Sistema avançado de realidade virtual para visualização de estruturas odontológicas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Visão sistêmica do Sítio Arqueológico Piracanjuba: a descoberta de conhecimento em sítios arqueológicos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A internet na sociedade: um estudo com professores e alunos da comunidade acadêmica de nível superior na cidade de Franca-SP

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Using 3D Animations in Teaching Chemistry

This paper discuss possibilities of use of 3D animation as a tool for teaching Chemistry support. The research proposes to investigate the conception process and development of educational animations to use in a Blended Learning environment in undergraduate chemistry. Associated with general chemistry teachers, were raised the demands and difficulties on the content transmission, and the most relevant topics, about "Atomic Theory" with propose to create appropriate animations to meeting needs of themes. Thinking about offering more dynamic materials, we elaborate animations in a format of "micro-documentary", with a length between 4 and 7 minutes. We use the narration aloud to the subject-matter understanding, leaving the external text as a complement of the animation. The conclusions are positives, students accepted well the format and they proved are able to remember, organize and systematize several information presented in animations. These skills don't ensure knowledge acquisition, but may be considered prerequisites to learning occur.

Conhecimento de mães sobre os procedimentos de emergência nos casos de avulsão dentária

Introduction: Despite tooth avulsion following trauma being relatively common in children, the available studies show that adults have limited knowledge about it. Aim: The purpose of this study was to assess, by a questionnaire, the mothers’ general knowledge about the immediate management of tooth avulsion. Material and method: This descriptive study was carried out on a convenient sample of mothers (n= 65) who participated of the “Pastoral da Criança”, from Araraquara, SP, Brazil. The questionnaire comprised 15 questions about personal data and knowledge on tooth avulsion management. Results: Participants were, on average, 35 years old. A total of 30.8% of the mothers reported that their children suffered dental trauma. The majority had never received advice on this subject (76.9%); and did not know how teeth are kept in the dental arch (69.2%). Almost a half of the sample believed that an avulsed tooth can be replanted (49.2%). In relation to the management of tooth avulsion, 40% of them would clean the avulsed tooth with water, even if it was not dirty (38.5%). Most of them (69.2%) would take the tooth by hand for cleaning purposes, regardless the tooth region; and believed that brushing the tooth was important to take the dirty out (67.7%). Conclusion: The general knowledge of mothers about the immediate management of tooth avulsion was considered inadequate endangering the successful treatment of tooth avulsion.

Metrics for Association Rule Clustering Assessment

Issues related to association mining have received attention, especially the ones aiming to discover and facilitate the search for interesting patterns. A promising approach, in this context, is the application of clustering in the pre-processing step. In this paper, eleven metrics are proposed to provide an assessment procedure in order to support the evaluation of this kind of approach. To propose the metrics, a subjective evaluation was done. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Besides, the metrics do the users think about aspects related to the problems and provide a flexible way to solve them. Some experiments were done in order to present how the metrics can be used and their usefulness.

Measurement of differential cross sections for the production of a pair of isolated photons in pp collisions at root s=7TeV

A measurement of differential cross sections for the production of a pair of isolated photons in proton-proton collisions at root s = 7 TeV is presented. The data sample corresponds to an integrated luminosity of 5.0 fb(-1) collected with the CMS detector. A data-driven isolation template method is used to extract the prompt diphoton yield. The measured cross section for two isolated photons, with transverse energy above 40 and 25 GeV respectively, in the pseudorapidity range vertical bar eta vertical bar < 2.5, vertical bar eta vertical bar (sic) [1.44, 1.57] and with an angular separation Delta R > 0.45, is 17.2 +/-0.2 (stat) +/-1.9 (syst) +/- 0.4 (lumi) pb. Differential cross sections are measured as a function of the diphoton invariant mass, the diphoton transverse momentum, the azimuthal angle difference between the two photons, and the cosine of the polar angle in the Collins-Soper reference frame of the diphoton system. The results are compared to theoretical predictions at leading, next-to-leading, and next-to-next-to-leading order in quantum chromodynamics.

Tecnologias digitais na produção e análise de dados qualitativos

This article aims to discuss the role of digital technology in the process of collecting and analyzing data in qualitative research. We present two qualitative studies, with different objectives and contexts, that illustrate how data can be collected using different media and how they may shape the researcher's analysis and the results. Based on a theoretical perspective in which knowledge is seen as being produced by a collective of humans-with-media, as opposed to an individual or collective of humans, we believe that the technology used to produce knowledge and to analyze the data conditions the findings. Thus, technologies play a key role in doing research and the scientific knowledge generated from it.

Gestão, território e desenvolvimento: dificuldades e perspectivas do município de Tabatinga - SP

Pós-graduação em Geografia - IGCE

Hybrid visual-driven decision support system in video monitor manufacturing

An intelligent system that emulates human decision behaviour based on visual data acquisition is proposed. The approach is useful in applications where images are used to supply information to specialists who will choose suitable actions. An artificial neural classifier aids a fuzzy decision support system to deal with uncertainty and imprecision present in available information. Advantages of both techniques are exploited complementarily. As an example, this method was applied in automatic focus checking and adjustment in video monitor manufacturing. Copyright © 2005 IFAC.

Methodology proposal for acquisition of biotelemetric data in a real time

This work presents a methodological proposal for acquisition of biometric data through telemetry basing its development on a research-action and a case study. Nowadays, the qualified professionals of physical evaluation have to use specific devices to obtain biometric signals and data. These devices in the most of the time are high cost and difficult to use and handling. Therefore, the methodological proposal was elaborate in order to develop, conceptually, a bio telemetric device which could acquire the desirable biometric signals: oxymetry, biometrics, corporal temperature and pedometry which are essential for the area of physical evaluation. It was researched the existent biometrics sensors, the possible ways for the remote transmission of signals and the computer systems available so that the acquisition of data could be possible. This methodological proposal of remote acquisition of biometrical signals is structured in four modules: Acquisitor of biometrics data; Converser and transmitter of biometric signals; Receiver and Processor of biometrics signals and Generator of Interpretative Graphs. The modules aim the obtention of interpretative graphics of human biometric signals. In order to validate this proposal a functional prototype was developed and it is presented in the development of this work.

Sliding Mode Control for Active Suspension System with Data Acquisition Delay

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A NEURAL NET FOR EXTRACTING KNOWLEDGE FROM NATURAL-LANGUAGE DATA-BASES

The present paper introduces a new model of fuzzy neuron, one which increases the computational power of the artificial neuron, turning it also into a symbolic processing device. This model proposes the synapsis to be symbolically and numerically defined, by means of the assignment of tokens to the presynaptic and postsynaptic neurons. The matching or concatenation compatibility between these tokens is used to decided about the possible connections among neurons of a given net. The strength of the compatible synapsis is made dependent on the amount of the available presynaptic and post synaptic tokens. The symbolic and numeric processing capacity of the new fuzzy neuron is used here to build a neural net (JARGON) to disclose the existing knowledge in natural language data bases such as medical files, set of interviews, and reports about engineering operations.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

BtoxDB: a comprehensive database of protein structural data on toxin-antitoxin systems

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