114 resultados para Process control Statistical methods
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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No clear evidence is available in the literature regarding the acute effect of different styles of music on cardiac autonomic control. The present study aimed to evaluate the acute effects of classical baroque and heavy metal musical auditory stimulation on Heart Rate Variability (HRV) in healthy men. In this study, HRV was analyzed regarding time (SDNN, RMSSD, NN50, and pNN50) and frequency domain (LF, HF, and LF / HF) in 12 healthy men. HRV was recorded at seated rest for 10 minutes. Subsequently, the participants were exposed to classical baroque or heavy metal music for five minutes through an earphone at seated rest. After exposure to the first song, they remained at rest for five minutes and they were again exposed to classical baroque or heavy metal music. The music sequence was random for each individual. Standard statistical methods were used for calculation of means and standard deviations. Besides, ANOVA and Friedman test were used for parametric and non-parametric distributions, respectively. While listening to heavy metal music, SDNN was reduced compared to the baseline (P = 0.023). In addition, the LF index (ms(2) and nu) was reduced during exposure to both heavy metal and classical baroque musical auditory stimulation compared to the control condition (P = 0.010 and P = 0.048, respectively). However, the HF index (ms(2)) was reduced only during auditory stimulation with music heavy metal (P = 0.01). The LF/HF ratio on the other hand decreased during auditory stimulation with classical baroque music (P = 0.019). Acute auditory stimulation with the selected heavy metal musical auditory stimulation decreased the sympathetic and parasympathetic modulation on the heart, while exposure to a selected classical baroque music reduced sympathetic regulation on the heart.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Aims and Objectives: The aim of this study was to analyze the microhardness of three resin cements used in cementing glass fiber posts in bovine incisor. The microhardness was analyzed in cervical, middle and apical thirds before and after thermocycling process. Materials and Methods: Bovine teeth were instrumented and divided into 3 groups composed of 10 teeth each. Then, the teeth were sectioned and obturated and had their canals prepared at a depth of 12mm. Once proceeded the desobturation, the roots and glass fiber posts were prepared for adhesive cementation. After cementation, the microhardness reading was carried out. After initial reading, the samples were placed in a thermocycler and subjected to 2,000 cycles and a new microhardness reading. The data collected were subjected to analysis of variance (ANOVA) and Turkey’s test. Results: It was observed a statistical difference among the microhardness of resin cements. However, the statistical difference of microhardness before and after thermocycling appeared only in group U-200. Conclusion: Thermocycling reduced microhardness values in all cements evaluated in this study. The autopolymerizing cement Multilink presented the most stable microhardness mean values after thermocycling in the coronal, middle and apical thirds.
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Pós-graduação em Agronomia (Produção Vegetal) - FCAV
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Agronomia (Ciência do Solo) - FCAV
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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.