Mithibai College of Arts, Chauhan Institute of Science & Amruthben Jivanlal College of Commerce and Economics is a college affiliated to the University of Mumbai. The college was established in 1961 in Vile Parle, Mumbai by Shri Vile Parle Kelavani Mandal. It has consistently featured on the top 5 ranks in India Today College rankings for years 2017, 2018 and 2019. In 2018 it was granted autonomous status by UGC.
This study investigates the negative-thermal expansion of the Sc 2 Mo 3 O 12 :Dy 3+ phosphor for thermally stable luminescence in sustainable WLED lighting and explores cryogenic phase transition and charge transfer band shift-based optical thermometry.
The discovery of neutron ushered in a paradigm shift in our understanding of the subatomic world. The electrically neutral neutron does not experience Coulomb repulsion produced by the positively charged protons inside the nucleus. As a result, a neutron can penetrate deep into the nucleus and thus is a powerful probe to unravel the nuclear structure. In 1920, Rutherford prophetically predicted the existence of an electrically neutral particle inside the nucleus. It took more than a decade of painstaking experimentation to finally discover it in 1932. The historical aspects of a scientific discovery, for one reason or another, often, do not find their place in the normal discourse at both school and college levels. Studying the historical development of an idea can inspire, excite curiosity among, and familiarize students about how science works. In this article, we have tried to show how the relevant historical information can be integrated in traditional instructions while discussing the discovery of neutron which may enhance the curriculum outcomes and increase scientific literacy. We have discussed two important experiments that hinted at the existence of a new particle inside the nucleus. The arguments that led Chadwick to the ultimate discovery of neutron are also presented.
Alzheimer’s Disease (AD) is the most prevalent form of age-related dementia. Even though a century has passed since the discovery of AD, the exact cause of the disease still remains unknown. As a result, this poses a major hindrance in developing effective therapies for treating AD. Glycogen synthase kinase-3 (GSK-3) is one of the kinases that has been investigated recently as a potential therapeutic target for the treatment of AD. It is also known as human tau protein kinase and is a proline-directed serine-threonine kinase. Since dysregulation of this kinase affects all the major characteristic features of the disease, such as tau phosphorylation, amyloid formation, memory, and synaptic function, it is thought to be a major player in the pathogenesis of AD. In this review, we present the most recent information on the role of this kinase in the onset and progression of AD, as well as significant findings that identify GSK-3 as one of the most important targets for AD therapy. We further discuss the potential of treating AD by targeting GSK-3 and give an overview of the ongoing studies aimed at developing GSK-3 inhibitors in preclinical and clinical investigations.
Data is increasing in volume day by day, this data can be processed and classified into various categories. Users all over the internet ask tons of questions to which they want precise answers. The Question Answering system is the best solution in such scenarios. Traditional approach mainly focuses on providing the documents which include the keywords related to the query asked by the users. While the question answering approach provides a better alternative by further refining the results, it not only returns the related documents but also retrieves the most relevant answer from the available corpus of data. Converting the questions asked by the users into an appropriate query string, classifying the question, retrieving the documents and extracting the valid answer are the main steps involved in this system.
Recommendation System is basically an algorithm that recommends useful content like videos, images or information to people. Like, choosing which movie to watch, which item to purchase, which book to read, etc. based on user’s actions on previous views. There are various algorithms used to perform this recommendation technique. This paper reviews different algorithms of recommendation system used in different applications where video/visual broadcasting is involved and it concludes how collaborative filtering is the most commonly used method out of all.
