The Flower Workshop: a social self-organizing practice in mental health nursing

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

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

Influence of the concentration of Sb2O3 and the viscosity of the precursor solution on the electrical and optical properties of SnO2 thin films produced by the Pechini method

SnO2:Sb multi-layer coatings were prepared by the Pechini method. An investigation was made of the influence of the concentration of Sb2O3 and the viscosity of the precursor solution on the electrical and optical properties of SnO2 thin films. The use of a multi-layer system as an alternative form of increasing the packing and. thus. decreasing porosity proved to be efficient, decreasing the system's resistivity without altering its optical properties. (C) 2002 Elsevier B.V. B.V. All rights reserved.

A Non-Destructive Testing Based on Electromagnetic Measurements and Neural Networks for the Inspection of Concrete Structures

This paper presents a new non-destructive testing (NDT) for reinforced concrete structures, in order to identify the components of their reinforcement. A time varying electromagnetic field is generated close to the structure by electromagnetic devices specially designed for this purpose. The presence of ferromagnetic materials (the steel bars of the reinforcement) immersed in the concrete disturbs the magnetic field at the surface of the structure. These field alterations are detected by sensors coils placed on the concrete surface. Variations in position and cross section (the size) of steel bars immersed in concrete originate slightly different values for the induced voltages at the coils.. The values for the induced voltages were obtained in laboratory tests, and multi-layer perceptron artificial neural networks with Levemberg-Marquardt training algorithm were used to identify the location and size of the bar. Preliminary results can be considered very good.

First telemetry study of bush dogs: Home range, activity and habitat selection

Context The bush dog (Speothos venaticus) is difficult to observe, capture, and study. To date, indirect evidence and opportunistic field observations have been the primary sources of information about the species' ecology. Field data are urgently needed to clarify the species' ecological requirements, behaviour and movement patterns. Aims The present study uses 13 months of telemetry data from a group of bush dogs to begin to address questions about area requirements, habitat preferences and movement patterns of this difficult-to-study species. Methods We tracked a group of bush dogs (two adults, one juvenile, four young) in an area of intact and altered Cerrado (woodlandsavanna biome) in eastern Mato Grosso, Brazil (Nova Xavantina District). Key results The group had a total home range of 140km2 (fixed kernel 95%), with smaller seasonal 'subareas' (areas used for 12 months before moving to another area, with repetition of some areas over time) and demonstrated a preference for native habitats. Conclusions The bush dog's home range is greater than that of other canids of the same size, even correcting for group size. Patterns of seasonal movement are also different from what has been observed in other South American canids. Implications From our observations in the Brazilian savanna, bush dogs need large tracks of native habitat for their long-term persistence. Although the present study is based on a single pack, it is highly relevant for bush dog conservation because it provides novel information on the species' spatial requirements and habitat preferences. © 2012 CSIRO.

Information Visualization to support decision making in the context of distance learning: A study with VLE TelEduc

Digital data sets constitute rich sources of information, which can be extracted and evaluated applying computational tools, for example, those ones for Information Visualization. Web-based applications, such as social network environments, forums and virtual environments for Distance Learning, are good examples for such sources. The great amount of data has direct impact on processing and analysis tasks. This paper presents the computational tool Mapper, defined and implemented to use visual representations - maps, graphics and diagrams - for supporting the decision making process by analyzing data stored in Virtual Learning Environment TelEduc-Unesp. © 2012 IEEE.

The dimensionality of ecological networks

How many dimensions (trait-axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small (< 10), with model selection favouring less than five. Using 18 high-quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large-scale community structure. © 2013 Blackwell Publishing Ltd/CNRS.

Caracterização de leite utilizando técnicas de ultra-som e redes neurais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Músicos conectados, fãs colaborativos: uma análise do site MySpace

Pós-graduação em Comunicação - FAAC

As marcas de uma escola apontadas pelos membros de uma comunidade no Orkut

Pós-graduação em Educação - IBRC

Desenvolvimento moral e história de vida: um estudo sobre os princípios éticos subjacentes às ações do líder comunidade

Pós-graduação em Educação - IBRC

As sociedades antiescravistas na cidade de São Paulo (1850-1871)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estudo etnobotânico de plantas alimentares cultivadas por moradores da periferia de Santo Antonio de Leverger, MT

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Qualidade de vida e processos sócio-ambientais em Indaiatuba - SP: estudo de caso do bairro Jardim Morada do Sol

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

Mães adolescentes: estrutura e funcionamento familiar

Pós-graduação em Psicologia do Desenvolvimento e Aprendizagem - FC