Hybrid Method Based on NARX models and Machine Learning for Pattern Recognition

This work presents a novel technique that integrates the methodologies of machine learning and system identification to solve multiclass problems. Such an approach allows to extract and select sets of representative features with reduced dimensionality, as well as predicts categorical outputs. The efficiency of the method was tested by running case studies investigated in machine learning, obtaining better absolute results when compared with traditional classification algorithms

Sistema automático de detecção e classificação de distúrbios elétricos em qualidade da energia elétrica

This contribution outlines signal processing-based algorithms for the detection and classification of voltage disturbances in power system. Basically filtering technique is applied to decompose the voltage signal into two primitive components which are named fundamental and error ones, then higher-order statistics (HOS)-based feature are selected and applied to detect and classify disturbances. Bayes- and Neural Network-based techniques are designed for the detection and classification respectively. The system was simulated considering six classes of disturbances, achieving a global efficiency about 100% to such disturbances. The performance of the method is compared with other methods presented in the literature.Este trabalho apresenta um sistema de detecção e classificação de distúrbios de qualidade da energia elétrica (QEE) que se baseia na decomposição do sinal de tensão em dois novos sinais, referentes à componente fundamental e ao sinal de erro e, em seguida, utiliza Estatísticas de Ordem Superior (EOS) para extrair parâmetros representativos de cada classe para simplificar o algoritmo de detecção e classificação. Como detector é utilizado um algoritmo baseado na teoria de Bayes e para implementar o algoritmo de classificação utilizou-se uma rede neural artificial. O sistema foi testado em simulações para seis classes de distúrbios, apresentando uma eficiência global próxima a 100% para tais distúrbios. Os resultados aqui apresentados são comparados com os resultados de outros sistemas propostos na literatura

A model validation scale based on multiple indices

Validation of an estimated model is not a trivial task because it depends on the purpose of the model, which usually defines the most important features of the model. Thus, in a validation process, the use of diverse tools that exploit different domains is recommended. Here, with this aim, a scale for model validation is proposed that combines the Normalized Root Mean Square Error (NRMSE) with two new indices: the coherence-based index and the fourth-order cross-cumulant index. The proposed scale was used for the validation of three models: the Logistic Map, the Duffing–Ueda oscillator, and the Buck converter. The results demonstrated that the proposed model validation scale produces a more complete validation process that takes into account both time and frequency information and provides robustness against Gaussian noise

Reconstruction of primary vertices at the ATLAS experiment in Run 1 proton–proton collisions at the LHC

This paper presents the method and performance of primary vertex reconstruction in proton–proton collision data recorded by the ATLAS experiment during Run 1 of the LHC. The studies presented focus on data taken during 2012 at a centre-of-mass energy of √s=8 TeV. The performance has been measured as a function of the number of interactions per bunch crossing over a wide range, from one to seventy. The measurement of the position and size of the luminous region and its use as a constraint to improve the primary vertex resolution are discussed. A longitudinal vertex position resolution of about 30μm is achieved for events with high multiplicity of reconstructed tracks. The transverse position resolution is better than 20μm and is dominated by the precision on the size of the luminous region. An analytical model is proposed to describe the primary vertex reconstruction efficiency as a function of the number of interactions per bunch crossing and of the longitudinal size of the luminous region. Agreement between the data and the predictions of this model is better than 3% up to seventy interactions per bunch crossing

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Abstracts

Tradução, para a língua inglesa, dos resumos dos artigos publicados nesta edição

Suplementação de coenzima Q10 e redução dos efeitos colaterais da terapêutica com estatinas: uma revisão sistemática / Coenzyme Q10 supplementation and reduction of side effects of statin therapy: a systematic review

A coenzima Q10 (CoQ10), conhecida também como ubiquinona é um componente essencial da cadeia respiratória mitocondrial. Uma das causas da sua deficiência é o uso crônico de estatinas, classe de medicamentos anticolesterolêmicos largamente prescrito. Sua redução pode ocasionar efeitos colaterais indesejáveis, tais como dispneia, alterações hepáticas, sintomas musculares e/ou gastrointestinais, rabdomiólise, neuropatias periféricas, Diabetes Mellitus tipo 2, dentre outros.  Esta revisão literária objetivou entender se a suplementação de CoQ10 reduz os efeitos colaterais provocados pelo uso de estatinas, descrevê-los e indicar qual a dose segura e eficaz para o sucesso dessa estratégia nutricional. Trata-se de uma revisão sistemática da literatura, cuja busca se deu na base de dados MEDLINE/PubMed, de estudos publicados no período entre 2004 a 09/2020, com uso dos descritores e combinação Ubiquinone AND Anticholesteremic Agents e Ubiquinone AND Cholesterol. Foram identificados 462 artigos e após leitura do título, resumo e aplicação dos critérios de exclusão foram incluídos 18 trabalhos científicos para análise. Os estudos apresentaram população e metodologias variadas e os métodos de avaliação dos resultados também foram heterogêneos, principalmente devido a variedade de efeitos colaterais estudados. Dos 18 estudos, dez (66,6%) encontraram algum benefício da suplementação. Foi evidenciado que a dose usual de suplementação (entre 100 e 300 mg) foi capaz de trazer benefícios quanto aos seguintes parâmetros: função diastólica, endotelial e mitocondrial, fadiga, miopatias, dispneia, perda de memória, neuropatia periférica, perfil lipídico, atividade antioxidante e anti-inflamatória e hepatotoxicidade evidenciados a partir de 30 dias de suplementação e, ainda, a redução do risco cardiovascular

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

Constraining the Higgs boson self-coupling from single- and double-Higgs production with the ATLAS detector using pppp collisions at s=13\sqrt{s}=13 TeV

Constraints on the Higgs boson self-coupling are set by combining double-Higgs boson analyses in the $b\bar{b}b\bar{b}$, $b\bar{b}\tau^+\tau^-$ and $b\bar{b} \gamma \gamma$ decay channels with single-Higgs boson analyses targeting the $\gamma \gamma$, $ZZ^*$, $WW^*$, $\tau^+ \tau^-$ and $b\bar{b}$ decay channels. The data used in these analyses were recorded by the ATLAS detector at the LHC in proton$-$proton collisions at $\sqrt{s}=13$ TeV and correspond to an integrated luminosity of 126$-$139 fb$^{-1}$. The combination of the double-Higgs analyses sets an upper limit of $\mu_{HH} < 2.4$ at 95% confidence level on the double-Higgs production cross-section normalised to its Standard Model prediction. Combining the single-Higgs and double-Higgs analyses, with the assumption that new physics affects only the Higgs boson self-coupling ($\lambda_{HHH}$), values outside the interval $-0.4< \kappa_{\lambda}=(\lambda_{HHH}/\lambda_{HHH}^{\textrm{SM}})< 6.3$ are excluded at 95% confidence level. The combined single-Higgs and double-Higgs analyses provide results with fewer assumptions, by adding in the fit more coupling modifiers introduced to account for the Higgs boson interactions with the other Standard Model particles.Comment: 17 pages in total, author list starting page 1, 6 figures, 2 tables, submitted to Phys. Lett. B. All figures including auxiliary figures are available at http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/HDBS-2022-0