SPATIAL-ORGANIZATION OF FORAGING ACTIVITY AND COLONIZATION BY COLONY EMIGRATION IN THE PHARAOH ANT, MONOMORIUM-PHARAONIS

Using food bait stations and colony trap nests, the spatial relation between the foraging activity of established colonies of the polygynous and unicolonial exotic pharaoh's ant, Monomorium pharaonis, and colonization by colony fragments was studied over an 8 month period in a large institutional setting in Brazil. Both foraging activity and colonizations demonstrated significant spatial clumping. However, colonizations were significantly negatively clumped spatially with respect to foraging activity. This suggests that the colonization strategy of this species under the studied conditions was that of complete habitat domination.

Strategy to detect ground control in digital images

The identification of ground control on photographs or images is usually carried out by a human operator, who uses his natural skills to make interpretations. In Digital Photogrammetry, which uses techniques of digital image processing extraction of ground control can be automated by using an approach based on relational matching and a heuristic that uses the analytical relation between straight features of object space and its homologous in the image space. A build-in self-diagnosis is also used in this method. It is based on implementation of data snooping statistic test in the process of spatial resection using the Iterated Extended Kalman Filtering (IEKF). The aim of this paper is to present the basic principles of the proposed approach and results based on real data.

A new implementation of population based incremental learning method for optimizations in electromagnetics

To enhance the global search ability of population based incremental learning (PBIL) methods, it is proposed that multiple probability vectors are to be included on available PBIL algorithms. The strategy for updating those probability vectors and the negative learning and mutation operators are thus re-defined correspondingly. Moreover, to strike the best tradeoff between exploration and exploitation searches, an adaptive updating strategy for the learning rate is designed. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm.

In-process thermal damage detection in grinding with monitoring via Internet

This work aims the development of a dedicated system for detection of burning in surface grinding process, where the process will constantly be monitored through the acoustic emission and electric power of the induction motor drive. Acquired by an analog-digital converter, algorithms process the signals and a control signal is generated to inform the operator or interrupt the process in case of burning occurrence. Moreover, the system makes possible the process monitoring via Internet. Additionally, a comparative study between parameters DPO and FKS is carried through. In the experimental work one type of. steel (ABNT-1020 annealed) and one type of grinding wheel referred to as TARGA, model ART 3TG80.3 NVHB, were employed.

Conceptual model for adaptable and extensible visual data exploration

Interactive visual representations complement traditional statistical and machine learning techniques for data analysis, allowing users to play a more active role in a knowledge discovery process and making the whole process more understandable. Though visual representations are applicable to several stages of the knowledge discovery process, a common use of visualization is in the initial stages to explore and organize a sometimes unknown and complex data set. In this context, the integrated and coordinated - that is, user actions should be capable of affecting multiple visualizations when desired - use of multiple graphical representations allows data to be observed from several perspectives and offers richer information than isolated representations. In this paper we propose an underlying model for an extensible and adaptable environment that allows independently developed visualization components to be gradually integrated into a user configured knowledge discovery application. Because a major requirement when using multiple visual techniques is the ability to link amongst them, so that user actions executed on a representation propagate to others if desired, the model also allows runtime configuration of coordinated user actions over different visual representations. We illustrate how this environment is being used to assist data exploration and organization in a climate classification problem.

Standardization of bioassays for monitoring resistance to insecticides in Aedes aegypti

Since the reintroduction of Aedes aegypti in Brazil in the 1980s, insecticide use for its control is routine. The chemical control efficacy is threatened by vectors developing resistance to insecticides. The World Health Organization, recognizing the impact of insecticide resistance in vector control programmes, proposed standardizing bioassays for detecting and monitoring resistance using a diagnostic dose method. As Brazil has a national programme for monitoring the resistance of Ae. aegypti populations to insecticides, this study was designed to compare diagnostic bioassays at WHO suggested concentrations and those estimated for local conditions. Populations were resistant to both temephos doses. But important differences were seen for fenitrothion and malathion, which could lead to under- or over-estimation of resistance respectively. These results and inclusion of a diagnostic dose bioassay standard for larvae are discussed.

A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics

To enhance the global search ability of Population Based Incremental Learning (PBIL) methods, It Is proposed that multiple probability vectors are to be Included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly Implemented algorithm. ©2006 IEEE.

Regulation, worker protection and active labour-market policies in Latin America

Includes bibliography

Labour markets, worker protection and lifelong learning in a global economy: experiences and perspectives of Latin America and the Caribbean

Includes bibliography

Public policies for the information society: a shared vision?

Includes bibliography

Achieving educational quality: what schools teach us: learning from Chile's P900 primary schools

Includes bibliography

The millennium development goals: strategic implications for the Latin American and Caribbean statistical systems

Includes bibliography

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

A Markov random field model for combining optimum-path forest classifiers using decision graphs and game strategy approach

The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of OPF ensembles and the framework to design multiple classifier systems. © 2011 Springer-Verlag.

ECLAC, transnational corporations and the quest for a Latin American development strategy

Includes bibliography