrínseco à RV. Esta dissertação produziu uma framework 3D que promove a comunicação não-verbal que tem um papel fundamental na interação humana e é principalmente baseada em emoção. Na academia é sabido que a confusão influencia os ganhos na aprendizagem quando gerida adequadamente. Desenhámos um estudo para avaliar como as características lexicais, sintáticas e n-gramas influenciam a confusão percecionada. Construímos e testámos um modelo de aprendizagem automática que prevê o nível de confusão baseado nestas características, produzindo resultados não estatisticamente significativos que suportam esta hipótese. Este modelo foi usado para manipular o texto de uma apresentação e o feedback dos utilizadores demonstra uma tendência na diminuição do nível de confusão reportada no texto e aumento da sensação de presença. Outra contribuição vem das características intrínsecas de um ambiente 3D onde se podem executar ações que no mundo real não seriam possíveis. Desenhámos um sistema automático de iluminação adaptativa que reage ao engagement percecionado do utilizador. Os resultados não suportam o que hipotetizámos mas não têm significância estatística, pelo que esta hipótese foi parcialmente rejeitada. Três linhas de investigação podem provir desta dissertação. Primeiro, criar características mais complexas para treinar o modelo de aprendizagem, tais como árvores de sintaxe. Além disso, num Intelligent Tutoring System este modelo poderá ajustar o discurso do avatar em tempo real, alimentado por um detetor de confusão. As emoções básicas ajustam-se a um cenário social e podem enriquecê-lo. A emoção expressada facialmente pode estender este efeito ao corpo do avatar para alimentar o sincronismo social e aumentar a sensação de presença. Finalmente, baseámo-nos em dispositivos ubíquos, mas com a rápida evolução da tecnologia, podemos considerar que novos dispositivos irão estar presentes em escritórios. Isto abre possibilidades para novas modalidades

Score CTo-aBCDE : um novo score preditor de sucesso nas CTOs

© 2020 Sociedade Portuguesa de Cardiologia. Published by Elsevier España, S.L.U. This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Introduction: Patient selection for percutaneous coronary intervention (PCI) in chronic total occlusions (CTOs) is crucial to procedural success. Our aim was to identify independent predictors of success in CTO PCI in order to create an accurate score. Methods: In a single-center observational registry of CTO PCI, demographic and clinical data and anatomical characteristics of coronary lesions were recorded. Linear and logistic regression analysis were used to identify predictors of success. A score to predict success was created and its accuracy was measured by receiver operating curve analysis. Results: A total of 377 interventions were performed (334 patients, age 68±11 years, 75% male). The success rate was 65% per patient and 60% per procedure. Predictors of success in univariate analysis were absence of active smoking (OR 2.02, 95% CI 1.243-3.29; p=0.005), presence of tapered stump (OR 5.2, 95% CI 2.7-10.2; p8 with high probability (95%). Conclusion: In our sample only anatomical characteristics were predictors of success. The creation of a score to predict success, with good accuracy, may enable selection of cases that can be treated by any operator, those in which a dedicated operator will be desirable, and those with an extremely low probability of success, which should be considered individually for conservative management, surgical revascularization or PCI by a team experienced in CTO.info:eu-repo/semantics/publishedVersio

Cultura Material, Cultura Científica: Património Industrial para o Futuro

IH4Future (PTDC/FIS-aQM/30292/2017Material, Culture, Scientific Culture: Industrial Heritage for the Futurepublishersversionpublishe

Vinte anos dos Estudos Eleitorais Portugueses, 2002-2022: A Base de Dados integrada

Para uma descrição completa do estudo, http://www.apis.ics.ulisboa.pt/catalogo/APIS0097.“Vinte anos dos Estudos Eleitorais Portugueses, 2002-2022” é uma base de dados integrada que junta os inquéritos pós-eleitorais conduzidos nos anos 2002, 2005, 2009, 2011, 2015, 2019 e 2022. Estes inquéritos junto da população adulta portuguesa têm o objetivo de medir o comportamento eleitoral (participação e opção de voto) em eleições legislativas, assim como uma série de potenciais correlatos, incluindo exposição aos media, discussão política e uma série de atitudes e valores políticos.info:eu-repo/semantics/updatedVersio

Observation of Higgs boson production in association with a top quark pair at the LHC with the ATLAS detector

The observation of Higgs boson production in association with a top quark pair ( tt¯H ), based on the analysis of proton–proton collision data at a centre-of-mass energy of 13 TeV recorded with the ATLAS detector at the Large Hadron Collider, is presented. Using data corresponding to integrated luminosities of up to 79.8 fb −1 , and considering Higgs boson decays into bb¯ , WW⁎ , τ+τ− , γγ , and ZZ⁎ , the observed significance is 5.8 standard deviations, compared to an expectation of 4.9 standard deviations. Combined with the tt¯H searches using a dataset corresponding to integrated luminosities of 4.5 fb −1 at 7 TeV and 20.3 fb −1 at 8 TeV, the observed (expected) significance is 6.3 (5.1) standard deviations. Assuming Standard Model branching fractions, the total tt¯H production cross section at 13 TeV is measured to be 670 ± 90 (stat.) −100+110 (syst.) fb, in agreement with the Standard Model prediction.Peer Reviewe

Measurement of photon–jet transverse momentum correlations in 5.02 TeV Pb + Pb and pppp collisions with ATLAS

Jets created in association with a photon can be used as a calibrated probe to study energy loss in the medium created in nuclear collisions. Measurements of the transverse momentum balance between isolated photons and inclusive jets are presented using integrated luminosities of 0.49 nb$^{-1}$ of Pb+Pb collision data at $\sqrt{s_\mathrm{NN}}=5.02$ TeV and 25 pb$^{-1}$ of $pp$ collision data at $\sqrt{s}=5.02$ TeV recorded with the ATLAS detector at the LHC. Photons with transverse momentum $63.1 31.6$ GeV and pseudorapidity $\left|\eta^\mathrm{jet}\right| 7\pi/8$. Distributions of the per-photon jet yield as a function of $x_\mathrm{J\gamma}$, $(1/N_\gamma)(\mathrm{d}N/\mathrm{d}x_\mathrm{J\gamma})$, are corrected for detector effects via a two-dimensional unfolding procedure and reported at the particle level. In $pp$ collisions, the distributions are well described by Monte Carlo event generators. In Pb+Pb collisions, the $x_\mathrm{J\gamma}$ distribution is modified from that observed in $pp$ collisions with increasing centrality, consistent with the picture of parton energy loss in the hot nuclear medium. The data are compared with a suite of energy-loss models and calculations.Peer Reviewe

description of the methodological approach

publishersversionpublishe

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info: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 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