Prediction of alkaline treatment effect on the slow pyrolysis of the Pachira aquatica Aubl.fruit bark using artificial neural networks / Predição do efeito do tratamento alcalino na pirólise lenta da casca da fruta Pachira aquatica Aubl. utilizando redes neurais artificiais

As crescentes preocupações com as fontes de fósseis fósseis geraram um crescimento no investimento em fontes renováveis. Uma pirólise lenta de materiais lignocelulósicos tornou-se favorável para produzir energia e ser capaz de fornecer produtos de alto valor agregado. Nesse sentido, por meio da aplicação de redes neurais artificiais, este estudo avaliou a cinética da pirólise lenta do pó compactação da casca do fruto de Pachira aquática Aubl. na forma natural e modificada quimicamente para determinar os parâmetros cinéticos usando os métodos de isoconversão de Friedman, Kissinger e Ozawa e introdução do método de deconvolução Fraser-Suzuki para obter os parâmetros cinéticos individuais para o componente de pseudo-celulose.Os resultados permitiram concluir que uma rede neural aplicada foi eficiente na predição dos dados térmicos, obtendo perfis termogravimétricos semelhantes aos experimentais e altos valores de determinação. O método de Friedman foi o melhor se ajustou aos dados, e como energias de ativação indiferentes que as submetidas ao tratamento químico obtiveram menor energia de ativação, devido à modificação dos componentes da matriz lignocelulósica

Prediction of alkaline treatment effect on the slow pyrolysis of the Pachira aquatica Aubl. fruit bark using artificial neural networks / Predição do efeito do tratamento alcalino na pirólise lenta da casca da fruta Pachira aquatica Aubl. utilizando redes neurais artificiais

The increasing concerns about fossil fuel sources have generated growth in investment in renewable sources. The slow pyrolysis of lignocellulosic materials has become favourable because to producing energy and is capable of supplying products with high added value. Accordingly, through the application of artificial neural networks, this study evaluated the kinetics of the slow pyrolysis of the powder obtained from the fruit peel of Pachira aquatica Aubl. in natural and chemical modified form to determine the kinetic parameters using the Friedman, Kissinger and Ozawa isoconversion methods and introduction of Fraser-Suzuki deconvolution method to obtain the individual kinetic parameters for the pseudo-cellulose component. The results allowed the conclusion that the applied neural network was efficient in the prediction of the thermal data, obtaining similar thermogravimetric profiles to experimental ones and high determination values. The Friedman method was the best fit for the data, and the activation energies showed that the samples submitted to chemical treatment obtained lower activation energy, due to the modification of the components of the lignocellulosic matrix

O direito a políticas públicas de saúde de um paciente com transtorno do espectro autista e sua consequência nas relações familiares: um relato de caso / The right to public health policies for a patient with autistic spectrum disorder and its consequence in family relations: a case report

Objetivo: Realizar um estudo de um paciente com transtorno do espectro autista, evidenciando a consequência nas relações familiares, bem como o seu acesso a políticas públicas de saúde. Métodos: Foi realizada uma entrevista semiestruturada, a qual se caracterizou por um relato de caso apoiado pela análise qualitativa dos dados coletados com membros da família de uma criança autista, com o médico Psiquiatra que o assiste, bem como um médico Neuropediatra reconhecido na comunidade científica sobre o referido tema. O estudo foi aprovado por Comitê de Ética em Pesquisa. Resultados: Foram extraídas 25 respostas das entrevistas, agrupadas em 2 unidades temáticas: O acesso a políticas públicas de saúde; consequências nas relações familiares. Conclusão: É necessário se colocar em prática o apoio jurídico oferecido à família da criança com Transtorno do Espectro. Sugere-se o desenvolvimento de grupos para pais baseados na troca de experiências que auxiliem e orientem a família neste assunto, bem como a necessidade de investigações futuras sobre outras esferas, tais como a inclusão escolar e a inserção no mercado de trabalho dos indivíduos com TEA

Measurement of differential cross sections for top quark pair production using the lepton plus jets final state in proton-proton collisions at 13 TeV

National Science Foundation (U.S.

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated $\mathrm{t}\overline{\mathrm{t}}$ events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The heavy-flavour jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV)

Particle-flow reconstruction and global event description with the CMS detector

The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic tau decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8 TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions

Search for heavy resonances decaying to a top quark and a bottom quark in the lepton+jets final state in proton–proton collisions at 13 TeV

info:eu-repo/semantics/publishe

Evidence for the Higgs boson decay to a bottom quark–antiquark pair

info:eu-repo/semantics/publishe

Pseudorapidity and transverse momentum dependence of flow harmonics in pPb and PbPb collisions

info:eu-repo/semantics/publishe

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis