Well productivity controlling factors in crystalline terrains of southeastern Brazil

Over the last decades, increasing water demands have fostered research to obtain high well yields in crystalline terrains where, besides the intrinsic properties of rocks, the groundwater flow depends on several factors. The depth of the wells, the lithotypes, the presence and thickness of sedimentary coverings and weathered layers, the landforms, the geological structures, and the effects of tectonic stresses are among the most investigated factors considered as determinant of well productivity. The influence of these factors on productivity of wells that exploit the Crystalline Aquifer System in the Jundiai-River Catchment, southeastern Brazil, is investigated in this work. The largest region of the studied area is located on the Precambrian Basement, partially covered by sedimentary deposits. The results show that the sedimentary deposits and the weathered layer are important for high well yield, but it also depends on the existence of a net of open fractures, in order to maintain high productivity. The sites that have more possibility of occurrence of such structures are the regional shear and fault zones and other minor structures with NW-SE and E-W directions, which characterize areas subjected to transtensional stress related to the neotectonics.

The generation and utilization of a cancer-oriented representation of the human transcriptome by using expressed sequence tags

Whereas genome sequencing defines the genetic potential of an organism, transcript sequencing defines the utilization of this potential and links the genome with most areas of biology. To exploit the information within the human genome in the fight against cancer, we have deposited some two million expressed sequence tags (ESTs) from human tumors and their corresponding normal tissues in the public databases. The data currently define approximate to23,500 genes, of which only approximate to1,250 are still represented only by ESTs. Examination of the EST coverage of known cancer-related (CR) genes reveals that <1% do not have corresponding ESTs, indicating that the representation of genes associated with commonly studied tumors is high. The careful recording of the origin of all ESTs we have produced has enabled detailed definition of where the genes they represent are expressed in the human body. More than 100,000 ESTs are available for seven tissues, indicating a surprising variability of gene usage that has led to the discovery of a significant number of genes with restricted expression, and that may thus be therapeutically useful. The ESTs also reveal novel nonsynonymous germline variants (although the one-pass nature of the data necessitates careful validation) and many alternatively spliced transcripts. Although widely exploited by the scientific community, vindicating our totally open source policy, the EST data generated still provide extensive information that remains to be systematically explored, and that may further facilitate progress toward both the understanding and treatment of human cancers.

Dirac equation in magnetic-solenoid field

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Utilization of epiphytes by birds in a Brazilian Atlantic Forest

Epiphytes constitute a great part of the vegetation biomass in Neotropical forests, offering a large variety of resources to birds. Despite their structural and ecological importance, few studies investigated the use of epiphytes by birds in the Neotropical region. We studied the bird species that exploit vascular epiphytes (and hemi-epiphytes) in an Atlantic forest site in southeastern Brazil. The resources exploited, seasonal variation in the use of epiphytes, the frequency of foraging and selectivity in epiphytes, and the relationship between the use of epiphytes and the participation in mixed-species bird flocks were investigated. After 360 h of observations along trails crossing the forest, 24 bird species (12 families) were recorded in a total of 74 events of epiphyte exploitation. Thamnophilidae (four species), Trochiliclae, Thraupidae and Furnariidae (three species) were the richest bird families in our sample, while Furnariidae and Dendrocolaptidae were the more frequently recorded families. Plants in the Bromeliaceae and Araceae families were the most abundant and more frequently exploited epiphytes. Nectar, water, nest material and invertebrates were the most frequently exploited resources, mainly from Bromeliaceae. None of the species for which we had enough data revealed to be a frequent user of epiphytes for foraging or selective to any epiphyte group. The White-eyed Foliage-gleaner (Automolus leucophthalmus; Furnariidae), a common participant of understory mixed-species flocks, exploited epiphytes more frequently when associated with mixed-species flocks. The utilization of epiphytes was opportunistic for most of the bird species recorded and occurred throughout the year with no seasonal variation.

Solucao de sistemas lineares com matrizes e vetores esparsos em computadores vetoriais

This paper presents a methodology for solving a set of linear sparse equations on vector computers. The new methodology is able to exploit the matrix and vector sparsities. The implementation was made on a CRAY Y-MP 2E/232 computer and the results were taken from electric power systems with 118, 320, 725 and 1729 buses. The proposed methodology was compared with three previous methods and the results show the superior performance of the new one.

Automation in fault detection using neural network and model updating

In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.

The influence of fruit morphology and habitat structure on ant-seed interactions: A study with artificial fruits

Artificial fruits designed to simulate lipid-rich non-myrecochorous diaspores were used to test for the effect of fruit morphology and habitat structure on ant-seed interactions in an Atlantic Forest site in SE Brazil. The outcome of the interaction (i.e., if the fruit was removed, cleaned by ants on the spot or had no interaction with ants) and the time of ant response were the investigated variables. Models simulating drupes and arilate diaspores were used to test for morphological effects and four habitat attributes (litter depth, number of logs, number of trees, and percentage of bromeliad coverage on the forest floor), likely to be correlated with the ant diversity and abundance in the study site, were measured to test for the effect of habitat structure. The proportion of fruits removed or cleaned did not differ between the two morphological models. Sites in which fruits were cleaned had more trees than those in which no interaction occurred. This may be a result of the foraging behavior of arboreal ants that frequently descend to the forest floor to exploit fleshy diaspores. Sites in which model removal occurred had lower litter depth than both those in which models were cleaned and those in which no interaction occurred. A negative correlation was observed between litter depth and ant response time. Accumulation of leaf litter at a given point may have constrained the movements of large ants in general, and ponerine ants (that are important seed removers) in particular. We conclude that that local pattern in litter depth and tree density influence the frequency and outcome of interactions between ants and non-myrmecochorous, fleshy diaspores.

Foraging activity and demographic patterns of two termite species (Isoptera: Rhinotermitidae) living in urban landscapes in southeastern Brazil

Coptotermes gestroi and Heterotermes tenuis (Isoptera: Rbinotermitidae) are important pests in southeastern Brazil causing serious economic damage. In this study we determined the demographic patterns and foraging activity of these species using mark-release-recapture and the consumption of wooden stakes. Using both the weighted mean and Lincoln index methods, population estimates ranged from ≈ 0.57 to 1.99 million individuals for C. gestroi and from ≈ 0.20 to 1.37 million for H. tenuis. Territory size of the colonies ranged from 172.5 to 5235 m 2 for C. gestroi and from 16 to 40 m 2 for H. tenuis. Our results also indicate that foraging activity was dependent on the minimum temperature; however, the existence of a compensation strategy in the foraging activities may permit foragers to exploit food sources under different environmental conditions.

Economic reforms, growth and employment: labour markets in Latin America and the Caribbean

Includes bibliography

The income distribution problem in Latin America and the Caribbean

Includes bibliography

Investment and economic reform in Latin America

Includes bibliography

Rethinking the development agenda

Includes bibliography

Environmental value, valuation methods, and natural disaster damage assessment

Includes bibliography

The strategies of multinational companies in 1990s Argentina

Includes bibliography

OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.