Objective. This study aimed to evaluate effects of type 1 diabetes mellitus and mini-implant placement method on the primary stability of mini-implants by comparing mechanical stability and microstructural/histological differences. Methods. After 4 weeks of diabetic induction, 48 mini-implants (24 self-tapping and 24 self-drilling implants) were placed on the tibia of 6 diabetic and 6 normal ra...

Hydrogen (H2) is considered as an ideal fuel for the future. The photo-electrochemical (PEC) water splitting employing semiconducting materials and induced irradiation, preferably of solar spectrum, presents a viable route H2 production. In this work, self-ordered chromium-doped TiO2 nanotube (CT) was fabricated using in-situ electro-anodization. CT surface modification then performed by photo-...

A good abdominal probabilistic atlas can provide important information to guide segmentation and registration applications in the abdomen. Here we build and test probabilistic atlases using 24 abdominal CT scans with available expert manual segmentations. Atlases are built by picking a target and mapping other training scans onto that target and then summing the results into one probabilistic a...

Accurate segmentation of organs at risk is essential for radiation therapy planning. However, manual time-consuming and prone to inter intra-observer variability. This study proposes a self-supervision based attention UNet model OAR abdominal CT images. The utilizes mechanism train itself without the need annotations. used highlight important features suppress irrelevant ones, thus improving mo...

Computed tomography (CT) devices are routinely employed to obtain three-dimensional images of the human body. The reconstructed CT numbers represent weighted x-ray attenuation coefficients. Their spectral weighting is influenced by the selected x-ray source spectrum, the detector characteristics, and the attenuating object itself. The quantitative ground truth of the scanned object is given by ...

Abstract Machine learning (ML) models have been widely successful in the prediction of material properties. However, large labeled datasets required for training accurate ML are elusive and computationally expensive to generate. Recent advances Self-Supervised Learning (SSL) frameworks capable on unlabeled data mitigate this problem demonstrate superior performance computer vision natural langu...