Clinical Evaluation of Denture Retention by Multi-suction Cup and Denture Adhesive

AIM: The aim of the study was to compare the retention of two modalities: Multi-suction cup denture, and denture adhesive and to evaluate the change of retention by different time intervals. PATIENTS AND METHODS: Twelve completely edentulous patients were selected. The patients received two dentures: One conventional denture, and the other with multi-suction cups. The retention was measured by a universal testing machine at insertion, 15 min, 30 min, 1 h, 2 h, and 4 h. All values were recorded in Newtons. Statistical analysis was carried out using two-way analysis of variance with post hoc Tukey’s test. RESULTS: Retention was higher in denture adhesive than multi-suction cup, and the change of retention was not statistically significant by time. CONCLUSION: Denture adhesive showed better retention clinically and simplified laboratory procedures than multi-suction denture

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN) based deep-learning models is a powerful technique to detect features from material micrographs in an automated manner. Manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. In this study, we attempt to overcome such limitations by utilizing multi-modal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy (SEM) images of 347H stainless steel as training data and electron backscatter diffraction (EBSD) micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that na\"ive pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization