Holocene Climatic Optimum centennial-scale paleoceanography in the NE Aegean (Mediterranean sea)

Combined micropaleontological and geochemical analyses of the high-sedimentation gravity core M-4G provided new centennial-scale paleoceanographic data for sapropel S1 deposition in the NE Aegean Sea during the Holocene Climatic Optimum. Sapropel layer S1a (10.2–8.0 ka) was deposited in dysoxic to oxic bottom waters characterized by a high abundance of benthic foraminiferal species tolerating surface sediment and/or pore water oxygen depletion (e.g., Chilostomella mediterranensis, Globobulimina affinis), and the presence of Uvigerina mediterranea, which thrives in oxic mesotrophic-eutrophic environments. Preservation of organic matter (OM) is inferred based on high organic carbon as well as loliolide and isololiolide contents, while the biomarker record and the abundances of eutrophic planktonic foraminifera document enhanced productivity. High inputs of terrigenous OM are attributed to north Aegean borderland riverine inputs. Both alkenone-based sea surface temperatures (SSTs) and δO18G. bulloides records indicate cooling at 8.2 ka (S1a) and ~7.8 ka (S1 interruption). Sapropelic layer S1b (7.7–6.4 ka) is characterized by rather oxic conditions; abundances of foraminiferal species tolerant to oxygen depletion are very low compared with the U. mediterranea rise. Strongly fluctuating SSTs demonstrate repeated cooling and associated dense water formation, with a major event at 7.4 ka followed by cold spells at 7.0, 6.8, and 6.5 ka. The prominent rise of the carbon preference index within the S1b layer indicates the delivery of less degraded terrestrial OM. The increase of algal biomarkers, labile OM-feeding foraminifera and eutrophic planktonic species pinpoints an enhanced in situ marine productivity, promoted by more efficient vertical convection due to repeated cold events. The associated contributions of labile marine OM along with fresher terrestrial OM inputs after ~7.7 ka imply sources alternative/additional to the north Aegean riverine borderland sources for the influx of organic matter in the south Limnos Basin, plausibly related to the inflow of highly productive Marmara/Black Sea waters.

MODELING VERY LONG BASELINE INTERFEROMETRIC IMAGES WITH THE CROSS-ENTROPY GLOBAL OPTIMIZATION TECHNIQUE

We present a new technique for obtaining model fittings to very long baseline interferometric images of astrophysical jets. The method minimizes a performance function proportional to the sum of the squared difference between the model and observed images. The model image is constructed by summing N(s) elliptical Gaussian sources characterized by six parameters: two-dimensional peak position, peak intensity, eccentricity, amplitude, and orientation angle of the major axis. We present results for the fitting of two main benchmark jets: the first constructed from three individual Gaussian sources, the second formed by five Gaussian sources. Both jets were analyzed by our cross-entropy technique in finite and infinite signal-to-noise regimes, the background noise chosen to mimic that found in interferometric radio maps. Those images were constructed to simulate most of the conditions encountered in interferometric images of active galactic nuclei. We show that the cross-entropy technique is capable of recovering the parameters of the sources with a similar accuracy to that obtained from the very traditional Astronomical Image Processing System Package task IMFIT when the image is relatively simple (e. g., few components). For more complex interferometric maps, our method displays superior performance in recovering the parameters of the jet components. Our methodology is also able to show quantitatively the number of individual components present in an image. An additional application of the cross-entropy technique to a real image of a BL Lac object is shown and discussed. Our results indicate that our cross-entropy model-fitting technique must be used in situations involving the analysis of complex emission regions having more than three sources, even though it is substantially slower than current model-fitting tasks (at least 10,000 times slower for a single processor, depending on the number of sources to be optimized). As in the case of any model fitting performed in the image plane, caution is required in analyzing images constructed from a poorly sampled (u, v) plane.

Spectral analysis and modeling of microcyclostratigraphy in late Paleozoic glaciogenic rhythmites, Parana Basin, Brazil

We investigate the depositional time scale of lithological couplets (fine sandstone/siltstone-siltstone/mudstone) from two distinctive outcrops of Permo-Carboniferous glacial rhythmites in the Itarare Group (Parana Basin, Brazil). Resolving the fundamental issue of time scale for these rhythmites is important in light of recent evidence for paleosecular variation measured in these sequences. Spectral analysis and tuning of high-resolution gray scale scans of sediment core microstratigraphy, which comprises pervasive laminations, reveal a comparable spectral content at both localities, with a frequency suite interpreted as that of short-term climate variability of Recent and modern times. This evidence for decadal- to centennial-scale deposition of these lithological couplets is discussed in light of the `varvic` character, i.e., annual time scale that was previously assumed for the rhythmites.

Importance of estimating matrix quality for modeling species distribution in complex tropical landscapes: a test with Atlantic forest small mammals

Information to guide decision making is especially urgent in human dominated landscapes in the tropics, where urban and agricultural frontiers are still expanding in an unplanned manner. Nevertheless, most studies that have investigated the influence of landscape structure on species distribution have not considered the heterogeneity of altered habitats of the matrix, which is usually high in human dominated landscapes. Using the distribution of small mammals in forest remnants and in the four main altered habitats in an Atlantic forest landscape, we investigated 1) how explanatory power of models describing species distribution in forest remnants varies between landscape structure variables that do or do not incorporate matrix quality and 2) the importance of spatial scale for analyzing the influence of landscape structure. We used standardized sampling in remnants and altered habitats to generate two indices of habitat quality, corresponding to the abundance and to the occurrence of small mammals. For each remnant, we calculated habitat quantity and connectivity in different spatial scales, considering or not the quality of surrounding habitats. The incorporation of matrix quality increased model explanatory power across all spatial scales for half the species that occurred in the matrix, but only when taking into account the distance between habitat patches (connectivity). These connectivity models were also less affected by spatial scale than habitat quantity models. The few consistent responses to the variation in spatial scales indicate that despite their small size, small mammals perceive landscape features at large spatial scales. Matrix quality index corresponding to species occurrence presented a better or similar performance compared to that of species abundance. Results indicate the importance of the matrix for the dynamics of fragmented landscapes and suggest that relatively simple indices can improve our understanding of species distribution, and could be applied in modeling, monitoring and managing complex tropical landscapes.

MODELING THE EVOLUTION OF COMPLEX NETWORKS THROUGH THE PATH-STAR TRANSFORMATION AND OPTIMAL MULTIVARIATE METHODS

The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modi. cations in their intrinsic organization. For instance, the network of transportation routes between cities and towns ( hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways ( paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation ( in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.

Shape classification using complex network and Multi-scale Fractal Dimension

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.

Meteomap: Generation Of Meteorological Variables For Distributed Physically-Based Hydrological Modeling

Distributed energy and water balance models require time-series surfaces of the meteorological variables involved in hydrological processes. Most of the hydrological GIS-based models apply simple interpolation techniques to extrapolate the point scale values registered at weather stations at a watershed scale. In mountainous areas, where the monitoring network ineffectively covers the complex terrain heterogeneity, simple geostatistical methods for spatial interpolation are not always representative enough, and algorithms that explicitly or implicitly account for the features creating strong local gradients in the meteorological variables must be applied. Originally developed as a meteorological pre-processing tool for a complete hydrological model (WiMMed), MeteoMap has become an independent software. The individual interpolation algorithms used to approximate the spatial distribution of each meteorological variable were carefully selected taking into account both, the specific variable being mapped, and the common lack of input data from Mediterranean mountainous areas. They include corrections with height for both rainfall and temperature (Herrero et al., 2007), and topographic corrections for solar radiation (Aguilar et al., 2010). MeteoMap is a GIS-based freeware upon registration. Input data include weather station records and topographic data and the output consists of tables and maps of the meteorological variables at hourly, daily, predefined rainfall event duration or annual scales. It offers its own pre and post-processing tools, including video outlook, map printing and the possibility of exporting the maps to images or ASCII ArcGIS formats. This study presents the friendly user interface of the software and shows some case studies with applications to hydrological modeling.

Ecological modeling and pest population management: a possible and necessary connection in a changing world

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

A thermoporometry and small-angle x-ray scattering study of wet silica sonogels as the pore volume fraction is varied

Silica sonogels with different porosities were prepared by acid sono-hydrolysis of tetraethoxysilane. Wet sonogels were studied using small-angle x-ray scattering (SAXS) and differential scanning calorimetry (DSC). The DSC shows a broad thermal peak below the normal water melting point associated with the melting of confined ice nanocrystals, or nanoporosity. The nanopore size distribution was determined from the Gibbs-Thomson equation. As the porosity is increased, a second sharp DSC thermal peak with onset temperature at the water melting point is apparent, which was associated with the melting of ice macrocrystals, or macroporosity. The DSC result could be causing misinterpretation of the macroporosity because water may not be exactly confined in very feeble silica network regions in sonogels with high porosity. The structure of the wet gels can be described fairly well as mutually self-similar mass fractal structures with characteristic length. increasing from similar to 1.8 to similar to 5.4 nm and mass fractal dimension D diminishing discretely from similar to 2.6 to similar to 2.3 as the porosity increases in the range studied. More specifically, such a structure could be described using a two-parameter correlation function gamma(r) similar to r(D-3) exp(-r/xi), which is limited at larger scale by the cut-off distance xi but without a well-defined small scale cut-off distance, at least up to the maximum angular domain probed using SAXS in the present study.

Soil loss risk and habitat quality in streams of a meso-scale river basin

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

Mass fractal characteristics of wet sonogels as determined by small-angle x-ray scattering and differential scanning calorimetry

Low density silica sonogels were prepared from acid sonohydrolysis of tetraethoxysilane. Wet gels were studied by small-angle x-ray scattering (SAXS) and differential scanning calorimetry (DSC). The DSC tests were carried out under a heating rate of 2 degrees C/min from -120 degrees C up to 30 degrees C. Aerogels were obtained by CO(2) supercritical extraction and characterized by nitrogen adsorption and SAXS. The DSC thermogram displays two distinct endothermic peaks. The first, a broad peak extending from about -80 degrees C up to practically 0 degrees C, was associated to the melting of ice nanocrystals with a crystal size distribution with pore diameter ranging from 1 or 2 nm up to about 60 nm, as estimated from Thomson's equation. The second, a sharp peak with onset temperature close to 0 degrees C, was attributed to the melting of macroscopic crystals. The DSC incremental nanopore volume distribution is in reasonable agreement with the incremental pore volume distribution of the aerogel as determined from nitrogen adsorption. No macroporosity was detected by nitrogen adsorption, probably because the adsorption method applies stress on the sample during measurement, leading to a underestimation of pore volume, or because often positive curvature of the solid surface is in aerogels, making the nitrogen condensation more difficult. According to the SAXS results, the solid network of the wet gels behaves as a mass fractal structure with mass fractal dimension D=2.20 +/- 0.01 in a characteristic length scale below xi=7.9 +/- 0.1 nm. The mass fractal characteristics of the wet gels have also been probed from DSC data by means of an earlier applied modeling for generation of a mass fractal from the incremental pore volume distribution curves. The results are shown to be in interesting agreement with the results from SAXS.

Structural characteristics of gels prepared from sonohydrolysis and conventional hydrolysis of TEOS: an emphasis on the mass fractal as determined from the pore size distribution

Silica gels were preparated from fixed proportion mixtures of tetraethoxysilane, water and hydrocloric acid, using either ultrasound stimulation (US) or conventional method (CO) in the hydrolysis step of the process. Wet gets were obtained with the same silica volume concentration and density. According to small-angle X-ray scattering, the structure of the wet gels can be described as mass fractal structures with mass fractal dimension D = 2.20 in a length scale xi = 7.9 nm, in the case of wet gels US, and D = 2.26 in a length scale 6.9 nm, in the case of wet gels CO. The mass fractal characteristics of the wet gels US and CO account for the different structures evolved in the drying of the gels US and CO in the obtaining of xerogels and aerogels. The pore structure of the dried gels was studied by nitrogen adsorption as a function of the temperature. Aerogels (US and CO) present high porosity with pore size distribution (PSD) curves in the mesopore region while xerogels (US and CO) present minor porosity with PSD curves mainly in the micropore region. The dried gels US (aerogels and xerogels) generally present pore volume and specific surface area greater than the dried gels CO. The mass fractal structure of the aerogels has been studied from an approach based on the PSD curves exclusively. (c) 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique

dIn this work, a perceptron neural-network technique is applied to estimate hourly values of the diffuse solar-radiation at the surface in São Paulo City, Brazil, using as input the global solar-radiation and other meteorological parameters measured from 1998 to 2001. The neural-network verification was performed using the hourly measurements of diffuse solar-radiation obtained during the year 2002. The neural network was developed based on both feature determination and pattern selection techniques. It was found that the inclusion of the atmospheric long-wave radiation as input improves the neural-network performance. on the other hand traditional meteorological parameters, like air temperature and atmospheric pressure, are not as important as long-wave radiation which acts as a surrogate for cloud-cover information on the regional scale. An objective evaluation has shown that the diffuse solar-radiation is better reproduced by neural network synthetic series than by a correlation model. (C) 2004 Elsevier Ltd. All rights reserved.

Including the sub-grid scale plume rise of vegetation fires in low resolution atmospheric transport models

We describe and begin to evaluate a parameterization to include the vertical transport of hot gases and particles emitted from biomass burning in low resolution atmospheric-chemistry transport models. This sub-grid transport mechanism is simulated by embedding a 1-D cloud-resolving model with appropriate lower boundary conditions in each column of the 3-D host model. Through assimilation of remote sensing fire products, we recognize which columns have fires. Using a land use dataset appropriate fire properties are selected. The host model provides the environmental conditions, allowing the plume rise to be simulated explicitly. The derived height of the plume is then used in the source emission field of the host model to determine the effective injection height, releasing the material emitted during the flaming phase at this height. Model results are compared with CO aircraft profiles from an Amazon basin field campaign and with satellite data, showing the huge impact that this mechanism has on model performance. We also show the relative role of each main vertical transport mechanisms, shallow and deep moist convection and the pyro-convection (dry or moist) induced by vegetation fires, on the distribution of biomass burning CO emissions in the troposphere.

Modeling regional macroeconomic interactions: situation and perspectives for macroeconomic coordination in Latin America. Background paper prepared for the REDIMA workshop on Modeling Macroeconomic Coordination in the Andean Community, (Santiago, Chile 22 October 2003)""

Includes bibliography