967 resultados para Modeling methods
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We introduce the Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS). CATT-BRAMS is an on-line transport model fully consistent with the simulated atmospheric dynamics. Emission sources from biomass burning and urban-industrial-vehicular activities for trace gases and from biomass burning aerosol particles are obtained from several published datasets and remote sensing information. The tracer and aerosol mass concentration prognostics include the effects of sub-grid scale turbulence in the planetary boundary layer, convective transport by shallow and deep moist convection, wet and dry deposition, and plume rise associated with vegetation fires in addition to the grid scale transport. The radiation parameterization takes into account the interaction between the simulated biomass burning aerosol particles and short and long wave radiation. The atmospheric model BRAMS is based on the Regional Atmospheric Modeling System (RAMS), with several improvements associated with cumulus convection representation, soil moisture initialization and surface scheme tuned for the tropics, among others. In this paper the CATT-BRAMS model is used to simulate carbon monoxide and particulate material (PM(2.5)) surface fluxes and atmospheric transport during the 2002 LBA field campaigns, conducted during the transition from the dry to wet season in the southwest Amazon Basin. Model evaluation is addressed with comparisons between model results and near surface, radiosondes and airborne measurements performed during the field campaign, as well as remote sensing derived products. We show the matching of emissions strengths to observed carbon monoxide in the LBA campaign. A relatively good comparison to the MOPITT data, in spite of the fact that MOPITT a priori assumptions imply several difficulties, is also obtained.
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Context. The turbulent pumping effect corresponds to the transport of magnetic flux due to the presence of density and turbulence gradients in convectively unstable layers. In the induction equation it appears as an advective term and for this reason it is expected to be important in the solar and stellar dynamo processes. Aims. We explore the effects of turbulent pumping in a flux-dominated Babcock-Leighton solar dynamo model with a solar-like rotation law. Methods. As a first step, only vertical pumping has been considered through the inclusion of a radial diamagnetic term in the induction equation. In the second step, a latitudinal pumping term was included and then, a near-surface shear was included. Results. The results reveal the importance of the pumping mechanism in solving current limitations in mean field dynamo modeling, such as the storage of the magnetic flux and the latitudinal distribution of the sunspots. If a meridional flow is assumed to be present only in the upper part of the convective zone, it is the full turbulent pumping that regulates both the period of the solar cycle and the latitudinal distribution of the sunspot activity. In models that consider shear near the surface, a second shell of toroidal field is generated above r = 0.95 R(circle dot) at all latitudes. If the full pumping is also included, the polar toroidal fields are efficiently advected inwards, and the toroidal magnetic activity survives only at the observed latitudes near the equator. With regard to the parity of the magnetic field, only models that combine turbulent pumping with near-surface shear always converge to the dipolar parity. Conclusions. This result suggests that, under the Babcock-Leighton approach, the equartorward motion of the observed magnetic activity is governed by the latitudinal pumping of the toroidal magnetic field rather than by a large scale coherent meridional flow. Our results support the idea that the parity problem is related to the quadrupolar imprint of the meridional flow on the poloidal component of the magnetic field and the turbulent pumping positively contributes to wash out this imprint.
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Context. Previous analyses of lithium abundances in main sequence and red giant stars have revealed the action of mixing mechanisms other than convection in stellar interiors. Beryllium abundances in stars with Li abundance determinations can offer valuable complementary information on the nature of these mechanisms. Aims. Our aim is to derive Be abundances along the whole evolutionary sequence of an open cluster. We focus on the well-studied open cluster IC 4651. These Be abundances are used with previously determined Li abundances, in the same sample stars, to investigate the mixing mechanisms in a range of stellar masses and evolutionary stages. Methods. Atmospheric parameters were adopted from a previous abundance analysis by the same authors. New Be abundances have been determined from high-resolution, high signal-to-noise UVES spectra using spectrum synthesis and model atmospheres. The careful synthetic modeling of the Be lines region is used to calculate reliable abundances in rapidly rotating stars. The observed behavior of Be and Li is compared to theoretical predictions from stellar models including rotation-induced mixing, internal gravity waves, atomic diffusion, and thermohaline mixing. Results. Beryllium is detected in all the main sequence and turn-off sample stars, both slow- and fast-rotating stars, including the Li-dip stars, but is not detected in the red giants. Confirming previous results, we find that the Li dip is also a Be dip, although the depletion of Be is more modest than for Li in the corresponding effective temperature range. For post-main-sequence stars, the Be dilution starts earlier within the Hertzsprung gap than expected from classical predictions, as does the Li dilution. A clear dispersion in the Be abundances is also observed. Theoretical stellar models including the hydrodynamical transport processes mentioned above are able to reproduce all the observed features well. These results show a good theoretical understanding of the Li and Be behavior along the color-magnitude diagram of this intermediate-age cluster for stars more massive than 1.2 M(circle dot).
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Several experimental studies have altered the phase relationship between photic and non-photic environmental, 24 h cycles (zeitgebers) in order to assess their role in the synchronization of circadian rhythms. To assist in the interpretation of the complex activity patterns that emerge from these ""conflicting zeitgeber'' protocols, we present computer simulations of coupled circadian oscillators forced by two independent zeitgebers. This circadian system configuration was first employed by Pittendrigh and Bruce (1959), to model their studies of the light and temperature entrainment of the eclosion oscillator in Drosophila. Whereas most of the recent experiments have restricted conflicting zeitgeber experiments to two experimental conditions, by comparing circadian oscillator phases under two distinct phase relationships between zeitgebers (usually 0 and 12 h), Pittendrigh and Bruce compared eclosion phase under 12 distinct phase relationships, spanning the 24 h interval. Our simulations using non-linear differential equations replicated complex non-linear phenomena, such as ""phase jumps'' and sudden switches in zeitgeber preferences, which had previously been difficult to interpret. Our simulations reveal that these phenomena generally arise when inter-oscillator coupling is high in relation to the zeitgeber strength. Manipulations in the structural symmetry of the model indicated that these results can be expected to apply to a wide range of system configurations. Finally, our studies recommend the use of the complete protocol employed by Pittendrigh and Bruce, because different system configurations can generate similar results when a ""conflicting zeitgeber experiment'' incorporates only two phase relationships between zeitgebers.
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Complicated patterns showing various spatial scales have been obtained in the past by coupling Turing systems in such a way that the scales of the independent systems resonate. This produces superimposed patterns with different length scales. Here we propose a model consisting of two identical reaction-diffusion systems coupled together in such a way that one of them produces a simple Turing pattern of spots or stripes, and the other traveling wave fronts that eventually become stationary. The basic idea is to assume that one of the systems becomes fixed after some time and serves as a source of morphogens for the other system. This mechanism produces patterns very similar to the pigmentation patterns observed in different species of stingrays and other fishes. The biological mechanisms that support the realization of this model are discussed.
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We describe an estimation technique for biomass burning emissions in South America based on a combination of remote-sensing fire products and field observations, the Brazilian Biomass Burning Emission Model (3BEM). For each fire pixel detected by remote sensing, the mass of the emitted tracer is calculated based on field observations of fire properties related to the type of vegetation burning. The burnt area is estimated from the instantaneous fire size retrieved by remote sensing, when available, or from statistical properties of the burn scars. The sources are then spatially and temporally distributed and assimilated daily by the Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) in order to perform the prognosis of related tracer concentrations. Three other biomass burning inventories, including GFEDv2 and EDGAR, are simultaneously used to compare the emission strength in terms of the resultant tracer distribution. We also assess the effect of using the daily time resolution of fire emissions by including runs with monthly-averaged emissions. We evaluate the performance of the model using the different emission estimation techniques by comparing the model results with direct measurements of carbon monoxide both near-surface and airborne, as well as remote sensing derived products. The model results obtained using the 3BEM methodology of estimation introduced in this paper show relatively good agreement with the direct measurements and MOPITT data product, suggesting the reliability of the model at local to regional scales.
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Atmospheric aerosol particles serving as cloud condensation nuclei (CCN) are key elements of the hydrological cycle and climate. We have measured and characterized CCN at water vapor supersaturations in the range of S=0.10-0.82% in pristine tropical rainforest air during the AMAZE-08 campaign in central Amazonia. The effective hygroscopicity parameters describing the influence of chemical composition on the CCN activity of aerosol particles varied in the range of kappa approximate to 0.1-0.4 (0.16+/-0.06 arithmetic mean and standard deviation). The overall median value of kappa approximate to 0.15 was by a factor of two lower than the values typically observed for continental aerosols in other regions of the world. Aitken mode particles were less hygroscopic than accumulation mode particles (kappa approximate to 0.1 at D approximate to 50 nm; kappa approximate to 0.2 at D approximate to 200 nm), which is in agreement with earlier hygroscopicity tandem differential mobility analyzer (H-TDMA) studies. The CCN measurement results are consistent with aerosol mass spectrometry (AMS) data, showing that the organic mass fraction (f(org)) was on average as high as similar to 90% in the Aitken mode (D <= 100 nm) and decreased with increasing particle diameter in the accumulation mode (similar to 80% at D approximate to 200 nm). The kappa values exhibited a negative linear correlation with f(org) (R(2)=0.81), and extrapolation yielded the following effective hygroscopicity parameters for organic and inorganic particle components: kappa(org)approximate to 0.1 which can be regarded as the effective hygroscopicity of biogenic secondary organic aerosol (SOA) and kappa(inorg)approximate to 0.6 which is characteristic for ammonium sulfate and related salts. Both the size dependence and the temporal variability of effective particle hygroscopicity could be parameterized as a function of AMS-based organic and inorganic mass fractions (kappa(p)=kappa(org) x f(org)+kappa(inorg) x f(inorg)). The CCN number concentrations predicted with kappa(p) were in fair agreement with the measurement results (similar to 20% average deviation). The median CCN number concentrations at S=0.1-0.82% ranged from N(CCN,0.10)approximate to 35 cm(-3) to N(CCN,0.82)approximate to 160 cm(-3), the median concentration of aerosol particles larger than 30 nm was N(CN,30)approximate to 200 cm(-3), and the corresponding integral CCN efficiencies were in the range of N(CCN,0.10/NCN,30)approximate to 0.1 to N(CCN,0.82/NCN,30)approximate to 0.8. Although the number concentrations and hygroscopicity parameters were much lower in pristine rainforest air, the integral CCN efficiencies observed were similar to those in highly polluted megacity air. Moreover, model calculations of N(CCN,S) assuming an approximate global average value of kappa approximate to 0.3 for continental aerosols led to systematic overpredictions, but the average deviations exceeded similar to 50% only at low water vapor supersaturation (0.1%) and low particle number concentrations (<= 100 cm(-3)). Model calculations assuming aconstant aerosol size distribution led to higher average deviations at all investigated levels of supersaturation: similar to 60% for the campaign average distribution and similar to 1600% for a generic remote continental size distribution. These findings confirm earlier studies suggesting that aerosol particle number and size are the major predictors for the variability of the CCN concentration in continental boundary layer air, followed by particle composition and hygroscopicity as relatively minor modulators. Depending on the required and applicable level of detail, the information and parameterizations presented in this paper should enable efficient description of the CCN properties of pristine tropical rainforest aerosols of Amazonia in detailed process models as well as in large-scale atmospheric and climate models.
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Aerosol samples were collected at a pasture site in the Amazon Basin as part of the project LBA-SMOCC-2002 (Large-Scale Biosphere-Atmosphere Experiment in Amazonia - Smoke Aerosols, Clouds, Rainfall and Climate: Aerosols from Biomass Burning Perturb Global and Regional Climate). Sampling was conducted during the late dry season, when the aerosol composition was dominated by biomass burning emissions, especially in the submicron fraction. A 13-stage Dekati low-pressure impactor (DLPI) was used to collect particles with nominal aerodynamic diameters (D(p)) ranging from 0.03 to 0.10 mu m. Gravimetric analyses of the DLPI substrates and filters were performed to obtain aerosol mass concentrations. The concentrations of total, apparent elemental, and organic carbon (TC, EC(a), and OC) were determined using thermal and thermal-optical analysis (TOA) methods. A light transmission method (LTM) was used to determine the concentration of equivalent black carbon (BC(e)) or the absorbing fraction at 880 nm for the size-resolved samples. During the dry period, due to the pervasive presence of fires in the region upwind of the sampling site, concentrations of fine aerosols (D(p) < 2.5 mu m: average 59.8 mu g m(-3)) were higher than coarse aerosols (D(p) > 2.5 mu m: 4.1 mu g m(-3)). Carbonaceous matter, estimated as the sum of the particulate organic matter (i.e., OC x 1.8) plus BC(e), comprised more than 90% to the total aerosol mass. Concentrations of EC(a) (estimated by thermal analysis with a correction for charring) and BC(e) (estimated by LTM) averaged 5.2 +/- 1.3 and 3.1 +/- 0.8 mu g m(-3), respectively. The determination of EC was improved by extracting water-soluble organic material from the samples, which reduced the average light absorption Angstrom exponent of particles in the size range of 0.1 to 1.0 mu m from >2.0 to approximately 1.2. The size-resolved BC(e) measured by the LTM showed a clear maximum between 0.4 and 0.6 mu m in diameter. The concentrations of OC and BC(e) varied diurnally during the dry period, and this variation is related to diurnal changes in boundary layer thickness and in fire frequency.
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The band-edge optical absorption in EuTe is studied in the framework of the 5d conduction band atomic model. Both relaxed antiferromagnetic order, and ferromagnetic order induced by an external magnetic field, were analyzed. For ferromagnetic arrangement, the absorption is characterized by a hugely dichroic doublet of narrow lines. In the antiferromagnetic order, the spectrum is blueshifted, becomes much broader and weaker, and dichroism is suppressed. These results are in excellent qualitative and quantitative agreement with experimental observations on EuTe and EuSe published by us previously [Phys. Rev. B 72, 155337 (2005)]. The possibility of inducing ferromagnetic order by illuminating the material at photon energies resonant with the band gap is also discussed.
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A thermodynamic approach is presented to model devices manufactured with cellular polymers. They are heterogeneous nonpolar space-charge electrets that exhibit much higher piezoelectricity than the well-known ferroelectric polymers. Their pyroelectric and piezoelectric properties are characterized by adequate coefficients which quantify the performance of devices manufactured with those materials. The method presented in this contribution to calculate those coefficients is exact and consistent avoiding ad hoc simplifications introduced in other approaches. The results obtained by this method allow drawing conclusions regarding device optimization.
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Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.
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Background: Mutations in TP53 are common events during carcinogenesis. In addition to gene mutations, several reports have focused on TP53 polymorphisms as risk factors for malignant disease. Many studies have highlighted that the status of the TP53 codon 72 polymorphism could influence cancer susceptibility. However, the results have been inconsistent and various methodological features can contribute to departures from Hardy-Weinberg equilibrium, a condition that may influence the disease risk estimates. The most widely accepted method of detecting genotyping error is to confirm genotypes by sequencing and/or via a separate method. Results: We developed two new genotyping methods for TP53 codon 72 polymorphism detection: Denaturing High Performance Liquid Chromatography (DHPLC) and Dot Blot hybridization. These methods were compared with Restriction Fragment Length Polymorphism (RFLP) using two different restriction enzymes. We observed high agreement among all methodologies assayed. Dot-blot hybridization and DHPLC results were more highly concordant with each other than when either of these methods was compared with RFLP. Conclusions: Although variations may occur, our results indicate that DHPLC and Dot Blot hybridization can be used as reliable screening methods for TP53 codon 72 polymorphism detection, especially in molecular epidemiologic studies, where high throughput methodologies are required.
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Southeastern Brazil has seen dramatic landscape modifications in recent decades, due to expansion of agriculture and urban areas; these changes have influenced the distribution and abundance of vertebrates. We developed predictive models of ecological and spatial distributions of capybaras (Hydrochoerus hydrochaeris) using ecological niche modeling. Most Occurrences of capybaras were in flat areas with water bodies Surrounded by sugarcane and pasture. More than 75% of the Piracicaba River basin was estimated as potentially habitable by capybara. The models had low omission error (2.3-3.4%), but higher commission error (91.0-98.5%); these ""model failures"" seem to be more related to local habitat characteristics than to spatial ones. The potential distribution of capybaras in the basin is associated with anthropogenic habitats, particularly with intensive land use for agriculture.
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It has been demonstrated that laser induced breakdown spectrometry (LIBS) can be used as an alternative method for the determination of macro (P, K. Ca, Mg) and micronutrients (B, Fe, Cu, Mn, Zn) in pellets of plant materials. However, information is required regarding the sample preparation for plant analysis by LIBS. In this work, methods involving cryogenic grinding and planetary ball milling were evaluated for leaves comminution before pellets preparation. The particle sizes were associated to chemical sample properties such as fiber and cellulose contents, as well as to pellets porosity and density. The pellets were ablated at 30 different sites by applying 25 laser pulses per site (Nd:YAG@1064 nm, 5 ns, 10 Hz, 25J cm(-2)). The plasma emission collected by lenses was directed through an optical fiber towards a high resolution echelle spectrometer equipped with an ICCD. Delay time and integration time gate were fixed at 2.0 and 4.5 mu s, respectively. Experiments carried out with pellets of sugarcane, orange tree and soy leaves showed a significant effect of the plant species for choosing the most appropriate grinding conditions. By using ball milling with agate materials, 20 min grinding for orange tree and soy, and 60 min for sugarcane leaves led to particle size distributions generally lower than 75 mu m. Cryogenic grinding yielded similar particle size distributions after 10 min for orange tree, 20 min for soy and 30 min for sugarcane leaves. There was up to 50% emission signal enhancement on LIBS measurements for most elements by improving particle size distribution and consequently the pellet porosity. (C) 2011 Elsevier B.V. All rights reserved.
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Science is a fundamental human activity and we trust its results because it has several error-correcting mechanisms. It is subject to experimental tests that are replicated by independent parts. Given the huge amount of information available and the information asymetry between producers and users of knowledge, scientists have to rely on the reports of others. This makes it possible for social effects to influence the scientific community. Here, an Opinion Dynamics agent model is proposed to describe this situation. The influence of Nature through experiments is described as an external field that acts on the experimental agents. We will see that the retirement of old scientists can be fundamental in the acceptance of a new theory. We will also investigate the interplay between social influence and observations. This will allow us to gain insight in the problem of when social effects can have negligible effects in the conclusions of a scientific community and when we should worry about them.