AGEING RATE OF PAPER INSULATION IN TRANSFORMERS ON ITS MECHANICAL STRENGTH

It is well known that the paper and oil used in transformers Degrade with time at rates which depend on the temperature and the amount of air and water present [1, 2]. Various devices such as completely sealing the units, using nitrogen blankets and using flexible membranes, have been suggested as means of improving the lifetime of the oil. In addition, thermally upgraded papers have been devised and have been available for some years. Some supply authorities and transformer manufacturers have been using some or all of these devices with varying degrees of success. Many others have taken the stance of cautious awareness. In the absence of any evidence that normal papers are inadequate, they have continued to use them in transformers fitted with conservators. As part of this current overall package of research, paper aging was re-examined with a view to reassessing the need for change, either in the materials or practices Used or in the preservation systems

Deep Learning-Based Diagnosis of Alzheimer’s Disease

Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed