Relationship between Candida and nocturnal denture wear: quantitative study

The purpose of this study was to evaluate the relationship between Candida and denture wear during the night. Twenty-four edentulous volunteers were randomly divided into two groups. Group I (GI, n = 11) was composed of volunteers who wore their complete dentures day and night and Group H (GII, n = 13) was composed of volunteers who wore their complete dentures only during the day. Three examination periods were performed for both groups. In GI, the first examination (A) was carried out after overnight denture wearing. Subsequent examinations were conducted after one (B) and seven nights (C) without denture use during sleep. In GII, the first (A) was done without previous use during sleep, and the following were carried out after one (B) and seven nights (C) of overnight denture wearing. Total un-stimulated saliva was collected in a sterile container and cultured in duplicate inside Petri dishes. The values of colony forming units (CFU mL(-1) +/- s.d.) were obtained: GI A - 10.1 x 10(3) +/- 1.2 x 10(4), B - 2.0 x 10(3) +/- 2.6 x 10(3), and C - 2.6 x 10(3) +/- 5.9 x 10(3) and GII: A - 0.4 x 10(3) +/- 0.6 x 10(3), B - 9.4 x 10(3) +/- 17.7 x 10(3) and C - 6.3 x 10(3) +/- 15.3 x 10(3). The mean counts for Candida sp. were expressed as log (CFU + 1) mL(-1) and statistical significance of differences among groups was tested by ANOVA (alpha = 0.05). Multiple comparisons were performed according to Bonferroni test and indicated significant differences between A-B and A-C, but not between B and C for both groups. It was concluded that there is a significant relationship between continuous denture wear and Candida sp.

Application of MLP and Kohonen networks for recognition of wear patterns of single-point dressers

Grinding is a parts finishing process for advanced products and surfaces. However, continuous friction between the workpiece and the grinding wheel causes the latter to lose its sharpness, thus impairing the grinding results. This is when the dressing process is required, which consists of sharpening the worn grains of the grinding wheel. The dressing conditions strongly affect the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The objective of this study was to estimate the wear of a single-point dresser using intelligent systems whose inputs were obtained by the digital processing of acoustic emission signals. Two intelligent systems, the multilayer perceptron and the Kohonen neural network, were compared in terms of their classifying ability. The harmonic content of the acoustic emission signal was found to be influenced by the condition of dresser, and when used to feed the neural networks it is possible to classify the condition of the tool under study.