Análise do processo de fresamento de MDF em centro de usinagem CNC

Pós-graduação em Engenharia Mecânica - FEG

Detecção e restauração de áreas sombreadas em imagens digitais de alta resolução espacial

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Ethanol is inefficient to remove endodontic sealer residues of dentinal surface

Endodontic sealer residues on dentinal surface have negative effects on adhesion of adhesives system and/or can cause discoloration of the dental crown. Objective: To evaluate the efficacy of 95% ethanol in removal of residues of epoxy-based (AH Plus), methacrylate-based (Epiphany SE) or calcium-based (Sealapex) sealers on dentinal surface. Material and methods: Thirty-two bovine incisor dental crown fragments (0.5 mm x 0.5 mm) were treated with 17% EDTA and 2.5% NaOCl. The specimens were divided into three experimental groups (n = 10): G1 (AH Plus), G2 (Epiphany SE) and G3 (Sealapex). In each group was applied a coating of one endodontic sealer type and were left undisturbed for 5 minutes. After this period, the specimens were cleaned with 95% ethanol. The control group was composed by two specimens that did not receive any sealer or cleaning treatment. The sealer residues persistence after cleaning with 95% ethanol was evaluated by scanning electron microscopy (x500) and a score system was applied. Data obtained were analyzed by Kruskal-Wallis and Dunn tests (α = 5%). Results: Moderate amount of endodontic sealer residues were observed in all groups, regardless of the endodontic sealer compositions. G1, G2 and G3 presented similar amount of sealer residues on dentinal surface after cleaning with 95% ethanol (p > 0.05). Conclusion: 95% ethanol was inefficiency to remove completely AH Plus, Epiphany SE and Sealapex residues of sealer-contaminated dentin.

Ethanol is inneficient to remove endodontic sealer residues of dentinal surface

Introduction: Endodontic sealer residues on dentinal surface have negative effects on adhesion of adhesives system and/or can cause discoloration of the dental crown. Objective: To evaluate the efficacy of 95% ethanol in removal of residues of epoxy-based (AH Plus), methacrylate-based (Epiphany SE) or calcium-based (Sealapex) sealers on dentinal surface. Material and methods: Thirty-two bovine incisor dental crown fragments (0.5 mm x 0.5 mm) were treated with 17% EDTA and 2.5% NaOCl. The specimens were divided into three experimental groups (n = 10): G1 (AH Plus), G2 (Epiphany SE) and G3 (Sealapex). In each group was applied a coating of one endodontic sealer type and were left undisturbed for 5 minutes. After this period, the specimens were cleaned with 95% ethanol. The control group was composed by two specimens that did not receive any sealer or cleaning treatment. The sealer residues persistence after cleaning with 95% ethanol was evaluated by scanning electron microscopy (x500) and a score system was applied. Data obtained were analyzed by Kruskal-Wallis and Dunn tests (α = 5%). Results: Moderate amount of endodontic sealer residues were observed in all groups, regardless of the endodontic sealer compositions. G1, G2 and G3 presented similar amount of sealer residues on dentinal surface after cleaning with 95% ethanol (p > 0.05). Conclusion: 95% ethanol was inefficiency to remove completely AH Plus, Epiphany SE and Sealapex residues of sealercontaminated dentin.

Técnicas de análise de dados distribuídos em áreas

Pós-graduação em Biometria - IBB

Fractal descriptors for discrimination of microscopy images of plant leaves

This study proposes the application of fractal descriptors method to the discrimination of microscopy images of plant leaves. Fractal descriptors have demonstrated to be a powerful discriminative method in image analysis, mainly for the discrimination of natural objects. In fact, these descriptors express the spatial arrangement of pixels inside the texture under different scales and such arrangements are directly related to physical properties inherent to the material depicted in the image. Here, we employ the Bouligand-Minkowski descriptors. These are obtained by the dilation of a surface mapping the gray-level texture. The classification of the microscopy images is performed by the well-known Support Vector Machine (SVM) method and we compare the success rate with other literature texture analysis methods. The proposed method achieved a correctness rate of 89%, while the second best solution, the Co-occurrence descriptors, yielded only 78%. This clear advantage of fractal descriptors demonstrates the potential of such approach in the analysis of the plant microscopy images.