Predicting structural models for silicon clusters

This article introduces an efficient method to generate structural models for medium-sized silicon clusters. Geometrical information obtained from previous investigations of small clusters is initially sorted and then introduced into our predictor algorithm in order to generate structural models for large clusters. The method predicts geometries whose binding energies are close (95%) to the corresponding value for the ground-state with very low computational cost. These predictions can be used as a very good initial guess for any global optimization algorithm. As a test case, information from clusters up to 14 atoms was used to predict good models for silicon clusters up to 20 atoms. We believe that the new algorithm may enhance the performance of most optimization methods whenever some previous information is available. (C) 2003 Wiley Periodicals, Inc.

Simulation of dynamic pinion course using Runge-Kutta's method and impact modeling

Once defined the relationship between the Starter Motor components and their functions, it is possible to develop a mathematical model capable to predict the Starter behavior during operation. One important aspect is the engagement system behavior. The development of a mathematical tool capable of predicting it is a valuable step in order to reduce the design time, cost and engineering efforts. A mathematical model, represented by differential equations, can be developed using physics laws, evaluating force balance and energy flow through the systems degrees of freedom. Another important physical aspect to be considered in this modeling is the impact conditions (particularly on the pinion and ring-gear contact). This work is a report of those equations application on available mathematical software and the resolution of those equations by Runge-Kutta's numerical integration method, in order to build an accessible engineering tool. Copyright © 2011 SAE International.

Estimation of spontaneous blinking main sequence in normal subjects and patients with Graves' upper eyelid retraction

Purpose. We quantified the main sequence of spontaneous blinks in normal subjects and Graves' disease patients with upper eyelid retraction using a nonlinear and two linear models, and examined the variability of the main sequence estimated with standard linear regression for 10-minute periods of time. Methods. A total of 20 normal subjects and 12 patients had their spontaneous blinking measured with the magnetic search coil technique when watching a video during one hour. The main sequence was estimated with a power-law function, and with standard and trough the origin linear regressions. Repeated measurements ANOVA was used to test the mean sequence stability of 10-minute bins measured with standard linear regression. Results. In 95% of the sample the correlation coefficients of the main sequence ranged from 0.60 to 0.94. Homoscedasticity of the peak velocity was not verified in 20% of the subjects and 25% of the patients. The power-law function provided the best main sequence fitting for subjects and patients. The mean sequence of 10-minute bins measured with standard linear regression did not differ from the one-hour period value. For the entire period of observation and the slope obtained by standard linear regression, the main sequence of the patients was reduced significantly compared to the normal subjects. Conclusions. Standard linear regression is a valid and stable approximation for estimating the main sequence of spontaneous blinking. However, the basic assumptions of the linear regression model should be examined on an individual basis. The maximum velocity of large blinks is slower in Graves' disease patients than in normal subjects. © 2013 The Association for Research in Vision and Ophthalmology, Inc.

Estimation of Submerged Aquatic Vegetation Height and Distribution in Nova Avanhandava Reservoir (São Paulo State, Brazil) Using Bio-Optical Modeling

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estimation of hourly, daily and monthly mean diffuse radiation based on MEO shadowring correction

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

Estimation of chlorophyll-a concentration and the trophic state of the Barra Bonita hydroelectric reservoir using oli/landsat-8 images

Reservoirs are artificial environments built by humans, and the impacts of these environments are not completely known. Retention time and high nutrient availability in the water increases the eutrophic level. Eutrophication is directly correlated to primary productivity by phytoplankton. These organisms have an important role in the environment. However, high concentrations of determined species can lead to public health problems. Species of cyanobacteria produce toxins that in determined concentrations can cause serious diseases in the liver and nervous system, which could lead to death. Phytoplankton has photoactive pigments that can be used to identify these toxins. Thus, remote sensing data is a viable alternative for mapping these pigments, and consequently, the trophic. Chlorophyll-a (Chl-a) is present in all phytoplankton species. Therefore, the aim of this work was to evaluate the performance of images of the sensor Operational Land Imager (OLI) onboard the Landsat-8 satellite in determining Chl-a concentrations and estimating the trophic level in a tropical reservoir. Empirical models were fitted using data from two field surveys conducted in May and October 2014 (Austral Autumn and Austral Spring, respectively). Models were applied in a temporal series of OLI images from May 2013 to October 2014. The estimated Chl-a concentration was used to classify the trophic level from a trophic state index that adopted the concentration of this pigment-like parameter. The models of Chl-a concentration showed reasonable results, but their performance was likely impaired by the atmospheric correction. Consequently, the trophic level classification also did not obtain better results.

Estimation of fecal production, digesta flow and digestibility in dairy heifers with different indicator

Objetivou-se com este trabalho avaliar a produção fecal por meio do indicador interno, fibra em detergente neutro indigestível (FDNi), e externos, cromo complexado com ácido etilenodiaminotetracético (Cr-EDTA ) e o cloreto de itérbio (YbCl3), além de estimar o fluxo duodenal da matéria seca e os coeficientes de digestibilidades aparentes total, ruminal e pós-ruminal, de diferentes nutrientes. Foram utilizadas oito novilhas mestiças Holandês/Zebu, distribuídas em duplo quadrado latino 4 x 4. Os indicadores Cr-EDTA, YbCl3 e o FDNi não estimaram produção fecal de forma eficiente (p < 0,05), obtendo resultado de 1,64; 1,71 e 2,71 kg dia-1, respectivamente, quando comparado à coleta total de fezes, que obteve resultado de 1,39 kg dia-1. Os valores estimados de fluxo de matéria seca, tanto para a metodologia de único, quanto para de duplo indicador, podem ser considerados biologicamente aceitáveis. Contudo, o valor obtido pela associação Cr-EDTA/YbCl3, utilizada na forma de duplo indicador, foi o mais confiável, pela melhor recuperação dos indicadores externos (Cr-EDTA e YbCl3), que obtiveram médias de 89 e 85%, respectivamente, em comparação ao interno (FDNi), que obteve média 67%. Os coeficientes de digestibilidade ruminal e pós ruminal, estimados pela associação Cr-EDTA/YbCl3, foram considerados melhores, em consequência do valor de fluxo de matéria seca estimado por esta associação.

Estimation of genetic parameters for body weights, scrotal circumference, and testicular volume measured at different ages in Nellore cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Chemical Modification of Botryosphaeran: Structural Characterization and Anticoagulant Activity of a Water-Soluble Sulfonated (1 -> 3)(1 -> 6)-beta-D-Glucan

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

Estimation of Maximal Work Rate Based on the 6-Minute Walk Test and Fat-Free Mass in Chronic Obstructive Pulmonary Disease

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

Tungsten permanent chemical modifier for fast estimation of Se contents in soil by graphite furnace atomic absorption spectrometry

A tungsten carbide coating on the integrated platform of a transversely heated graphite atomizer was used as a modifier for the direct determination of Se in soil extracts by graphite furnace atomic absorption spectrometry. Diethylenetriaminepentaacetic acid (0.0050 mol L-1) plus ammonium hydrogencarbonate (1.0 mol L-1) extracted predominantly available inorganic selenate from soil. The formation of a large amount of carbonaceous residue inside the atomizer was avoided with a first pyrolysis step at 600 degreesC assisted by air during 30 s. For 20 muL of soil extracts delivered to the atomizer and calibration by matrix matching, an analytical curve (10.0-100 mug of L-1) with good linear correlation (r = 0.999) between integrated absorbance and analyte concentration was established. The characteristic mass was similar to63 pg of Se, and the lifetime of the tube was similar to750 firings. The limit of detection was 1.6 mug L-1, and the relative standard deviations (n = 12) were typically <4% for a soil extract containing 50 mug of L-1. The accuracy of the determination of Se was checked for soil samples by means of addition/recovery tests. Recovery data of Se added to four enriched soil samples varied from 80 to 90% and indicated an accurate method.

Using neural networks for estimation of aquifer dynamical behavior

The systems of water distribution from groundwater wells can be monitored using the changes observed on its dynamical behavior. In this paper, artificial neural networks are used to estimate the depth of the dynamical water level of groundwater wells in relation to water flow, operation time and rest time. Simulation results are presented to demonstrate the validity of the proposed approach. These results have shown that artificial neural networks can be effectively used for the identification and estimation of parameters related to systems of water distribution.

Neural network based estimation of torque in induction motors for real-time applications

Induction motors are largely used in several industry sectors. The selection of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this article is to use artificial neural networks for torque estimation with the purpose of best selecting the induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Since proposed approach estimates the torque behavior from the transient to the steady state, one of its main contributions is the potential to also be implemented in control schemes for real-time applications. Simulation results are also presented to validate the proposed approach.

Fermions bounded by kinks of false vacuum models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)