772 resultados para training and jobs


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The present study was designed to analyze the effects of the association between cinnamon extract and aerobic exercise on the glycemic control and serum lipid profile of diabetic rats. Fifty Wistar male rats divided into five groups: control (C), sedentary nondiabetic rats; diabetic (D), sedentary diabetic rats; diabetic cinnamon (DC), sedentary diabetic rats that received cinnamon extract; diabetic exercise (DE), sedentary diabetic rats subjected to physical training; and diabetic cinnamon exercise (DCE), diabetic rats that received cinnamon extract and were subjected to physical training. For the induction of diabetes, the rats received alloxan. The cinnamon was administered to once a day for four weeks. The groups performed swimming exercises for one hour each day with lead overloads (3% - 5% of b.w) for five days a week for four weeks. Body weight loss was lower in the DE group compared to the other diabetic groups. The basal serum glucose of all the diabetic groups was higher compared to the control group. Group D had higher serum cholesterol concentrations compared to the DE and DCE groups. The resting blood lactate in group D was higher than the resting blood lactate in the DC and DE groups. Aerobic exercise partially counteracted the diabetic effects on body weight, serum cholesterol and blood lactate concentrations. No additional beneficial effects of cinnamon extract and aerobic exercise were observed on the parameters studied.

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Although the research on the relationship between human factors and environmental sustainability is slowly progressing, environmental training has attracted the most attention from researchers and practitioners. However, there remains a lack of research that integrates and systematises the available knowledge on organisational environmental training. Environmental training is fundamental to any successful activity of environmental management, conservation and recycling of resources. Thus, the aim of this paper was to present the results of a systematic literature review on environmental training in organisations. The main studies in this area were classified and coded, and a research agenda with 9 recommendations that may advance the field was presented. As a result of the gaps in the current literature, a framework was proposed aiming guide and strengthens the state-of-the-art research on environmental training. Additionally, results show that more research is needed on environmental training, combining training and green human resource management and defining/measuring the objectives of the environmental training actions. Future studies should also consider mixed methodologies and comparative perspectives. © 2013 Elsevier B.V. All rights reserved.

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This paper presents an experimental research on the use of eddy current testing (ECT) and artificial neural networks (ANNs) in order to identify the gauge and position of steel bars immersed in concrete structures. The paper presents details of the ECT probe and concrete specimens constructed for the tests, and a study about the influence of the concrete on the values of measured voltages. After this, new measurements were done with a greater number of specimens, simulating a field condition and the results were used to generate training and validation vectors for multilayer perceptron ANNs. The results show a high percentage of correct identification with respect to both, the gauge of the bar and of the thickness of the concrete cover. © 2013 Copyright Taylor and Francis Group, LLC.

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Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defined to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fluorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artificial neural networks.