Effect of fluoride varnish supplemented with sodium trimetaphosphate on enamel erosion and abrasion

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Phase space properties and chaotic transport for a particle moving in a time dependent step potential well

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Efeito da partícula e do momento de jateamento na caracterização superficial da zircônia e na resistência de união com cimento resinoso

Pós-graduação em Reabilitação Oral - FOAR

Caracterização de superfície, adesiva e mecânica de uma zircônia parcialmente estabilizada por ítria em função da partícula ou do momento do jateamento

Pós-graduação em Reabilitação Oral - FOAR

Particle-vortex and Maxwell duality in the AdS(4) x CP3/ABJM correspondence

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Single-Particle Momentum Distributions for Bosonic Trimer States in Two and Three Dimensions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Particle creation in a f(R) theory with cosmological constraints

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Photoluminescence activity of Ba1-xCaxTiO3: dependence on particle size and morphology

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

CCDM model from quantum particle creation: constraints on dark matter mass

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

MASSLESS PARTICLE CREATION IN A F(R) ACCELERATING UNIVERSE

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Stability of a Lipase Extracted from Seeds of Pachira aquatica in Commercial Detergents and Application Tests in Poultry Wastewater Pretreatment and Fat Particle Hydrolysis

A protein extract containing a plant lipase from oleaginous seeds of Pachira aquatica was tested using soybean oil, wastewater from a poultry processing plant, and beef fat particles as substrate. The hydrolysis experiments were carried out at a temperature of 40°C, an incubation time of 90 minutes, and pH 8.0-9.0. The enzyme had the best stability at pH 9.0 and showed good stability in the alkaline range. It was found that P. aquatica lipase was stable in the presence of some commercial laundry detergent formulations, and it retained full activity up to 0.35% in hydrogen peroxide, despite losing activity at higher concentrations. Concerning wastewater, the lipase increased free fatty acids release by 7.4 times and promoted the hydrolysis of approximately 10% of the fats, suggesting that it could be included in a pretreatment stage, especially for vegetable oil degradation.

Particle Physics in the teaching practice of the participants of the School of Physics CERN

A atualização dos currículos escolares e, em especial, a inserção de assuntos de física moderna e contemporânea já foram defendidos com justificativas satisfatórias, tanto por professores em atividade escolar como por pesquisadores da área de ensino de física. Entre os assuntos que deveriam ser discutidos encontramos a Física das Partículas Elementares. O ensino deste tópico é justificado pelo fato dele permitir a discussão: a) de uma nova visão de mundo; b) de uma visão mais adequada da ciência; c) da reinterpretação da Física Clássica; d) da dinâmica da Ciência e seu desenvolvimento; e) da contribuição dos diversos cientistas; f) do papel da experimentação; g) do investimento financeiro e cooperativo de diversos países e pesquisadores. No entanto, para que a inserção de assuntos de física moderna e contemporânea ocorra de maneira eficiente é necessária a atualização dos professores que já estão em docência escolar, bem como uma formação adequada daqueles que estão em processo de formação inicial. Neste sentido, desde 2010 a Sociedade Brasileira de Física realiza anualmente a Escola de Física do CERN, na qual participam professores brasileiros de física de escolas públicas do Ensino Médio. Nesta escola são desenvolvidas aulas sobre física de partículas, sessões experimentais e visitas aos laboratórios do CERN. Perante isso, investigamos como os professores participantes da Escola de Física do CERN abordam a física de partículas em suas aulas após participarem dela.

Moderating effect of ammonia on particle growth and stability of quasi-monodisperse silver nanoparticles synthesized by the Turkevich method

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)