796 resultados para On-Line Learning Resources


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A presente tese parte do conceito de autonomia, de Cornelius Castoriadis, com o objetivo de produzir interrogações sobre EAD on-line e seu potencial inovador, principalmente no que diz respeito ao possível fortalecimento de uma educação mais democrática e do favorecimento da construção da autonomia do aluno auxiliada pelas novas tecnologias. Para realizar os objetivos que são os seus, o presente estudo propõe-se a refletir sobre as características do modelo de formação humana que, emergindo dos documentos oficiais e da farta literatura que vem sendo produzida para introduzir a EAD on-line nos cursos superiores públicos e privados no país. A intenção manifesta é a de tentar examinar os possíveis avanços e retrocessos que muitas propostas de EAD on-line apresentam para o processo de autonomia do sujeito atualmente no Brasil. Para tentar cumprir os objetivos apresentados, foi realizado o exame de parte da vasta produção acadêmica sobre o tema, dando especial atenção aos escritos que trata do ciberespaço e da cibercultura, destacando-se nesses trabalhos as elaborações de Pierre Lévy, um dos autores mais citados pela literatura especializada. Para aprofundar as reflexões críticas sobre a questão antropológica, usamos o referencial winnicottiano da construção da singularidade, e a reflexão de Hannah Arendt sobre a dimensão política da educação.

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Este trabalho tem por objetivo propor um modelo de ontologia simples e generalista, capaz de descrever os conceitos mais básicos que permeiam o domínio de conhecimento dos jornais on-line brasileiros não especializados, fundamentado tanto na prática quanto conceitualmente, em conformidade com os princípios da Web Semântica. A partir de uma nova forma de classificação e organização do conteúdo, a ontologia proposta deve ter condições de atender as necessidades comuns de ambas as partes, jornal e leitor, que são, resumidamente, a busca e a recuperação das informações.

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La identidad digital y la reputación online como elementos claves a la hora de relacionarse con el consumidor y generar valor

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Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.

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The measurement of high speed laser beam parameters during processing is a topic that has seen growing attention over the last few years as quality assurance places greater demand on the monitoring of the manufacturing process. The targets for any monitoring system is to be non-intrusive, low cost, simple to operate, high speed and capable of operation in process. A new ISO compliant system is presented based on the integration of an imaging plate and camera located behind a proprietary mirror sampling device. The general layout of the device is presented along with the thermal and optical performance of the sampling optic. Diagnostic performance of the system is compared with industry standard devices, demonstrating the high quality high speed data which has been generated using this system.

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In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed

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In 1999, the Chesapeake Bay Program completed a survey of existing data on chemical contaminants and the potential for bioeffects in 38 tidal river systems of Chesapeake Bay. This review led to the identification of 20 areas for which there were insufficient data to adequately characterize the potential for contaminant bioeffects on the Bay’s living resources. The goal of the present study was to estimate the current status of ecological condition in five of these areas and thus help to complete the overall toxics inventory for the Bay. These five systems included the Chester River, Nanticoke River, Pocomoke River, Lower Mobjack Bay (Poquosin and Back Rivers) and the South and Rhode Rivers. This study utilized a Sediment Quality Triad (SQT) approach in combination with additional water-column contaminant analysis to allow for a “weight of evidence” assessment of environmental condition. A total of 60 stations distributed among the five systems, using a probabilistic stratified random design, were sampled during the summer of 2004 to allow for synoptic measures of sediment contamination, sediment toxicity, and benthic condition. Upon completion of all analyses, stations were assigned to one of four categories based on the three legs of the triad. Stations with high sediment quality had no hits on any of the three legs of the triad; those with moderate quality had one hit; those with marginal quality had two hits; and those with poor quality had hits for all three legs of the triad. The Pocomoke River had by far the largest proportion of the total area (97.5%) classified as having high sediment quality, while the Rhode/South system had the highest proportion (11.4%) classified as poor. None of the stations in the Chester River, Nanticoke River, and Lower Mobjack Bay systems were classified as poor. More than 65% of the area of each of the five systems was classified with high to moderate sediment quality. The Rhode/South system had 30.4% of total area classified with marginally to severely poor quality. The results of this study highlight the importance of using multiple indicators and a “weight of evidence” approach to characterize environmental quality and the potential bioeffects of toxic contaminants.

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West Bengal holds an important position in fisheries development as the state has all types of captive, culture freshwater and brackish water fisheries. A survey of forest areas of Sundarbans indicates the total annual catch to be 2500 metric tonnes. On average 4,000 persons are engaged in daily fishing with 1.5 kilogrammes of fish catch per fisherman per day and during the 6 months from September to February on an average 6,000 persons are engaged in fishing. About 70% of total catch of fish is collected during this period. Statistical analysis of the data collected for the study has clearly indicated that a rational and scientific exploitation of fish species inhabiting the rivers and creeks of Sundarbans estuary has immense economic potentialities.