Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage

We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S\&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive (VHAR) models with the least absolute shrinkage and selection operator (LASSO). Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios

Semantic Web-based Software Product Line for Building Intelligent Tutoring Systems

Intelligent Tutoring Systems (ITS) have been assumed as an important learning resource to be added as a module in e-learning systems. However, the construction of such systems is still a hard and complex task that involves, for instance, representation and manipulation of different knowledge source. To alleviate these issues, this paper proposes a new approach for building ITS by integrating Software Product Line and Semantic Web technologies focusing on two software engineering aspects: large-scale production and customization for different learners, and how to allow these knowledge to be automatically shared between software and authors in both reuse and knowledge evolution points of view. This paper shows a modeling for the proposed product line, as well as how the Semantic Web technologies was used to achieve the effective shared knowledge

Instituto Compartilhado: uma parceria IFRN e uma escola estadual para manutenção do projeto de inclusão digital Um Computador por Aluno (UCA)

O presente trabalho encontra-se sendo realizado como projeto deextensão de uma escola pública federal e tem como o objetivo principal darcontinuidade ao programa governamental Um Computador por Aluno (UCA),fazendo uma atualização e manutenção do mesmo numa escola estadual nomunicípio de Parnamirim/RN onde estão localizadas. Funcionando nasescolas públicas desde 2010, passou-se a existir necessidade de atualizar ossoftwares, treinar os usuários e realizar a manutenção das máquinas para queo uso seja satisfatório e que o programa possa trazer ainda mais benefícios.Nessa perspectiva, este trabalho visa a melhoria e a continuidade doprograma UCA

Enhancing satiety and aerobic performance with beer microparticles-based non-alcoholic drinks: exploring dose and duration effects

Beer is an alcoholic beverage, rich in carbohydrates, amino acids, vitamins and polyphenols, consumed worldwide as a social drink. There is a large number of beer styles which depends on the ingredients and brewing process. The consumption of beer as a fluid replacement after sport practice is a current discussion in literature. A non-alcoholic pale-ale microparticles-based beverage (PABM) have been previously designed, however, its phenolic profile and ergogenic effect remain unknown. Thus, this study aims to verify the ergogenic potential (increase of running performance) of PAMB in male Wistar rats. Beer microparticles were obtained by spray drying and beverages with different concentrations were prepared in water. Wistar rats were subjected to a training protocol on a treadmill (5 times/week, 60 min/day) and daily intake of PABM (20 mg.kg-1 or 200 mg.kg-1) or water by gavage. Chlorogenic acid was found to be the main component in the phenolic profile (12.28 mg·g-1) of PABM analyzed with high-performance liquid chromatography and mass spectrometry. An increase in the aerobic performance was observed after 4 weeks in the 20 mg.kg-1 group, but the same dose after 8 weeks and a higher dose (200 mg.kg-1) blunted this effect. A higher dose was also related to decrease in food intake. These data suggest that PABM can improve satiety and aerobic performance, but its effect depends on the dose and time of consumption

Whole-genome sequencing of 1,171 elderly admixed individuals from Brazil

As whole-genome sequencing (WGS) becomes the gold standard tool for studying population genomics and medical applications, data on diverse non-European and admixed individuals are still scarce. Here, we present a high-coverage WGS dataset of 1,171 highly admixed elderly Brazilians from a census-based cohort, providing over 76 million variants, of which ~2 million are absent from large public databases. WGS enables identification of ~2,000 previously undescribed mobile element insertions without previous description, nearly 5 Mb of genomic segments absent from the human genome reference, and over 140 alleles from HLA genes absent from public resources. We reclassify and curate pathogenicity assertions for nearly four hundred variants in genes associated with dominantly-inherited Mendelian disorders and calculate the incidence for selected recessive disorders, demonstrating the clinical usefulness of the present study. Finally, we observe that whole-genome and HLA imputation could be significantly improved compared to available datasets since rare variation represents the largest proportion of input from WGS. These results demonstrate that even smaller sample sizes of underrepresented populations bring relevant data for genomic studies, especially when exploring analyses allowed only by WGS

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