Time-dependent creep-fatigue crack growth (CFCG) is an important consideration in the design and remaining life estimation of high temperature components. CFCG tests were carried out on compact type (CT) specimens of 2.25 Cr-1.0 Mo steel and its behavior, for hold times ranging from 10 seconds to 50 seconds, at 594°C (1100°F) was characterized using the average value of the Gt-parameter, (Ct)avg. The trends in the creep-crack growth (CCG) data for this material are also compared with the CFCG data. The analytically estimated values of (Ct)avg are compared with the experimental values of (Ct)avg obtained from the measured values of load-line deflection rates. It is also shown that even in the absence of accurate creep deformation constants, accurate estimates of the measured values of (Ct)avg can be obtained in CT specimens.
Alzheimer’s disease is a chronic neurodegenerative disease that causes brain cells to degenerate, resulting in decreased physical and mental abilities and, in severe cases, permanent memory loss. It is considered as the most common and fatal form of dementia. Although mild cognitive impairment (MCI) precedes Alzheimer’s disease (AD), it does not necessarily show the obvious symptoms of AD. As a result, it becomes challenging to distinguish between mild cognitive impairment and cognitively normal. In this paper, we propose an ensemble of deep learners based on convolutional neural networks for the early diagnosis of Alzheimer’s disease. The proposed approach utilises simple averaging ensemble and weighted averaging ensemble methods. The ensemble-based transfer learning model demonstrates enhanced generalization and performance for AD diagnosis compared to traditional transfer learning methods. Extensive experiments on the OASIS-3 dataset validate the effectiveness of the proposed model, showcasing its superiority over state-of-the-art transfer learning approaches in terms of accuracy, robustness, and efficiency.
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