• Restorative Dentistry and Endodontics
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• v.34 no.5
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• pp.424-429
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• 2009

This study aimed to assess the influence of different cross-sectional area on the cyclic fatigue fracture of Ni-Ti rotary files using a fatigue tester incorporating cyclical axial movement. Six brands of Ni-Ti rotary files (ISO 30 size with. 04 taper) of 10 each were tested: Alpha system (KOMET), HeroShaper (MicroMega), K3 (SybronEndo), Mtwo (VDW), NRT (Mani), and ProFile (Dentsply). A fatigue-tester (Denbotix) was designed to allow cyclic tension and compressive stress on the tip of the instrument. Each file was mounted on a torque controlled motor (Aseptico) using a 1:20 reduction contra-angle and was rotated at 300 rpm with a continuous, 6 mm axial oscillating motion inside an artificial steel canal. The canal had a $60^{\circ}$ angle and a 5 mm radius of curvature. Instrument fracture was visually detected and the time until fracture was recorded by a digital stop watch. The data were analyzed statistically. Fractographic analysis of all fractured surfaces was performed to determine the fracture modes using a scanning electron microscope. Cross-sectional area at 3 mm from the tip of 3 unused Ni-Ti instruments for each group was calculated using Image-Pro Plus (Imagej 1.34n, NIH). Results showed that NRT and ProFile had significantly longer time to fracture compared to the other groups (p < .05). The cross-sectional area was not significantly associated with fatigue resistance. Fractographycally, all fractured surfaces demonstrated a combination of ductile and brittle fracture. In conclusion, there was no significant relationship between fatigue resistance and the cross-sectional area of Ni-Ti instruments under experimental conditions.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.