Metaheurística particle swarm utilizada para alocação ótima de bancos de capacitores em sistemas de distribuição radial

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Determination of reactivity rates of silicate particle-size fractions

The efficiency of sources used for soil acidity correction depends on reactivity rate (RR) and neutralization power (NP), indicated by effective calcium carbonate (ECC). Few studies establish relative efficiency of reactivity (RER) for silicate particle-size fractions, therefore, the RER applied for lime are used. This study aimed to evaluate the reactivity of silicate materials affected by particle size throughout incubation periods in comparison to lime, and to calculate the RER for silicate particle-size fractions. Six correction sources were evaluated: three slags from distinct origins, dolomitic and calcitic lime separated into four particle-size fractions (2, 0.84, 0.30 and <0.30-mm sieves), and wollastonite, as an additional treatment. The treatments were applied to three soils with different texture classes. The dose of neutralizing material (calcium and magnesium oxides) was applied at equal quantities, and the only variation was the particle-size material. After a 90-day incubation period, the RER was calculated for each particle-size fraction, as well as the RR and ECC of each source. The neutralization of soil acidity of the same particle-size fraction for different sources showed distinct solubility and a distinct reaction between silicates and lime. The RER for slag were higher than the limits established by Brazilian legislation, indicating that the method used for limes should not be used for the slags studied here.

Particle swarm optimization aplicada ao planejamento da expansão de sistemas de transmissão

Pós-graduação em Engenharia Elétrica - FEIS

Jet and underlying event properties as a function of charged-particle multiplicity in proton-proton collisions at root s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Influence of air-particle deposition protocols on the surface topography and adhesion of resin cement to zirconia

Objectives: This study evaluated the influence of air-particle abrasion protocols on the surface roughness (SR) of zirconia and the shear bond strength (SBS) of dual-polymerized resin cement to this ceramic. Materials and methods. Sintered zirconia blocks (n = 115) (Lava, 3M ESPE) were embedded in acrylic resin and polished. The specimens were divided according to the 'particle type' (Al: 110 mu m Al2O3; Si: 110 mu m SiO2) and 'pressure' factors (2.5 or 3.5 bar) (n = 3 per group): (a) Control (no air-abrasion); (b) Al2.5; (c) Si2.5; (d) Al3.5; (e) Si3.5. SR (Ra) was measured 3-times from each specimen after 20 s of air-abrasion (distance: 10 mm) using a digital optical profilometer. Surface topography was evaluated under SEM analyses. For the SBS test, 'particle type', 'pressure' and 'thermocycling' (TC) factors were considered (n = 10; n = 10 per group): Control (no air-abrasion); Al2.5; Si2.5; Al3.5; Si3.5; Control(TC); Al2.5(TC); Si2.5(TC); Al3.5(TC); Si3.5(TC). After silane application, resin cement (Panavia F2.0) was bonded and polymerized. Specimens were thermocycled (6.000 cycles, 5-55 degrees C) and subjected to SBS (1 mm/min). Data were analyzed using ANOVA, Tukey's and Dunnett tests (5%). Results. 'Particle' (p = 0.0001) and 'pressure' (p = 0.0001) factors significantly affected the SR. All protocols significantly increased the SR (Al2.5: 0.45 +/- 0.02; Si2.5: 0.39 +/- 0.01; Al3.5: 0.80 +/- 0.01; Si3.5: 0.64 +/- 0.01 mu m) compared to the control group (0.16 +/- 0.01 mu m). For SBS, only 'particle' factor significantly affected the results (p = 0.015). The SiO2 groups presented significantly higher SBS results than Al2O3 (Al2.5: 4.78 +/- 1.86; Si2.5: 7.17 +/- 2.62; Al3.5: 4.97 +/- 3.74; Si3.5: 9.14 +/- 4.09 MPa) and the control group (3.67 +/- 3.0 MPa). All TC specimens presented spontaneous debondings. SEM analysis showed that Al2O3 created damage in zirconia in the form of grooves, different from those observed with SiO2 groups. Conclusions. Air-abrasion with 110 mu m Al2O3 resulted in higher roughness, but air-abrasion protocols with SiO2 promoted better adhesion.

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)

Avaliação da eficácia de primers para metal na resistência ao cisalhamento da união entre cimentos resinosos e metais não nobres. Efeito do armazenamento em água

O sucesso das restaurações metálicas indiretas depende, dentre outros fatores, de uma união eficaz entre a estrutura metálica e o cimento dentário empregados. Sabe-se que os primers para metal atuam com comprovada eficácia na resistência adesiva dos cimentos resinosos às ligas nobres. Entretanto, pouco foi estudado sobre o efeito de tais materiais em ligas não nobres. O propósito deste estudo foi avaliar a eficácia de primers para metal na resistência ao cisalhamento da união entre cimentos resinosos e metais não nobres. Discos (9 mm de diâmetro e 3 mm de altura) foram fundidos em liga de NiCr (n=80) e em titânio comercialmente puro (Ti c.p.) (n=80) e foram incluídos em anel de PVC com resina acrílica quimicamente ativada. As superfícies dos discos foram regularizadas com lixas de carbeto de silício de granulação 320, 400 e 600 e jateadas com partículas de óxido de alumínio de 50 µm. Espécimes de cada metal foram divididos em quatro grupos (n=20). Uma matriz metálica bi-partida (5 mm de diâmetro interno e 2 mm de altura) foi posicionada na superfície do espécime. As áreas adesivas receberam um dos seguintes tratamentos: 1) Panavia F; 2) Alloy Primer e Panavia F; 3) Bistite DC e 4) Metaltite e Bistite DC. Para evitar a exposição dos cimentos à luz, estes foram espatulados e inseridos na matriz dentro de uma câmara de revelação radiográfica. Quarenta minutos após a confecção, os espécimes foram armazenados em água destilada a 37ºC por 24 horas e então termociclados (1.000 ciclos, 5ºC e 55ºC, 30 segundos cada banho). Após a termociclagem, os espécimes foram armazenados novamente nas mesmas condições descritas anteriormente por um período de 24 horas (n=10) ou de 6 meses (n=10) antes do ensaio de cisalhamento em uma máquina de ensaios mecânicos (Material Test System 810).

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)

EVALUATION OF ROUGHNESS, WETTABILITY, AND MORPHOLOGY OF AN YTTRIA-STABILIZED TETRAGONAL ZIRCONIA POLYCRYSTAL CERAMIC AFTER DIFFERENT AIRBORNE-PARTICLE ABRASION PROTOCOL

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.